Re: BUG :: UI Spark

[Mich Talebzadeh]

sorry i thought i gave an explanation

The issue you are encountering with incorrect record numbers in the
"ShuffleWrite Size/Records" column in the Spark DAG UI when data is read
from cache/persist is a known limitation. This discrepancy arises due to
the way Spark handles and reports shuffle data when caching is involved.

Mich Talebzadeh,

Technologist | Architect | Data Engineer  | Generative AI | FinCrime

London
United Kingdom


   view my Linkedin profile


 https://en.everybodywiki.com/Mich_Talebzadeh



Disclaimer: The information provided is correct to the best of my knowledge
but of course cannot be guaranteed . It is essential to note that, as with
any advice, quote "one test result is worth one-thousand expert opinions
(Werner Von Braun)".


Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Sun, 26 May 2024 at 21:16, Prem Sahoo  wrote:

> Can anyone please assist me ?
>
> On Fri, May 24, 2024 at 12:29 AM Prem Sahoo  wrote:
>
>> Does anyone have a clue ?
>>
>> On Thu, May 23, 2024 at 11:40 AM Prem Sahoo  wrote:
>>
>>> Hello Team,
>>> in spark DAG UI , we have Stages tab. Once you click on each stage you
>>> can view the tasks.
>>>
>>> In each task we have a column "ShuffleWrite Size/Records " that column
>>> prints wrong data when it gets the data from cache/persist . it
>>> typically will show the wrong record number though the data size is correct
>>> for e.g  3.2G/ 7400 which is wrong .
>>>
>>> please advise.
>>>
>>

Re: BUG :: UI Spark

[Mich Talebzadeh]

Just to further clarify that the Shuffle Write Size/Records column in
the Spark UI can be misleading when working with cached/persisted data
because it reflects the shuffled data size and record count, not the
entire cached/persisted data., So it is fair to say that this is a
limitation of the UI's display, not necessarily a bug in the Spark
framework itself.

HTH

Mich Talebzadeh,

Technologist | Architect | Data Engineer  | Generative AI | FinCrime

London
United Kingdom


   view my Linkedin profile


 https://en.everybodywiki.com/Mich_Talebzadeh



Disclaimer: The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner Von Braun)".

Mich Talebzadeh,

Technologist | Architect | Data Engineer  | Generative AI | FinCrime

London
United Kingdom


   view my Linkedin profile


 https://en.everybodywiki.com/Mich_Talebzadeh



Disclaimer: The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner Von Braun)".



On Sun, 26 May 2024 at 16:45, Mich Talebzadeh  wrote:
>
> Yep, the Spark UI's Shuffle Write Size/Records" column can sometimes show 
> incorrect record counts when data is retrieved from cache or persisted data. 
> This happens because the record count reflects the number of records written 
> to disk for shuffling, and not the actual number of records in the cached or 
> persisted data itself. Add to it, because of lazy evaluation:, Spark may only 
> materialize a portion of the cached or persisted data when a task needs it. 
> The "Shuffle Write Size/Records" might only reflect the materialized portion, 
> not the total number of records in the cache/persistence. While the "Shuffle 
> Write Size/Records" might be inaccurate for cached/persisted data, the 
> "Shuffle Read Size/Records" column can be more reliable. This metric shows 
> the number of records read from shuffle by the following stage, which should 
> be closer to the actual number of records processed.
>
> HTH
>
> Mich Talebzadeh,
>
> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
>
> London
> United Kingdom
>
>
>view my Linkedin profile
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> Disclaimer: The information provided is correct to the best of my knowledge 
> but of course cannot be guaranteed . It is essential to note that, as with 
> any advice, quote "one test result is worth one-thousand expert opinions 
> (Werner Von Braun)".
>
>
>
> On Thu, 23 May 2024 at 17:45, Prem Sahoo  wrote:
>>
>> Hello Team,
>> in spark DAG UI , we have Stages tab. Once you click on each stage you can 
>> view the tasks.
>>
>> In each task we have a column "ShuffleWrite Size/Records " that column 
>> prints wrong data when it gets the data from cache/persist . it typically 
>> will show the wrong record number though the data size is correct for e.g  
>> 3.2G/ 7400 which is wrong .
>>
>> please advise.

-
To unsubscribe e-mail: user-unsubscr...@spark.apache.org

Re: BUG :: UI Spark

[Mich Talebzadeh]

Yep, the Spark UI's Shuffle Write Size/Records" column can sometimes show
incorrect record counts *when data is retrieved from cache or persisted
data*. This happens because the record count reflects the number of records
written to disk for shuffling, and not the actual number of records in the
cached or persisted data itself. Add to it, because of lazy evaluation:,
Spark may only materialize a portion of the cached or persisted data when a
task needs it. The "Shuffle Write Size/Records" might only reflect the
materialized portion, not the total number of records in the
cache/persistence. While the "Shuffle Write Size/Records" might be
inaccurate for cached/persisted data, the "Shuffle Read Size/Records"
column can be more reliable. This metric shows the number of records read
from shuffle by the following stage, which should be closer to the actual
number of records processed.

HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Thu, 23 May 2024 at 17:45, Prem Sahoo  wrote:

> Hello Team,
> in spark DAG UI , we have Stages tab. Once you click on each stage you can
> view the tasks.
>
> In each task we have a column "ShuffleWrite Size/Records " that column
> prints wrong data when it gets the data from cache/persist . it
> typically will show the wrong record number though the data size is correct
> for e.g  3.2G/ 7400 which is wrong .
>
> please advise.
>

Re: BUG :: UI Spark

[Sathi Chowdhury]

Can you please explain how did you realize it’s wrong? Did you check cloudwatch 
for the same metrics and compare? Also are you using do.cache() and expecting 
that shuffle read/write to go away ?


Sent from Yahoo Mail for iPhone


On Sunday, May 26, 2024, 7:53 AM, Prem Sahoo  wrote:

Can anyone please assist me ?
On Fri, May 24, 2024 at 12:29 AM Prem Sahoo  wrote:

Does anyone have a clue ?
On Thu, May 23, 2024 at 11:40 AM Prem Sahoo  wrote:

Hello Team,in spark DAG UI , we have Stages tab. Once you click on each stage 
you can view the tasks.
In each task we have a column "ShuffleWrite Size/Records " that column prints 
wrong data when it gets the data from cache/persist . it typically will show 
the wrong record number though the data size is correct for e.g  3.2G/ 7400 
which is wrong . 
please advise. 

Re: BUG :: UI Spark

[Prem Sahoo]

Can anyone please assist me ?

On Fri, May 24, 2024 at 12:29 AM Prem Sahoo  wrote:

> Does anyone have a clue ?
>
> On Thu, May 23, 2024 at 11:40 AM Prem Sahoo  wrote:
>
>> Hello Team,
>> in spark DAG UI , we have Stages tab. Once you click on each stage you
>> can view the tasks.
>>
>> In each task we have a column "ShuffleWrite Size/Records " that column
>> prints wrong data when it gets the data from cache/persist . it
>> typically will show the wrong record number though the data size is correct
>> for e.g  3.2G/ 7400 which is wrong .
>>
>> please advise.
>>
>

Re: Can Spark Catalog Perform Multimodal Database Query Analysis

[Mich Talebzadeh]

Something like this in Python

from pyspark.sql import SparkSession

# Configure Spark Session with JDBC URLs
spark_conf = SparkConf() \
  .setAppName("SparkCatalogMultipleSources") \
  .set("hive.metastore.uris",
"thrift://hive1-metastore:9080,thrift://hive2-metastore:9080")

jdbc_urls = ["jdbc:hive2://hive1-jdbc:1",
"jdbc:hive2://hive2-jdbc:1"]
mysql_jdbc_url = "jdbc:mysql://mysql-host:3306/mysql_database"

spark = SparkSession.builder \
  .config(spark_conf) \
  .enableHiveSupport() \
  .getOrCreate()

# Accessing tables from Hive1, Hive2, and MySQL
spark.sql("SELECT * FROM hive1.table1").show()
spark.sql("SELECT * FROM hive2.table2").show()
spark.sql("SELECT * FROM mysql.table1").show()

# Optional: Create temporary views for easier joining (if needed)
spark.sql("CREATE TEMPORARY VIEW hive1_table1 AS SELECT * FROM
hive1.table1")
spark.sql("CREATE TEMPORARY VIEW hive2_table2 AS SELECT * FROM
hive2.table2")
spark.sql("CREATE TEMPORARY VIEW mysql_table1 AS SELECT * FROM
mysql.table1")

HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Fri, 24 May 2024 at 09:41, 志阳 <308027...@qq.com.invalid> wrote:

> I have two clusters hive1 and hive2, as well as a MySQL database. Can I
> use Spark Catalog for registration, but can I only use one catalog at a
> time? Can multiple catalogs be joined across databases.
> select * from
>  hive1.table1 join hive2.table2 join mysql.table1
> where 
>
> --
> 志阳
> 308027...@qq.com
>
> 
>
>

Re: Dstream HasOffsetRanges equivalent in Structured streaming

[Anil Dasari]

It appears that structured streaming and Dstream have entirely different
microbatch metadata representation
Can someone assist me in finding the following Dstream microbatch metadata
equivalent in Structured streaming.

1. microbatch timestamp : structured streaming foreachBatch gives batchID
which is not a timestamp. Is there a way to get the microbatch timestamp ?
2. microbatch start event ?
3. scheduling delay of a microbatch ?
4. pending microbatches in case of fixed internal microbatches ?

Thanks

On Wed, May 22, 2024 at 5:23 PM Anil Dasari  wrote:

> You are right.
> - another question on migration. Is there a way to get the microbatch id
> during the microbatch dataset `trasform` operation like in rdd transform ?
> I am attempting to implement the following pseudo functionality with
> structured streaming. In this approach, recordCategoriesMetadata is fetched
> and rdd metrics like rdd size etc using microbatch idin the transform
> operation.
> ```code
> val rddQueue = new mutable.Queue[RDD[Int]]()
> // source components
> val sources = Seq.empty[String]
> val consolidatedDstream = sources
> .map(source => {
> val inputStream = ssc.queueStream(rddQueue)
> inputStream.transform((rdd, ts) => {
> // emit metrics of microbatch ts : rdd size etc.
>
> val recordCategories = rdd.map(..).collect();
> val recordCategoriesMetadata = ...
> rdd
> .map(r =>
> val customRecord = CustomRecord(r, SomeMetadataOfRecordSchema)
> (source, customRecord)
> )
> })
> }
> )
> .reduceLeft(_ union _)
>
> consolidatedDstream
> .foreachRDD((rdd, ts) => {
> // get pipes for each source
> val pipes = Seq.empty[String] // pipes of given source
> pipes.foreach(pipe => {
> val pipeSource = null; // get from pipe variable
> val psRDD = rdd
> .filter {
> case (source, sourceRDD) => source.equals(pipeSource)
> }
> // apply pipe transformation and sink
>
> })
> })
> ```
>
> In structured streaming, it can look like -
>
> ```code
> val consolidatedDstream = sources
> .map(source => {
> val inputStream = ... (for each source)
> inputStream
> }
> )
> .reduceLeft(_ union _)
>
> consolidatedDstream
> .writeStream
> .foreachBatch((ds, ts) => {
> val newDS = ds.transform((internalDS => {
> // emit metrics of microbatch ts : rdd size etc.
>
> val recordCategories = rdd.map(..).collect();
> val recordCategoriesMetadata = ...
> internalDS
> .map(r =>
> val customRecord = CustomRecord(r, SomeMetadataOfRecordSchema)
> (source, customRecord)
> )
> })(... )
> // get pipes for each source
> val pipes = Seq.empty[String] // pipes of given source
> pipes.foreach(pipe => {
> val pipeSource = null; // get from pipe variable
> val psRDD = newDS
> .filter {
> case (source, sourceDS) => source.equals(pipeSource)
> }
> // apply pipe transformation and sink
>
> })
> })
> ```
> ^ is just pseudo code and still not sure if it works. Let me know your
> suggestions if any. thanks.
>
> On Wed, May 22, 2024 at 8:34 AM Tathagata Das 
> wrote:
>
>> The right way to associated microbatches when committing to external
>> storage is to use the microbatch id that you can get in foreachBatch. That
>> microbatch id guarantees that the data produced in the batch is the always
>> the same no matter any recomputations (assuming all processing logic is
>> deterministic). So you can commit the batch id + batch data together. And
>> then async commit the batch id + offsets.
>>
>> On Wed, May 22, 2024 at 11:27 AM Anil Dasari 
>> wrote:
>>
>>> Thanks Das, Mtich.
>>>
>>> Mitch,
>>> We process data from Kafka and write it to S3 in Parquet format using
>>> Dstreams. To ensure exactly-once delivery and prevent data loss, our
>>> process records micro-batch offsets to an external storage at the end of
>>> each micro-batch in foreachRDD, which is then used when the job restarts.
>>>
>>> Das,
>>> Thanks for sharing the details. I will look into them.
>>> Unfortunately, the listeners process is async and can't
>>> guarantee happens before association with microbatch to commit offsets to
>>> external storage. But still they will work. Is there a way to access
>>> lastProgress in foreachBatch ?
>>>
>>>
>>> On Wed, May 22, 2024 at 7:35 AM Tathagata Das <
>>> tathagata.das1...@gmail.com> wrote:
>>>
 If you want to find what offset ranges are present in a microbatch in
 Structured Streaming, you have to look at the
 StreamingQuery.lastProgress or use the QueryProgressListener
 .
 Both of these approaches gives you access to the SourceProgress
 
 which gives Kafka offsets as a JSON string.

 Hope this helps!

 On Wed, May 22, 2024 at 10:04 AM Mich Talebzadeh <
 mich.talebza...@gmail.com> wrote:

> OK to understand better your current model relies on streaming data
> input through Kafka topic, Spark does some ETL and you send to a sink, 

Re: BUG :: UI Spark

[Prem Sahoo]

Does anyone have a clue ?

On Thu, May 23, 2024 at 11:40 AM Prem Sahoo  wrote:

> Hello Team,
> in spark DAG UI , we have Stages tab. Once you click on each stage you can
> view the tasks.
>
> In each task we have a column "ShuffleWrite Size/Records " that column
> prints wrong data when it gets the data from cache/persist . it
> typically will show the wrong record number though the data size is correct
> for e.g  3.2G/ 7400 which is wrong .
>
> please advise.
>

Re: [s3a] Spark is not reading s3 object content

[Mich Talebzadeh]

Could be a number of reasons

First test reading the file with a cli

aws s3 cp s3a://input/testfile.csv .
cat testfile.csv


Try this code with debug option to diagnose the problem

from pyspark.sql import SparkSession
from pyspark.sql.utils import AnalysisException

try:
# Initialize Spark session
spark = SparkSession.builder \
.appName("S3ReadTest") \
.config("spark.jars.packages",
"org.apache.hadoop:hadoop-aws:3.3.6") \
.config("spark.hadoop.fs.s3a.access.key", "R*6") \
.config("spark.hadoop.fs.s3a.secret.key", "1***e") \
.config("spark.hadoop.fs.s3a.endpoint", "192.168.52.63:8000") \
.config("spark.hadoop.fs.s3a.path.style.access", "true") \
.config("spark.hadoop.fs.s3a.impl",
"org.apache.hadoop.fs.s3a.S3AFileSystem") \
.getOrCreate()

# Read the CSV file from S3
df = spark.read \
.option("header", "true") \
.option("inferSchema", "true") \
.option("delimiter", " ") \  # ensure this is apace
.csv("s3a://input/testfile.csv")

# Show the data
df.show(n=1)

except AnalysisException as e:
print(f"AnalysisException: {e}")
except Exception as e:
print(f"Error: {e}")
finally:
# Stop the Spark session
spark.stop()

HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Thu, 23 May 2024 at 20:14, Amin Mosayyebzadeh 
wrote:

> I am trying to read an s3 object from a local S3 storage (Ceph based)
> using Spark 3.5.1. I see it can access the bucket and list the files (I
> have verified it on Ceph side by checking its logs), even returning the
> correct size of the object. But the content is not read.
>
> The object url is:
> s3a://input/testfile.csv (I have also tested a nested bucket:
> s3a://test1/test2/test3/testfile.csv)
>
>
> Object's content:
>
> =
> name int1 int2
> first 1 2
> second 3 4
> =
>
>
> Here is the config I have set so far:
>
> ("spark.jars.packages", "org.apache.hadoop:hadoop-aws:3.3.6")
> ("spark.hadoop.fs.s3a.access.key", "R*6")
> ("spark.hadoop.fs.s3a.secret.key", "1***e")
> ("spark.hadoop.fs.s3a.endpoint", "192.168.52.63:8000")
> ("spark.hadoop.fs.s3a.path.style.access", "true")
> ("spark.hadoop.fs.s3a.impl", "org.apache.hadoop.fs.s3a.S3AFileSystem")
>
>
> The outop for my following Pyspark application:
> df = spark.read \
> .option("header", "true") \
> .schema(schema) \
> .csv("s3a://input/testfile.csv", sep=' ')
>
> df.show(n=1)
> ==
> 24/05/20 02:35:00 INFO MetricsSystemImpl: s3a-file-system metrics system 
> started24/05/20 02:35:01 INFO MetadataLogFileIndex: Reading streaming file 
> log from s3a://input/testfile.csv/_spark_metadata24/05/20 02:35:01 INFO 
> FileStreamSinkLog: BatchIds found from listing:24/05/20 02:35:03 INFO 
> FileSourceStrategy: Pushed Filters:24/05/20 02:35:03 INFO FileSourceStrategy: 
> Post-Scan Filters:24/05/20 02:35:03 INFO CodeGenerator: Code generated in 
> 176.139675 ms24/05/20 02:35:03 INFO MemoryStore: Block broadcast_0 stored as 
> values in memory (estimated size 496.6 KiB, free 4.1 GiB)24/05/20 02:35:03 
> INFO MemoryStore: Block broadcast_0_piece0 stored as bytes in memory 
> (estimated size 54.4 KiB, free 4.1 GiB)24/05/20 02:35:03 INFO 
> BlockManagerInfo: Added broadcast_0_piece0 in memory on master:38197 (size: 
> 54.4 KiB, free: 4.1 GiB)24/05/20 02:35:03 INFO SparkContext: Created 
> broadcast 0 from showString at NativeMethodAccessorImpl.java:024/05/20 
> 02:35:03 INFO FileSourceScanExec: Planning scan with bin packing, max size: 
> 4194304 bytes, open cost is considered as scanning 4194304 bytes.
> ++++
> |name|int1|int2|
> ++++
> ++++
> 24/05/20 02:35:04 INFO SparkContext: Invoking stop() from shutdown 
> hook24/05/20 02:35:04 INFO SparkContext: SparkContext is stopping with 
> exitCode 0
> =
>
> Am I missing something here?
>
> P.S. I see OP_IS_DIRECTORY is set to 1. Is that a correct behavior?
>
>
> Thanks in advance!
>
>

Re: Dstream HasOffsetRanges equivalent in Structured streaming

[Anil Dasari]

You are right.
- another question on migration. Is there a way to get the microbatch id
during the microbatch dataset `trasform` operation like in rdd transform ?
I am attempting to implement the following pseudo functionality with
structured streaming. In this approach, recordCategoriesMetadata is fetched
and rdd metrics like rdd size etc using microbatch idin the transform
operation.
```code
val rddQueue = new mutable.Queue[RDD[Int]]()
// source components
val sources = Seq.empty[String]
val consolidatedDstream = sources
.map(source => {
val inputStream = ssc.queueStream(rddQueue)
inputStream.transform((rdd, ts) => {
// emit metrics of microbatch ts : rdd size etc.

val recordCategories = rdd.map(..).collect();
val recordCategoriesMetadata = ...
rdd
.map(r =>
val customRecord = CustomRecord(r, SomeMetadataOfRecordSchema)
(source, customRecord)
)
})
}
)
.reduceLeft(_ union _)

consolidatedDstream
.foreachRDD((rdd, ts) => {
// get pipes for each source
val pipes = Seq.empty[String] // pipes of given source
pipes.foreach(pipe => {
val pipeSource = null; // get from pipe variable
val psRDD = rdd
.filter {
case (source, sourceRDD) => source.equals(pipeSource)
}
// apply pipe transformation and sink

})
})
```

In structured streaming, it can look like -

```code
val consolidatedDstream = sources
.map(source => {
val inputStream = ... (for each source)
inputStream
}
)
.reduceLeft(_ union _)

consolidatedDstream
.writeStream
.foreachBatch((ds, ts) => {
val newDS = ds.transform((internalDS => {
// emit metrics of microbatch ts : rdd size etc.

val recordCategories = rdd.map(..).collect();
val recordCategoriesMetadata = ...
internalDS
.map(r =>
val customRecord = CustomRecord(r, SomeMetadataOfRecordSchema)
(source, customRecord)
)
})(... )
// get pipes for each source
val pipes = Seq.empty[String] // pipes of given source
pipes.foreach(pipe => {
val pipeSource = null; // get from pipe variable
val psRDD = newDS
.filter {
case (source, sourceDS) => source.equals(pipeSource)
}
// apply pipe transformation and sink

})
})
```
^ is just pseudo code and still not sure if it works. Let me know your
suggestions if any. thanks.

On Wed, May 22, 2024 at 8:34 AM Tathagata Das 
wrote:

> The right way to associated microbatches when committing to external
> storage is to use the microbatch id that you can get in foreachBatch. That
> microbatch id guarantees that the data produced in the batch is the always
> the same no matter any recomputations (assuming all processing logic is
> deterministic). So you can commit the batch id + batch data together. And
> then async commit the batch id + offsets.
>
> On Wed, May 22, 2024 at 11:27 AM Anil Dasari 
> wrote:
>
>> Thanks Das, Mtich.
>>
>> Mitch,
>> We process data from Kafka and write it to S3 in Parquet format using
>> Dstreams. To ensure exactly-once delivery and prevent data loss, our
>> process records micro-batch offsets to an external storage at the end of
>> each micro-batch in foreachRDD, which is then used when the job restarts.
>>
>> Das,
>> Thanks for sharing the details. I will look into them.
>> Unfortunately, the listeners process is async and can't guarantee happens
>> before association with microbatch to commit offsets to external storage.
>> But still they will work. Is there a way to access lastProgress in
>> foreachBatch ?
>>
>>
>> On Wed, May 22, 2024 at 7:35 AM Tathagata Das <
>> tathagata.das1...@gmail.com> wrote:
>>
>>> If you want to find what offset ranges are present in a microbatch in
>>> Structured Streaming, you have to look at the
>>> StreamingQuery.lastProgress or use the QueryProgressListener
>>> .
>>> Both of these approaches gives you access to the SourceProgress
>>> 
>>> which gives Kafka offsets as a JSON string.
>>>
>>> Hope this helps!
>>>
>>> On Wed, May 22, 2024 at 10:04 AM Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>
 OK to understand better your current model relies on streaming data
 input through Kafka topic, Spark does some ETL and you send to a sink, a
 database for file storage like HDFS etc?

 Your current architecture relies on Direct Streams (DStream) and RDDs
 and you want to move to Spark sStructured Streaming based on dataframes and
 datasets?

 You have not specified your sink

 With regard to your question?

 "Is there an equivalent of Dstream HasOffsetRanges in structure
 streaming to get the microbatch end offsets to the checkpoint in our
 external checkpoint store ?"

 There is not a direct equivalent of DStream HasOffsetRanges in Spark
 Structured Streaming. However, Structured Streaming provides mechanisms to
 achieve similar functionality:

 HTH

 Mich Talebzadeh,
 Technologist | Architect | Data Engineer  | 

Re: Dstream HasOffsetRanges equivalent in Structured streaming

[Mich Talebzadeh]

With regard to this sentence


*Offset Tracking with Structured Streaming:: While storing offsets in an
external storage with DStreams was necessary, SSS handles this
automatically through checkpointing. The checkpoints include the offsets
processed by each micro-batch. However, you can still access the most
recent offsets using the offset() method on your StreamingQuery object for
monitoring purposes that is if you need it*

In essence, with SSS and checkpointing in place, you can rely on the
automatic offset management provided by the framework,
*eliminating the need for the custom offset storage you had with DStreams.*

HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Wed, 22 May 2024 at 19:49, Mich Talebzadeh 
wrote:

> Hi  Anil,
>
> Ok let us put the complete picture here
>
>* Current DStreams Setup:*
>
>- Data Source: Kafka
>- Processing Engine: Spark DStreams
>- Data Transformation with Spark
>- Sink: S3
>- Data Format: Parquet
>- Exactly-Once Delivery (Attempted): You're attempting exactly-once
>delivery by recording micro-batch offsets in an external storage using
>foreachRDD at the end of each micro-batch. This allows you to potentially
>restart the job from the last processed offset in case of failures?
>- Challenges with DStreams for Exactly-Once Delivery: Spark DStreams
>offer limited built-in support for exactly-once delivery guarantees.
>
>
> *Moving to Spark Structured Streaming: (SSS)*
>
> All stays the same. except below
>
>
>- Exactly-Once Delivery which is guaranteed by SSS
>- Checkpointing: Enable checkpointing by setting the
>checkpointLocation option in  writeStream. Spark will periodically
>checkpoint the state of streaming query, including offsets, to a designated
>location (e.g., HDFS, cloud storage or SSD).
>- Offset Tracking with Structured Streaming:: While storing offsets in
>an external storage with DStreams was necessary, SSS handles this
>automatically through checkpointing. The checkpoints include the offsets
>processed by each micro-batch. However, you can still access the most
>recent offsets using the offset() method on your StreamingQuery object for
>monitoring purposes that is if you need it
>
> Have a look at this article of mine  about  structured streaming  and
> checkpointing
>
> Processing Change Data Capture with Spark Structured Streaming
> 
>
> In your case briefly
>
> def *store_offsets_to_checkpoint*(df, batchId):
> if(len(df.take(1))) > 0:
>  df. persist()
>  # Extract offsets from the DataFrame (assuming a column named
> 'offset')
>  offset_rows = df.select(col('offset')).rdd.collect()
>  # Create OffsetRange objects from extracted offsets
>  offsets = [OffsetRange(partition=row.partition,
> fromOffset=row.offset, toOffset=row.offset + 1) # Assuming 'partition'
> and 'offset' columns
> for row in offset_rows]
>  # Logic to store offsets in your external checkpoint store)
>   ..
>   df.unpersist()
> else:
>   print("DataFrame is empty")
>
> # Define your Structured Streaming application with Kafka source and sink
>
>"""
>"foreach" performs custom write logic on each row and
> "foreachBatch" performs custom write logic on each micro-batch through
> *store_offsets_to_checkpoint* function
> foreachBatch(*store_offsets_to_checkpoint*) expects 2
> parameters, first: micro-batch as DataFrame or Dataset and second: unique
> id for each batch
>Using foreachBatch, we write each micro batch to storage
> defined in our custom logic
> """
>
> streaming = spark.readStream \
>.format("kafka") \ .
> option("kafka.bootstrap.servers", "localhost:9092") \
>   .option("subscribe", "topic_name") \
>   .load()
>
> # Custom sink function to store offsets in checkpoint
> streaming = streaming.writeStream \
>  . format("memory")  \
>  *.option("checkpointLocation", "/path/to/checkpoint/store") \ *
>   .foreachBatch(*store_offsets_to_checkpoint*) \
>   .start()
>
> HTH
>
>
> 
>
> 

Re: Dstream HasOffsetRanges equivalent in Structured streaming

[Mich Talebzadeh]

Hi  Anil,

Ok let us put the complete picture here

   * Current DStreams Setup:*

   - Data Source: Kafka
   - Processing Engine: Spark DStreams
   - Data Transformation with Spark
   - Sink: S3
   - Data Format: Parquet
   - Exactly-Once Delivery (Attempted): You're attempting exactly-once
   delivery by recording micro-batch offsets in an external storage using
   foreachRDD at the end of each micro-batch. This allows you to potentially
   restart the job from the last processed offset in case of failures?
   - Challenges with DStreams for Exactly-Once Delivery: Spark DStreams
   offer limited built-in support for exactly-once delivery guarantees.


*Moving to Spark Structured Streaming: (SSS)*

All stays the same. except below


   - Exactly-Once Delivery which is guaranteed by SSS
   - Checkpointing: Enable checkpointing by setting the checkpointLocation
   option in  writeStream. Spark will periodically checkpoint the state of
   streaming query, including offsets, to a designated location (e.g., HDFS,
   cloud storage or SSD).
   - Offset Tracking with Structured Streaming:: While storing offsets in
   an external storage with DStreams was necessary, SSS handles this
   automatically through checkpointing. The checkpoints include the offsets
   processed by each micro-batch. However, you can still access the most
   recent offsets using the offset() method on your StreamingQuery object for
   monitoring purposes that is if you need it

Have a look at this article of mine  about  structured streaming  and
checkpointing

Processing Change Data Capture with Spark Structured Streaming


In your case briefly

def *store_offsets_to_checkpoint*(df, batchId):
if(len(df.take(1))) > 0:
 df. persist()
 # Extract offsets from the DataFrame (assuming a column named
'offset')
 offset_rows = df.select(col('offset')).rdd.collect()
 # Create OffsetRange objects from extracted offsets
 offsets = [OffsetRange(partition=row.partition,
fromOffset=row.offset, toOffset=row.offset + 1) # Assuming 'partition' and
'offset' columns
for row in offset_rows]
 # Logic to store offsets in your external checkpoint store)
  ..
  df.unpersist()
else:
  print("DataFrame is empty")

# Define your Structured Streaming application with Kafka source and sink

   """
   "foreach" performs custom write logic on each row and
"foreachBatch" performs custom write logic on each micro-batch through
*store_offsets_to_checkpoint* function
foreachBatch(*store_offsets_to_checkpoint*) expects 2
parameters, first: micro-batch as DataFrame or Dataset and second: unique
id for each batch
   Using foreachBatch, we write each micro batch to storage
defined in our custom logic
"""

streaming = spark.readStream \
   .format("kafka") \ .
option("kafka.bootstrap.servers", "localhost:9092") \
  .option("subscribe", "topic_name") \
  .load()

# Custom sink function to store offsets in checkpoint
streaming = streaming.writeStream \
 . format("memory")  \
 *.option("checkpointLocation", "/path/to/checkpoint/store") \ *
  .foreachBatch(*store_offsets_to_checkpoint*) \
  .start()

HTH



Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Wed, 22 May 2024 at 16:27, Anil Dasari  wrote:

> Thanks Das, Mtich.
>
> Mitch,
> We process data from Kafka and write it to S3 in Parquet format using
> Dstreams. To ensure exactly-once delivery and prevent data loss, our
> process records micro-batch offsets to an external storage at the end of
> each micro-batch in foreachRDD, which is then used when the job restarts.
>
> Das,
> Thanks for sharing the details. I will look into them.
> Unfortunately, the listeners process is async and can't guarantee happens
> before association with microbatch to commit offsets to external storage.
> But still they will work. Is there a way to access lastProgress in
> foreachBatch ?
>
>
> On Wed, May 22, 2024 at 7:35 AM Tathagata Das 
> wrote:
>
>> If you want to find what 

Re: Dstream HasOffsetRanges equivalent in Structured streaming

[Tathagata Das]

The right way to associated microbatches when committing to external
storage is to use the microbatch id that you can get in foreachBatch. That
microbatch id guarantees that the data produced in the batch is the always
the same no matter any recomputations (assuming all processing logic is
deterministic). So you can commit the batch id + batch data together. And
then async commit the batch id + offsets.

On Wed, May 22, 2024 at 11:27 AM Anil Dasari  wrote:

> Thanks Das, Mtich.
>
> Mitch,
> We process data from Kafka and write it to S3 in Parquet format using
> Dstreams. To ensure exactly-once delivery and prevent data loss, our
> process records micro-batch offsets to an external storage at the end of
> each micro-batch in foreachRDD, which is then used when the job restarts.
>
> Das,
> Thanks for sharing the details. I will look into them.
> Unfortunately, the listeners process is async and can't guarantee happens
> before association with microbatch to commit offsets to external storage.
> But still they will work. Is there a way to access lastProgress in
> foreachBatch ?
>
>
> On Wed, May 22, 2024 at 7:35 AM Tathagata Das 
> wrote:
>
>> If you want to find what offset ranges are present in a microbatch in
>> Structured Streaming, you have to look at the StreamingQuery.lastProgress or
>> use the QueryProgressListener
>> .
>> Both of these approaches gives you access to the SourceProgress
>> 
>> which gives Kafka offsets as a JSON string.
>>
>> Hope this helps!
>>
>> On Wed, May 22, 2024 at 10:04 AM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>> OK to understand better your current model relies on streaming data
>>> input through Kafka topic, Spark does some ETL and you send to a sink, a
>>> database for file storage like HDFS etc?
>>>
>>> Your current architecture relies on Direct Streams (DStream) and RDDs
>>> and you want to move to Spark sStructured Streaming based on dataframes and
>>> datasets?
>>>
>>> You have not specified your sink
>>>
>>> With regard to your question?
>>>
>>> "Is there an equivalent of Dstream HasOffsetRanges in structure
>>> streaming to get the microbatch end offsets to the checkpoint in our
>>> external checkpoint store ?"
>>>
>>> There is not a direct equivalent of DStream HasOffsetRanges in Spark
>>> Structured Streaming. However, Structured Streaming provides mechanisms to
>>> achieve similar functionality:
>>>
>>> HTH
>>>
>>> Mich Talebzadeh,
>>> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>> 
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> *Disclaimer:* The information provided is correct to the best of my
>>> knowledge but of course cannot be guaranteed . It is essential to note
>>> that, as with any advice, quote "one test result is worth one-thousand
>>> expert opinions (Werner
>>> Von Braun
>>> )".
>>>
>>>
>>> On Wed, 22 May 2024 at 10:32, ashok34...@yahoo.com.INVALID
>>>  wrote:
>>>
 Hello,

 what options are you considering yourself?

 On Wednesday 22 May 2024 at 07:37:30 BST, Anil Dasari <
 adas...@guidewire.com> wrote:


 Hello,

 We are on Spark 3.x and using Spark dstream + kafka and planning to use
 structured streaming + Kafka.
 Is there an equivalent of Dstream HasOffsetRanges in structure
 streaming to get the microbatch end offsets to the checkpoint in our
 external checkpoint store ? Thanks in advance.

 Regards

Re: Dstream HasOffsetRanges equivalent in Structured streaming

[Anil Dasari]

Thanks Das, Mtich.

Mitch,
We process data from Kafka and write it to S3 in Parquet format using
Dstreams. To ensure exactly-once delivery and prevent data loss, our
process records micro-batch offsets to an external storage at the end of
each micro-batch in foreachRDD, which is then used when the job restarts.

Das,
Thanks for sharing the details. I will look into them.
Unfortunately, the listeners process is async and can't guarantee happens
before association with microbatch to commit offsets to external storage.
But still they will work. Is there a way to access lastProgress in
foreachBatch ?


On Wed, May 22, 2024 at 7:35 AM Tathagata Das 
wrote:

> If you want to find what offset ranges are present in a microbatch in
> Structured Streaming, you have to look at the StreamingQuery.lastProgress or
> use the QueryProgressListener
> .
> Both of these approaches gives you access to the SourceProgress
> 
> which gives Kafka offsets as a JSON string.
>
> Hope this helps!
>
> On Wed, May 22, 2024 at 10:04 AM Mich Talebzadeh <
> mich.talebza...@gmail.com> wrote:
>
>> OK to understand better your current model relies on streaming data input
>> through Kafka topic, Spark does some ETL and you send to a sink, a
>> database for file storage like HDFS etc?
>>
>> Your current architecture relies on Direct Streams (DStream) and RDDs and
>> you want to move to Spark sStructured Streaming based on dataframes and
>> datasets?
>>
>> You have not specified your sink
>>
>> With regard to your question?
>>
>> "Is there an equivalent of Dstream HasOffsetRanges in structure
>> streaming to get the microbatch end offsets to the checkpoint in our
>> external checkpoint store ?"
>>
>> There is not a direct equivalent of DStream HasOffsetRanges in Spark
>> Structured Streaming. However, Structured Streaming provides mechanisms to
>> achieve similar functionality:
>>
>> HTH
>>
>> Mich Talebzadeh,
>> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>> 
>>
>>
>>
>> *Disclaimer:* The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner
>> Von
>> Braun
>> 
>> )".
>>
>>
>> On Wed, 22 May 2024 at 10:32, ashok34...@yahoo.com.INVALID
>>  wrote:
>>
>>> Hello,
>>>
>>> what options are you considering yourself?
>>>
>>> On Wednesday 22 May 2024 at 07:37:30 BST, Anil Dasari <
>>> adas...@guidewire.com> wrote:
>>>
>>>
>>> Hello,
>>>
>>> We are on Spark 3.x and using Spark dstream + kafka and planning to use
>>> structured streaming + Kafka.
>>> Is there an equivalent of Dstream HasOffsetRanges in structure streaming
>>> to get the microbatch end offsets to the checkpoint in our external
>>> checkpoint store ? Thanks in advance.
>>>
>>> Regards
>>>
>>>

Re: Dstream HasOffsetRanges equivalent in Structured streaming

[Tathagata Das]

If you want to find what offset ranges are present in a microbatch in
Structured Streaming, you have to look at the StreamingQuery.lastProgress or
use the QueryProgressListener
.
Both of these approaches gives you access to the SourceProgress

which gives Kafka offsets as a JSON string.

Hope this helps!

On Wed, May 22, 2024 at 10:04 AM Mich Talebzadeh 
wrote:

> OK to understand better your current model relies on streaming data input
> through Kafka topic, Spark does some ETL and you send to a sink, a
> database for file storage like HDFS etc?
>
> Your current architecture relies on Direct Streams (DStream) and RDDs and
> you want to move to Spark sStructured Streaming based on dataframes and
> datasets?
>
> You have not specified your sink
>
> With regard to your question?
>
> "Is there an equivalent of Dstream HasOffsetRanges in structure streaming
> to get the microbatch end offsets to the checkpoint in our external
> checkpoint store ?"
>
> There is not a direct equivalent of DStream HasOffsetRanges in Spark
> Structured Streaming. However, Structured Streaming provides mechanisms to
> achieve similar functionality:
>
> HTH
>
> Mich Talebzadeh,
> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
>
> On Wed, 22 May 2024 at 10:32, ashok34...@yahoo.com.INVALID
>  wrote:
>
>> Hello,
>>
>> what options are you considering yourself?
>>
>> On Wednesday 22 May 2024 at 07:37:30 BST, Anil Dasari <
>> adas...@guidewire.com> wrote:
>>
>>
>> Hello,
>>
>> We are on Spark 3.x and using Spark dstream + kafka and planning to use
>> structured streaming + Kafka.
>> Is there an equivalent of Dstream HasOffsetRanges in structure streaming
>> to get the microbatch end offsets to the checkpoint in our external
>> checkpoint store ? Thanks in advance.
>>
>> Regards
>>
>>

Re: Dstream HasOffsetRanges equivalent in Structured streaming

[Mich Talebzadeh]

OK to understand better your current model relies on streaming data input
through Kafka topic, Spark does some ETL and you send to a sink, a
database for file storage like HDFS etc?

Your current architecture relies on Direct Streams (DStream) and RDDs and
you want to move to Spark sStructured Streaming based on dataframes and
datasets?

You have not specified your sink

With regard to your question?

"Is there an equivalent of Dstream HasOffsetRanges in structure streaming
to get the microbatch end offsets to the checkpoint in our external
checkpoint store ?"

There is not a direct equivalent of DStream HasOffsetRanges in Spark
Structured Streaming. However, Structured Streaming provides mechanisms to
achieve similar functionality:

HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Wed, 22 May 2024 at 10:32, ashok34...@yahoo.com.INVALID
 wrote:

> Hello,
>
> what options are you considering yourself?
>
> On Wednesday 22 May 2024 at 07:37:30 BST, Anil Dasari <
> adas...@guidewire.com> wrote:
>
>
> Hello,
>
> We are on Spark 3.x and using Spark dstream + kafka and planning to use
> structured streaming + Kafka.
> Is there an equivalent of Dstream HasOffsetRanges in structure streaming
> to get the microbatch end offsets to the checkpoint in our external
> checkpoint store ? Thanks in advance.
>
> Regards
>
>

Re: Dstream HasOffsetRanges equivalent in Structured streaming

[ashok34...@yahoo.com.INVALID]

 Hello,
what options are you considering yourself?
On Wednesday 22 May 2024 at 07:37:30 BST, Anil Dasari 
 wrote:  
 
 Hello,

We are on Spark 3.x and using Spark dstream + kafka and planning to use 
structured streaming + Kafka. Is there an equivalent of Dstream HasOffsetRanges 
in structure streaming to get the microbatch end offsets to the checkpoint in 
our external checkpoint store ? Thanks in advance. 
Regards
  

Re: Re: EXT: Dual Write to HDFS and MinIO in faster way

[Prem Sahoo]

I am looking for writer/comitter optimization which can make the spark
write faster.

On Tue, May 21, 2024 at 9:15 PM eab...@163.com  wrote:

> Hi，
> I think you should write to HDFS then copy file (parquet or orc) from
> HDFS to MinIO.
>
> --
> eabour
>
>
> *From:* Prem Sahoo 
> *Date:* 2024-05-22 00:38
> *To:* Vibhor Gupta ; user
> 
> *Subject:* Re: EXT: Dual Write to HDFS and MinIO in faster way
>
>
> On Tue, May 21, 2024 at 6:58 AM Prem Sahoo  wrote:
>
>> Hello Vibhor,
>> Thanks for the suggestion .
>> I am looking for some other alternatives where I can use the same
>> dataframe can be written to two destinations without re execution and cache
>> or persist .
>>
>> Can some one help me in scenario 2 ?
>> How to make spark write to MinIO faster ?
>> Sent from my iPhone
>>
>> On May 21, 2024, at 1:18 AM, Vibhor Gupta 
>> wrote:
>>
>> 
>>
>> Hi Prem,
>>
>>
>>
>> You can try to write to HDFS then read from HDFS and write to MinIO.
>>
>>
>>
>> This will prevent duplicate transformation.
>>
>>
>>
>> You can also try persisting the dataframe using the DISK_ONLY level.
>>
>>
>>
>> Regards,
>>
>> Vibhor
>>
>> *From: *Prem Sahoo 
>> *Date: *Tuesday, 21 May 2024 at 8:16 AM
>> *To: *Spark dev list 
>> *Subject: *EXT: Dual Write to HDFS and MinIO in faster way
>>
>> *EXTERNAL: *Report suspicious emails to *Email Abuse.*
>>
>> Hello Team,
>>
>> I am planning to write to two datasource at the same time .
>>
>>
>>
>> Scenario:-
>>
>>
>>
>> Writing the same dataframe to HDFS and MinIO without re-executing the
>> transformations and no cache(). Then how can we make it faster ?
>>
>>
>>
>> Read the parquet file and do a few transformations and write to HDFS and
>> MinIO.
>>
>>
>>
>> here in both write spark needs execute the transformation again. Do we
>> know how we can avoid re-execution of transformation  without
>> cache()/persist ?
>>
>>
>>
>> Scenario2 :-
>>
>> I am writing 3.2G data to HDFS and MinIO which takes ~6mins.
>>
>> Do we have any way to make writing this faster ?
>>
>>
>>
>> I don't want to do repartition and write as repartition will have
>> overhead of shuffling .
>>
>>
>>
>> Please provide some inputs.
>>
>>
>>
>>
>>
>>

Re: Re: EXT: Dual Write to HDFS and MinIO in faster way

[eab...@163.com]

Hi，
I think you should write to HDFS then copy file (parquet or orc) from HDFS 
to MinIO.



eabour
 
From: Prem Sahoo
Date: 2024-05-22 00:38
To: Vibhor Gupta; user
Subject: Re: EXT: Dual Write to HDFS and MinIO in faster way


On Tue, May 21, 2024 at 6:58 AM Prem Sahoo  wrote:
Hello Vibhor,
Thanks for the suggestion .
I am looking for some other alternatives where I can use the same dataframe can 
be written to two destinations without re execution and cache or persist .

Can some one help me in scenario 2 ?
How to make spark write to MinIO faster ?
Sent from my iPhone

On May 21, 2024, at 1:18 AM, Vibhor Gupta  wrote:

 
Hi Prem,
 
You can try to write to HDFS then read from HDFS and write to MinIO.
 
This will prevent duplicate transformation.
 
You can also try persisting the dataframe using the DISK_ONLY level.
 
Regards,
Vibhor
From: Prem Sahoo 
Date: Tuesday, 21 May 2024 at 8:16 AM
To: Spark dev list 
Subject: EXT: Dual Write to HDFS and MinIO in faster way
EXTERNAL: Report suspicious emails to Email Abuse.
Hello Team,
I am planning to write to two datasource at the same time . 
 
Scenario:-
 
Writing the same dataframe to HDFS and MinIO without re-executing the 
transformations and no cache(). Then how can we make it faster ?
 
Read the parquet file and do a few transformations and write to HDFS and MinIO.
 
here in both write spark needs execute the transformation again. Do we know how 
we can avoid re-execution of transformation  without cache()/persist ?
 
Scenario2 :-
I am writing 3.2G data to HDFS and MinIO which takes ~6mins.
Do we have any way to make writing this faster ?
 
I don't want to do repartition and write as repartition will have overhead of 
shuffling .
 
Please provide some inputs. 
 
 

Re: A handy tool called spark-column-analyser

[ashok34...@yahoo.com.INVALID]

 Great work. Very handy for identifying problems
thanks
On Tuesday 21 May 2024 at 18:12:15 BST, Mich Talebzadeh 
 wrote:  
 
 A colleague kindly pointed out about giving an example of output which wll be 
added to README
Doing analysis for column Postcode
Json formatted output
{    "Postcode": {        "exists": true,        "num_rows": 93348,        
"data_type": "string",        "null_count": 21921,        "null_percentage": 
23.48,        "distinct_count": 38726,        "distinct_percentage": 41.49    }}
Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom



   view my Linkedin profile




 https://en.everybodywiki.com/Mich_Talebzadeh

 



Disclaimer: The information provided is correct to the best of my knowledge but 
of course cannot be guaranteed . It is essential to note that, as with any 
advice, quote "one test result is worth one-thousand expert opinions (Werner 
Von Braun)".


On Tue, 21 May 2024 at 16:21, Mich Talebzadeh  wrote:


I just wanted to share a tool I built called spark-column-analyzer. It's a 
Python package that helps you dig into your Spark DataFrames with ease. 

Ever spend ages figuring out what's going on in your columns? Like, how many 
null values are there, or how many unique entries? Built with data preparation 
for Generative AI in mind, it aids in data imputation and augmentation – key 
steps for creating realistic synthetic data.

Basics
   
   - Effortless Column Analysis: It calculates all the important stats you need 
for each column, like null counts, distinct values, percentages, and more. No 
more manual counting or head scratching!
   - Simple to Use: Just toss in your DataFrame and call the analyze_column 
function. Bam! Insights galore.
   - Makes Data Cleaning easier: Knowing your data's quality helps you clean it 
up way faster. This package helps you figure out where the missing values are 
hiding and how much variety you've got in each column.
   - Detecting skewed columns
   - Open Source and Friendly: Feel free to tinker, suggest improvements, or 
even contribute some code yourself! We love collaboration in the Spark 
community.

Installation: 


Using pip from the link: https://pypi.org/project/spark-column-analyzer/

pip install spark-column-analyzer

Also you can clone the project from gitHub

git clone https://github.com/michTalebzadeh/spark_column_analyzer.git

The details are in the attached RENAME file
Let me know what you think! Feedback is always welcome.

HTH

Mich Talebzadeh,

Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom



   view my Linkedin profile




 https://en.everybodywiki.com/Mich_Talebzadeh

 



Disclaimer: The information provided is correct to the best of my knowledge but 
of course cannot be guaranteed . It is essential to note that, as with any 
advice, quote "one test result is worth one-thousand expert opinions (Werner 
Von Braun)".

  

Re: A handy tool called spark-column-analyser

[Mich Talebzadeh]

A colleague kindly pointed out about giving an example of output which wll
be added to README

Doing analysis for column Postcode

Json formatted output

{
"Postcode": {
"exists": true,
"num_rows": 93348,
"data_type": "string",
"null_count": 21921,
"null_percentage": 23.48,
"distinct_count": 38726,
"distinct_percentage": 41.49
}
}

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Tue, 21 May 2024 at 16:21, Mich Talebzadeh 
wrote:

> I just wanted to share a tool I built called *spark-column-analyzer*.
> It's a Python package that helps you dig into your Spark DataFrames with
> ease.
>
> Ever spend ages figuring out what's going on in your columns? Like, how
> many null values are there, or how many unique entries? Built with data
> preparation for Generative AI in mind, it aids in data imputation and
> augmentation – key steps for creating realistic synthetic data.
>
> *Basics*
>
>- *Effortless Column Analysis:* It calculates all the important stats
>you need for each column, like null counts, distinct values, percentages,
>and more. No more manual counting or head scratching!
>- *Simple to Use:* Just toss in your DataFrame and call the
>analyze_column function. Bam! Insights galore.
>- *Makes Data Cleaning easier:* Knowing your data's quality helps you
>clean it up way faster. This package helps you figure out where the missing
>values are hiding and how much variety you've got in each column.
>- *Detecting skewed columns*
>- *Open Source and Friendly:* Feel free to tinker, suggest
>improvements, or even contribute some code yourself! We love collaboration
>in the Spark community.
>
> *Installation:*
>
> Using pip from the link: https://pypi.org/project/spark-column-analyzer/
>
>
> *pip install spark-column-analyzer*
> Also you can clone the project from gitHub
>
>
> *git clone https://github.com/michTalebzadeh/spark_column_analyzer.git
> *
>
> The details are in the attached RENAME file
>
> Let me know what you think! Feedback is always welcome.
>
> HTH
>
> Mich Talebzadeh,
> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>

Re: EXT: Dual Write to HDFS and MinIO in faster way

[Prem Sahoo]

On Tue, May 21, 2024 at 6:58 AM Prem Sahoo  wrote:

> Hello Vibhor,
> Thanks for the suggestion .
> I am looking for some other alternatives where I can use the same
> dataframe can be written to two destinations without re execution and cache
> or persist .
>
> Can some one help me in scenario 2 ?
> How to make spark write to MinIO faster ?
> Sent from my iPhone
>
> On May 21, 2024, at 1:18 AM, Vibhor Gupta 
> wrote:
>
> 
>
> Hi Prem,
>
>
>
> You can try to write to HDFS then read from HDFS and write to MinIO.
>
>
>
> This will prevent duplicate transformation.
>
>
>
> You can also try persisting the dataframe using the DISK_ONLY level.
>
>
>
> Regards,
>
> Vibhor
>
> *From: *Prem Sahoo 
> *Date: *Tuesday, 21 May 2024 at 8:16 AM
> *To: *Spark dev list 
> *Subject: *EXT: Dual Write to HDFS and MinIO in faster way
>
> *EXTERNAL: *Report suspicious emails to *Email Abuse.*
>
> Hello Team,
>
> I am planning to write to two datasource at the same time .
>
>
>
> Scenario:-
>
>
>
> Writing the same dataframe to HDFS and MinIO without re-executing the
> transformations and no cache(). Then how can we make it faster ?
>
>
>
> Read the parquet file and do a few transformations and write to HDFS and
> MinIO.
>
>
>
> here in both write spark needs execute the transformation again. Do we
> know how we can avoid re-execution of transformation  without
> cache()/persist ?
>
>
>
> Scenario2 :-
>
> I am writing 3.2G data to HDFS and MinIO which takes ~6mins.
>
> Do we have any way to make writing this faster ?
>
>
>
> I don't want to do repartition and write as repartition will have overhead
> of shuffling .
>
>
>
> Please provide some inputs.
>
>
>
>
>
>

Re: pyspark dataframe join with two different data type

[Karthick Nk]

Hi All,

I have tried the same result with pyspark and with SQL query by creating
with tempView, I could able to achieve whereas I have to do in the pyspark
code itself, Could you help on this

incoming_data = [["a"], ["b"], ["d"]]
column_names = ["column1"]
df = spark.createDataFrame(incoming_data, column_names)

view_data_df = spark.createDataFrame([(["a", "b", "c"], 1), (['f'], 2)],
['data_col', 'id'])

df.createOrReplaceTempView(f"id_table")
view_data_df.createOrReplaceTempView(f"view_data")

*%sql*
*select * from view_data*
*where exists (select 1 from id_table where array_contains(data_col,
column1))*

*Result:*

*data_col id*
*["a","b","c"] 1*

I need this equivalent SQL query with pyspark code to achieve the result.

One of the solution, I have tried is below, but here I am doing explode and
doing distinct again, But I need to perform the action without doing this
since this will impact performance again for the huge data.

Thanks,


solutions

On Thu, May 16, 2024 at 8:33 AM Karthick Nk  wrote:

> Thanks Mich,
>
> I have tried this solution, but i want all the columns from the dataframe
> df_1, if i explode the df_1 i am getting only data column. But the
> resultant should get the all the column from the df_1 with distinct result
> like below.
>
> Results in
>
> *df:*
> +---+
> |column1|
> +---+
> |  a|
> |  b|
> |  d|
> +---+
>
> *df_1:*
> +-+
> | id| data| field
> +-+
> |1 | [a, b, c]| ['company']
> | 3| [b, c, d]| ['hello']
> | 4| [e, f, s]| ['hello']
> +-+
>
> *Result:*
> ++
> |id| data| field
> ++
> |1| ['company']
> | 3|  ['helo']|
> ++
>
> Explanation: id with 1, 3 why -> because a or b present in both records 1
> and 3 so returning distinct result from the join.
>
>
> Here I would like to get the result like above, even if I get the
> duplicate element in the column data, I need to get the distinct data
> with respect to id field, But when I try to use array_contain, it will
> return duplicate result since data column has multiple occurrence.
>
> If you need more clarification, please let me know.
>
> Thanks,
>
>
>
>
>
>
> On Tue, May 14, 2024 at 6:12 PM Mich Talebzadeh 
> wrote:
>
>> You can use a combination of explode and distinct before joining.
>>
>> from pyspark.sql import SparkSession
>> from pyspark.sql.functions import explode
>>
>> # Create a SparkSession
>> spark = SparkSession.builder \
>> .appName("JoinExample") \
>> .getOrCreate()
>>
>> sc = spark.sparkContext
>> # Set the log level to ERROR to reduce verbosity
>> sc.setLogLevel("ERROR")
>>
>> # Create DataFrame df
>> data = [
>> ["a"],
>> ["b"],
>> ["d"],
>> ]
>> column_names = ["column1"]
>> df = spark.createDataFrame(data, column_names)
>> print("df:")
>> df.show()
>>
>> # Create DataFrame df_1
>> df_1 = spark.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])
>> print("df_1:")
>> df_1.show()
>>
>> # Explode the array column in df_1
>> exploded_df_1 = df_1.select(explode("data").alias("data"))
>>
>> # Join with df using the exploded column
>> final_df = exploded_df_1.join(df, exploded_df_1.data == df.column1)
>>
>> # Distinct to ensure only unique rows are returned from df_1
>> final_df = final_df.select("data").distinct()
>>
>> print("Result:")
>> final_df.show()
>>
>>
>> Results in
>>
>> df:
>> +---+
>> |column1|
>> +---+
>> |  a|
>> |  b|
>> |  d|
>> +---+
>>
>> df_1:
>> +-+
>> | data|
>> +-+
>> |[a, b, c]|
>> |   []|
>> +-+
>>
>> Result:
>> ++
>> |data|
>> ++
>> |   a|
>> |   b|
>> ++
>>
>> HTH
>>
>> Mich Talebzadeh,
>>
>> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *Disclaimer:* The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner
>> Von Braun
>> )".
>>
>>
>> On Tue, 14 May 2024 at 13:19, Karthick Nk  wrote:
>>
>>> Hi All,
>>>
>>> Could anyone have any idea or suggestion of any alternate way to achieve
>>> this scenario?
>>>
>>> Thanks.
>>>
>>> On Sat, May 11, 2024 at 6:55 AM Damien Hawes 
>>> wrote:
>>>
 Right now, with the structure of your data, it isn't possible.

 The rows aren't duplicates of each other. "a" and "b" both exist in the
 array. So Spark is correctly performing the join. It looks like you need to
 find another way to model this data to get what you want to achieve.

 Are the values of "a" and "b" related to each other in any way?

 - Damien

 Op vr 10 mei 2024 18:08 schreef Karthick Nk :

> Hi Mich,
>

Re: pyspark dataframe join with two different data type

[Karthick Nk]

Thanks Mich,

I have tried this solution, but i want all the columns from the dataframe
df_1, if i explode the df_1 i am getting only data column. But the
resultant should get the all the column from the df_1 with distinct result
like below.

Results in

*df:*
+---+
|column1|
+---+
|  a|
|  b|
|  d|
+---+

*df_1:*
+-+
| id| data| field
+-+
|1 | [a, b, c]| ['company']
| 3| [b, c, d]| ['hello']
| 4| [e, f, s]| ['hello']
+-+

*Result:*
++
|id| data| field
++
|1| ['company']
| 3|  ['helo']|
++

Explanation: id with 1, 3 why -> because a or b present in both records 1
and 3 so returning distinct result from the join.


Here I would like to get the result like above, even if I get the
duplicate element in the column data, I need to get the distinct data
with respect to id field, But when I try to use array_contain, it will
return duplicate result since data column has multiple occurrence.

If you need more clarification, please let me know.

Thanks,






On Tue, May 14, 2024 at 6:12 PM Mich Talebzadeh 
wrote:

> You can use a combination of explode and distinct before joining.
>
> from pyspark.sql import SparkSession
> from pyspark.sql.functions import explode
>
> # Create a SparkSession
> spark = SparkSession.builder \
> .appName("JoinExample") \
> .getOrCreate()
>
> sc = spark.sparkContext
> # Set the log level to ERROR to reduce verbosity
> sc.setLogLevel("ERROR")
>
> # Create DataFrame df
> data = [
> ["a"],
> ["b"],
> ["d"],
> ]
> column_names = ["column1"]
> df = spark.createDataFrame(data, column_names)
> print("df:")
> df.show()
>
> # Create DataFrame df_1
> df_1 = spark.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])
> print("df_1:")
> df_1.show()
>
> # Explode the array column in df_1
> exploded_df_1 = df_1.select(explode("data").alias("data"))
>
> # Join with df using the exploded column
> final_df = exploded_df_1.join(df, exploded_df_1.data == df.column1)
>
> # Distinct to ensure only unique rows are returned from df_1
> final_df = final_df.select("data").distinct()
>
> print("Result:")
> final_df.show()
>
>
> Results in
>
> df:
> +---+
> |column1|
> +---+
> |  a|
> |  b|
> |  d|
> +---+
>
> df_1:
> +-+
> | data|
> +-+
> |[a, b, c]|
> |   []|
> +-+
>
> Result:
> ++
> |data|
> ++
> |   a|
> |   b|
> ++
>
> HTH
>
> Mich Talebzadeh,
>
> Technologist | Architect | Data Engineer  | Generative AI | FinCrimeLondon
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
>
> On Tue, 14 May 2024 at 13:19, Karthick Nk  wrote:
>
>> Hi All,
>>
>> Could anyone have any idea or suggestion of any alternate way to achieve
>> this scenario?
>>
>> Thanks.
>>
>> On Sat, May 11, 2024 at 6:55 AM Damien Hawes 
>> wrote:
>>
>>> Right now, with the structure of your data, it isn't possible.
>>>
>>> The rows aren't duplicates of each other. "a" and "b" both exist in the
>>> array. So Spark is correctly performing the join. It looks like you need to
>>> find another way to model this data to get what you want to achieve.
>>>
>>> Are the values of "a" and "b" related to each other in any way?
>>>
>>> - Damien
>>>
>>> Op vr 10 mei 2024 18:08 schreef Karthick Nk :
>>>
 Hi Mich,

 Thanks for the solution, But I am getting duplicate result by using
 array_contains. I have explained the scenario below, could you help me on
 that, how we can achieve i have tried different way bu i could able to
 achieve.

 For example

 data = [
 ["a"],
 ["b"],
 ["d"],
 ]
 column_names = ["column1"]
 df = spark.createDataFrame(data, column_names)
 df.display()

 [image: image.png]

 df_1 = spark.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])
 df_1.display()
 [image: image.png]


 final_df = df_1.join(df, expr("array_contains(data, column1)"))
 final_df.display()

 Resul:
 [image: image.png]

 But i need the result like below:

 [image: image.png]

 Why because

 In the df_1 i have only two records, in that first records onlly i have
 matching value.
 But both records from the df i.e *a, b* are present in the first
 records itself, it is returning two records as resultant, but my
 expectation is to return only one records means if any of the records from
 the df is present in the df_1 it should return only one records from the
 df_1.

Re: pyspark dataframe join with two different data type

[Mich Talebzadeh]

You can use a combination of explode and distinct before joining.

from pyspark.sql import SparkSession
from pyspark.sql.functions import explode

# Create a SparkSession
spark = SparkSession.builder \
.appName("JoinExample") \
.getOrCreate()

sc = spark.sparkContext
# Set the log level to ERROR to reduce verbosity
sc.setLogLevel("ERROR")

# Create DataFrame df
data = [
["a"],
["b"],
["d"],
]
column_names = ["column1"]
df = spark.createDataFrame(data, column_names)
print("df:")
df.show()

# Create DataFrame df_1
df_1 = spark.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])
print("df_1:")
df_1.show()

# Explode the array column in df_1
exploded_df_1 = df_1.select(explode("data").alias("data"))

# Join with df using the exploded column
final_df = exploded_df_1.join(df, exploded_df_1.data == df.column1)

# Distinct to ensure only unique rows are returned from df_1
final_df = final_df.select("data").distinct()

print("Result:")
final_df.show()


Results in

df:
+---+
|column1|
+---+
|  a|
|  b|
|  d|
+---+

df_1:
+-+
| data|
+-+
|[a, b, c]|
|   []|
+-+

Result:
++
|data|
++
|   a|
|   b|
++

HTH

Mich Talebzadeh,

Technologist | Architect | Data Engineer  | Generative AI | FinCrimeLondon
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Tue, 14 May 2024 at 13:19, Karthick Nk  wrote:

> Hi All,
>
> Could anyone have any idea or suggestion of any alternate way to achieve
> this scenario?
>
> Thanks.
>
> On Sat, May 11, 2024 at 6:55 AM Damien Hawes 
> wrote:
>
>> Right now, with the structure of your data, it isn't possible.
>>
>> The rows aren't duplicates of each other. "a" and "b" both exist in the
>> array. So Spark is correctly performing the join. It looks like you need to
>> find another way to model this data to get what you want to achieve.
>>
>> Are the values of "a" and "b" related to each other in any way?
>>
>> - Damien
>>
>> Op vr 10 mei 2024 18:08 schreef Karthick Nk :
>>
>>> Hi Mich,
>>>
>>> Thanks for the solution, But I am getting duplicate result by using
>>> array_contains. I have explained the scenario below, could you help me on
>>> that, how we can achieve i have tried different way bu i could able to
>>> achieve.
>>>
>>> For example
>>>
>>> data = [
>>> ["a"],
>>> ["b"],
>>> ["d"],
>>> ]
>>> column_names = ["column1"]
>>> df = spark.createDataFrame(data, column_names)
>>> df.display()
>>>
>>> [image: image.png]
>>>
>>> df_1 = spark.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])
>>> df_1.display()
>>> [image: image.png]
>>>
>>>
>>> final_df = df_1.join(df, expr("array_contains(data, column1)"))
>>> final_df.display()
>>>
>>> Resul:
>>> [image: image.png]
>>>
>>> But i need the result like below:
>>>
>>> [image: image.png]
>>>
>>> Why because
>>>
>>> In the df_1 i have only two records, in that first records onlly i have
>>> matching value.
>>> But both records from the df i.e *a, b* are present in the first
>>> records itself, it is returning two records as resultant, but my
>>> expectation is to return only one records means if any of the records from
>>> the df is present in the df_1 it should return only one records from the
>>> df_1.
>>>
>>> Note:
>>> 1. Here we are able to filter the duplicate records by using distinct of
>>> ID field in the resultant df, bu I am thinking that shouldn't be effective
>>> way, rather i am thinking of updating in array_contains steps itself.
>>>
>>> Thanks.
>>>
>>>
>>> On Fri, Mar 1, 2024 at 4:11 AM Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>

 This is what you want, how to join two DFs with a string column in one
 and an array of strings in the other, keeping only rows where the
 string is present in the array.

 from pyspark.sql import SparkSession
 from pyspark.sql import Row
 from pyspark.sql.functions import expr

 spark = SparkSession.builder.appName("joins").getOrCreate()

 data1 = [Row(combined_id=[1, 2, 3])  # this one has a column
 combined_id as an array of integers
 data2 = [Row(mr_id=2), Row(mr_id=5)] # this one has column mr_id with
 single integers

 df1 = spark.createDataFrame(data1)
 df2 = spark.createDataFrame(data2)

 df1.printSchema()
 df2.printSchema()

 # Perform the join with array_contains. It takes two arguments: an
 array and a value. It returns True if the value exists as an element
 within the array, otherwise False.
 joined_df = df1.join(df2, 

Re: pyspark dataframe join with two different data type

[Karthick Nk]

Hi All,

Could anyone have any idea or suggestion of any alternate way to achieve
this scenario?

Thanks.

On Sat, May 11, 2024 at 6:55 AM Damien Hawes  wrote:

> Right now, with the structure of your data, it isn't possible.
>
> The rows aren't duplicates of each other. "a" and "b" both exist in the
> array. So Spark is correctly performing the join. It looks like you need to
> find another way to model this data to get what you want to achieve.
>
> Are the values of "a" and "b" related to each other in any way?
>
> - Damien
>
> Op vr 10 mei 2024 18:08 schreef Karthick Nk :
>
>> Hi Mich,
>>
>> Thanks for the solution, But I am getting duplicate result by using
>> array_contains. I have explained the scenario below, could you help me on
>> that, how we can achieve i have tried different way bu i could able to
>> achieve.
>>
>> For example
>>
>> data = [
>> ["a"],
>> ["b"],
>> ["d"],
>> ]
>> column_names = ["column1"]
>> df = spark.createDataFrame(data, column_names)
>> df.display()
>>
>> [image: image.png]
>>
>> df_1 = spark.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])
>> df_1.display()
>> [image: image.png]
>>
>>
>> final_df = df_1.join(df, expr("array_contains(data, column1)"))
>> final_df.display()
>>
>> Resul:
>> [image: image.png]
>>
>> But i need the result like below:
>>
>> [image: image.png]
>>
>> Why because
>>
>> In the df_1 i have only two records, in that first records onlly i have
>> matching value.
>> But both records from the df i.e *a, b* are present in the first records
>> itself, it is returning two records as resultant, but my expectation is to
>> return only one records means if any of the records from the df is present
>> in the df_1 it should return only one records from the df_1.
>>
>> Note:
>> 1. Here we are able to filter the duplicate records by using distinct of
>> ID field in the resultant df, bu I am thinking that shouldn't be effective
>> way, rather i am thinking of updating in array_contains steps itself.
>>
>> Thanks.
>>
>>
>> On Fri, Mar 1, 2024 at 4:11 AM Mich Talebzadeh 
>> wrote:
>>
>>>
>>> This is what you want, how to join two DFs with a string column in one
>>> and an array of strings in the other, keeping only rows where the
>>> string is present in the array.
>>>
>>> from pyspark.sql import SparkSession
>>> from pyspark.sql import Row
>>> from pyspark.sql.functions import expr
>>>
>>> spark = SparkSession.builder.appName("joins").getOrCreate()
>>>
>>> data1 = [Row(combined_id=[1, 2, 3])  # this one has a column combined_id
>>> as an array of integers
>>> data2 = [Row(mr_id=2), Row(mr_id=5)] # this one has column mr_id with
>>> single integers
>>>
>>> df1 = spark.createDataFrame(data1)
>>> df2 = spark.createDataFrame(data2)
>>>
>>> df1.printSchema()
>>> df2.printSchema()
>>>
>>> # Perform the join with array_contains. It takes two arguments: an
>>> array and a value. It returns True if the value exists as an element
>>> within the array, otherwise False.
>>> joined_df = df1.join(df2, expr("array_contains(combined_id, mr_id)"))
>>>
>>> # Show the result
>>> joined_df.show()
>>>
>>> root
>>>  |-- combined_id: array (nullable = true)
>>>  ||-- element: long (containsNull = true)
>>>
>>> root
>>>  |-- mr_id: long (nullable = true)
>>>
>>> +---+-+
>>> |combined_id|mr_id|
>>> +---+-+
>>> |  [1, 2, 3]|2|
>>> |  [4, 5, 6]|5|
>>> +---+-+
>>>
>>> HTH
>>>
>>> Mich Talebzadeh,
>>> Dad | Technologist | Solutions Architect | Engineer
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>> 
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> *Disclaimer:* The information provided is correct to the best of my
>>> knowledge but of course cannot be guaranteed . It is essential to note
>>> that, as with any advice, quote "one test result is worth one-thousand
>>> expert opinions (Werner
>>> Von Braun
>>> )".
>>>
>>>
>>> On Thu, 29 Feb 2024 at 20:50, Karthick Nk  wrote:
>>>
 Hi All,

 I have two dataframe with below structure, i have to join these two
 dataframe - the scenario is one column is string in one dataframe and in
 other df join column is array of string, so we have to inner join two df
 and get the data if string value is present in any of the array of string
 value in another dataframe,


 df1 = spark.sql("""
 SELECT
 mr.id as mr_id,
 pv.id as pv_id,
 array(mr.id, pv.id) as combined_id
 FROM
 table1 mr
 INNER JOIN table2 pv ON pv.id = Mr.recordid
where
 pv.id = '35122806-4cd2-4916-a149-24ea55c2dc36'
 or pv.id = 'a5f03625-6cc5-49df-95eb-df741fe9139b'
 """)

 # df1.display()

 # Your second query
 df2 = spark.sql("""
 SELECT
 

Re: [spark-graphframes]: Generating incorrect edges

[Nijland, J.G.W. (Jelle, Student M-CS)]

Hi all,

The issue is solved.
I conducted a lot more testing and built checkers to verify at which size it's 
going wrong.
When checking for specific edges, I could construct successful graphs up to 
261k records.
When verifying all edges created, is breaks somewhere in the 200-250k records.
I didn't bother finding the specific error threshold, as runs take up to 7 
minutes per slice.

I started looking at all underlying assumptions of my code along with my 
supervisor.
We located the problem in the generate_ids() function.
I selected all distinct values to give them an ID and subsequently joining 
those results back to the main DataFrame.
I replaced this by generating unique IDs for each value occurrence by hashing 
them with 'withColumn' rather than joining them back.
This resolved my issues and ended up to be a significant performance boost as 
well.

My fixed generate_ids() code
def generate_ids(df: DataFrame) -> DataFrame:
   """
   Generates a unique ID for each distinct maintainer, prefix, origin and 
organisation

   Parameters
   --
   df : DataFrame
   DataFrame to generate IDs for
   """
   df = df.withColumn(MAINTAINER_ID, psf.concat(psf.lit(PREFIX_M), 
psf.sha2(df.mnt_by, 256)))
   df = df.withColumn(PREFIX_ID, psf.concat(psf.lit(PREFIX_P), 
psf.sha2(df.prefix, 256)))
   df = df.withColumn(ORIGIN_ID, psf.concat(psf.lit(PREFIX_O), 
psf.sha2(df.origin, 256)))
   df = df.withColumn(ORGANISATION_ID, psf.concat(psf.lit(PREFIX_ORG), 
psf.sha2(df.descr, 256)))
   return df

Hope this email finds someone running into a similar issue in the future.

Kind regards,
Jelle




From: Mich Talebzadeh 
Sent: Wednesday, May 1, 2024 11:56 AM
To: Stephen Coy 
Cc: Nijland, J.G.W. (Jelle, Student M-CS) ; 
user@spark.apache.org 
Subject: Re: [spark-graphframes]: Generating incorrect edges

Hi Steve,

Thanks for your statement. I tend to use uuid myself to avoid collisions. This 
built-in function generates random IDs that are highly likely to be unique 
across systems. My concerns are on edge so to speak. If the Spark application 
runs for a very long time or encounters restarts, the 
monotonically_increasing_id() sequence might restart from the beginning. This 
could again cause duplicate IDs if other Spark applications are running 
concurrently or if data is processed across multiple runs of the same 
application..

HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


 
[https://ci3.googleusercontent.com/mail-sig/AIorK4zholKucR2Q9yMrKbHNn-o1TuS4mYXyi2KO6Xmx6ikHPySa9MLaLZ8t2hrA6AUcxSxDgHIwmKE]
   view my Linkedin 
profile<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>


 https://en.everybodywiki.com/Mich_Talebzadeh



Disclaimer: The information provided is correct to the best of my knowledge but 
of course cannot be guaranteed . It is essential to note that, as with any 
advice, quote "one test result is worth one-thousand expert opinions (Werner 
<https://en.wikipedia.org/wiki/Wernher_von_Braun> Von 
Braun<https://en.wikipedia.org/wiki/Wernher_von_Braun>)".


On Wed, 1 May 2024 at 01:22, Stephen Coy 
mailto:s...@infomedia.com.au>> wrote:
Hi Mich,

I was just reading random questions on the user list when I noticed that you 
said:

On 25 Apr 2024, at 2:12 AM, Mich Talebzadeh 
mailto:mich.talebza...@gmail.com>> wrote:

1) You are using monotonically_increasing_id(), which is not 
collision-resistant in distributed environments like Spark. Multiple hosts
   can generate the same ID. I suggest switching to UUIDs (e.g., uuid.uuid4()) 
for guaranteed uniqueness.


It’s my understanding that the *Spark* `monotonically_increasing_id()` function 
exists for the exact purpose of generating a collision-resistant unique id 
across nodes on different hosts.
We use it extensively for this purpose and have never encountered an issue.

Are we wrong or are you thinking of a different (not Spark) function?

Cheers,

Steve C




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Re: pyspark dataframe join with two different data type

[Damien Hawes]

Right now, with the structure of your data, it isn't possible.

The rows aren't duplicates of each other. "a" and "b" both exist in the
array. So Spark is correctly performing the join. It looks like you need to
find another way to model this data to get what you want to achieve.

Are the values of "a" and "b" related to each other in any way?

- Damien

Op vr 10 mei 2024 18:08 schreef Karthick Nk :

> Hi Mich,
>
> Thanks for the solution, But I am getting duplicate result by using
> array_contains. I have explained the scenario below, could you help me on
> that, how we can achieve i have tried different way bu i could able to
> achieve.
>
> For example
>
> data = [
> ["a"],
> ["b"],
> ["d"],
> ]
> column_names = ["column1"]
> df = spark.createDataFrame(data, column_names)
> df.display()
>
> [image: image.png]
>
> df_1 = spark.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])
> df_1.display()
> [image: image.png]
>
>
> final_df = df_1.join(df, expr("array_contains(data, column1)"))
> final_df.display()
>
> Resul:
> [image: image.png]
>
> But i need the result like below:
>
> [image: image.png]
>
> Why because
>
> In the df_1 i have only two records, in that first records onlly i have
> matching value.
> But both records from the df i.e *a, b* are present in the first records
> itself, it is returning two records as resultant, but my expectation is to
> return only one records means if any of the records from the df is present
> in the df_1 it should return only one records from the df_1.
>
> Note:
> 1. Here we are able to filter the duplicate records by using distinct of
> ID field in the resultant df, bu I am thinking that shouldn't be effective
> way, rather i am thinking of updating in array_contains steps itself.
>
> Thanks.
>
>
> On Fri, Mar 1, 2024 at 4:11 AM Mich Talebzadeh 
> wrote:
>
>>
>> This is what you want, how to join two DFs with a string column in one
>> and an array of strings in the other, keeping only rows where the string
>> is present in the array.
>>
>> from pyspark.sql import SparkSession
>> from pyspark.sql import Row
>> from pyspark.sql.functions import expr
>>
>> spark = SparkSession.builder.appName("joins").getOrCreate()
>>
>> data1 = [Row(combined_id=[1, 2, 3])  # this one has a column combined_id
>> as an array of integers
>> data2 = [Row(mr_id=2), Row(mr_id=5)] # this one has column mr_id with
>> single integers
>>
>> df1 = spark.createDataFrame(data1)
>> df2 = spark.createDataFrame(data2)
>>
>> df1.printSchema()
>> df2.printSchema()
>>
>> # Perform the join with array_contains. It takes two arguments: an array
>> and a value. It returns True if the value exists as an element within
>> the array, otherwise False.
>> joined_df = df1.join(df2, expr("array_contains(combined_id, mr_id)"))
>>
>> # Show the result
>> joined_df.show()
>>
>> root
>>  |-- combined_id: array (nullable = true)
>>  ||-- element: long (containsNull = true)
>>
>> root
>>  |-- mr_id: long (nullable = true)
>>
>> +---+-+
>> |combined_id|mr_id|
>> +---+-+
>> |  [1, 2, 3]|2|
>> |  [4, 5, 6]|5|
>> +---+-+
>>
>> HTH
>>
>> Mich Talebzadeh,
>> Dad | Technologist | Solutions Architect | Engineer
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *Disclaimer:* The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner
>> Von Braun
>> )".
>>
>>
>> On Thu, 29 Feb 2024 at 20:50, Karthick Nk  wrote:
>>
>>> Hi All,
>>>
>>> I have two dataframe with below structure, i have to join these two
>>> dataframe - the scenario is one column is string in one dataframe and in
>>> other df join column is array of string, so we have to inner join two df
>>> and get the data if string value is present in any of the array of string
>>> value in another dataframe,
>>>
>>>
>>> df1 = spark.sql("""
>>> SELECT
>>> mr.id as mr_id,
>>> pv.id as pv_id,
>>> array(mr.id, pv.id) as combined_id
>>> FROM
>>> table1 mr
>>> INNER JOIN table2 pv ON pv.id = Mr.recordid
>>>where
>>> pv.id = '35122806-4cd2-4916-a149-24ea55c2dc36'
>>> or pv.id = 'a5f03625-6cc5-49df-95eb-df741fe9139b'
>>> """)
>>>
>>> # df1.display()
>>>
>>> # Your second query
>>> df2 = spark.sql("""
>>> SELECT
>>> id
>>> FROM
>>> table2
>>> WHERE
>>> id = '35122806-4cd2-4916-a149-24ea55c2dc36'
>>>
>>> """)
>>>
>>>
>>>
>>> Result data:
>>> 35122806-4cd2-4916-a149-24ea55c2dc36 only, because this records alone is
>>> common between string and array of string value.
>>>
>>> Can you share the sample snippet, how we 

Re: pyspark dataframe join with two different data type

[Karthick Nk]

Hi Mich,

Thanks for the solution, But I am getting duplicate result by using
array_contains. I have explained the scenario below, could you help me on
that, how we can achieve i have tried different way bu i could able to
achieve.

For example

data = [
["a"],
["b"],
["d"],
]
column_names = ["column1"]
df = spark.createDataFrame(data, column_names)
df.display()

[image: image.png]

df_1 = spark.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])
df_1.display()
[image: image.png]


final_df = df_1.join(df, expr("array_contains(data, column1)"))
final_df.display()

Resul:
[image: image.png]

But i need the result like below:

[image: image.png]

Why because

In the df_1 i have only two records, in that first records onlly i have
matching value.
But both records from the df i.e *a, b* are present in the first records
itself, it is returning two records as resultant, but my expectation is to
return only one records means if any of the records from the df is present
in the df_1 it should return only one records from the df_1.

Note:
1. Here we are able to filter the duplicate records by using distinct of ID
field in the resultant df, bu I am thinking that shouldn't be effective
way, rather i am thinking of updating in array_contains steps itself.

Thanks.


On Fri, Mar 1, 2024 at 4:11 AM Mich Talebzadeh 
wrote:

>
> This is what you want, how to join two DFs with a string column in one and
> an array of strings in the other, keeping only rows where the string is
> present in the array.
>
> from pyspark.sql import SparkSession
> from pyspark.sql import Row
> from pyspark.sql.functions import expr
>
> spark = SparkSession.builder.appName("joins").getOrCreate()
>
> data1 = [Row(combined_id=[1, 2, 3])  # this one has a column combined_id
> as an array of integers
> data2 = [Row(mr_id=2), Row(mr_id=5)] # this one has column mr_id with
> single integers
>
> df1 = spark.createDataFrame(data1)
> df2 = spark.createDataFrame(data2)
>
> df1.printSchema()
> df2.printSchema()
>
> # Perform the join with array_contains. It takes two arguments: an array
> and a value. It returns True if the value exists as an element within the
> array, otherwise False.
> joined_df = df1.join(df2, expr("array_contains(combined_id, mr_id)"))
>
> # Show the result
> joined_df.show()
>
> root
>  |-- combined_id: array (nullable = true)
>  ||-- element: long (containsNull = true)
>
> root
>  |-- mr_id: long (nullable = true)
>
> +---+-+
> |combined_id|mr_id|
> +---+-+
> |  [1, 2, 3]|2|
> |  [4, 5, 6]|5|
> +---+-+
>
> HTH
>
> Mich Talebzadeh,
> Dad | Technologist | Solutions Architect | Engineer
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
>
> On Thu, 29 Feb 2024 at 20:50, Karthick Nk  wrote:
>
>> Hi All,
>>
>> I have two dataframe with below structure, i have to join these two
>> dataframe - the scenario is one column is string in one dataframe and in
>> other df join column is array of string, so we have to inner join two df
>> and get the data if string value is present in any of the array of string
>> value in another dataframe,
>>
>>
>> df1 = spark.sql("""
>> SELECT
>> mr.id as mr_id,
>> pv.id as pv_id,
>> array(mr.id, pv.id) as combined_id
>> FROM
>> table1 mr
>> INNER JOIN table2 pv ON pv.id = Mr.recordid
>>where
>> pv.id = '35122806-4cd2-4916-a149-24ea55c2dc36'
>> or pv.id = 'a5f03625-6cc5-49df-95eb-df741fe9139b'
>> """)
>>
>> # df1.display()
>>
>> # Your second query
>> df2 = spark.sql("""
>> SELECT
>> id
>> FROM
>> table2
>> WHERE
>> id = '35122806-4cd2-4916-a149-24ea55c2dc36'
>>
>> """)
>>
>>
>>
>> Result data:
>> 35122806-4cd2-4916-a149-24ea55c2dc36 only, because this records alone is
>> common between string and array of string value.
>>
>> Can you share the sample snippet, how we can do the join for this two
>> different datatype in the dataframe.
>>
>> if any clarification needed, pls feel free to ask.
>>
>> Thanks
>>
>>

Re: [Spark Streaming]: Save the records that are dropped by watermarking in spark structured streaming

[Mich Talebzadeh]

you may consider

- Increase Watermark Retention: Consider increasing the watermark retention
duration. This allows keeping records for a longer period before dropping
them. However, this might increase processing latency and violate
at-least-once semantics if the watermark lags behind real-time.

OR

- Use a separate stream for dropped records: Create a separate streaming
pipeline to process the dropped records. Try:


   - Filter: Filter out records older than the watermark in the main
   pipeline.  say

   resultC = streamingDataFrame.select( \
 col("parsed_value.rowkey").alias("rowkey") \
   , col("parsed_value.timestamp").alias("timestamp") \
   , col("parsed_value.temperature").alias("temperature"))

"""
We work out the window and the AVG(temperature) in the window's
timeframe below
This should return back the following Dataframe as struct

 root
 |-- window: struct (nullable = false)
 ||-- start: timestamp (nullable = true)
 ||-- end: timestamp (nullable = true)
 |-- avg(temperature): double (nullable = true)

"""
resultM = resultC. \
 *withWatermark("timestamp", "5 minutes").* \
 groupBy(window(resultC.timestamp, "5 minutes", "5
minutes")). \
 avg('temperature')

   - Write to Sink: Write the filtered records (dropped records) to a
   separate Kafka topic.
   - Consume and Store: Consume the dropped records topic with another
   streaming job and store them in a Postgres table or S3 using lib


HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Wed, 8 May 2024 at 05:13, Nandha Kumar  wrote:

> Hi Team,
>We are trying to use *spark structured streaming *for our use
> case. We will be joining 2 streaming sources(from kafka topic) with
> watermarks. As time progresses, the records that are prior to the watermark
> timestamp are removed from the state. For our use case, we want to *store
> these dropped records* in some postgres table or s3.
>
> When searching, we found a similar question
> in
> StackOverflow which is unanswered.
> *We would like to know how to store these dropped records due to the
> watermark.*
>

Re: ********Spark streaming issue to Elastic data**********

[Mich Talebzadeh]

Hi Kartrick,

Unfortunately Materialised views are not available in Spark as yet. I
raised Jira [SPARK-48117] Spark Materialized Views: Improve Query
Performance and Data Management - ASF JIRA (apache.org)
 as a feature request.

Let me think about another way and revert

HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Mon, 6 May 2024 at 07:54, Karthick Nk  wrote:

> Thanks Mich,
>
> can you please confirm me is my understanding correct?
>
> First, we have to create the materialized view based on the mapping
> details we have by using multiple tables as source(since we have multiple
> join condition from different tables). From the materialised view we can
> stream the view data into elastic index by using cdc?
>
> Thanks in advance.
>
> On Fri, May 3, 2024 at 3:39 PM Mich Talebzadeh 
> wrote:
>
>> My recommendation! is using materialized views (MVs) created in Hive with
>> Spark Structured Streaming and Change Data Capture (CDC) is a good
>> combination for efficiently streaming view data updates in your scenario.
>>
>> HTH
>>
>> Mich Talebzadeh,
>> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *Disclaimer:* The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner
>> Von Braun
>> )".
>>
>>
>> On Thu, 2 May 2024 at 21:25, Karthick Nk  wrote:
>>
>>> Hi All,
>>>
>>> Requirements:
>>> I am working on the data flow, which will use the view definition(view
>>> definition already defined in schema), there are multiple tables used in
>>> the view definition. Here we want to stream the view data into elastic
>>> index based on if any of the table(used in the view definition) data got
>>> changed.
>>>
>>>
>>> Current flow:
>>> 1. we are inserting id's from the table(which used in the view
>>> definition) into the common table.
>>> 2. From the common table by using the id, we will be streaming the view
>>> data (by using if any of the incomming id is present in the collective id
>>> of all tables used from view definition) by using spark structured
>>> streaming.
>>>
>>>
>>> Issue:
>>> 1. Here we are facing issue - For each incomming id here we running view
>>> definition(so it will read all the data from all the data) and check if any
>>> of the incomming id is present in the collective id's of view result, Due
>>> to which it is taking more memory in the cluster driver and taking more
>>> time to process.
>>>
>>>
>>> I am epxpecting an alternate solution, if we can avoid full scan of view
>>> definition every time, If you have any alternate deisgn flow how we can
>>> achieve the result, please suggest for the same.
>>>
>>>
>>> Note: Also, it will be helpfull, if you can share the details like
>>> community forum or platform to discuss this kind of deisgn related topics,
>>> it will be more helpfull.
>>>
>>

Re: ********Spark streaming issue to Elastic data**********

[Karthick Nk]

Thanks Mich,

can you please confirm me is my understanding correct?

First, we have to create the materialized view based on the mapping details
we have by using multiple tables as source(since we have multiple
join condition from different tables). From the materialised view we can
stream the view data into elastic index by using cdc?

Thanks in advance.

On Fri, May 3, 2024 at 3:39 PM Mich Talebzadeh 
wrote:

> My recommendation! is using materialized views (MVs) created in Hive with
> Spark Structured Streaming and Change Data Capture (CDC) is a good
> combination for efficiently streaming view data updates in your scenario.
>
> HTH
>
> Mich Talebzadeh,
> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
>
> On Thu, 2 May 2024 at 21:25, Karthick Nk  wrote:
>
>> Hi All,
>>
>> Requirements:
>> I am working on the data flow, which will use the view definition(view
>> definition already defined in schema), there are multiple tables used in
>> the view definition. Here we want to stream the view data into elastic
>> index based on if any of the table(used in the view definition) data got
>> changed.
>>
>>
>> Current flow:
>> 1. we are inserting id's from the table(which used in the view
>> definition) into the common table.
>> 2. From the common table by using the id, we will be streaming the view
>> data (by using if any of the incomming id is present in the collective id
>> of all tables used from view definition) by using spark structured
>> streaming.
>>
>>
>> Issue:
>> 1. Here we are facing issue - For each incomming id here we running view
>> definition(so it will read all the data from all the data) and check if any
>> of the incomming id is present in the collective id's of view result, Due
>> to which it is taking more memory in the cluster driver and taking more
>> time to process.
>>
>>
>> I am epxpecting an alternate solution, if we can avoid full scan of view
>> definition every time, If you have any alternate deisgn flow how we can
>> achieve the result, please suggest for the same.
>>
>>
>> Note: Also, it will be helpfull, if you can share the details like
>> community forum or platform to discuss this kind of deisgn related topics,
>> it will be more helpfull.
>>
>

Re: Issue with Materialized Views in Spark SQL

[Mich Talebzadeh]

Sadly Apache Spark sounds like it has nothing to do within materialised
views. I was hoping it could read it!

>>> *spark.sql("SELECT * FROM test.mv ").show()*
Traceback (most recent call last):
  File "", line 1, in 
  File "/opt/spark/python/pyspark/sql/session.py", line 1440, in sql
return DataFrame(self._jsparkSession.sql(sqlQuery, litArgs), self)
  File
"/usr/src/Python-3.9.16/venv/venv3.9/lib/python3.9/site-packages/py4j/java_gateway.py",
line 1321, in __call__
return_value = get_return_value(
  File "/opt/spark/python/pyspark/errors/exceptions/captured.py", line 175,
in deco
raise converted from None
*Pyspark.errors.exceptions.captured.AnalysisException: Hive materialized
view is not supported.*


HTH

Mch Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Fri, 3 May 2024 at 11:03, Mich Talebzadeh 
wrote:

> Thanks for the comments I received.
>
> So in summary, Apache Spark itself doesn't directly manage materialized
> views,(MV)  but it can work with them through integration with the
> underlying data storage systems like Hive or through iceberg. I believe
> databricks through unity catalog support MVs as well.
>
> Moreover, there is a case for supporting MVs. However, Spark can utilize
> materialized views even though it doesn't directly manage them.. This came
> about because someone in the Spark user forum enquired about "Spark
> streaming issue to Elastic data*". One option I thought of was that uUsing
> materialized views with Spark Structured Streaming and Change Data Capture
> (CDC) is a potential solution for efficiently streaming view data updates
> in this scenario. .
>
>
> Mich Talebzadeh,
> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
>
> On Fri, 3 May 2024 at 00:54, Mich Talebzadeh 
> wrote:
>
>> An issue I encountered while working with Materialized Views in Spark
>> SQL. It appears that there is an inconsistency between the behavior of
>> Materialized Views in Spark SQL and Hive.
>>
>> When attempting to execute a statement like DROP MATERIALIZED VIEW IF
>> EXISTS test.mv in Spark SQL, I encountered a syntax error indicating
>> that the keyword MATERIALIZED is not recognized. However, the same
>> statement executes successfully in Hive without any errors.
>>
>> pyspark.errors.exceptions.captured.ParseException:
>> [PARSE_SYNTAX_ERROR] Syntax error at or near 'MATERIALIZED'.(line 1, pos
>> 5)
>>
>> == SQL ==
>> DROP MATERIALIZED VIEW IF EXISTS test.mv
>> -^^^
>>
>> Here are the versions I am using:
>>
>>
>>
>> *Hive: 3.1.1Spark: 3.4*
>> my Spark session:
>>
>> spark = SparkSession.builder \
>>   .appName("test") \
>>   .enableHiveSupport() \
>>   .getOrCreate()
>>
>> Has anyone seen this behaviour or encountered a similar issue or if there
>> are any insights into why this discrepancy exists between Spark SQL and
>> Hive.
>>
>> Thanks
>>
>> Mich Talebzadeh,
>>
>> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
>>
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> Disclaimer: The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner Von Braun)".
>>
>

Re: ********Spark streaming issue to Elastic data**********

[Mich Talebzadeh]

My recommendation! is using materialized views (MVs) created in Hive with
Spark Structured Streaming and Change Data Capture (CDC) is a good
combination for efficiently streaming view data updates in your scenario.

HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Thu, 2 May 2024 at 21:25, Karthick Nk  wrote:

> Hi All,
>
> Requirements:
> I am working on the data flow, which will use the view definition(view
> definition already defined in schema), there are multiple tables used in
> the view definition. Here we want to stream the view data into elastic
> index based on if any of the table(used in the view definition) data got
> changed.
>
>
> Current flow:
> 1. we are inserting id's from the table(which used in the view definition)
> into the common table.
> 2. From the common table by using the id, we will be streaming the view
> data (by using if any of the incomming id is present in the collective id
> of all tables used from view definition) by using spark structured
> streaming.
>
>
> Issue:
> 1. Here we are facing issue - For each incomming id here we running view
> definition(so it will read all the data from all the data) and check if any
> of the incomming id is present in the collective id's of view result, Due
> to which it is taking more memory in the cluster driver and taking more
> time to process.
>
>
> I am epxpecting an alternate solution, if we can avoid full scan of view
> definition every time, If you have any alternate deisgn flow how we can
> achieve the result, please suggest for the same.
>
>
> Note: Also, it will be helpfull, if you can share the details like
> community forum or platform to discuss this kind of deisgn related topics,
> it will be more helpfull.
>

Re: Issue with Materialized Views in Spark SQL

[Mich Talebzadeh]

Thanks for the comments I received.

So in summary, Apache Spark itself doesn't directly manage materialized
views,(MV)  but it can work with them through integration with the
underlying data storage systems like Hive or through iceberg. I believe
databricks through unity catalog support MVs as well.

Moreover, there is a case for supporting MVs. However, Spark can utilize
materialized views even though it doesn't directly manage them.. This came
about because someone in the Spark user forum enquired about "Spark
streaming issue to Elastic data*". One option I thought of was that uUsing
materialized views with Spark Structured Streaming and Change Data Capture
(CDC) is a potential solution for efficiently streaming view data updates
in this scenario. .


Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Fri, 3 May 2024 at 00:54, Mich Talebzadeh 
wrote:

> An issue I encountered while working with Materialized Views in Spark SQL.
> It appears that there is an inconsistency between the behavior of
> Materialized Views in Spark SQL and Hive.
>
> When attempting to execute a statement like DROP MATERIALIZED VIEW IF
> EXISTS test.mv in Spark SQL, I encountered a syntax error indicating that
> the keyword MATERIALIZED is not recognized. However, the same statement
> executes successfully in Hive without any errors.
>
> pyspark.errors.exceptions.captured.ParseException:
> [PARSE_SYNTAX_ERROR] Syntax error at or near 'MATERIALIZED'.(line 1, pos 5)
>
> == SQL ==
> DROP MATERIALIZED VIEW IF EXISTS test.mv
> -^^^
>
> Here are the versions I am using:
>
>
>
> *Hive: 3.1.1Spark: 3.4*
> my Spark session:
>
> spark = SparkSession.builder \
>   .appName("test") \
>   .enableHiveSupport() \
>   .getOrCreate()
>
> Has anyone seen this behaviour or encountered a similar issue or if there
> are any insights into why this discrepancy exists between Spark SQL and
> Hive.
>
> Thanks
>
> Mich Talebzadeh,
>
> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
>
> London
> United Kingdom
>
>
>view my Linkedin profile
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> Disclaimer: The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner Von Braun)".
>

Re: Issue with Materialized Views in Spark SQL

[Jungtaek Lim]

(removing dev@ as I don't think this is dev@ related thread but more about
"question")

My understanding is that Apache Spark does not support Materialized View.
That's all. IMHO it's not a proper expectation that all operations in
Apache Hive will be supported in Apache Spark. They are different projects
and Apache Spark does not aim to be 100% compatible with Apache Hive. There
was a time the community tried to provide some sort of compatibility, but
both projects are 10+ years old, and mature enough to have their own
roadmap to drive.

That said, that's not a bug or an issue. You can initiate a feature request
and wish the community to include that into the roadmap.

On Fri, May 3, 2024 at 12:01 PM Mich Talebzadeh 
wrote:

> An issue I encountered while working with Materialized Views in Spark SQL.
> It appears that there is an inconsistency between the behavior of
> Materialized Views in Spark SQL and Hive.
>
> When attempting to execute a statement like DROP MATERIALIZED VIEW IF
> EXISTS test.mv in Spark SQL, I encountered a syntax error indicating that
> the keyword MATERIALIZED is not recognized. However, the same statement
> executes successfully in Hive without any errors.
>
> pyspark.errors.exceptions.captured.ParseException:
> [PARSE_SYNTAX_ERROR] Syntax error at or near 'MATERIALIZED'.(line 1, pos 5)
>
> == SQL ==
> DROP MATERIALIZED VIEW IF EXISTS test.mv
> -^^^
>
> Here are the versions I am using:
>
>
>
> *Hive: 3.1.1Spark: 3.4*
> my Spark session:
>
> spark = SparkSession.builder \
>   .appName("test") \
>   .enableHiveSupport() \
>   .getOrCreate()
>
> Has anyone seen this behaviour or encountered a similar issue or if there
> are any insights into why this discrepancy exists between Spark SQL and
> Hive.
>
> Thanks
>
> Mich Talebzadeh,
>
> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
>
> London
> United Kingdom
>
>
>view my Linkedin profile
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> Disclaimer: The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner Von Braun)".
>

Re: Issue with Materialized Views in Spark SQL

[Walaa Eldin Moustafa]

I do not think the issue is with DROP MATERIALIZED VIEW only, but also with
CREATE MATERIALIZED VIEW, because neither is supported in Spark. I guess
you must have created the view from Hive and are trying to drop it from
Spark and that is why you are running to the issue with DROP first.

There is some work in the Iceberg community to add the support to Spark
through SQL extensions, and Iceberg support for views and
materialization tables. Some recent discussions can be found here [1] along
with a WIP Iceberg-Spark PR.

[1] https://lists.apache.org/thread/rotmqzmwk5jrcsyxhzjhrvcjs5v3yjcc

Thanks,
Walaa.

On Thu, May 2, 2024 at 4:55 PM Mich Talebzadeh 
wrote:

> An issue I encountered while working with Materialized Views in Spark SQL.
> It appears that there is an inconsistency between the behavior of
> Materialized Views in Spark SQL and Hive.
>
> When attempting to execute a statement like DROP MATERIALIZED VIEW IF
> EXISTS test.mv in Spark SQL, I encountered a syntax error indicating that
> the keyword MATERIALIZED is not recognized. However, the same statement
> executes successfully in Hive without any errors.
>
> pyspark.errors.exceptions.captured.ParseException:
> [PARSE_SYNTAX_ERROR] Syntax error at or near 'MATERIALIZED'.(line 1, pos 5)
>
> == SQL ==
> DROP MATERIALIZED VIEW IF EXISTS test.mv
> -^^^
>
> Here are the versions I am using:
>
>
>
> *Hive: 3.1.1Spark: 3.4*
> my Spark session:
>
> spark = SparkSession.builder \
>   .appName("test") \
>   .enableHiveSupport() \
>   .getOrCreate()
>
> Has anyone seen this behaviour or encountered a similar issue or if there
> are any insights into why this discrepancy exists between Spark SQL and
> Hive.
>
> Thanks
>
> Mich Talebzadeh,
>
> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
>
> London
> United Kingdom
>
>
>view my Linkedin profile
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> Disclaimer: The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner Von Braun)".
>

Re: [spark-graphframes]: Generating incorrect edges

[Mich Talebzadeh]

Hi Steve,

Thanks for your statement. I tend to use uuid myself to avoid
collisions. This built-in function generates random IDs that are highly
likely to be unique across systems. My concerns are on edge so to speak. If
the Spark application runs for a very long time or encounters restarts, the
monotonically_increasing_id() sequence might restart from the beginning.
This could again cause duplicate IDs if other Spark applications are
running concurrently or if data is processed across multiple runs of the
same application..

HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Wed, 1 May 2024 at 01:22, Stephen Coy  wrote:

> Hi Mich,
>
> I was just reading random questions on the user list when I noticed that
> you said:
>
> On 25 Apr 2024, at 2:12 AM, Mich Talebzadeh 
> wrote:
>
> 1) You are using monotonically_increasing_id(), which is not
> collision-resistant in distributed environments like Spark. Multiple hosts
>can generate the same ID. I suggest switching to UUIDs (e.g.,
> uuid.uuid4()) for guaranteed uniqueness.
>
>
> It’s my understanding that the *Spark* `monotonically_increasing_id()`
> function exists for the exact purpose of generating a collision-resistant
> unique id across nodes on different hosts.
> We use it extensively for this purpose and have never encountered an issue.
>
> Are we wrong or are you thinking of a different (not Spark) function?
>
> Cheers,
>
> Steve C
>
>
>
>
> This email contains confidential information of and is the copyright of
> Infomedia. It must not be forwarded, amended or disclosed without consent
> of the sender. If you received this message by mistake, please advise the
> sender and delete all copies. Security of transmission on the internet
> cannot be guaranteed, could be infected, intercepted, or corrupted and you
> should ensure you have suitable antivirus protection in place. By sending
> us your or any third party personal details, you consent to (or confirm you
> have obtained consent from such third parties) to Infomedia’s privacy
> policy. http://www.infomedia.com.au/privacy-policy/
>

Re: [spark-graphframes]: Generating incorrect edges

[Stephen Coy]

Hi Mich,

I was just reading random questions on the user list when I noticed that you 
said:

On 25 Apr 2024, at 2:12 AM, Mich Talebzadeh  wrote:

1) You are using monotonically_increasing_id(), which is not 
collision-resistant in distributed environments like Spark. Multiple hosts
   can generate the same ID. I suggest switching to UUIDs (e.g., uuid.uuid4()) 
for guaranteed uniqueness.


It’s my understanding that the *Spark* `monotonically_increasing_id()` function 
exists for the exact purpose of generating a collision-resistant unique id 
across nodes on different hosts.
We use it extensively for this purpose and have never encountered an issue.

Are we wrong or are you thinking of a different (not Spark) function?

Cheers,

Steve C




This email contains confidential information of and is the copyright of 
Infomedia. It must not be forwarded, amended or disclosed without consent of 
the sender. If you received this message by mistake, please advise the sender 
and delete all copies. Security of transmission on the internet cannot be 
guaranteed, could be infected, intercepted, or corrupted and you should ensure 
you have suitable antivirus protection in place. By sending us your or any 
third party personal details, you consent to (or confirm you have obtained 
consent from such third parties) to Infomedia’s privacy policy. 
http://www.infomedia.com.au/privacy-policy/

Re: spark.sql.shuffle.partitions=auto

[Mich Talebzadeh]

spark.sql.shuffle.partitions=auto

Because Apache Spark does not build clusters. This configuration option is
specific to Databricks, with their managed Spark offering. It allows
Databricks to automatically determine an optimal number of shuffle
partitions for your workload.

HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Tue, 30 Apr 2024 at 11:51, second_co...@yahoo.com.INVALID
 wrote:

> May i know is
>
> spark.sql.shuffle.partitions=auto
>
> only available on Databricks? what about on vanilla Spark ? When i set
> this, it gives error need to put int.  Any open source library that auto
> find the best partition , block size for dataframe?
>
>
>

Re: Spark on Kubernetes

[Mich Talebzadeh]

Hi,
In k8s the driver is responsible for executor creation. The likelihood of
your problem is that Insufficient memory allocated for executors in the K8s
cluster. Even with dynamic allocation, k8s won't  schedule executor pods if
there is not enough free memory to fulfill their resource requests.

My suggestions

   - Increase Executor Memory: Allocate more memory per executor (e.g., 2GB
   or 3GB) to allow for multiple executors within available cluster memory.
   - Adjust Driver Pod Resources: Ensure the driver pod has enough memory
   to run Spark and manage executors.
   - Optimize Resource Management: Explore on-demand allocation or
   adjusting allocation granularity for better resource utilization. For
   example look at documents for Executor On-Demand Allocation
   (spark.executor.cores=0): and spark.dynamicAllocation.minExecutors &
   spark.dynamicAllocation.maxExecutors

HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Tue, 30 Apr 2024 at 04:29, Tarun raghav 
wrote:

> Respected Sir/Madam,
> I am Tarunraghav. I have a query regarding spark on kubernetes.
>
> We have an eks cluster, within which we have spark installed in the pods.
> We set the executor memory as 1GB and set the executor instances as 2, I
> have also set dynamic allocation as true. So when I try to read a 3 GB CSV
> file or parquet file, it is supposed to increase the number of pods by 2.
> But the number of executor pods is zero.
> I don't know why executor pods aren't being created, even though I set
> executor instance as 2. Please suggest a solution for this.
>
> Thanks & Regards,
> Tarunraghav
>
>

Re: Python for the kids and now PySpark

[Meena Rajani]

Mitch, you are right these days the attention span is getting shorter.
Christian could work on a completely new thing for 3 hours and is proud to
explain. It is amazing.

Thanks for sharing.



On Sat, Apr 27, 2024 at 9:40 PM Farshid Ashouri 
wrote:

> Mich, this is absolutely amazing.
>
> Thanks for sharing.
>
> On Sat, 27 Apr 2024, 22:26 Mich Talebzadeh, 
> wrote:
>
>> Python for the kids. Slightly off-topic but worthwhile sharing.
>>
>> One of the things that may benefit kids is starting to learn something
>> new. Basically anything that can focus their attention away from games for
>> a few hours. Around 2020, my son Christian (now nearly 15) decided to
>> learn a programming language. So somehow he picked Python to start with.
>> The kids are good when they focus. However, they live in a virtual reality
>> world and they cannot focus for long hours. I let him explore Python on his
>> Windows 10 laptop and download it himself. In the following video Christian
>> explains to his mother what he started to do just before going to bed. BTW,
>> when he says 32M he means 32-bit. I leave it to you to judge :) Now the
>> idea is to start learning PySpark. So I will let him do it himself and
>> learn from his mistakes.  For those who have kids, I would be interested to
>> know their opinion.
>>
>> Cheers
>>
>>
>> Mich Talebzadeh,
>> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *Disclaimer:* The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner
>> Von Braun
>> )".
>>
>> -
>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>
>

Re: Python for the kids and now PySpark

[Farshid Ashouri]

Mich, this is absolutely amazing.

Thanks for sharing.

On Sat, 27 Apr 2024, 22:26 Mich Talebzadeh, 
wrote:

> Python for the kids. Slightly off-topic but worthwhile sharing.
>
> One of the things that may benefit kids is starting to learn something
> new. Basically anything that can focus their attention away from games for
> a few hours. Around 2020, my son Christian (now nearly 15) decided to
> learn a programming language. So somehow he picked Python to start with.
> The kids are good when they focus. However, they live in a virtual reality
> world and they cannot focus for long hours. I let him explore Python on his
> Windows 10 laptop and download it himself. In the following video Christian
> explains to his mother what he started to do just before going to bed. BTW,
> when he says 32M he means 32-bit. I leave it to you to judge :) Now the
> idea is to start learning PySpark. So I will let him do it himself and
> learn from his mistakes.  For those who have kids, I would be interested to
> know their opinion.
>
> Cheers
>
>
> Mich Talebzadeh,
> Technologist | Architect | Data Engineer  | Generative AI | FinCrime
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
> -
> To unsubscribe e-mail: user-unsubscr...@spark.apache.org

Re: [spark-graphframes]: Generating incorrect edges

[Nijland, J.G.W. (Jelle, Student M-CS)]

Hi Mich,

Thanks for your suggestions.
1) It currently runs on one server with plenty of resources assigned. But I 
will keep it in mind to replace monotonically_increasing_id() with uuid() once 
we scale up.
2) I have replaced the null values in origin with a string 
{prefix}-{mnt_by}-{organisation}

replacement_string = psf.concat_ws("-", psf.col("prefix"), psf.col("mnt_by"), 
psf.col("descr"))
df = df.withColumn("origin", psf.coalesce(psf.col("origin"), 
replacement_string))

I have verified my other columns have no Null values.

3) This is my logic how i generate IDs

mnt_by_id = df.select(MNT_BY).distinct().withColumn(MAINTAINER_ID, 
psf.concat(psf.lit('m_'), psf.monotonically_increasing_id()))
prefix_id = df.select(PREFIX).distinct().withColumn(PREFIX_ID, 
psf.concat(psf.lit('p_'), psf.monotonically_increasing_id()))
origin_id = df.select(ORIGIN).distinct().withColumn(ORIGIN_ID, 
psf.concat(psf.lit('o_'), psf.monotonically_increasing_id()))
organisation_id = df.select(DESCR).distinct().withColumn(ORGANISATION_ID, 
psf.concat(psf.lit('org_'), psf.monotonically_increasing_id()))

df = df.join(mnt_by_id, on=MNT_BY, how="left").join(prefix_id, on=PREFIX, 
how="left").join(origin_id, on=ORIGIN, how="left").join(organisation_id, 
on=DESCR, how="left")

I create the ID using the distinct values in the columns "mnt_by", "prefix", 
"origin" and "descr". The same columns I join "on".

4) This is my current resource allocation, I run it on the server of my 
university.
It has 112 cores and 1.48T ram, I can request more resources but in my eyes 
this sound be plenty.
If you think more resource would help, I will ask them.

spark_conf = SparkConf().setAppName(f"pyspark-{APP_NAME}-{int(time())}").set(
"spark.submit.deployMode", "client"
).set("spark.sql.parquet.binaryAsString", "true"
).set("spark.driver.bindAddress", "localhost"
).set("spark.driver.host", "127.0.0.1"
# ).set("spark.driver.port", "0"
).set("spark.ui.port", "4041"
).set("spark.executor.instances", "1"
).set("spark.executor.cores", "50"
).set("spark.executor.memory", "128G"
).set("spark.executor.memoryOverhead", "32G"
).set("spark.driver.cores", "16"
).set("spark.driver.memory", "64G"
)

I dont think b) applies as its a single machine.

Kind regards,
Jelle


From: Mich Talebzadeh 
Sent: Wednesday, April 24, 2024 6:12 PM
To: Nijland, J.G.W. (Jelle, Student M-CS) 
Cc: user@spark.apache.org 
Subject: Re: [spark-graphframes]: Generating incorrect edges

OK let us have a look at these

1) You are using monotonically_increasing_id(), which is not 
collision-resistant in distributed environments like Spark. Multiple hosts
   can generate the same ID. I suggest switching to UUIDs (e.g., uuid.uuid4()) 
for guaranteed uniqueness.

2) Missing values in the Origin column lead to null IDs, potentially causing 
problems downstream. You can handle missing values appropriately, say
   a) Filter out rows with missing origins or b) impute missing values with a 
strategy that preserves relationships (if applicable).

3) With join code, you mentioned left joining on the same column used for ID 
creation, not very clear!

4) Edge Issue, it appears to me the issue seems to occur with larger datasets 
(>100K records). Possible causes could be
   a) Resource Constraints as data size increases, PySpark might struggle with 
joins or computations if resources are limited (memory, CPU).
   b) Data Skew: Uneven distribution of values in certain columns could lead to 
imbalanced processing across machines.  Check Spark UI (4040) on staging and 
execution tabs

HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


 
[https://ci3.googleusercontent.com/mail-sig/AIorK4zholKucR2Q9yMrKbHNn-o1TuS4mYXyi2KO6Xmx6ikHPySa9MLaLZ8t2hrA6AUcxSxDgHIwmKE]
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Disclaimer: The information provided is correct to the best of my knowledge but 
of course cannot be guaranteed . It is essential to note that, as with any 
advice, quote "one test result is worth one-thousand expert opinions (Werner 
<https://en.wikipedia.org/wiki/Wernher_von_Braun> Von 
Braun<https://en.wikipedia.org/wiki/Wernher_von_Braun>)".


On Wed, 24 Apr 2024 at 16:44, Nijland, J.G.W. (Jelle, Student M-CS) 
mailto:j.g.w.nijl...@student.utwente.nl>> 
wrote:
Hi Mich,

Thanks for your reply,
1) ID generation is done using 
monotonically_increasing_id()<https://spark.apache.org/

Re: [spark-graphframes]: Generating incorrect edges

[Mich Talebzadeh]

OK let us have a look at these

1) You are using monotonically_increasing_id(), which is not
collision-resistant in distributed environments like Spark. Multiple hosts
   can generate the same ID. I suggest switching to UUIDs (e.g.,
uuid.uuid4()) for guaranteed uniqueness.

2) Missing values in the Origin column lead to null IDs, potentially
causing problems downstream. You can handle missing values appropriately,
say
   a) Filter out rows with missing origins or b) impute missing values with
a strategy that preserves relationships (if applicable).

3) With join code, you mentioned left joining on the same column used for
ID creation, not very clear!

4) Edge Issue, it appears to me the issue seems to occur with larger
datasets (>100K records). Possible causes could be
   a) Resource Constraints as data size increases, PySpark might struggle
with joins or computations if resources are limited (memory, CPU).
   b) Data Skew: Uneven distribution of values in certain columns could
lead to imbalanced processing across machines.  Check Spark UI (4040) on
staging and execution tabs

HTH

Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI | FinCrime
London
United Kingdom


   view my Linkedin profile
<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>


 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von
Braun <https://en.wikipedia.org/wiki/Wernher_von_Braun>)".


On Wed, 24 Apr 2024 at 16:44, Nijland, J.G.W. (Jelle, Student M-CS) <
j.g.w.nijl...@student.utwente.nl> wrote:

> Hi Mich,
>
> Thanks for your reply,
> 1) ID generation is done using monotonically_increasing_id()
> <https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.monotonically_increasing_id.html>
>  this
> is then prefixed with "p_", "m_", "o_" or "org_" depending on the type of
> the value it identifies.
> 2) There are some missing values in the Origin column, these will result
> in a Null ID
> 3) The join code is present in [1], I join "left" on the same column
> I create the ID on
> 4) I dont think the issue is in ID or edge generation, if i limit my input
> dataframe and union it with my Utwente data row, I can verify those edges
> are created correctly up to 100K records.
> Once I go past that amount of records the results become inconsistent and
> incorrect.
>
> Kind regards,
> Jelle Nijland
>
>
> --
> *From:* Mich Talebzadeh 
> *Sent:* Wednesday, April 24, 2024 4:40 PM
> *To:* Nijland, J.G.W. (Jelle, Student M-CS) <
> j.g.w.nijl...@student.utwente.nl>
> *Cc:* user@spark.apache.org 
> *Subject:* Re: [spark-graphframes]: Generating incorrect edges
>
> OK few observations
>
> 1) ID Generation Method: How are you generating unique IDs (UUIDs,
> sequential numbers, etc.)?
> 2) Data Inconsistencies: Have you checked for missing values impacting ID
> generation?
> 3) Join Verification: If relevant, can you share the code for joining data
> points during ID creation? Are joins matching columns correctly?
> 4) Specific Edge Issues: Can you share examples of vertex IDs with
> incorrect connections? Is this related to ID generation or edge creation
> logic?
>
> HTH
> Mich Talebzadeh,
> Technologist | Architect | Data Engineer  | Generative AI, FinCrime
> London
> United Kingdom
>
>
>view my Linkedin profile
> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von
> Braun <https://en.wikipedia.org/wiki/Wernher_von_Braun>)".
>
>
> On Wed, 24 Apr 2024 at 12:24, Nijland, J.G.W. (Jelle, Student M-CS) <
> j.g.w.nijl...@student.utwente.nl> wrote:
>
> tags: pyspark,spark-graphframes
>
> Hello,
>
> I am running pyspark in a podman container and I have issues with
> incorrect edges when I build my graph.
> I start with loading a source dataframe from a parquet directory on my
> server. The source dataframe has the following columns:
>
> +-+---+-+-+--+-+--+---+
> |created |descr |last_modified

Re: [spark-graphframes]: Generating incorrect edges

[Nijland, J.G.W. (Jelle, Student M-CS)]

Hi Mich,

Thanks for your reply,
1) ID generation is done using 
monotonically_increasing_id()<https://spark.apache.org/docs/latest/api/python/reference/pyspark.sql/api/pyspark.sql.functions.monotonically_increasing_id.html>
 this is then prefixed with "p_", "m_", "o_" or "org_" depending on the type of 
the value it identifies.
2) There are some missing values in the Origin column, these will result in a 
Null ID
3) The join code is present in [1], I join "left" on the same column I create 
the ID on
4) I dont think the issue is in ID or edge generation, if i limit my input 
dataframe and union it with my Utwente data row, I can verify those edges are 
created correctly up to 100K records.
Once I go past that amount of records the results become inconsistent and 
incorrect.

Kind regards,
Jelle Nijland



From: Mich Talebzadeh 
Sent: Wednesday, April 24, 2024 4:40 PM
To: Nijland, J.G.W. (Jelle, Student M-CS) 
Cc: user@spark.apache.org 
Subject: Re: [spark-graphframes]: Generating incorrect edges

OK few observations

1) ID Generation Method: How are you generating unique IDs (UUIDs, sequential 
numbers, etc.)?
2) Data Inconsistencies: Have you checked for missing values impacting ID 
generation?
3) Join Verification: If relevant, can you share the code for joining data 
points during ID creation? Are joins matching columns correctly?
4) Specific Edge Issues: Can you share examples of vertex IDs with incorrect 
connections? Is this related to ID generation or edge creation logic?

HTH
Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI, FinCrime
London
United Kingdom


 
[https://ci3.googleusercontent.com/mail-sig/AIorK4zholKucR2Q9yMrKbHNn-o1TuS4mYXyi2KO6Xmx6ikHPySa9MLaLZ8t2hrA6AUcxSxDgHIwmKE]
   view my Linkedin 
profile<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>


 https://en.everybodywiki.com/Mich_Talebzadeh



Disclaimer: The information provided is correct to the best of my knowledge but 
of course cannot be guaranteed . It is essential to note that, as with any 
advice, quote "one test result is worth one-thousand expert opinions (Werner 
<https://en.wikipedia.org/wiki/Wernher_von_Braun> Von 
Braun<https://en.wikipedia.org/wiki/Wernher_von_Braun>)".


On Wed, 24 Apr 2024 at 12:24, Nijland, J.G.W. (Jelle, Student M-CS) 
mailto:j.g.w.nijl...@student.utwente.nl>> 
wrote:
tags: pyspark,spark-graphframes

Hello,

I am running pyspark in a podman container and I have issues with incorrect 
edges when I build my graph.
I start with loading a source dataframe from a parquet directory on my server. 
The source dataframe has the following columns:
+-+---+-+-+--+-+--+---+
|created |descr |last_modified|mnt_by |origin|start_address|prefix 
|external_origin|
+-+---+-+-+--+-+--+---+

I aim to build a graph connecting prefix, mnt_by, origin and descr with edges 
storing the created and last_modified values.
I start with generating IDs for the prefix, mnt_by, origin and descr using 
monotonically_increasing_id() [1]
These IDs are prefixed with "m_", "p_", "o_" or "org_" to ensure they are 
unique IDs across the dataframe.

Then I construct the vertices dataframe by collecting the ID, value and whether 
they are external for each vertex. [2]
These vertices are then unioned together.
Following the vertices, I construct the edges dataframe by selecting the IDs 
that I want to be the src and the dst and union those together. [3]
These edges store the created and last_modified.

Now I am ready to construct the graph. Here is where I run into my issue.

When verifying my graph, I looked at a couple of vertices to see if they have 
the correct edges.
I looked at the Utwente prefix, origin, descr and mnt_by and found that it 
generates incorrect edges.

I saw edges going out to vertices that are not associated with the utwente 
values at all.
The methods to find the vertices, edges and the output can be found in [4]
We can already observe inconsistencies by viewing the prefix->maintainer and 
origin -> prefix edges. [5]
Depending on what column I filter on the results are inconsistent.
To make matters worse some edges contain IDs that are not connected to the 
original values in the source dataframe at all.

What I have tried to resolve my issue:

  *
Write a checker that verifies edges created against the source dataframe. [6]
The aim of this checker was to determine where the inconsistency comes fro, to 
locate the bug and resolve it.
I ran this checker a limited graphs from n=10 upwards to n=100 (or 1m).
This felt close enough as there are only ~6.5m records in my source dataframe.
This ran correctly, near the 1m it did experience significant slowdown at the 
f

Re: [spark-graphframes]: Generating incorrect edges

[Mich Talebzadeh]

OK few observations

1) ID Generation Method: How are you generating unique IDs (UUIDs,
sequential numbers, etc.)?
2) Data Inconsistencies: Have you checked for missing values impacting ID
generation?
3) Join Verification: If relevant, can you share the code for joining data
points during ID creation? Are joins matching columns correctly?
4) Specific Edge Issues: Can you share examples of vertex IDs with
incorrect connections? Is this related to ID generation or edge creation
logic?

HTH
Mich Talebzadeh,
Technologist | Architect | Data Engineer  | Generative AI, FinCrime
London
United Kingdom


   view my Linkedin profile
<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>


 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von
Braun <https://en.wikipedia.org/wiki/Wernher_von_Braun>)".


On Wed, 24 Apr 2024 at 12:24, Nijland, J.G.W. (Jelle, Student M-CS) <
j.g.w.nijl...@student.utwente.nl> wrote:

> tags: pyspark,spark-graphframes
>
> Hello,
>
> I am running pyspark in a podman container and I have issues with
> incorrect edges when I build my graph.
> I start with loading a source dataframe from a parquet directory on my
> server. The source dataframe has the following columns:
>
> +-+---+-+-+--+-+--+---+
> |created |descr |last_modified|mnt_by |origin|start_address|prefix
> |external_origin|
>
> +-+---+-+-+--+-+--+---+
>
> I aim to build a graph connecting prefix, mnt_by, origin and descr with
> edges storing the created and last_modified values.
> I start with generating IDs for the prefix, mnt_by, origin and descr using
> monotonically_increasing_id() [1]
> These IDs are prefixed with "m_", "p_", "o_" or "org_" to ensure they are
> unique IDs across the dataframe.
>
> Then I construct the vertices dataframe by collecting the ID, value and
> whether they are external for each vertex. [2]
> These vertices are then unioned together.
> Following the vertices, I construct the edges dataframe by selecting the
> IDs that I want to be the src and the dst and union those together. [3]
> These edges store the created and last_modified.
>
> Now I am ready to construct the graph. Here is where I run into my issue.
>
> When verifying my graph, I looked at a couple of vertices to see if they
> have the correct edges.
> I looked at the Utwente prefix, origin, descr and mnt_by and found that it
> generates incorrect edges.
>
> I saw edges going out to vertices that are not associated with the utwente
> values at all.
> The methods to find the vertices, edges and the output can be found in [4]
> We can already observe inconsistencies by viewing the prefix->maintainer
> and origin -> prefix edges. [5]
> Depending on what column I filter on the results are inconsistent.
> To make matters worse some edges contain IDs that are not connected to the
> original values in the source dataframe at all.
>
> What I have tried to resolve my issue:
>
>- Write a checker that verifies edges created against the source
>dataframe. [6]
>The aim of this checker was to determine where the inconsistency comes
>fro, to locate the bug and resolve it.
>I ran this checker a limited graphs from n=10 upwards to n=100 (or
>1m).
>This felt close enough as there are only ~6.5m records in my source
>dataframe.
>This ran correctly, near the 1m it did experience significant slowdown
>at the full dataframe it errors/times out.
>I blamed this on the large joins that it performs on the source
>dataframe.
>- I found a github issue of someone with significantly larger graphs
>have similar issues.
>One suggestion there blamed indexing using strings rather than ints or
>longs.
>I rewrote my system to use int for IDs but I ran into the same issue.
>The amount of incorrect edges was the same, the link to which
>incorrects vertices it links to was the same too.
>- I re-ordered my source dataframe to see what the impact was.
>This results in considerably more incorrect edges using the checker in
>[4]
>If helpful I can post the output of this checker as well.
>
>
> Can you give me any pointers in what I can try or what I can do to clarify
> my situation better?
> Thanks in advance for your time.
>
> Kind regards,
> 

RE: How to add MaxDOP option in spark mssql JDBC

[Appel, Kevin]

You might be able to leverage the prepareQuery option, that is at 
https://spark.apache.org/docs/3.5.1/sql-data-sources-jdbc.html#data-source-option
 ... this was introduced in Spark 3.4.0 to handle temp table query and CTE 
query against MSSQL server since what you send in is not actually what gets 
sent, there is some items that get wrapped.

There is more of the technical info in 
https://issues.apache.org/jira/browse/SPARK-37259 with the PR's linked that had 
the fix done for this


From: Elite 
Sent: Tuesday, April 23, 2024 10:28 PM
To: user@spark.apache.org
Subject: How to add MaxDOP option in spark mssql JDBC

[QUESTION] How to pass MAXDOP option * Issue #2395 * microsoft/mssql-jdbc 
(github.com)

Hi team,

I am suggested to require help form spark community.

We suspect spark rewerite the query before pass to ms sql, and it lead to 
syntax error.
Is there any work around to let make my codes work?


spark.read()
.format("jdbc")
.option("driver","com.microsoft.sqlserver.jdbc.SQLServerDriver")
.option("url", "jdbc:sqlserver://xxx.database.windows.net;databaseName=")
.option("query", "SELECT TOP 10 * FROM dbo.Demo with (nolock) WHERE Id = 1 
option (maxdop 1)")
.load()
.show();

com.microsoft.sqlserver.jdbc.SQLServerException: Incorrect syntax near the 
keyword 'option'.
at 
com.microsoft.sqlserver.jdbc.SQLServerException.makeFromDatabaseError(SQLServerException.java:270)
at 
com.microsoft.sqlserver.jdbc.SQLServerStatement.getNextResult(SQLServerStatement.java:1778)
at 
com.microsoft.sqlserver.jdbc.SQLServerPreparedStatement.doExecutePreparedStatement(SQLServerPreparedStatement.java:697)
at 
com.microsoft.sqlserver.jdbc.SQLServerPreparedStatement$PrepStmtExecCmd.doExecute(SQLServerPreparedStatement.java:616)
at com.microsoft.sqlserver.jdbc.TDSCommand.execute(IOBuffer.java:7775)
at 
com.microsoft.sqlserver.jdbc.SQLServerConnection.executeCommand(SQLServerConnection.java:4397)
at 
com.microsoft.sqlserver.jdbc.SQLServerStatement.executeCommand(SQLServerStatement.java:293)
at 
com.microsoft.sqlserver.jdbc.SQLServerStatement.executeStatement(SQLServerStatement.java:263)
at 
com.microsoft.sqlserver.jdbc.SQLServerPreparedStatement.executeQuery(SQLServerPreparedStatement.java:531)
at 
org.apache.spark.sql.execution.datasources.jdbc.JDBCRDD$.resolveTable(JDBCRDD.scala:61)
at 
org.apache.spark.sql.execution.datasources.jdbc.JDBCRelation$.getSchema(JDBCRelation.scala:226)
at 
org.apache.spark.sql.execution.datasources.jdbc.JdbcRelationProvider.createRelation(JdbcRelationProvider.scala:35)
at 
org.apache.spark.sql.execution.datasources.DataSource.resolveRelation(DataSource.scala:344)
at org.apache.spark.sql.DataFrameReader.loadV1Source(DataFrameReader.scala:297)
at 
org.apache.spark.sql.DataFrameReader.$anonfun$load$2(DataFrameReader.scala:286)
at scala.Option.getOrElse(Option.scala:189)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:286)
at org.apache.spark.sql.DataFrameReader.load(DataFrameReader.scala:221)

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Re: Spark streaming job for kafka transaction does not consume read_committed messages correctly.

[Kidong Lee]

Thanks, Mich for your reply.

I agree, it is not so scalable and efficient. But it works correctly for
kafka transaction, and there is no problem with committing offset to kafka
async for now.

I try to tell you some more details about my streaming job.
CustomReceiver does not receive anything from outside and just forward
notice message to run an executor in which kafka consumer will be run.
See my CustomReceiver.

private static class CustomReceiver extends Receiver {

public CustomReceiver() {
super(StorageLevel.MEMORY_AND_DISK_2());
}

@Override
public void onStart() {
new Thread(this::receive).start();
}

private void receive() {
String input = "receiver input " + UUID.randomUUID().toString();
store(input);
}

@Override
public void onStop() {

}
}


Actually, just one Kafka consumer will be run which consumes committed
messages from kafka directly(, which is not so scalable, I think.).
But the main point of this approach which I need is that spark
session needs to be used to save rdd(parallelized consumed messages) to
iceberg table.
Consumed messages will be converted to spark rdd which will be saved to
iceberg table using spark session.

I have tested this spark streaming job with transactional producers which
send several millions of messages. Correctly consumed and saved to iceberg
tables correctly.

- Kidong.



2024년 4월 14일 (일) 오후 11:05, Mich Talebzadeh 님이 작성:

> Interesting
>
> My concern is infinite Loop in* foreachRDD*: The *while(true)* loop
> within foreachRDD creates an infinite loop within each Spark executor. This
> might not be the most efficient approach, especially since offsets are
> committed asynchronously.?
>
> HTH
>
> Mich Talebzadeh,
> Technologist | Solutions Architect | Data Engineer  | Generative AI
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
>
> On Sun, 14 Apr 2024 at 13:40, Kidong Lee  wrote:
>
>>
>> Because spark streaming for kafk transaction does not work correctly to
>> suit my need, I moved to another approach using raw kafka consumer which
>> handles read_committed messages from kafka correctly.
>>
>> My codes look like the following.
>>
>> JavaDStream stream = ssc.receiverStream(new CustomReceiver()); // 
>> CustomReceiver does nothing special except awaking foreach task.
>>
>> stream.foreachRDD(rdd -> {
>>
>>   KafkaConsumer consumer = new 
>> KafkaConsumer<>(consumerProperties);
>>
>>   consumer.subscribe(Arrays.asList(topic));
>>
>>   while(true){
>>
>> ConsumerRecords records =
>> consumer.poll(java.time.Duration.ofMillis(intervalMs));
>>
>> Map offsetMap = new HashMap<>();
>>
>> List someList = new ArrayList<>();
>>
>> for (ConsumerRecord consumerRecord : records) {
>>
>>   // add something to list.
>>
>>   // put offset to offsetMap.
>>
>> }
>>
>> // process someList.
>>
>> // commit offset.
>>
>> consumer.commitAsync(offsetMap, null);
>>
>>   }
>>
>> });
>>
>>
>> In addition, I increased max.poll.records to 10.
>>
>> Even if this raw kafka consumer approach is not so scalable, it consumes
>> read_committed messages from kafka correctly and is enough for me at the
>> moment.
>>
>> - Kidong.
>>
>>
>>
>> 2024년 4월 12일 (금) 오후 9:19, Kidong Lee 님이 작성:
>>
>>> Hi,
>>>
>>> I have a kafka producer which sends messages transactionally to kafka
>>> and spark streaming job which should consume read_committed messages from
>>> kafka.
>>> But there is a problem for spark streaming to consume read_committed
>>> messages.
>>> The count of messages sent by kafka producer transactionally is not the
>>> same to the count of the read_committed messages consumed by spark
>>> streaming.
>>>
>>> Some consumer properties of my spark streaming job are as follows.
>>>
>>> auto.offset.reset=earliest
>>> enable.auto.commit=false
>>> isolation.level=read_committed
>>>
>>>
>>> I also added the following spark streaming configuration.
>>>
>>> sparkConf.set("spark.streaming.kafka.allowNonConsecutiveOffsets", "true");
>>> sparkConf.set("spark.streaming.kafka.consumer.poll.ms", String.valueOf(2 * 
>>> 60 * 1000));
>>>
>>>
>>> My spark streaming is using DirectStream like this.
>>>
>>> JavaInputDStream> stream =
>>> KafkaUtils.createDirectStream(
>>> ssc,
>>> LocationStrategies.PreferConsistent(),
>>> ConsumerStrategies.>> GenericRecord>Subscribe(topics, kafkaParams)
>>> );
>>>
>>>
>>> stream.foreachRDD(rdd -> O
>>>
>>> 

Re: Spark streaming job for kafka transaction does not consume read_committed messages correctly.

[Mich Talebzadeh]

Interesting

My concern is infinite Loop in* foreachRDD*: The *while(true)* loop within
foreachRDD creates an infinite loop within each Spark executor. This might
not be the most efficient approach, especially since offsets are committed
asynchronously.?

HTH

Mich Talebzadeh,
Technologist | Solutions Architect | Data Engineer  | Generative AI
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Sun, 14 Apr 2024 at 13:40, Kidong Lee  wrote:

>
> Because spark streaming for kafk transaction does not work correctly to
> suit my need, I moved to another approach using raw kafka consumer which
> handles read_committed messages from kafka correctly.
>
> My codes look like the following.
>
> JavaDStream stream = ssc.receiverStream(new CustomReceiver()); // 
> CustomReceiver does nothing special except awaking foreach task.
>
> stream.foreachRDD(rdd -> {
>
>   KafkaConsumer consumer = new 
> KafkaConsumer<>(consumerProperties);
>
>   consumer.subscribe(Arrays.asList(topic));
>
>   while(true){
>
> ConsumerRecords records =
> consumer.poll(java.time.Duration.ofMillis(intervalMs));
>
> Map offsetMap = new HashMap<>();
>
> List someList = new ArrayList<>();
>
> for (ConsumerRecord consumerRecord : records) {
>
>   // add something to list.
>
>   // put offset to offsetMap.
>
> }
>
> // process someList.
>
> // commit offset.
>
> consumer.commitAsync(offsetMap, null);
>
>   }
>
> });
>
>
> In addition, I increased max.poll.records to 10.
>
> Even if this raw kafka consumer approach is not so scalable, it consumes
> read_committed messages from kafka correctly and is enough for me at the
> moment.
>
> - Kidong.
>
>
>
> 2024년 4월 12일 (금) 오후 9:19, Kidong Lee 님이 작성:
>
>> Hi,
>>
>> I have a kafka producer which sends messages transactionally to kafka and
>> spark streaming job which should consume read_committed messages from kafka.
>> But there is a problem for spark streaming to consume read_committed
>> messages.
>> The count of messages sent by kafka producer transactionally is not the
>> same to the count of the read_committed messages consumed by spark
>> streaming.
>>
>> Some consumer properties of my spark streaming job are as follows.
>>
>> auto.offset.reset=earliest
>> enable.auto.commit=false
>> isolation.level=read_committed
>>
>>
>> I also added the following spark streaming configuration.
>>
>> sparkConf.set("spark.streaming.kafka.allowNonConsecutiveOffsets", "true");
>> sparkConf.set("spark.streaming.kafka.consumer.poll.ms", String.valueOf(2 * 
>> 60 * 1000));
>>
>>
>> My spark streaming is using DirectStream like this.
>>
>> JavaInputDStream> stream =
>> KafkaUtils.createDirectStream(
>> ssc,
>> LocationStrategies.PreferConsistent(),
>> ConsumerStrategies.Subscribe(topics, 
>> kafkaParams)
>> );
>>
>>
>> stream.foreachRDD(rdd -> O
>>
>>// get offset ranges.
>>
>>OffsetRange[] offsetRanges = ((HasOffsetRanges) 
>> rdd.rdd()).offsetRanges();
>>
>>// process something.
>>
>>
>>// commit offset.
>>((CanCommitOffsets) stream.inputDStream()).commitAsync(offsetRanges);
>>
>> }
>> );
>>
>>
>>
>> I have tested with a kafka consumer written with raw kafka-clients jar
>> library without problem that it consumes read_committed messages correctly,
>> and the count of consumed read_committed messages is equal to the count of
>> messages sent by kafka producer.
>>
>>
>> And sometimes, I got the following exception.
>>
>> Job aborted due to stage failure: Task 0 in stage 324.0 failed 1 times,
>> most recent failure: Lost task 0.0 in stage 324.0 (TID 1674)
>> (chango-private-1.chango.private executor driver):
>> java.lang.IllegalArgumentException: requirement failed: Failed to get
>> records for compacted spark-executor-school-student-group school-student-7
>> after polling for 12
>>
>> at scala.Predef$.require(Predef.scala:281)
>>
>> at
>> org.apache.spark.streaming.kafka010.InternalKafkaConsumer.compactedNext(KafkaDataConsumer.scala:186)
>>
>> at
>> org.apache.spark.streaming.kafka010.KafkaDataConsumer.compactedNext(KafkaDataConsumer.scala:60)
>>
>> at
>> org.apache.spark.streaming.kafka010.KafkaDataConsumer.compactedNext$(KafkaDataConsumer.scala:59)
>>
>> at
>> org.apache.spark.streaming.kafka010.KafkaDataConsumer$CachedKafkaDataConsumer.compactedNext(KafkaDataConsumer.scala:219)
>>
>>
>>
>> I have experienced spark streaming job which works fine with kafka
>> messages which are non-transactional, and I 

Re: Spark streaming job for kafka transaction does not consume read_committed messages correctly.

[Kidong Lee]

Because spark streaming for kafk transaction does not work correctly to
suit my need, I moved to another approach using raw kafka consumer which
handles read_committed messages from kafka correctly.

My codes look like the following.

JavaDStream stream = ssc.receiverStream(new CustomReceiver());
// CustomReceiver does nothing special except awaking foreach task.

stream.foreachRDD(rdd -> {

  KafkaConsumer consumer = new
KafkaConsumer<>(consumerProperties);

  consumer.subscribe(Arrays.asList(topic));

  while(true){

ConsumerRecords records =
consumer.poll(java.time.Duration.ofMillis(intervalMs));

Map offsetMap = new HashMap<>();

List someList = new ArrayList<>();

for (ConsumerRecord consumerRecord : records) {

  // add something to list.

  // put offset to offsetMap.

}

// process someList.

// commit offset.

consumer.commitAsync(offsetMap, null);

  }

});


In addition, I increased max.poll.records to 10.

Even if this raw kafka consumer approach is not so scalable, it consumes
read_committed messages from kafka correctly and is enough for me at the
moment.

- Kidong.



2024년 4월 12일 (금) 오후 9:19, Kidong Lee 님이 작성:

> Hi,
>
> I have a kafka producer which sends messages transactionally to kafka and
> spark streaming job which should consume read_committed messages from kafka.
> But there is a problem for spark streaming to consume read_committed
> messages.
> The count of messages sent by kafka producer transactionally is not the
> same to the count of the read_committed messages consumed by spark
> streaming.
>
> Some consumer properties of my spark streaming job are as follows.
>
> auto.offset.reset=earliest
> enable.auto.commit=false
> isolation.level=read_committed
>
>
> I also added the following spark streaming configuration.
>
> sparkConf.set("spark.streaming.kafka.allowNonConsecutiveOffsets", "true");
> sparkConf.set("spark.streaming.kafka.consumer.poll.ms", String.valueOf(2 * 60 
> * 1000));
>
>
> My spark streaming is using DirectStream like this.
>
> JavaInputDStream> stream =
> KafkaUtils.createDirectStream(
> ssc,
> LocationStrategies.PreferConsistent(),
> ConsumerStrategies.Subscribe(topics, 
> kafkaParams)
> );
>
>
> stream.foreachRDD(rdd -> O
>
>// get offset ranges.
>
>OffsetRange[] offsetRanges = ((HasOffsetRanges) 
> rdd.rdd()).offsetRanges();
>
>// process something.
>
>
>// commit offset.
>((CanCommitOffsets) stream.inputDStream()).commitAsync(offsetRanges);
>
> }
> );
>
>
>
> I have tested with a kafka consumer written with raw kafka-clients jar
> library without problem that it consumes read_committed messages correctly,
> and the count of consumed read_committed messages is equal to the count of
> messages sent by kafka producer.
>
>
> And sometimes, I got the following exception.
>
> Job aborted due to stage failure: Task 0 in stage 324.0 failed 1 times,
> most recent failure: Lost task 0.0 in stage 324.0 (TID 1674)
> (chango-private-1.chango.private executor driver):
> java.lang.IllegalArgumentException: requirement failed: Failed to get
> records for compacted spark-executor-school-student-group school-student-7
> after polling for 12
>
> at scala.Predef$.require(Predef.scala:281)
>
> at
> org.apache.spark.streaming.kafka010.InternalKafkaConsumer.compactedNext(KafkaDataConsumer.scala:186)
>
> at
> org.apache.spark.streaming.kafka010.KafkaDataConsumer.compactedNext(KafkaDataConsumer.scala:60)
>
> at
> org.apache.spark.streaming.kafka010.KafkaDataConsumer.compactedNext$(KafkaDataConsumer.scala:59)
>
> at
> org.apache.spark.streaming.kafka010.KafkaDataConsumer$CachedKafkaDataConsumer.compactedNext(KafkaDataConsumer.scala:219)
>
>
>
> I have experienced spark streaming job which works fine with kafka
> messages which are non-transactional, and I never encountered the
> exceptions like above.
> It seems that spark streaming for kafka transaction does not handle such
> as kafka consumer properties like isolation.level=read_committed and
> enable.auto.commit=false correctly.
>
> Any help appreciated.
>
> - Kidong.
>
>
> --
> *이기동 *
> *Kidong Lee*
>
> Email: mykid...@gmail.com
> Chango: https://cloudcheflabs.github.io/chango-private-docs
> Web Site: http://www.cloudchef-labs.com/
> Mobile: +82 10 4981 7297
> 
>


-- 
*이기동 *
*Kidong Lee*

Email: mykid...@gmail.com
Chango: https://cloudcheflabs.github.io/chango-private-docs
Web Site: http://www.cloudchef-labs.com/
Mobile: +82 10 4981 7297

Re: Spark streaming job for kafka transaction does not consume read_committed messages correctly.

[Kidong Lee]

Thank you Mich for your reply.

Actually, I tried to do most of your advice.

When spark.streaming.kafka.allowNonConsecutiveOffsets=false, I got the
following error.

Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most
recent failure: Lost task 0.0 in stage 1.0 (TID 3)
(chango-private-1.chango.private executor driver):
java.lang.IllegalArgumentException: requirement failed: Got wrong record
for spark-executor-school-student-group school-student-7 even after seeking
to offset 11206961 got offset 11206962 instead. If this is a compacted
topic, consider enabling spark.streaming.kafka.allowNonConsecutiveOffsets

at scala.Predef$.require(Predef.scala:281)

at
org.apache.spark.streaming.kafka010.InternalKafkaConsumer.get(KafkaDataConsumer.scala:155)

at
org.apache.spark.streaming.kafka010.KafkaDataConsumer.get(KafkaDataConsumer.scala:40)

at
org.apache.spark.streaming.kafka010.KafkaDataConsumer.get$(KafkaDataConsumer.scala:39)

at
org.apache.spark.streaming.kafka010.KafkaDataConsumer$CachedKafkaDataConsumer.get(KafkaDataConsumer.scala:219)


And I tried to increase spark.streaming.kafka.consumer.poll.ms to avoid the
exceptions, but it did not help.


- Kidong.




2024년 4월 14일 (일) 오전 4:25, Mich Talebzadeh 님이 작성:

> Hi Kidong,
>
> There may be few potential reasons why the message counts from your Kafka
> producer and Spark Streaming consumer might not match, especially with
> transactional messages and read_committed isolation level.
>
> 1) Just ensure that both your Spark Streaming job and the Kafka consumer
> written with raw kafka-clients use the same consumer group. Messages are
> delivered to specific consumer groups, and if they differ, Spark Streaming
> might miss messages consumed by the raw consumer.
> 2) Your Spark Streaming configuration sets *enable.auto.commit=false* and
> uses *commitAsync manually*. However, I noted
> *spark.streaming.kafka.allowNonConsecutiveOffsets=true* which may be
> causing the problem. This setting allows Spark Streaming to read offsets
> that are not strictly increasing, which can happen with transactional
> reads. Generally recommended to set this to* false *for transactional
> reads to ensure Spark Streaming only reads committed messages.
> 3) Missed messages, in transactional messages, Kafka guarantees *delivery
> only after the transaction commits successfully. *There could be a slight
> delay between the producer sending the message and it becoming visible to
> consumers under read_committed isolation level. Spark Streaming could
> potentially miss messages during this window.
> 4) The exception Lost task 0.0 in stage 324.0, suggests a problem fetching
> records for a specific topic partition. Review your code handling of
> potential exceptions during rdd.foreachRDD processing. Ensure retries or
> appropriate error handling if encountering issues with specific partitions.
> 5) Try different configurations for *spark.streaming.kafka.consumer.poll.ms
> * to adjust polling
> frequency and potentially improve visibility into committed messages.
>
> HTH
>
> Mich Talebzadeh,
> Technologist | Solutions Architect | Data Engineer  | Generative AI
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
>
> On Fri, 12 Apr 2024 at 21:38, Kidong Lee  wrote:
>
>> Hi,
>>
>> I have a kafka producer which sends messages transactionally to kafka and
>> spark streaming job which should consume read_committed messages from kafka.
>> But there is a problem for spark streaming to consume read_committed
>> messages.
>> The count of messages sent by kafka producer transactionally is not the
>> same to the count of the read_committed messages consumed by spark
>> streaming.
>>
>> Some consumer properties of my spark streaming job are as follows.
>>
>> auto.offset.reset=earliest
>> enable.auto.commit=false
>> isolation.level=read_committed
>>
>>
>> I also added the following spark streaming configuration.
>>
>> sparkConf.set("spark.streaming.kafka.allowNonConsecutiveOffsets", "true");
>> sparkConf.set("spark.streaming.kafka.consumer.poll.ms", String.valueOf(2 * 
>> 60 * 1000));
>>
>>
>> My spark streaming is using DirectStream like this.
>>
>> JavaInputDStream> stream =
>> KafkaUtils.createDirectStream(
>> ssc,
>> LocationStrategies.PreferConsistent(),
>> ConsumerStrategies.Subscribe(topics, 
>> kafkaParams)
>> );
>>
>>
>> stream.foreachRDD(rdd -> O
>>
>>// get offset ranges.
>>
>> 

Re: Spark streaming job for kafka transaction does not consume read_committed messages correctly.

[Mich Talebzadeh]

Hi Kidong,

There may be few potential reasons why the message counts from your Kafka
producer and Spark Streaming consumer might not match, especially with
transactional messages and read_committed isolation level.

1) Just ensure that both your Spark Streaming job and the Kafka consumer
written with raw kafka-clients use the same consumer group. Messages are
delivered to specific consumer groups, and if they differ, Spark Streaming
might miss messages consumed by the raw consumer.
2) Your Spark Streaming configuration sets *enable.auto.commit=false* and
uses *commitAsync manually*. However, I noted
*spark.streaming.kafka.allowNonConsecutiveOffsets=true* which may be
causing the problem. This setting allows Spark Streaming to read offsets
that are not strictly increasing, which can happen with transactional
reads. Generally recommended to set this to* false *for transactional reads
to ensure Spark Streaming only reads committed messages.
3) Missed messages, in transactional messages, Kafka guarantees *delivery
only after the transaction commits successfully. *There could be a slight
delay between the producer sending the message and it becoming visible to
consumers under read_committed isolation level. Spark Streaming could
potentially miss messages during this window.
4) The exception Lost task 0.0 in stage 324.0, suggests a problem fetching
records for a specific topic partition. Review your code handling of
potential exceptions during rdd.foreachRDD processing. Ensure retries or
appropriate error handling if encountering issues with specific partitions.
5) Try different configurations for *spark.streaming.kafka.consumer.poll.ms
* to adjust polling
frequency and potentially improve visibility into committed messages.

HTH

Mich Talebzadeh,
Technologist | Solutions Architect | Data Engineer  | Generative AI
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Fri, 12 Apr 2024 at 21:38, Kidong Lee  wrote:

> Hi,
>
> I have a kafka producer which sends messages transactionally to kafka and
> spark streaming job which should consume read_committed messages from kafka.
> But there is a problem for spark streaming to consume read_committed
> messages.
> The count of messages sent by kafka producer transactionally is not the
> same to the count of the read_committed messages consumed by spark
> streaming.
>
> Some consumer properties of my spark streaming job are as follows.
>
> auto.offset.reset=earliest
> enable.auto.commit=false
> isolation.level=read_committed
>
>
> I also added the following spark streaming configuration.
>
> sparkConf.set("spark.streaming.kafka.allowNonConsecutiveOffsets", "true");
> sparkConf.set("spark.streaming.kafka.consumer.poll.ms", String.valueOf(2 * 60 
> * 1000));
>
>
> My spark streaming is using DirectStream like this.
>
> JavaInputDStream> stream =
> KafkaUtils.createDirectStream(
> ssc,
> LocationStrategies.PreferConsistent(),
> ConsumerStrategies.Subscribe(topics, 
> kafkaParams)
> );
>
>
> stream.foreachRDD(rdd -> O
>
>// get offset ranges.
>
>OffsetRange[] offsetRanges = ((HasOffsetRanges) 
> rdd.rdd()).offsetRanges();
>
>// process something.
>
>
>// commit offset.
>((CanCommitOffsets) stream.inputDStream()).commitAsync(offsetRanges);
>
> }
> );
>
>
>
> I have tested with a kafka consumer written with raw kafka-clients jar
> library without problem that it consumes read_committed messages correctly,
> and the count of consumed read_committed messages is equal to the count of
> messages sent by kafka producer.
>
>
> And sometimes, I got the following exception.
>
> Job aborted due to stage failure: Task 0 in stage 324.0 failed 1 times,
> most recent failure: Lost task 0.0 in stage 324.0 (TID 1674)
> (chango-private-1.chango.private executor driver):
> java.lang.IllegalArgumentException: requirement failed: Failed to get
> records for compacted spark-executor-school-student-group school-student-7
> after polling for 12
>
> at scala.Predef$.require(Predef.scala:281)
>
> at
> org.apache.spark.streaming.kafka010.InternalKafkaConsumer.compactedNext(KafkaDataConsumer.scala:186)
>
> at
> org.apache.spark.streaming.kafka010.KafkaDataConsumer.compactedNext(KafkaDataConsumer.scala:60)
>
> at
> org.apache.spark.streaming.kafka010.KafkaDataConsumer.compactedNext$(KafkaDataConsumer.scala:59)
>
> at
> 

Re: External Spark shuffle service for k8s

[Bjørn Jørgensen]

I think this answers your question about what to do if you need more space
on nodes.

https://spark.apache.org/docs/latest/running-on-kubernetes.html#local-storage

Local Storage


Spark supports using volumes to spill data during shuffles and other
operations. To use a volume as local storage, the volume’s name should
starts with spark-local-dir-, for example:

--conf 
spark.kubernetes.driver.volumes.[VolumeType].spark-local-dir-[VolumeName].mount.path=
--conf 
spark.kubernetes.driver.volumes.[VolumeType].spark-local-dir-[VolumeName].mount.readOnly=false

Specifically, you can use persistent volume claims if the jobs require
large shuffle and sorting operations in executors.

spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.options.claimName=OnDemand
spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.options.storageClass=gp
spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.options.sizeLimit=500Gi
spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.mount.path=/data
spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.mount.readOnly=false

To enable shuffle data recovery feature via the built-in
KubernetesLocalDiskShuffleDataIO plugin, we need to have the followings.
You may want to enable
spark.kubernetes.driver.waitToReusePersistentVolumeClaim additionally.

spark.kubernetes.executor.volumes.persistentVolumeClaim.spark-local-dir-1.mount.path=/data/spark-x/executor-x
spark.shuffle.sort.io.plugin.class=org.apache.spark.shuffle.KubernetesLocalDiskShuffleDataIO

If no volume is set as local storage, Spark uses temporary scratch space to
spill data to disk during shuffles and other operations. When using
Kubernetes as the resource manager the pods will be created with an emptyDir
 volume
mounted for each directory listed in spark.local.dir or the environment
variable SPARK_LOCAL_DIRS . If no directories are explicitly specified then
a default directory is created and configured appropriately.

emptyDir volumes use the ephemeral storage feature of Kubernetes and do not
persist beyond the life of the pod.

tor. 11. apr. 2024 kl. 10:29 skrev Bjørn Jørgensen :

> " In the end for my usecase I started using pvcs and pvc aware scheduling
> along with decommissioning. So far performance is good with this choice."
> How did you do this?
>
>
> tor. 11. apr. 2024 kl. 04:13 skrev Arun Ravi :
>
>> Hi Everyone,
>>
>> I had to explored IBM's and AWS's S3 shuffle plugins (some time back), I
>> had also explored AWS FSX lustre in few of my production jobs which has
>> ~20TB of shuffle operations with 200-300 executors. What I have observed is
>> S3 and fax behaviour was fine during the write phase, however I faced iops
>> throttling during the read phase(read taking forever to complete). I think
>> this might be contributed by the heavy use of shuffle index file (I didn't
>> perform any extensive research on this), so I believe the shuffle manager
>> logic have to be intelligent enough to reduce the fetching of files from
>> object store. In the end for my usecase I started using pvcs and pvc aware
>> scheduling along with decommissioning. So far performance is good with this
>> choice.
>>
>> Thank you
>>
>> On Tue, 9 Apr 2024, 15:17 Mich Talebzadeh, 
>> wrote:
>>
>>> Hi,
>>>
>>> First thanks everyone for their contributions
>>>
>>> I was going to reply to @Enrico Minack   but
>>> noticed additional info. As I understand for example,  Apache Uniffle is an
>>> incubating project aimed at providing a pluggable shuffle service for
>>> Spark. So basically, all these "external shuffle services" have in common
>>> is to offload shuffle data management to external services, thus reducing
>>> the memory and CPU overhead on Spark executors. That is great.  While
>>> Uniffle and others enhance shuffle performance and scalability, it would be
>>> great to integrate them with Spark UI. This may require additional
>>> development efforts. I suppose  the interest would be to have these
>>> external matrices incorporated into Spark with one look and feel. This may
>>> require customizing the UI to fetch and display metrics or statistics from
>>> the external shuffle services. Has any project done this?
>>>
>>> Thanks
>>>
>>> Mich Talebzadeh,
>>> Technologist | Solutions Architect | Data Engineer  | Generative AI
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>> 
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> *Disclaimer:* The information provided is correct to the best of my
>>> knowledge but of course cannot be guaranteed . It is essential to note
>>> that, as with any advice, quote "one test result is worth one-thousand
>>> expert opinions (Werner
>>> 

Re: [Spark SQL]: Source code for PartitionedFile

[Ashley McManamon]

Hi Mich,

Thanks for the reply.

I did come across that file but it didn't align with the appearance of
`PartitionedFile`:
https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/PartitionedFileUtil.scala

In fact, the code snippet you shared also references the type
`PartitionedFile`.

There's actually this javadoc.io page for a `PartitionedFile`
at org.apache.spark.sql.execution.datasources for spark-sql_2.12:3.0.2:
https://javadoc.io/doc/org.apache.spark/spark-sql_2.12/3.0.2/org/apache/spark/sql/execution/datasources/PartitionedFile.html.
I double checked the source code for version 3.0.2 and doesn't seem to
exist there either:
https://github.com/apache/spark/tree/v3.0.2/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources

Ashley


On Mon, 8 Apr 2024 at 22:41, Mich Talebzadeh 
wrote:

> Hi,
>
> I believe this is the package
>
>
> https://raw.githubusercontent.com/apache/spark/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FilePartition.scala
>
> And the code
>
> case class FilePartition(index: Int, files: Array[PartitionedFile])
>   extends Partition with InputPartition {
>   override def preferredLocations(): Array[String] = {
> // Computes total number of bytes that can be retrieved from each host.
> val hostToNumBytes = mutable.HashMap.empty[String, Long]
> files.foreach { file =>
>   file.locations.filter(_ != "localhost").foreach { host =>
> hostToNumBytes(host) = hostToNumBytes.getOrElse(host, 0L) +
> file.length
>   }
> }
>
> // Selects the first 3 hosts with the most data to be retrieved.
> hostToNumBytes.toSeq.sortBy {
>   case (host, numBytes) => numBytes
> }.reverse.take(3).map {
>   case (host, numBytes) => host
> }.toArray
>   }
> }
>
> HTH
>
> Mich Talebzadeh,
> Technologist | Solutions Architect | Data Engineer  | Generative AI
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
>
> On Mon, 8 Apr 2024 at 20:31, Ashley McManamon <
> ashley.mcmana...@quantcast.com> wrote:
>
>> Hi All,
>>
>> I've been diving into the source code to get a better understanding of
>> how file splitting works from a user perspective. I've hit a deadend at
>> `PartitionedFile`, for which I cannot seem to find a definition? It appears
>> though it should be found at
>> org.apache.spark.sql.execution.datasources but I find no definition in
>> the entire source code. Am I missing something?
>>
>> I appreciate there may be an obvious answer here, apologies if I'm being
>> naive.
>>
>> Thanks,
>> Ashley McManamon
>>
>>

Re: External Spark shuffle service for k8s

[Bjørn Jørgensen]

" In the end for my usecase I started using pvcs and pvc aware scheduling
along with decommissioning. So far performance is good with this choice."
How did you do this?


tor. 11. apr. 2024 kl. 04:13 skrev Arun Ravi :

> Hi Everyone,
>
> I had to explored IBM's and AWS's S3 shuffle plugins (some time back), I
> had also explored AWS FSX lustre in few of my production jobs which has
> ~20TB of shuffle operations with 200-300 executors. What I have observed is
> S3 and fax behaviour was fine during the write phase, however I faced iops
> throttling during the read phase(read taking forever to complete). I think
> this might be contributed by the heavy use of shuffle index file (I didn't
> perform any extensive research on this), so I believe the shuffle manager
> logic have to be intelligent enough to reduce the fetching of files from
> object store. In the end for my usecase I started using pvcs and pvc aware
> scheduling along with decommissioning. So far performance is good with this
> choice.
>
> Thank you
>
> On Tue, 9 Apr 2024, 15:17 Mich Talebzadeh, 
> wrote:
>
>> Hi,
>>
>> First thanks everyone for their contributions
>>
>> I was going to reply to @Enrico Minack   but
>> noticed additional info. As I understand for example,  Apache Uniffle is an
>> incubating project aimed at providing a pluggable shuffle service for
>> Spark. So basically, all these "external shuffle services" have in common
>> is to offload shuffle data management to external services, thus reducing
>> the memory and CPU overhead on Spark executors. That is great.  While
>> Uniffle and others enhance shuffle performance and scalability, it would be
>> great to integrate them with Spark UI. This may require additional
>> development efforts. I suppose  the interest would be to have these
>> external matrices incorporated into Spark with one look and feel. This may
>> require customizing the UI to fetch and display metrics or statistics from
>> the external shuffle services. Has any project done this?
>>
>> Thanks
>>
>> Mich Talebzadeh,
>> Technologist | Solutions Architect | Data Engineer  | Generative AI
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *Disclaimer:* The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner
>> Von Braun
>> )".
>>
>>
>> On Mon, 8 Apr 2024 at 14:19, Vakaris Baškirov <
>> vakaris.bashki...@gmail.com> wrote:
>>
>>> I see that both Uniffle and Celebron support S3/HDFS backends which is
>>> great.
>>> In the case someone is using S3/HDFS, I wonder what would be the
>>> advantages of using Celebron or Uniffle vs IBM shuffle service plugin
>>>  or Cloud Shuffle Storage
>>> Plugin from AWS
>>> 
>>> ?
>>>
>>> These plugins do not require deploying a separate service. Are there any
>>> advantages to using Uniffle/Celebron in the case of using S3 backend, which
>>> would require deploying a separate service?
>>>
>>> Thanks
>>> Vakaris
>>>
>>> On Mon, Apr 8, 2024 at 10:03 AM roryqi  wrote:
>>>
 Apache Uniffle (incubating) may be another solution.
 You can see
 https://github.com/apache/incubator-uniffle

 https://uniffle.apache.org/blog/2023/07/21/Uniffle%20-%20New%20chapter%20for%20the%20shuffle%20in%20the%20cloud%20native%20era

 Mich Talebzadeh  于2024年4月8日周一 07:15写道：

> Splendid
>
> The configurations below can be used with k8s deployments of Spark.
> Spark applications running on k8s can utilize these configurations to
> seamlessly access data stored in Google Cloud Storage (GCS) and Amazon S3.
>
> For Google GCS we may have
>
> spark_config_gcs = {
> "spark.kubernetes.authenticate.driver.serviceAccountName":
> "service_account_name",
> "spark.hadoop.fs.gs.impl":
> "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem",
> "spark.hadoop.google.cloud.auth.service.account.enable": "true",
> "spark.hadoop.google.cloud.auth.service.account.json.keyfile":
> "/path/to/keyfile.json",
> }
>
> For Amazon S3 similar
>
> spark_config_s3 = {
> "spark.kubernetes.authenticate.driver.serviceAccountName":
> "service_account_name",
> "spark.hadoop.fs.s3a.impl":
> "org.apache.hadoop.fs.s3a.S3AFileSystem",
> "spark.hadoop.fs.s3a.access.key": "s3_access_key",
> "spark.hadoop.fs.s3a.secret.key": "secret_key",
> }
>
>
> To implement these configurations and enable Spark applications to
> interact with GCS and S3, I 

Re: External Spark shuffle service for k8s

[Arun Ravi]

Hi Everyone,

I had to explored IBM's and AWS's S3 shuffle plugins (some time back), I
had also explored AWS FSX lustre in few of my production jobs which has
~20TB of shuffle operations with 200-300 executors. What I have observed is
S3 and fax behaviour was fine during the write phase, however I faced iops
throttling during the read phase(read taking forever to complete). I think
this might be contributed by the heavy use of shuffle index file (I didn't
perform any extensive research on this), so I believe the shuffle manager
logic have to be intelligent enough to reduce the fetching of files from
object store. In the end for my usecase I started using pvcs and pvc aware
scheduling along with decommissioning. So far performance is good with this
choice.

Thank you

On Tue, 9 Apr 2024, 15:17 Mich Talebzadeh, 
wrote:

> Hi,
>
> First thanks everyone for their contributions
>
> I was going to reply to @Enrico Minack   but
> noticed additional info. As I understand for example,  Apache Uniffle is an
> incubating project aimed at providing a pluggable shuffle service for
> Spark. So basically, all these "external shuffle services" have in common
> is to offload shuffle data management to external services, thus reducing
> the memory and CPU overhead on Spark executors. That is great.  While
> Uniffle and others enhance shuffle performance and scalability, it would be
> great to integrate them with Spark UI. This may require additional
> development efforts. I suppose  the interest would be to have these
> external matrices incorporated into Spark with one look and feel. This may
> require customizing the UI to fetch and display metrics or statistics from
> the external shuffle services. Has any project done this?
>
> Thanks
>
> Mich Talebzadeh,
> Technologist | Solutions Architect | Data Engineer  | Generative AI
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
>
> On Mon, 8 Apr 2024 at 14:19, Vakaris Baškirov 
> wrote:
>
>> I see that both Uniffle and Celebron support S3/HDFS backends which is
>> great.
>> In the case someone is using S3/HDFS, I wonder what would be the
>> advantages of using Celebron or Uniffle vs IBM shuffle service plugin
>>  or Cloud Shuffle Storage
>> Plugin from AWS
>> 
>> ?
>>
>> These plugins do not require deploying a separate service. Are there any
>> advantages to using Uniffle/Celebron in the case of using S3 backend, which
>> would require deploying a separate service?
>>
>> Thanks
>> Vakaris
>>
>> On Mon, Apr 8, 2024 at 10:03 AM roryqi  wrote:
>>
>>> Apache Uniffle (incubating) may be another solution.
>>> You can see
>>> https://github.com/apache/incubator-uniffle
>>>
>>> https://uniffle.apache.org/blog/2023/07/21/Uniffle%20-%20New%20chapter%20for%20the%20shuffle%20in%20the%20cloud%20native%20era
>>>
>>> Mich Talebzadeh  于2024年4月8日周一 07:15写道：
>>>
 Splendid

 The configurations below can be used with k8s deployments of Spark.
 Spark applications running on k8s can utilize these configurations to
 seamlessly access data stored in Google Cloud Storage (GCS) and Amazon S3.

 For Google GCS we may have

 spark_config_gcs = {
 "spark.kubernetes.authenticate.driver.serviceAccountName":
 "service_account_name",
 "spark.hadoop.fs.gs.impl":
 "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem",
 "spark.hadoop.google.cloud.auth.service.account.enable": "true",
 "spark.hadoop.google.cloud.auth.service.account.json.keyfile":
 "/path/to/keyfile.json",
 }

 For Amazon S3 similar

 spark_config_s3 = {
 "spark.kubernetes.authenticate.driver.serviceAccountName":
 "service_account_name",
 "spark.hadoop.fs.s3a.impl":
 "org.apache.hadoop.fs.s3a.S3AFileSystem",
 "spark.hadoop.fs.s3a.access.key": "s3_access_key",
 "spark.hadoop.fs.s3a.secret.key": "secret_key",
 }


 To implement these configurations and enable Spark applications to
 interact with GCS and S3, I guess we can approach it this way

 1) Spark Repository Integration: These configurations need to be added
 to the Spark repository as part of the supported configuration options for
 k8s deployments.

 2) Configuration Settings: Users need to specify these configurations
 when submitting Spark applications to a Kubernetes cluster. They can
 include 

Re: Re: [Spark SQL] How can I use .sql() in conjunction with watermarks?

[Mich Talebzadeh]

interesting. So below should be the corrected code with the suggestion in
the [SPARK-47718] .sql() does not recognize watermark defined upstream -
ASF JIRA (apache.org) 

# Define schema for parsing Kafka messages
schema = StructType([
StructField('createTime', TimestampType(), True),
StructField('orderId', LongType(), True),
StructField('payAmount', DoubleType(), True),
StructField('payPlatform', IntegerType(), True),
StructField('provinceId', IntegerType(), True),
])

# Read streaming data from Kafka source
streaming_df = session.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers", "localhost:9092") \
.option("subscribe", "payment_msg") \
.option("startingOffsets", "earliest") \
.load() \
.select(from_json(col("value").cast("string"),
schema).alias("parsed_value")) \
.select("parsed_value.*") \
.withWatermark("createTime", "10 seconds")

# Create temporary view for SQL queries
*streaming_df.createOrReplaceTempView("streaming_df")*
# Define SQL query with correct window function usage
query = """
*SELECT*
*window(start, '1 hour', '30 minutes') as window,*
provinceId,
sum(payAmount) as totalPayAmount
FROM streaming_df
GROUP BY provinceId, window(start, '1 hour', '30 minutes')
ORDER BY window.start
"""

# Write the aggregated results to Kafka sink
stream = session.sql(query) \
.writeStream \
.format("kafka") \
.option("checkpointLocation", "checkpoint") \
.option("kafka.bootstrap.servers", "localhost:9092") \
.option("topic", "sink") \
.start()


Mich Talebzadeh,
Technologist | Solutions Architect | Data Engineer  | Generative AI
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Tue, 9 Apr 2024 at 21:45, 刘唯  wrote:

> Sorry this is not a bug but essentially a user error. Spark throws a
> really confusing error and I'm also confused. Please see the reply in the
> ticket for how to make things correct.
> https://issues.apache.org/jira/browse/SPARK-47718
>
> 刘唯  于2024年4月6日周六 11:41写道：
>
>> This indeed looks like a bug. I will take some time to look into it.
>>
>> Mich Talebzadeh  于2024年4月3日周三 01:55写道：
>>
>>>
>>> hm. you are getting below
>>>
>>> AnalysisException: Append output mode not supported when there are
>>> streaming aggregations on streaming DataFrames/DataSets without watermark;
>>>
>>> The problem seems to be that you are using the append output mode when
>>> writing the streaming query results to Kafka. This mode is designed for
>>> scenarios where you want to append new data to an existing dataset at the
>>> sink (in this case, the "sink" topic in Kafka). However, your query
>>> involves a streaming aggregation: group by provinceId, window('createTime',
>>> '1 hour', '30 minutes'). The problem is that Spark Structured Streaming
>>> requires a watermark to ensure exactly-once processing when using
>>> aggregations with append mode. Your code already defines a watermark on the
>>> "createTime" column with a delay of 10 seconds (withWatermark("createTime",
>>> "10 seconds")). However, the error message indicates it is missing on the
>>> start column. Try adding watermark to "start" Column: Modify your code as
>>> below  to include a watermark on the "start" column generated by the
>>> window function:
>>>
>>> from pyspark.sql.functions import col, from_json, explode, window, sum,
>>> watermark
>>>
>>> streaming_df = session.readStream \
>>>   .format("kafka") \
>>>   .option("kafka.bootstrap.servers", "localhost:9092") \
>>>   .option("subscribe", "payment_msg") \
>>>   .option("startingOffsets", "earliest") \
>>>   .load() \
>>>   .select(from_json(col("value").cast("string"),
>>> schema).alias("parsed_value")) \
>>>   .select("parsed_value.*") \
>>>   .withWatermark("createTime", "10 seconds")  # Existing watermark on
>>> createTime
>>>
>>> *# Modified section with watermark on 'start' column*
>>> streaming_df = streaming_df.groupBy(
>>>   col("provinceId"),
>>>   window(col("createTime"), "1 hour", "30 minutes")
>>> ).agg(
>>>   sum(col("payAmount")).alias("totalPayAmount")
>>> ).withWatermark(expr("start"), "10 seconds")  # Watermark on
>>> window-generated 'start'
>>>
>>> # Rest of the code remains the same
>>> streaming_df.createOrReplaceTempView("streaming_df")
>>>
>>> spark.sql("""
>>> SELECT
>>>   window.start, window.end, provinceId, totalPayAmount
>>> FROM streaming_df
>>> ORDER BY window.start
>>> """) \
>>> .writeStream \
>>> .format("kafka") \
>>> .option("checkpointLocation", 

Re: Re: [Spark SQL] How can I use .sql() in conjunction with watermarks?

[刘唯]

Sorry this is not a bug but essentially a user error. Spark throws a really
confusing error and I'm also confused. Please see the reply in the ticket
for how to make things correct.
https://issues.apache.org/jira/browse/SPARK-47718

刘唯  于2024年4月6日周六 11:41写道：

> This indeed looks like a bug. I will take some time to look into it.
>
> Mich Talebzadeh  于2024年4月3日周三 01:55写道：
>
>>
>> hm. you are getting below
>>
>> AnalysisException: Append output mode not supported when there are
>> streaming aggregations on streaming DataFrames/DataSets without watermark;
>>
>> The problem seems to be that you are using the append output mode when
>> writing the streaming query results to Kafka. This mode is designed for
>> scenarios where you want to append new data to an existing dataset at the
>> sink (in this case, the "sink" topic in Kafka). However, your query
>> involves a streaming aggregation: group by provinceId, window('createTime',
>> '1 hour', '30 minutes'). The problem is that Spark Structured Streaming
>> requires a watermark to ensure exactly-once processing when using
>> aggregations with append mode. Your code already defines a watermark on the
>> "createTime" column with a delay of 10 seconds (withWatermark("createTime",
>> "10 seconds")). However, the error message indicates it is missing on the
>> start column. Try adding watermark to "start" Column: Modify your code as
>> below  to include a watermark on the "start" column generated by the
>> window function:
>>
>> from pyspark.sql.functions import col, from_json, explode, window, sum,
>> watermark
>>
>> streaming_df = session.readStream \
>>   .format("kafka") \
>>   .option("kafka.bootstrap.servers", "localhost:9092") \
>>   .option("subscribe", "payment_msg") \
>>   .option("startingOffsets", "earliest") \
>>   .load() \
>>   .select(from_json(col("value").cast("string"),
>> schema).alias("parsed_value")) \
>>   .select("parsed_value.*") \
>>   .withWatermark("createTime", "10 seconds")  # Existing watermark on
>> createTime
>>
>> *# Modified section with watermark on 'start' column*
>> streaming_df = streaming_df.groupBy(
>>   col("provinceId"),
>>   window(col("createTime"), "1 hour", "30 minutes")
>> ).agg(
>>   sum(col("payAmount")).alias("totalPayAmount")
>> ).withWatermark(expr("start"), "10 seconds")  # Watermark on
>> window-generated 'start'
>>
>> # Rest of the code remains the same
>> streaming_df.createOrReplaceTempView("streaming_df")
>>
>> spark.sql("""
>> SELECT
>>   window.start, window.end, provinceId, totalPayAmount
>> FROM streaming_df
>> ORDER BY window.start
>> """) \
>> .writeStream \
>> .format("kafka") \
>> .option("checkpointLocation", "checkpoint") \
>> .option("kafka.bootstrap.servers", "localhost:9092") \
>> .option("topic", "sink") \
>> .start()
>>
>> Try and see how it goes
>>
>> HTH
>>
>> Mich Talebzadeh,
>>
>> Technologist | Solutions Architect | Data Engineer  | Generative AI
>>
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> Disclaimer: The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner Von Braun)".
>>
>> Mich Talebzadeh,
>> Technologist | Solutions Architect | Data Engineer  | Generative AI
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *Disclaimer:* The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner
>> Von Braun
>> )".
>>
>>
>> On Tue, 2 Apr 2024 at 22:43, Chloe He 
>> wrote:
>>
>>> Hi Mich,
>>>
>>> Thank you so much for your response. I really appreciate your help!
>>>
>>> You mentioned "defining the watermark using the withWatermark function
>>> on the streaming_df before creating the temporary view” - I believe this is
>>> what I’m doing and it’s not working for me. Here is the exact code snippet
>>> that I’m running:
>>>
>>> ```
>>> >>> streaming_df = session.readStream\
>>> .format("kafka")\
>>> .option("kafka.bootstrap.servers", "localhost:9092")\
>>> .option("subscribe", "payment_msg")\
>>> .option("startingOffsets","earliest")\
>>> .load()\
>>> .select(from_json(col("value").cast("string"),
>>> schema).alias("parsed_value"))\
>>> .select("parsed_value.*")\
>>> .withWatermark("createTime", "10 seconds")
>>>
>>> >>> streaming_df.createOrReplaceTempView("streaming_df”)
>>>
>>> >>> spark.sql("""
>>> SELECT
>>> window.start, window.end, provinceId, sum(payAmount) as
>>> totalPayAmount
>>> FROM 

Re: How to get db related metrics when use spark jdbc to read db table?

[Femi Anthony]

If you're using just Spark you could try turning on the history server
 and try to glean
statistics from there.  But there is no one location or log file which
stores them all.
Databricks, which is a managed Spark solution, provides such features in an
Enterprise setting.
I am unsure whether AWS EMR or Google Data Proc does the same.

Femi



On Mon, Apr 8, 2024 at 5:34 AM casel.chen  wrote:

> Hello, I have a spark application with jdbc source and do some
> calculation.
> To monitor application healthy, I need db related metrics per database
> like number of connections, sql execution time and sql fired time
> distribution etc.
> Does anybody know how to get them? Thanks!
>
>

-- 
http://dataphantik.com

"Great spirits have always encountered violent opposition from mediocre
minds." - Albert Einstein.

Re: [Spark SQL]: Source code for PartitionedFile

[Mich Talebzadeh]

Hi,

I believe this is the package

https://raw.githubusercontent.com/apache/spark/master/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/FilePartition.scala

And the code

case class FilePartition(index: Int, files: Array[PartitionedFile])
  extends Partition with InputPartition {
  override def preferredLocations(): Array[String] = {
// Computes total number of bytes that can be retrieved from each host.
val hostToNumBytes = mutable.HashMap.empty[String, Long]
files.foreach { file =>
  file.locations.filter(_ != "localhost").foreach { host =>
hostToNumBytes(host) = hostToNumBytes.getOrElse(host, 0L) +
file.length
  }
}

// Selects the first 3 hosts with the most data to be retrieved.
hostToNumBytes.toSeq.sortBy {
  case (host, numBytes) => numBytes
}.reverse.take(3).map {
  case (host, numBytes) => host
}.toArray
  }
}

HTH

Mich Talebzadeh,
Technologist | Solutions Architect | Data Engineer  | Generative AI
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Mon, 8 Apr 2024 at 20:31, Ashley McManamon <
ashley.mcmana...@quantcast.com> wrote:

> Hi All,
>
> I've been diving into the source code to get a better understanding of how
> file splitting works from a user perspective. I've hit a deadend at
> `PartitionedFile`, for which I cannot seem to find a definition? It appears
> though it should be found at
> org.apache.spark.sql.execution.datasources but I find no definition in the
> entire source code. Am I missing something?
>
> I appreciate there may be an obvious answer here, apologies if I'm being
> naive.
>
> Thanks,
> Ashley McManamon
>
>

Re: How to get db related metrics when use spark jdbc to read db table?

[Mich Talebzadeh]

Well you can do a fair bit with the available tools

The Spark UI, particularly the Staging and Executors tabs, do provide some
valuable insights related to database health metrics for applications using
a JDBC source.

Stage Overview:

This section provides a summary of all the stages executed during the
application's lifetime. It includes details such as the stage ID,
description, submission time, duration, and number of tasks.
Each Stage represents a set of tasks that perform the same computation,
typically applied to a partition of the input data. The Stages tab offers
insights into how these stages are executed and their associated metrics.
This tab may include a directed acyclic graph (DAG) visualization,
illustrating the logical and physical execution plan of the Spark
application.

Executors Tab:

The Executors tab provides detailed information about the executors running
in the Spark application. Executors are responsible for executing tasks on
behalf of the Spark application. The "Executors" tab offers insights into
the current state and resource usage of each executor.

In addition, the underlying database will have some instrumentation to
assist you with your work. say with Oracle (as an example), utilise tools
like OEM, VM StatPack, SQL*Plus scripts etc or third-party monitoring tools
to collect detailed database health metrics directly from the Oracle
database server.

HTH

Mich Talebzadeh,
Technologist | Solutions Architect | Data Engineer  | Generative AI
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Mon, 8 Apr 2024 at 19:35, casel.chen  wrote:

> Hello, I have a spark application with jdbc source and do some
> calculation.
> To monitor application healthy, I need db related metrics per database
> like number of connections, sql execution time and sql fired time
> distribution etc.
> Does anybody know how to get them? Thanks!
>
>

Re: External Spark shuffle service for k8s

[Mich Talebzadeh]

Hi,

First thanks everyone for their contributions

I was going to reply to @Enrico Minack   but
noticed additional info. As I understand for example,  Apache Uniffle is an
incubating project aimed at providing a pluggable shuffle service for
Spark. So basically, all these "external shuffle services" have in common
is to offload shuffle data management to external services, thus reducing
the memory and CPU overhead on Spark executors. That is great.  While
Uniffle and others enhance shuffle performance and scalability, it would be
great to integrate them with Spark UI. This may require additional
development efforts. I suppose  the interest would be to have these
external matrices incorporated into Spark with one look and feel. This may
require customizing the UI to fetch and display metrics or statistics from
the external shuffle services. Has any project done this?

Thanks

Mich Talebzadeh,
Technologist | Solutions Architect | Data Engineer  | Generative AI
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Mon, 8 Apr 2024 at 14:19, Vakaris Baškirov 
wrote:

> I see that both Uniffle and Celebron support S3/HDFS backends which is
> great.
> In the case someone is using S3/HDFS, I wonder what would be the
> advantages of using Celebron or Uniffle vs IBM shuffle service plugin
>  or Cloud Shuffle Storage Plugin
> from AWS
> 
> ?
>
> These plugins do not require deploying a separate service. Are there any
> advantages to using Uniffle/Celebron in the case of using S3 backend, which
> would require deploying a separate service?
>
> Thanks
> Vakaris
>
> On Mon, Apr 8, 2024 at 10:03 AM roryqi  wrote:
>
>> Apache Uniffle (incubating) may be another solution.
>> You can see
>> https://github.com/apache/incubator-uniffle
>>
>> https://uniffle.apache.org/blog/2023/07/21/Uniffle%20-%20New%20chapter%20for%20the%20shuffle%20in%20the%20cloud%20native%20era
>>
>> Mich Talebzadeh  于2024年4月8日周一 07:15写道：
>>
>>> Splendid
>>>
>>> The configurations below can be used with k8s deployments of Spark.
>>> Spark applications running on k8s can utilize these configurations to
>>> seamlessly access data stored in Google Cloud Storage (GCS) and Amazon S3.
>>>
>>> For Google GCS we may have
>>>
>>> spark_config_gcs = {
>>> "spark.kubernetes.authenticate.driver.serviceAccountName":
>>> "service_account_name",
>>> "spark.hadoop.fs.gs.impl":
>>> "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem",
>>> "spark.hadoop.google.cloud.auth.service.account.enable": "true",
>>> "spark.hadoop.google.cloud.auth.service.account.json.keyfile":
>>> "/path/to/keyfile.json",
>>> }
>>>
>>> For Amazon S3 similar
>>>
>>> spark_config_s3 = {
>>> "spark.kubernetes.authenticate.driver.serviceAccountName":
>>> "service_account_name",
>>> "spark.hadoop.fs.s3a.impl": "org.apache.hadoop.fs.s3a.S3AFileSystem",
>>> "spark.hadoop.fs.s3a.access.key": "s3_access_key",
>>> "spark.hadoop.fs.s3a.secret.key": "secret_key",
>>> }
>>>
>>>
>>> To implement these configurations and enable Spark applications to
>>> interact with GCS and S3, I guess we can approach it this way
>>>
>>> 1) Spark Repository Integration: These configurations need to be added
>>> to the Spark repository as part of the supported configuration options for
>>> k8s deployments.
>>>
>>> 2) Configuration Settings: Users need to specify these configurations
>>> when submitting Spark applications to a Kubernetes cluster. They can
>>> include these configurations in the Spark application code or pass them as
>>> command-line arguments or environment variables during application
>>> submission.
>>>
>>> HTH
>>>
>>> Mich Talebzadeh,
>>>
>>> Technologist | Solutions Architect | Data Engineer  | Generative AI
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>> 
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> *Disclaimer:* The information provided is correct to the best of my
>>> knowledge but of course cannot be guaranteed . It is essential to note
>>> that, as with any advice, quote "one test result is worth one-thousand
>>> expert opinions (Werner
>>> Von Braun
>>> )".
>>>
>>>
>>> On Sun, 7 Apr 2024 at 13:31, Vakaris Baškirov <
>>> vakaris.bashki...@gmail.com> wrote:
>>>
 There is an IBM shuffle 

Re: External Spark shuffle service for k8s

[Vakaris Baškirov]

I see that both Uniffle and Celebron support S3/HDFS backends which is
great.
In the case someone is using S3/HDFS, I wonder what would be the advantages
of using Celebron or Uniffle vs IBM shuffle service plugin
 or Cloud Shuffle Storage Plugin
from AWS

?

These plugins do not require deploying a separate service. Are there any
advantages to using Uniffle/Celebron in the case of using S3 backend, which
would require deploying a separate service?

Thanks
Vakaris

On Mon, Apr 8, 2024 at 10:03 AM roryqi  wrote:

> Apache Uniffle (incubating) may be another solution.
> You can see
> https://github.com/apache/incubator-uniffle
>
> https://uniffle.apache.org/blog/2023/07/21/Uniffle%20-%20New%20chapter%20for%20the%20shuffle%20in%20the%20cloud%20native%20era
>
> Mich Talebzadeh  于2024年4月8日周一 07:15写道：
>
>> Splendid
>>
>> The configurations below can be used with k8s deployments of Spark. Spark
>> applications running on k8s can utilize these configurations to seamlessly
>> access data stored in Google Cloud Storage (GCS) and Amazon S3.
>>
>> For Google GCS we may have
>>
>> spark_config_gcs = {
>> "spark.kubernetes.authenticate.driver.serviceAccountName":
>> "service_account_name",
>> "spark.hadoop.fs.gs.impl":
>> "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem",
>> "spark.hadoop.google.cloud.auth.service.account.enable": "true",
>> "spark.hadoop.google.cloud.auth.service.account.json.keyfile":
>> "/path/to/keyfile.json",
>> }
>>
>> For Amazon S3 similar
>>
>> spark_config_s3 = {
>> "spark.kubernetes.authenticate.driver.serviceAccountName":
>> "service_account_name",
>> "spark.hadoop.fs.s3a.impl": "org.apache.hadoop.fs.s3a.S3AFileSystem",
>> "spark.hadoop.fs.s3a.access.key": "s3_access_key",
>> "spark.hadoop.fs.s3a.secret.key": "secret_key",
>> }
>>
>>
>> To implement these configurations and enable Spark applications to
>> interact with GCS and S3, I guess we can approach it this way
>>
>> 1) Spark Repository Integration: These configurations need to be added to
>> the Spark repository as part of the supported configuration options for k8s
>> deployments.
>>
>> 2) Configuration Settings: Users need to specify these configurations
>> when submitting Spark applications to a Kubernetes cluster. They can
>> include these configurations in the Spark application code or pass them as
>> command-line arguments or environment variables during application
>> submission.
>>
>> HTH
>>
>> Mich Talebzadeh,
>>
>> Technologist | Solutions Architect | Data Engineer  | Generative AI
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *Disclaimer:* The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner
>> Von Braun
>> )".
>>
>>
>> On Sun, 7 Apr 2024 at 13:31, Vakaris Baškirov <
>> vakaris.bashki...@gmail.com> wrote:
>>
>>> There is an IBM shuffle service plugin that supports S3
>>> https://github.com/IBM/spark-s3-shuffle
>>>
>>> Though I would think a feature like this could be a part of the main
>>> Spark repo. Trino already has out-of-box support for s3 exchange (shuffle)
>>> and it's very useful.
>>>
>>> Vakaris
>>>
>>> On Sun, Apr 7, 2024 at 12:27 PM Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>

 Thanks for your suggestion that I take it as a workaround. Whilst this
 workaround can potentially address storage allocation issues, I was more
 interested in exploring solutions that offer a more seamless integration
 with large distributed file systems like HDFS, GCS, or S3. This would
 ensure better performance and scalability for handling larger datasets
 efficiently.


 Mich Talebzadeh,
 Technologist | Solutions Architect | Data Engineer  | Generative AI
 London
 United Kingdom


view my Linkedin profile
 


  https://en.everybodywiki.com/Mich_Talebzadeh



 *Disclaimer:* The information provided is correct to the best of my
 knowledge but of course cannot be guaranteed . It is essential to note
 that, as with any advice, quote "one test result is worth one-thousand
 expert opinions (Werner
 Von Braun
 )".


 On Sat, 6 Apr 2024 at 21:28, Bjørn Jørgensen 
 wrote:

> You can make a PVC on K8S call it 300GB
>
> make a folder in yours dockerfile
> 

Re: External Spark shuffle service for k8s

[roryqi]

Apache Uniffle (incubating) may be another solution.
You can see
https://github.com/apache/incubator-uniffle
https://uniffle.apache.org/blog/2023/07/21/Uniffle%20-%20New%20chapter%20for%20the%20shuffle%20in%20the%20cloud%20native%20era

Mich Talebzadeh  于2024年4月8日周一 07:15写道：

> Splendid
>
> The configurations below can be used with k8s deployments of Spark. Spark
> applications running on k8s can utilize these configurations to seamlessly
> access data stored in Google Cloud Storage (GCS) and Amazon S3.
>
> For Google GCS we may have
>
> spark_config_gcs = {
> "spark.kubernetes.authenticate.driver.serviceAccountName":
> "service_account_name",
> "spark.hadoop.fs.gs.impl":
> "com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem",
> "spark.hadoop.google.cloud.auth.service.account.enable": "true",
> "spark.hadoop.google.cloud.auth.service.account.json.keyfile":
> "/path/to/keyfile.json",
> }
>
> For Amazon S3 similar
>
> spark_config_s3 = {
> "spark.kubernetes.authenticate.driver.serviceAccountName":
> "service_account_name",
> "spark.hadoop.fs.s3a.impl": "org.apache.hadoop.fs.s3a.S3AFileSystem",
> "spark.hadoop.fs.s3a.access.key": "s3_access_key",
> "spark.hadoop.fs.s3a.secret.key": "secret_key",
> }
>
>
> To implement these configurations and enable Spark applications to
> interact with GCS and S3, I guess we can approach it this way
>
> 1) Spark Repository Integration: These configurations need to be added to
> the Spark repository as part of the supported configuration options for k8s
> deployments.
>
> 2) Configuration Settings: Users need to specify these configurations when
> submitting Spark applications to a Kubernetes cluster. They can include
> these configurations in the Spark application code or pass them as
> command-line arguments or environment variables during application
> submission.
>
> HTH
>
> Mich Talebzadeh,
>
> Technologist | Solutions Architect | Data Engineer  | Generative AI
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
>
> On Sun, 7 Apr 2024 at 13:31, Vakaris Baškirov 
> wrote:
>
>> There is an IBM shuffle service plugin that supports S3
>> https://github.com/IBM/spark-s3-shuffle
>>
>> Though I would think a feature like this could be a part of the main
>> Spark repo. Trino already has out-of-box support for s3 exchange (shuffle)
>> and it's very useful.
>>
>> Vakaris
>>
>> On Sun, Apr 7, 2024 at 12:27 PM Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>>
>>> Thanks for your suggestion that I take it as a workaround. Whilst this
>>> workaround can potentially address storage allocation issues, I was more
>>> interested in exploring solutions that offer a more seamless integration
>>> with large distributed file systems like HDFS, GCS, or S3. This would
>>> ensure better performance and scalability for handling larger datasets
>>> efficiently.
>>>
>>>
>>> Mich Talebzadeh,
>>> Technologist | Solutions Architect | Data Engineer  | Generative AI
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>> 
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> *Disclaimer:* The information provided is correct to the best of my
>>> knowledge but of course cannot be guaranteed . It is essential to note
>>> that, as with any advice, quote "one test result is worth one-thousand
>>> expert opinions (Werner
>>> Von Braun
>>> )".
>>>
>>>
>>> On Sat, 6 Apr 2024 at 21:28, Bjørn Jørgensen 
>>> wrote:
>>>
 You can make a PVC on K8S call it 300GB

 make a folder in yours dockerfile
 WORKDIR /opt/spark/work-dir
 RUN chmod g+w /opt/spark/work-dir

 start spark with adding this

 .config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.options.claimName",
 "300gb") \

 .config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.mount.path",
 "/opt/spark/work-dir") \

 .config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.mount.readOnly",
 "False") \

 .config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.options.claimName",
 "300gb") \

 .config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.mount.path",
 "/opt/spark/work-dir") \

 .config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.mount.readOnly",
 

Re: External Spark shuffle service for k8s

[Enrico Minack]

There is Apache incubator project Uniffle:
https://github.com/apache/incubator-uniffle

It stores shuffle data on remote servers in memory, on local disk and HDFS.

Cheers,
Enrico


Am 06.04.24 um 15:41 schrieb Mich Talebzadeh:

I have seen some older references for shuffle service for k8s,
although it is not clear they are talking about a generic shuffle
service for k8s.

Anyhow with the advent of genai and the need to allow for a larger
volume of data, I was wondering if there has been any more work on
this matter. Specifically larger and scalable file systems like HDFS,
GCS , S3 etc, offer significantly larger storage capacity than local
disks on individual worker nodes in a k8s cluster, thus allowing
handling much larger datasets more efficiently. Also the degree of
parallelism and fault tolerance  with these files systems come into
it. I will be interested in hearing more about any progress on this.

Thanks
.

Mich Talebzadeh,

Technologist | Solutions Architect | Data Engineer  | Generative AI

London
United Kingdom


view my Linkedin profile


  https://en.everybodywiki.com/Mich_Talebzadeh



Disclaimer: The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner Von Braun)".

-
To unsubscribe e-mail: user-unsubscr...@spark.apache.org




-
To unsubscribe e-mail: user-unsubscr...@spark.apache.org

Re: Idiomatic way to rate-limit streaming sources to avoid OutOfMemoryError?

[Mich Talebzadeh]

OK,

This is a common issue in Spark Structured Streaming (SSS), where the
source generates data faster than Spark can process it. SSS doesn't have a
built-in mechanism for directly rate-limiting the incoming data stream
itself. However, consider the following:


   - Limit the rate at which data is produced. This can involve configuring
   the data source itself to emit data at a controlled rate or implementing
   rate limiting mechanisms in the application or system that produces the
   data.
   - SSS supports backpressure, which allows it to dynamically adjust the
   ingestion rate based on the processing capacity of the system. This can
   help prevent overwhelming the system with data. To enable backpressure, set
   the appropriate configuration properties such as
spark.conf.set("spark.streaming.backpressure.enabled",
   "true") and spark.streaming.backpressure.initialRate.
   - Consider adjusting the micro-batch interval to control the rate at
   which data is processed. Increasing the micro-batch interval and reduce the
   frequency of processing, allowing more time for each batch to be processed
   and reducing the likelihood of out-of-memory
   errors.. spark.conf.set("spark.sql.streaming.trigger.interval", "
   seconds"
   -  Dynamic Resource Allocation (DRA), Not implemented yet. DRA will
   automatically adjust allocated resources based on workload. This ensures
   Spark has enough resources to process incoming data within the trigger
   interval, preventing backlogs and potential OOM issues.


>From Spark UI, look at the streaming tab. There are various statistics
there. In general your Processing Time has to be less than your batch
interval. The scheduling Delay and Total Delay are additional indicator of
health.

HTH

Mich Talebzadeh,
Technologist | Solutions Architect | Data Engineer  | Generative AI
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Sun, 7 Apr 2024 at 15:11, Baran, Mert  wrote:

> Hi Spark community,
>
> I have a Spark Structured Streaming application that reads data from a
> socket source (implemented very similarly to the
> TextSocketMicroBatchStream). The issue is that the source can generate
> data faster than Spark can process it, eventually leading to an
> OutOfMemoryError when Spark runs out of memory trying to queue up all
> the pending data.
>
> I'm looking for advice on the most idiomatic/recommended way in Spark to
> rate-limit data ingestion to avoid overwhelming the system.
>
> Approaches I've considered:
>
> 1. Using a BlockingQueue with a fixed size to throttle the data.
> However, this requires careful tuning of the queue size. If too small,
> it limits throughput; if too large, you risk batches taking too long.
>
> 2. Fetching a limited number of records in the PartitionReader's next(),
> adding the records into a queue and checking if the queue is empty.
> However, I'm not sure if there is a built-in way to dynamically scale
> the number of records fetched (i.e., dynamically calculating the offset)
> based on the system load and capabilities.
>
> So in summary, what is the recommended way to dynamically rate-limit a
> streaming source to match Spark's processing capacity and avoid
> out-of-memory issues? Are there any best practices or configuration
> options I should look at?
> Any guidance would be much appreciated! Let me know if you need any
> other details.
>
> Thanks,
> Mert
>
>
> -
> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>
>

Re: External Spark shuffle service for k8s

[Mich Talebzadeh]

Thanks Cheng for the heads up. I will have a look.

Cheers

Mich Talebzadeh,
Technologist | Solutions Architect | Data Engineer  | Generative AI
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Sun, 7 Apr 2024 at 15:08, Cheng Pan  wrote:

> Instead of External Shuffle Shufle, Apache Celeborn might be a good option
> as a Remote Shuffle Service for Spark on K8s.
>
> There are some useful resources you might be interested in.
>
> [1] https://celeborn.apache.org/
> [2] https://www.youtube.com/watch?v=s5xOtG6Venw
> [3] https://github.com/aws-samples/emr-remote-shuffle-service
> [4] https://github.com/apache/celeborn/issues/2140
>
> Thanks,
> Cheng Pan
>
>
> > On Apr 6, 2024, at 21:41, Mich Talebzadeh 
> wrote:
> >
> > I have seen some older references for shuffle service for k8s,
> > although it is not clear they are talking about a generic shuffle
> > service for k8s.
> >
> > Anyhow with the advent of genai and the need to allow for a larger
> > volume of data, I was wondering if there has been any more work on
> > this matter. Specifically larger and scalable file systems like HDFS,
> > GCS , S3 etc, offer significantly larger storage capacity than local
> > disks on individual worker nodes in a k8s cluster, thus allowing
> > handling much larger datasets more efficiently. Also the degree of
> > parallelism and fault tolerance  with these files systems come into
> > it. I will be interested in hearing more about any progress on this.
> >
> > Thanks
> > .
> >
> > Mich Talebzadeh,
> >
> > Technologist | Solutions Architect | Data Engineer  | Generative AI
> >
> > London
> > United Kingdom
> >
> >
> >   view my Linkedin profile
> >
> >
> > https://en.everybodywiki.com/Mich_Talebzadeh
> >
> >
> >
> > Disclaimer: The information provided is correct to the best of my
> > knowledge but of course cannot be guaranteed . It is essential to note
> > that, as with any advice, quote "one test result is worth one-thousand
> > expert opinions (Werner Von Braun)".
> >
> > -
> > To unsubscribe e-mail: dev-unsubscr...@spark.apache.org
> >
>
>

Re: External Spark shuffle service for k8s

[Vakaris Baškirov]

There is an IBM shuffle service plugin that supports S3
https://github.com/IBM/spark-s3-shuffle

Though I would think a feature like this could be a part of the main Spark
repo. Trino already has out-of-box support for s3 exchange (shuffle) and
it's very useful.

Vakaris

On Sun, Apr 7, 2024 at 12:27 PM Mich Talebzadeh 
wrote:

>
> Thanks for your suggestion that I take it as a workaround. Whilst this
> workaround can potentially address storage allocation issues, I was more
> interested in exploring solutions that offer a more seamless integration
> with large distributed file systems like HDFS, GCS, or S3. This would
> ensure better performance and scalability for handling larger datasets
> efficiently.
>
>
> Mich Talebzadeh,
> Technologist | Solutions Architect | Data Engineer  | Generative AI
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
>
> On Sat, 6 Apr 2024 at 21:28, Bjørn Jørgensen 
> wrote:
>
>> You can make a PVC on K8S call it 300GB
>>
>> make a folder in yours dockerfile
>> WORKDIR /opt/spark/work-dir
>> RUN chmod g+w /opt/spark/work-dir
>>
>> start spark with adding this
>>
>> .config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.options.claimName",
>> "300gb") \
>>
>> .config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.mount.path",
>> "/opt/spark/work-dir") \
>>
>> .config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.mount.readOnly",
>> "False") \
>>
>> .config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.options.claimName",
>> "300gb") \
>>
>> .config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.mount.path",
>> "/opt/spark/work-dir") \
>>
>> .config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.mount.readOnly",
>> "False") \
>>   .config("spark.local.dir", "/opt/spark/work-dir")
>>
>>
>>
>>
>> lør. 6. apr. 2024 kl. 15:45 skrev Mich Talebzadeh <
>> mich.talebza...@gmail.com>:
>>
>>> I have seen some older references for shuffle service for k8s,
>>> although it is not clear they are talking about a generic shuffle
>>> service for k8s.
>>>
>>> Anyhow with the advent of genai and the need to allow for a larger
>>> volume of data, I was wondering if there has been any more work on
>>> this matter. Specifically larger and scalable file systems like HDFS,
>>> GCS , S3 etc, offer significantly larger storage capacity than local
>>> disks on individual worker nodes in a k8s cluster, thus allowing
>>> handling much larger datasets more efficiently. Also the degree of
>>> parallelism and fault tolerance  with these files systems come into
>>> it. I will be interested in hearing more about any progress on this.
>>>
>>> Thanks
>>> .
>>>
>>> Mich Talebzadeh,
>>>
>>> Technologist | Solutions Architect | Data Engineer  | Generative AI
>>>
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> Disclaimer: The information provided is correct to the best of my
>>> knowledge but of course cannot be guaranteed . It is essential to note
>>> that, as with any advice, quote "one test result is worth one-thousand
>>> expert opinions (Werner Von Braun)".
>>>
>>> -
>>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>>>
>>>
>>
>> --
>> Bjørn Jørgensen
>> Vestre Aspehaug 4, 6010 Ålesund
>> Norge
>>
>> +47 480 94 297
>>
>

Re: External Spark shuffle service for k8s

[Cheng Pan]

Instead of External Shuffle Shufle, Apache Celeborn might be a good option as a 
Remote Shuffle Service for Spark on K8s.

There are some useful resources you might be interested in.

[1] https://celeborn.apache.org/
[2] https://www.youtube.com/watch?v=s5xOtG6Venw
[3] https://github.com/aws-samples/emr-remote-shuffle-service
[4] https://github.com/apache/celeborn/issues/2140

Thanks,
Cheng Pan


> On Apr 6, 2024, at 21:41, Mich Talebzadeh  wrote:
> 
> I have seen some older references for shuffle service for k8s,
> although it is not clear they are talking about a generic shuffle
> service for k8s.
> 
> Anyhow with the advent of genai and the need to allow for a larger
> volume of data, I was wondering if there has been any more work on
> this matter. Specifically larger and scalable file systems like HDFS,
> GCS , S3 etc, offer significantly larger storage capacity than local
> disks on individual worker nodes in a k8s cluster, thus allowing
> handling much larger datasets more efficiently. Also the degree of
> parallelism and fault tolerance  with these files systems come into
> it. I will be interested in hearing more about any progress on this.
> 
> Thanks
> .
> 
> Mich Talebzadeh,
> 
> Technologist | Solutions Architect | Data Engineer  | Generative AI
> 
> London
> United Kingdom
> 
> 
>   view my Linkedin profile
> 
> 
> https://en.everybodywiki.com/Mich_Talebzadeh
> 
> 
> 
> Disclaimer: The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner Von Braun)".
> 
> -
> To unsubscribe e-mail: dev-unsubscr...@spark.apache.org
> 


-
To unsubscribe e-mail: user-unsubscr...@spark.apache.org

Re: External Spark shuffle service for k8s

[Mich Talebzadeh]

Splendid

The configurations below can be used with k8s deployments of Spark. Spark
applications running on k8s can utilize these configurations to seamlessly
access data stored in Google Cloud Storage (GCS) and Amazon S3.

For Google GCS we may have

spark_config_gcs = {
"spark.kubernetes.authenticate.driver.serviceAccountName":
"service_account_name",
"spark.hadoop.fs.gs.impl":
"com.google.cloud.hadoop.fs.gcs.GoogleHadoopFileSystem",
"spark.hadoop.google.cloud.auth.service.account.enable": "true",
"spark.hadoop.google.cloud.auth.service.account.json.keyfile":
"/path/to/keyfile.json",
}

For Amazon S3 similar

spark_config_s3 = {
"spark.kubernetes.authenticate.driver.serviceAccountName":
"service_account_name",
"spark.hadoop.fs.s3a.impl": "org.apache.hadoop.fs.s3a.S3AFileSystem",
"spark.hadoop.fs.s3a.access.key": "s3_access_key",
"spark.hadoop.fs.s3a.secret.key": "secret_key",
}


To implement these configurations and enable Spark applications to interact
with GCS and S3, I guess we can approach it this way

1) Spark Repository Integration: These configurations need to be added to
the Spark repository as part of the supported configuration options for k8s
deployments.

2) Configuration Settings: Users need to specify these configurations when
submitting Spark applications to a Kubernetes cluster. They can include
these configurations in the Spark application code or pass them as
command-line arguments or environment variables during application
submission.

HTH

Mich Talebzadeh,

Technologist | Solutions Architect | Data Engineer  | Generative AI
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Sun, 7 Apr 2024 at 13:31, Vakaris Baškirov 
wrote:

> There is an IBM shuffle service plugin that supports S3
> https://github.com/IBM/spark-s3-shuffle
>
> Though I would think a feature like this could be a part of the main Spark
> repo. Trino already has out-of-box support for s3 exchange (shuffle) and
> it's very useful.
>
> Vakaris
>
> On Sun, Apr 7, 2024 at 12:27 PM Mich Talebzadeh 
> wrote:
>
>>
>> Thanks for your suggestion that I take it as a workaround. Whilst this
>> workaround can potentially address storage allocation issues, I was more
>> interested in exploring solutions that offer a more seamless integration
>> with large distributed file systems like HDFS, GCS, or S3. This would
>> ensure better performance and scalability for handling larger datasets
>> efficiently.
>>
>>
>> Mich Talebzadeh,
>> Technologist | Solutions Architect | Data Engineer  | Generative AI
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> 
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *Disclaimer:* The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner
>> Von Braun
>> )".
>>
>>
>> On Sat, 6 Apr 2024 at 21:28, Bjørn Jørgensen 
>> wrote:
>>
>>> You can make a PVC on K8S call it 300GB
>>>
>>> make a folder in yours dockerfile
>>> WORKDIR /opt/spark/work-dir
>>> RUN chmod g+w /opt/spark/work-dir
>>>
>>> start spark with adding this
>>>
>>> .config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.options.claimName",
>>> "300gb") \
>>>
>>> .config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.mount.path",
>>> "/opt/spark/work-dir") \
>>>
>>> .config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.mount.readOnly",
>>> "False") \
>>>
>>> .config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.options.claimName",
>>> "300gb") \
>>>
>>> .config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.mount.path",
>>> "/opt/spark/work-dir") \
>>>
>>> .config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.mount.readOnly",
>>> "False") \
>>>   .config("spark.local.dir", "/opt/spark/work-dir")
>>>
>>>
>>>
>>>
>>> lør. 6. apr. 2024 kl. 15:45 skrev Mich Talebzadeh <
>>> mich.talebza...@gmail.com>:
>>>
 I have seen some older references for shuffle service for k8s,
 although it is not clear they are talking about a generic shuffle
 service for k8s.

 Anyhow with the advent of genai and the need to allow for a larger
 volume of data, I was wondering if there has been any more work on
 this 

Re: External Spark shuffle service for k8s

[Mich Talebzadeh]

Thanks for your suggestion that I take it as a workaround. Whilst this
workaround can potentially address storage allocation issues, I was more
interested in exploring solutions that offer a more seamless integration
with large distributed file systems like HDFS, GCS, or S3. This would
ensure better performance and scalability for handling larger datasets
efficiently.


Mich Talebzadeh,
Technologist | Solutions Architect | Data Engineer  | Generative AI
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Sat, 6 Apr 2024 at 21:28, Bjørn Jørgensen 
wrote:

> You can make a PVC on K8S call it 300GB
>
> make a folder in yours dockerfile
> WORKDIR /opt/spark/work-dir
> RUN chmod g+w /opt/spark/work-dir
>
> start spark with adding this
>
> .config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.options.claimName",
> "300gb") \
>
> .config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.mount.path",
> "/opt/spark/work-dir") \
>
> .config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.mount.readOnly",
> "False") \
>
> .config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.options.claimName",
> "300gb") \
>
> .config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.mount.path",
> "/opt/spark/work-dir") \
>
> .config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.mount.readOnly",
> "False") \
>   .config("spark.local.dir", "/opt/spark/work-dir")
>
>
>
>
> lør. 6. apr. 2024 kl. 15:45 skrev Mich Talebzadeh <
> mich.talebza...@gmail.com>:
>
>> I have seen some older references for shuffle service for k8s,
>> although it is not clear they are talking about a generic shuffle
>> service for k8s.
>>
>> Anyhow with the advent of genai and the need to allow for a larger
>> volume of data, I was wondering if there has been any more work on
>> this matter. Specifically larger and scalable file systems like HDFS,
>> GCS , S3 etc, offer significantly larger storage capacity than local
>> disks on individual worker nodes in a k8s cluster, thus allowing
>> handling much larger datasets more efficiently. Also the degree of
>> parallelism and fault tolerance  with these files systems come into
>> it. I will be interested in hearing more about any progress on this.
>>
>> Thanks
>> .
>>
>> Mich Talebzadeh,
>>
>> Technologist | Solutions Architect | Data Engineer  | Generative AI
>>
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> Disclaimer: The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner Von Braun)".
>>
>> -
>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>>
>>
>
> --
> Bjørn Jørgensen
> Vestre Aspehaug 4, 6010 Ålesund
> Norge
>
> +47 480 94 297
>

Re: External Spark shuffle service for k8s

[Bjørn Jørgensen]

You can make a PVC on K8S call it 300GB

make a folder in yours dockerfile
WORKDIR /opt/spark/work-dir
RUN chmod g+w /opt/spark/work-dir

start spark with adding this

.config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.options.claimName",
"300gb") \

.config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.mount.path",
"/opt/spark/work-dir") \

.config("spark.kubernetes.driver.volumes.persistentVolumeClaim.300gb.mount.readOnly",
"False") \

.config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.options.claimName",
"300gb") \

.config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.mount.path",
"/opt/spark/work-dir") \

.config("spark.kubernetes.executor.volumes.persistentVolumeClaim.300gb.mount.readOnly",
"False") \
  .config("spark.local.dir", "/opt/spark/work-dir")




lør. 6. apr. 2024 kl. 15:45 skrev Mich Talebzadeh :

> I have seen some older references for shuffle service for k8s,
> although it is not clear they are talking about a generic shuffle
> service for k8s.
>
> Anyhow with the advent of genai and the need to allow for a larger
> volume of data, I was wondering if there has been any more work on
> this matter. Specifically larger and scalable file systems like HDFS,
> GCS , S3 etc, offer significantly larger storage capacity than local
> disks on individual worker nodes in a k8s cluster, thus allowing
> handling much larger datasets more efficiently. Also the degree of
> parallelism and fault tolerance  with these files systems come into
> it. I will be interested in hearing more about any progress on this.
>
> Thanks
> .
>
> Mich Talebzadeh,
>
> Technologist | Solutions Architect | Data Engineer  | Generative AI
>
> London
> United Kingdom
>
>
>view my Linkedin profile
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> Disclaimer: The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner Von Braun)".
>
> -
> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>
>

-- 
Bjørn Jørgensen
Vestre Aspehaug 4, 6010 Ålesund
Norge

+47 480 94 297

Re: Re: [Spark SQL] How can I use .sql() in conjunction with watermarks?

[刘唯]

This indeed looks like a bug. I will take some time to look into it.

Mich Talebzadeh  于2024年4月3日周三 01:55写道：

>
> hm. you are getting below
>
> AnalysisException: Append output mode not supported when there are
> streaming aggregations on streaming DataFrames/DataSets without watermark;
>
> The problem seems to be that you are using the append output mode when
> writing the streaming query results to Kafka. This mode is designed for
> scenarios where you want to append new data to an existing dataset at the
> sink (in this case, the "sink" topic in Kafka). However, your query
> involves a streaming aggregation: group by provinceId, window('createTime',
> '1 hour', '30 minutes'). The problem is that Spark Structured Streaming
> requires a watermark to ensure exactly-once processing when using
> aggregations with append mode. Your code already defines a watermark on the
> "createTime" column with a delay of 10 seconds (withWatermark("createTime",
> "10 seconds")). However, the error message indicates it is missing on the
> start column. Try adding watermark to "start" Column: Modify your code as
> below  to include a watermark on the "start" column generated by the
> window function:
>
> from pyspark.sql.functions import col, from_json, explode, window, sum,
> watermark
>
> streaming_df = session.readStream \
>   .format("kafka") \
>   .option("kafka.bootstrap.servers", "localhost:9092") \
>   .option("subscribe", "payment_msg") \
>   .option("startingOffsets", "earliest") \
>   .load() \
>   .select(from_json(col("value").cast("string"),
> schema).alias("parsed_value")) \
>   .select("parsed_value.*") \
>   .withWatermark("createTime", "10 seconds")  # Existing watermark on
> createTime
>
> *# Modified section with watermark on 'start' column*
> streaming_df = streaming_df.groupBy(
>   col("provinceId"),
>   window(col("createTime"), "1 hour", "30 minutes")
> ).agg(
>   sum(col("payAmount")).alias("totalPayAmount")
> ).withWatermark(expr("start"), "10 seconds")  # Watermark on
> window-generated 'start'
>
> # Rest of the code remains the same
> streaming_df.createOrReplaceTempView("streaming_df")
>
> spark.sql("""
> SELECT
>   window.start, window.end, provinceId, totalPayAmount
> FROM streaming_df
> ORDER BY window.start
> """) \
> .writeStream \
> .format("kafka") \
> .option("checkpointLocation", "checkpoint") \
> .option("kafka.bootstrap.servers", "localhost:9092") \
> .option("topic", "sink") \
> .start()
>
> Try and see how it goes
>
> HTH
>
> Mich Talebzadeh,
>
> Technologist | Solutions Architect | Data Engineer  | Generative AI
>
> London
> United Kingdom
>
>
>view my Linkedin profile
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> Disclaimer: The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner Von Braun)".
>
> Mich Talebzadeh,
> Technologist | Solutions Architect | Data Engineer  | Generative AI
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
>
> On Tue, 2 Apr 2024 at 22:43, Chloe He 
> wrote:
>
>> Hi Mich,
>>
>> Thank you so much for your response. I really appreciate your help!
>>
>> You mentioned "defining the watermark using the withWatermark function on
>> the streaming_df before creating the temporary view” - I believe this is
>> what I’m doing and it’s not working for me. Here is the exact code snippet
>> that I’m running:
>>
>> ```
>> >>> streaming_df = session.readStream\
>> .format("kafka")\
>> .option("kafka.bootstrap.servers", "localhost:9092")\
>> .option("subscribe", "payment_msg")\
>> .option("startingOffsets","earliest")\
>> .load()\
>> .select(from_json(col("value").cast("string"),
>> schema).alias("parsed_value"))\
>> .select("parsed_value.*")\
>> .withWatermark("createTime", "10 seconds")
>>
>> >>> streaming_df.createOrReplaceTempView("streaming_df”)
>>
>> >>> spark.sql("""
>> SELECT
>> window.start, window.end, provinceId, sum(payAmount) as totalPayAmount
>> FROM streaming_df
>> GROUP BY provinceId, window('createTime', '1 hour', '30 minutes')
>> ORDER BY window.start
>> """)\
>>   .withWatermark("start", "10 seconds")\
>>   .writeStream\
>>   .format("kafka") \
>>   .option("checkpointLocation", "checkpoint") \
>>   .option("kafka.bootstrap.servers", "localhost:9092") \
>>   .option("topic", "sink") \
>>   .start()
>>
>> AnalysisException: Append output mode not 

Re: [External] Re: Issue of spark with antlr version

[Bjørn Jørgensen]

[[VOTE] Release Plan for Apache Spark 4.0.0 (June 2024)](
https://lists.apache.org/thread/r0zn6rd8y25yn2dg59ktw3ttrwxzqrfb)

Apache Spark 4.0.0 Release Plan
===

1. After creating `branch-3.5`, set "4.0.0-SNAPSHOT" in master branch.

2. Creating `branch-4.0` on April 1st, 2024.

3. Apache Spark 4.0.0 RC1 on May 1st, 2024.

4. Apache Spark 4.0.0 Release in June, 2024.

tir. 2. apr. 2024 kl. 12:06 skrev Chawla, Parul :

> ++ Ashima
>
> --
> *From:* Chawla, Parul 
> *Sent:* Tuesday, April 2, 2024 9:56 AM
> *To:* Bjørn Jørgensen ; user@spark.apache.org <
> user@spark.apache.org>
> *Cc:* Sahni, Ashima ;
> user@spark.apache.org ; Misra Parashar, Jyoti <
> jyoti.misra.paras...@accenture.com>
> *Subject:* Re: [External] Re: Issue of spark with antlr version
>
> Hi Team,
> Any update on below query :when spark 4.x will be released to maven as on
> maven site i could see spark core 3.5.1 .
>
> Regards,
> Parul
>
> --
> *From:* Chawla, Parul 
> *Sent:* Monday, April 1, 2024 4:20 PM
> *To:* Bjørn Jørgensen 
> *Cc:* Sahni, Ashima ;
> user@spark.apache.org ; Misra Parashar, Jyoti <
> jyoti.misra.paras...@accenture.com>; Mekala, Rajesh <
> r.mek...@accenture.com>; Grandhi, Venkatesh <
> venkatesh.a.gran...@accenture.com>; George, Rejish <
> rejish.geo...@accenture.com>; Tayal, Aayushi 
> *Subject:* Re: [External] Re: Issue of spark with antlr version
>
> Hi Team,
>
> Can you let us know the when   this spark 4.x will be released to maven.
>
> regards,
> Parul
>
> Get Outlook for iOS <https://aka.ms/o0ukef>
> --
> *From:* Bjørn Jørgensen 
> *Sent:* Wednesday, February 28, 2024 5:06:54 PM
> *To:* Chawla, Parul 
> *Cc:* Sahni, Ashima ;
> user@spark.apache.org ; Misra Parashar, Jyoti <
> jyoti.misra.paras...@accenture.com>; Mekala, Rajesh <
> r.mek...@accenture.com>; Grandhi, Venkatesh <
> venkatesh.a.gran...@accenture.com>; George, Rejish <
> rejish.geo...@accenture.com>; Tayal, Aayushi 
> *Subject:* Re: [External] Re: Issue of spark with antlr version
>
> [image: image.png]
>
> ons. 28. feb. 2024 kl. 11:28 skrev Chawla, Parul <
> parul.cha...@accenture.com>:
>
>
> Hi ,
> Can we get spark version on whuich this is resolved.
> --
> *From:* Bjørn Jørgensen 
> *Sent:* Tuesday, February 27, 2024 7:05:36 PM
> *To:* Sahni, Ashima 
> *Cc:* Chawla, Parul ; user@spark.apache.org <
> user@spark.apache.org>; Misra Parashar, Jyoti <
> jyoti.misra.paras...@accenture.com>; Mekala, Rajesh <
> r.mek...@accenture.com>; Grandhi, Venkatesh <
> venkatesh.a.gran...@accenture.com>; George, Rejish <
> rejish.geo...@accenture.com>; Tayal, Aayushi 
> *Subject:* [External] Re: Issue of spark with antlr version
>
> *CAUTION:* External email. Be cautious with links and attachments.
> [SPARK-44366][BUILD] Upgrade antlr4 to 4.13.1
> <https://urldefense.com/v3/__https://github.com/apache/spark/pull/43075__;!!OrxsNty6D4my!5V_Vn4ayIAu7jPmZwkyA7qS7Pjmrg0Tp6jfeEYeA5p1GnG7Ks5v6nFfsCjZeHULghYge8_xl1to4vzFhly3d_figlCZ9-w$>
>
>
> tir. 27. feb. 2024 kl. 13:25 skrev Sahni, Ashima
> :
>
> Hi Team,
>
>
>
> Can you please let us know the update on below.
>
>
>
> Thanks,
>
> Ashima
>
>
>
> *From:* Chawla, Parul 
> *Sent:* Sunday, February 25, 2024 11:57 PM
> *To:* user@spark.apache.org
> *Cc:* Sahni, Ashima ; Misra Parashar, Jyoti <
> jyoti.misra.paras...@accenture.com>
> *Subject:* Issue of spark with antlr version
>
>
>
> Hi Spark Team,
>
>
>
>
>
> Our application is currently using spring framrwork 5.3.31 .To upgrade it
> to 6.x , as per application dependency we must upgrade Spark and
> Hibernate jars as well .
>
> With Hibernate compatible upgrade, the dependent Antlr4 jar version has
> been upgraded to 4.10.1 but there’s no Spark version available with the
> upgraded Antlr4 jar.
>
> Can u please update when we can have updated version with upgraded antl4
> version..
>
>
>
>
>
> Regards,
>
> Parul
>
> --
>
> This message is for the designated recipient only and may contain
> privileged, proprietary, or otherwise confidential information. If you have
> received it in error, please notify the sender immediately and delete the
> original. Any other use of the e-mail by you is prohibited. Where allowed
> by local law, electronic communications with Accenture and its affiliates,
> including e-mail and instant messaging (including content), may be scanned
> by our sy

Re: [EXTERNAL] Re: [Spark]: Spark / Iceberg / hadoop-aws compatibility matrix

[Oxlade, Dan]

I don't really understand how Iceberg and the hadoop libraries can coexist in a 
deployment.

The latest spark (3.5.1) base image contains the hadoop-client*-3.3.4.jar. The 
AWS v2 SDK is only supported in hadoop*-3.4.0.jar and onward.
Iceberg AWS integration states AWS v2 SDK is 
required<https://iceberg.apache.org/docs/latest/aws/>

Does anyone have a working combination of pyspark, iceberg and hadoop? Or, is 
there an alternative way to use pyspark to 
spark.read.parquet("s3a:///.parquet") such that I don't need the 
hadoop dependencies?

Kind regards,
Dan

From: Oxlade, Dan 
Sent: 03 April 2024 15:49
To: Oxlade, Dan ; Aaron Grubb 
; user@spark.apache.org 
Subject: Re: [EXTERNAL] Re: [Spark]: Spark / Iceberg / hadoop-aws compatibility 
matrix

Swapping out the iceberg-aws-bundle for the very latest aws provided sdk 
('software.amazon.awssdk:bundle:2.25.23') produces an incompatibility from a 
slightly different code path:

java.lang.NoSuchMethodError: 'void 
org.apache.hadoop.util.SemaphoredDelegatingExecutor.(java.util.concurrent.ExecutorService,
 int, boolean, org.apache.hadoop.fs.statistics.DurationTrackerFactory)'
at org.apache.hadoop.fs.s3a.S3AFileSystem.executeOpen(S3AFileSystem.java 
[s3afilesystem.java]<https://urldefense.com/v3/__http://S3AFileSystem.java__;!!EVw9PLhwfpc!ajy_-43U_YFTsDsGtsUXnaHdPzmpJCPeIYkmz-moYJTm7hKc1HL69MDQhPaUjyk_6Ka6XayBQNpTuf4c_roI0G8cYtHzLKu6sQ$>:1767)
at org.apache.hadoop.fs.s3a.S3AFileSystem.open(S3AFileSystem.java 
[s3afilesystem.java]<https://urldefense.com/v3/__http://S3AFileSystem.java__;!!EVw9PLhwfpc!ajy_-43U_YFTsDsGtsUXnaHdPzmpJCPeIYkmz-moYJTm7hKc1HL69MDQhPaUjyk_6Ka6XayBQNpTuf4c_roI0G8cYtHzLKu6sQ$>:1717)
at org.apache.hadoop.fs.FileSystem.open(FileSystem.java 
[filesystem.java]<https://urldefense.com/v3/__http://FileSystem.java__;!!EVw9PLhwfpc!ajy_-43U_YFTsDsGtsUXnaHdPzmpJCPeIYkmz-moYJTm7hKc1HL69MDQhPaUjyk_6Ka6XayBQNpTuf4c_roI0G8cYtFOz7Rg0A$>:976)
at 
org.apache.parquet.hadoop.util.HadoopInputFile.newStream(HadoopInputFile.java 
[hadoopinputfile.java]<https://urldefense.com/v3/__http://HadoopInputFile.java__;!!EVw9PLhwfpc!ajy_-43U_YFTsDsGtsUXnaHdPzmpJCPeIYkmz-moYJTm7hKc1HL69MDQhPaUjyk_6Ka6XayBQNpTuf4c_roI0G8cYtGnqRrxSg$>:69)
at org.apache.parquet.hadoop.ParquetFileReader.(ParquetFileReader.java 
[parquetfilereader.java]<https://urldefense.com/v3/__http://ParquetFileReader.java__;!!EVw9PLhwfpc!ajy_-43U_YFTsDsGtsUXnaHdPzmpJCPeIYkmz-moYJTm7hKc1HL69MDQhPaUjyk_6Ka6XayBQNpTuf4c_roI0G8cYtHDzEly0A$>:774)
at org.apache.parquet.hadoop.ParquetFileReader.open(ParquetFileReader.java 
[parquetfilereader.java]<https://urldefense.com/v3/__http://ParquetFileReader.java__;!!EVw9PLhwfpc!ajy_-43U_YFTsDsGtsUXnaHdPzmpJCPeIYkmz-moYJTm7hKc1HL69MDQhPaUjyk_6Ka6XayBQNpTuf4c_roI0G8cYtHDzEly0A$>:658)
at 
org.apache.spark.sql.execution.datasources.parquet.ParquetFooterReader.readFooter(ParquetFooterReader.java
 
[parquetfooterreader.java]<https://urldefense.com/v3/__http://ParquetFooterReader.java__;!!EVw9PLhwfpc!ajy_-43U_YFTsDsGtsUXnaHdPzmpJCPeIYkmz-moYJTm7hKc1HL69MDQhPaUjyk_6Ka6XayBQNpTuf4c_roI0G8cYtGEyk3Riw$>:53)
at 
org.apache.spark.sql.execution.datasources.parquet.ParquetFooterReader.readFooter(ParquetFooterReader.java
 
[parquetfooterreader.java]<https://urldefense.com/v3/__http://ParquetFooterReader.java__;!!EVw9PLhwfpc!ajy_-43U_YFTsDsGtsUXnaHdPzmpJCPeIYkmz-moYJTm7hKc1HL69MDQhPaUjyk_6Ka6XayBQNpTuf4c_roI0G8cYtGEyk3Riw$>:44)
at 
org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.$anonfun$readParquetFootersInParallel$1(ParquetFileFormat.scala:429)




From: Oxlade, Dan 
Sent: 03 April 2024 14:33
To: Aaron Grubb ; user@spark.apache.org 
Subject: Re: [EXTERNAL] Re: [Spark]: Spark / Iceberg / hadoop-aws compatibility 
matrix


[sorry; replying all this time]

With hadoop-*-3.3.6 in place of the 3.4.0 below I get 
java.lang.NoClassDefFoundError: com/amazonaws/AmazonClientException

I think that the below iceberg-aws-bundle version supplies the v2 sdk.

Dan


From: Aaron Grubb 
Sent: 03 April 2024 13:52
To: user@spark.apache.org 
Subject: [EXTERNAL] Re: [Spark]: Spark / Iceberg / hadoop-aws compatibility 
matrix

Downgrade to hadoop-*:3.3.x, Hadoop 3.4.x is based on the AWS SDK v2 and should 
probably be considered as breaking for tools that build on < 3.4.0 while using 
AWS.

From: Oxlade, Dan 
Sent: Wednesday, April 3, 2024 2:41:11 PM
To: user@spark.apache.org 
Subject: [Spark]: Spark / Iceberg / hadoop-aws compatibility matrix


Hi all,



I’ve struggled with this for quite some time.

My requirement is to read a parquet file from s3 to a Dataframe then append to 
an existing iceberg table.



In order to read the parquet I need the hadoop-aws dependency for s3a:// . In 
order to write to iceberg I need the iceberg dependency. Both of these 
dependencies have a transitive depend

Re: [EXTERNAL] Re: [Spark]: Spark / Iceberg / hadoop-aws compatibility matrix

[Oxlade, Dan]

Swapping out the iceberg-aws-bundle for the very latest aws provided sdk 
('software.amazon.awssdk:bundle:2.25.23') produces an incompatibility from a 
slightly different code path:

java.lang.NoSuchMethodError: 'void 
org.apache.hadoop.util.SemaphoredDelegatingExecutor.(java.util.concurrent.ExecutorService,
 int, boolean, org.apache.hadoop.fs.statistics.DurationTrackerFactory)'
at org.apache.hadoop.fs.s3a.S3AFileSystem.executeOpen(S3AFileSystem.java:1767)
at org.apache.hadoop.fs.s3a.S3AFileSystem.open(S3AFileSystem.java:1717)
at org.apache.hadoop.fs.FileSystem.open(FileSystem.java:976)
at 
org.apache.parquet.hadoop.util.HadoopInputFile.newStream(HadoopInputFile.java:69)
at 
org.apache.parquet.hadoop.ParquetFileReader.(ParquetFileReader.java:774)
at org.apache.parquet.hadoop.ParquetFileReader.open(ParquetFileReader.java:658)
at 
org.apache.spark.sql.execution.datasources.parquet.ParquetFooterReader.readFooter(ParquetFooterReader.java:53)
at 
org.apache.spark.sql.execution.datasources.parquet.ParquetFooterReader.readFooter(ParquetFooterReader.java:44)
at 
org.apache.spark.sql.execution.datasources.parquet.ParquetFileFormat$.$anonfun$readParquetFootersInParallel$1(ParquetFileFormat.scala:429)




From: Oxlade, Dan 
Sent: 03 April 2024 14:33
To: Aaron Grubb ; user@spark.apache.org 
Subject: Re: [EXTERNAL] Re: [Spark]: Spark / Iceberg / hadoop-aws compatibility 
matrix


[sorry; replying all this time]

With hadoop-*-3.3.6 in place of the 3.4.0 below I get 
java.lang.NoClassDefFoundError: com/amazonaws/AmazonClientException

I think that the below iceberg-aws-bundle version supplies the v2 sdk.

Dan


From: Aaron Grubb 
Sent: 03 April 2024 13:52
To: user@spark.apache.org 
Subject: [EXTERNAL] Re: [Spark]: Spark / Iceberg / hadoop-aws compatibility 
matrix

Downgrade to hadoop-*:3.3.x, Hadoop 3.4.x is based on the AWS SDK v2 and should 
probably be considered as breaking for tools that build on < 3.4.0 while using 
AWS.

From: Oxlade, Dan 
Sent: Wednesday, April 3, 2024 2:41:11 PM
To: user@spark.apache.org 
Subject: [Spark]: Spark / Iceberg / hadoop-aws compatibility matrix


Hi all,



I’ve struggled with this for quite some time.

My requirement is to read a parquet file from s3 to a Dataframe then append to 
an existing iceberg table.



In order to read the parquet I need the hadoop-aws dependency for s3a:// . In 
order to write to iceberg I need the iceberg dependency. Both of these 
dependencies have a transitive dependency on the aws SDK. I can’t find versions 
for Spark 3.4 that work together.





Current Versions:

Spark 3.4.1

iceberg-spark-runtime-3.4-2.12:1.4.1

iceberg-aws-bundle:1.4.1

hadoop-aws:3.4.0

hadoop-common:3.4.0



I’ve tried a number of combinations of the above and their respective versions 
but all fall over with their assumptions on the aws sdk version with class not 
found exceptions or method not found etc.



Is there a compatibility matrix somewhere that someone could point me to?



Thanks

Dan

T. Rowe Price International Ltd (registered number 3957748) is registered in 
England and Wales with its registered office at Warwick Court, 5 Paternoster 
Square, London EC4M 7DX. T. Rowe Price International Ltd is authorised and 
regulated by the Financial Conduct Authority. The company has a branch in Dubai 
International Financial Centre (regulated by the DFSA as a Representative 
Office).

T. Rowe Price (including T. Rowe Price International Ltd and its affiliates) 
and its associates do not provide legal or tax advice. Any tax-related 
discussion contained in this e-mail, including any attachments, is not intended 
or written to be used, and cannot be used, for the purpose of (i) avoiding any 
tax penalties or (ii) promoting, marketing, or recommending to any other party 
any transaction or matter addressed herein. Please consult your independent 
legal counsel and/or professional tax advisor regarding any legal or tax issues 
raised in this e-mail.

The contents of this e-mail and any attachments are intended solely for the use 
of the named addressee(s) and may contain confidential and/or privileged 
information. Any unauthorized use, copying, disclosure, or distribution of the 
contents of this e-mail is strictly prohibited by the sender and may be 
unlawful. If you are not the intended recipient, please notify the sender 
immediately and delete this e-mail.

T. Rowe Price International Ltd (registered number 3957748) is registered in 
England and Wales with its registered office at Warwick Court, 5 Paternoster 
Square, London EC4M 7DX. T. Rowe Price International Ltd is authorised and 
regulated by the Financial Conduct Authority. The company has a branch in Dubai 
International Financial Centre (regulated by the DFSA as a Representative 
Office).

T. Rowe Price (including T. Rowe Price International Ltd and its affiliates) 
and its associates do not provide legal or tax adv

Re: [EXTERNAL] Re: [Spark]: Spark / Iceberg / hadoop-aws compatibility matrix

[Oxlade, Dan]

[sorry; replying all this time]

With hadoop-*-3.3.6 in place of the 3.4.0 below I get 
java.lang.NoClassDefFoundError: com/amazonaws/AmazonClientException

I think that the below iceberg-aws-bundle version supplies the v2 sdk.

Dan


From: Aaron Grubb 
Sent: 03 April 2024 13:52
To: user@spark.apache.org 
Subject: [EXTERNAL] Re: [Spark]: Spark / Iceberg / hadoop-aws compatibility 
matrix

Downgrade to hadoop-*:3.3.x, Hadoop 3.4.x is based on the AWS SDK v2 and should 
probably be considered as breaking for tools that build on < 3.4.0 while using 
AWS.

From: Oxlade, Dan 
Sent: Wednesday, April 3, 2024 2:41:11 PM
To: user@spark.apache.org 
Subject: [Spark]: Spark / Iceberg / hadoop-aws compatibility matrix


Hi all,



I’ve struggled with this for quite some time.

My requirement is to read a parquet file from s3 to a Dataframe then append to 
an existing iceberg table.



In order to read the parquet I need the hadoop-aws dependency for s3a:// . In 
order to write to iceberg I need the iceberg dependency. Both of these 
dependencies have a transitive dependency on the aws SDK. I can’t find versions 
for Spark 3.4 that work together.





Current Versions:

Spark 3.4.1

iceberg-spark-runtime-3.4-2.12:1.4.1

iceberg-aws-bundle:1.4.1

hadoop-aws:3.4.0

hadoop-common:3.4.0



I’ve tried a number of combinations of the above and their respective versions 
but all fall over with their assumptions on the aws sdk version with class not 
found exceptions or method not found etc.



Is there a compatibility matrix somewhere that someone could point me to?



Thanks

Dan

T. Rowe Price International Ltd (registered number 3957748) is registered in 
England and Wales with its registered office at Warwick Court, 5 Paternoster 
Square, London EC4M 7DX. T. Rowe Price International Ltd is authorised and 
regulated by the Financial Conduct Authority. The company has a branch in Dubai 
International Financial Centre (regulated by the DFSA as a Representative 
Office).

T. Rowe Price (including T. Rowe Price International Ltd and its affiliates) 
and its associates do not provide legal or tax advice. Any tax-related 
discussion contained in this e-mail, including any attachments, is not intended 
or written to be used, and cannot be used, for the purpose of (i) avoiding any 
tax penalties or (ii) promoting, marketing, or recommending to any other party 
any transaction or matter addressed herein. Please consult your independent 
legal counsel and/or professional tax advisor regarding any legal or tax issues 
raised in this e-mail.

The contents of this e-mail and any attachments are intended solely for the use 
of the named addressee(s) and may contain confidential and/or privileged 
information. Any unauthorized use, copying, disclosure, or distribution of the 
contents of this e-mail is strictly prohibited by the sender and may be 
unlawful. If you are not the intended recipient, please notify the sender 
immediately and delete this e-mail.
T. Rowe Price International Ltd (registered number 3957748) is registered in 
England and Wales with its registered office at Warwick Court, 5 Paternoster 
Square, London EC4M 7DX. T. Rowe Price International Ltd is authorised and 
regulated by the Financial Conduct Authority. The company has a branch in Dubai 
International Financial Centre (regulated by the DFSA as a Representative 
Office).

T. Rowe Price (including T. Rowe Price International Ltd and its affiliates) 
and its associates do not provide legal or tax advice. Any tax-related 
discussion contained in this e-mail, including any attachments, is not intended 
or written to be used, and cannot be used, for the purpose of (i) avoiding any 
tax penalties or (ii) promoting, marketing, or recommending to any other party 
any transaction or matter addressed herein. Please consult your independent 
legal counsel and/or professional tax advisor regarding any legal or tax issues 
raised in this e-mail.

The contents of this e-mail and any attachments are intended solely for the use 
of the named addressee(s) and may contain confidential and/or privileged 
information. Any unauthorized use, copying, disclosure, or distribution of the 
contents of this e-mail is strictly prohibited by the sender and may be 
unlawful. If you are not the intended recipient, please notify the sender 
immediately and delete this e-mail.

Re: [Spark]: Spark / Iceberg / hadoop-aws compatibility matrix

[Aaron Grubb]

Downgrade to hadoop-*:3.3.x, Hadoop 3.4.x is based on the AWS SDK v2 and should 
probably be considered as breaking for tools that build on < 3.4.0 while using 
AWS.

From: Oxlade, Dan 
Sent: Wednesday, April 3, 2024 2:41:11 PM
To: user@spark.apache.org 
Subject: [Spark]: Spark / Iceberg / hadoop-aws compatibility matrix


Hi all,



I’ve struggled with this for quite some time.

My requirement is to read a parquet file from s3 to a Dataframe then append to 
an existing iceberg table.



In order to read the parquet I need the hadoop-aws dependency for s3a:// . In 
order to write to iceberg I need the iceberg dependency. Both of these 
dependencies have a transitive dependency on the aws SDK. I can’t find versions 
for Spark 3.4 that work together.





Current Versions:

Spark 3.4.1

iceberg-spark-runtime-3.4-2.12:1.4.1

iceberg-aws-bundle:1.4.1

hadoop-aws:3.4.0

hadoop-common:3.4.0



I’ve tried a number of combinations of the above and their respective versions 
but all fall over with their assumptions on the aws sdk version with class not 
found exceptions or method not found etc.



Is there a compatibility matrix somewhere that someone could point me to?



Thanks

Dan

T. Rowe Price International Ltd (registered number 3957748) is registered in 
England and Wales with its registered office at Warwick Court, 5 Paternoster 
Square, London EC4M 7DX. T. Rowe Price International Ltd is authorised and 
regulated by the Financial Conduct Authority. The company has a branch in Dubai 
International Financial Centre (regulated by the DFSA as a Representative 
Office).

T. Rowe Price (including T. Rowe Price International Ltd and its affiliates) 
and its associates do not provide legal or tax advice. Any tax-related 
discussion contained in this e-mail, including any attachments, is not intended 
or written to be used, and cannot be used, for the purpose of (i) avoiding any 
tax penalties or (ii) promoting, marketing, or recommending to any other party 
any transaction or matter addressed herein. Please consult your independent 
legal counsel and/or professional tax advisor regarding any legal or tax issues 
raised in this e-mail.

The contents of this e-mail and any attachments are intended solely for the use 
of the named addressee(s) and may contain confidential and/or privileged 
information. Any unauthorized use, copying, disclosure, or distribution of the 
contents of this e-mail is strictly prohibited by the sender and may be 
unlawful. If you are not the intended recipient, please notify the sender 
immediately and delete this e-mail.

Re: Re: [Spark SQL] How can I use .sql() in conjunction with watermarks?

[Mich Talebzadeh]

hm. you are getting below

AnalysisException: Append output mode not supported when there are
streaming aggregations on streaming DataFrames/DataSets without watermark;

The problem seems to be that you are using the append output mode when
writing the streaming query results to Kafka. This mode is designed for
scenarios where you want to append new data to an existing dataset at the
sink (in this case, the "sink" topic in Kafka). However, your query
involves a streaming aggregation: group by provinceId, window('createTime',
'1 hour', '30 minutes'). The problem is that Spark Structured Streaming
requires a watermark to ensure exactly-once processing when using
aggregations with append mode. Your code already defines a watermark on the
"createTime" column with a delay of 10 seconds (withWatermark("createTime",
"10 seconds")). However, the error message indicates it is missing on the
start column. Try adding watermark to "start" Column: Modify your code as
below  to include a watermark on the "start" column generated by the window
function:

from pyspark.sql.functions import col, from_json, explode, window, sum,
watermark

streaming_df = session.readStream \
  .format("kafka") \
  .option("kafka.bootstrap.servers", "localhost:9092") \
  .option("subscribe", "payment_msg") \
  .option("startingOffsets", "earliest") \
  .load() \
  .select(from_json(col("value").cast("string"),
schema).alias("parsed_value")) \
  .select("parsed_value.*") \
  .withWatermark("createTime", "10 seconds")  # Existing watermark on
createTime

*# Modified section with watermark on 'start' column*
streaming_df = streaming_df.groupBy(
  col("provinceId"),
  window(col("createTime"), "1 hour", "30 minutes")
).agg(
  sum(col("payAmount")).alias("totalPayAmount")
).withWatermark(expr("start"), "10 seconds")  # Watermark on
window-generated 'start'

# Rest of the code remains the same
streaming_df.createOrReplaceTempView("streaming_df")

spark.sql("""
SELECT
  window.start, window.end, provinceId, totalPayAmount
FROM streaming_df
ORDER BY window.start
""") \
.writeStream \
.format("kafka") \
.option("checkpointLocation", "checkpoint") \
.option("kafka.bootstrap.servers", "localhost:9092") \
.option("topic", "sink") \
.start()

Try and see how it goes

HTH

Mich Talebzadeh,

Technologist | Solutions Architect | Data Engineer  | Generative AI

London
United Kingdom


   view my Linkedin profile


 https://en.everybodywiki.com/Mich_Talebzadeh



Disclaimer: The information provided is correct to the best of my knowledge
but of course cannot be guaranteed . It is essential to note that, as with
any advice, quote "one test result is worth one-thousand expert opinions
(Werner Von Braun)".

Mich Talebzadeh,
Technologist | Solutions Architect | Data Engineer  | Generative AI
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Tue, 2 Apr 2024 at 22:43, Chloe He  wrote:

> Hi Mich,
>
> Thank you so much for your response. I really appreciate your help!
>
> You mentioned "defining the watermark using the withWatermark function on
> the streaming_df before creating the temporary view” - I believe this is
> what I’m doing and it’s not working for me. Here is the exact code snippet
> that I’m running:
>
> ```
> >>> streaming_df = session.readStream\
> .format("kafka")\
> .option("kafka.bootstrap.servers", "localhost:9092")\
> .option("subscribe", "payment_msg")\
> .option("startingOffsets","earliest")\
> .load()\
> .select(from_json(col("value").cast("string"),
> schema).alias("parsed_value"))\
> .select("parsed_value.*")\
> .withWatermark("createTime", "10 seconds")
>
> >>> streaming_df.createOrReplaceTempView("streaming_df”)
>
> >>> spark.sql("""
> SELECT
> window.start, window.end, provinceId, sum(payAmount) as totalPayAmount
> FROM streaming_df
> GROUP BY provinceId, window('createTime', '1 hour', '30 minutes')
> ORDER BY window.start
> """)\
>   .withWatermark("start", "10 seconds")\
>   .writeStream\
>   .format("kafka") \
>   .option("checkpointLocation", "checkpoint") \
>   .option("kafka.bootstrap.servers", "localhost:9092") \
>   .option("topic", "sink") \
>   .start()
>
> AnalysisException: Append output mode not supported when there are
> streaming aggregations on streaming DataFrames/DataSets without watermark;
> EventTimeWatermark start#37: timestamp, 10 seconds
> ```
>
> I’m using pyspark 3.5.1. Please let me know if I missed something. Thanks
> again!
>
> Best,
> Chloe
>
>
> On 2024/04/02 20:32:11 Mich Talebzadeh wrote:
> > ok let us 

RE: Re: [Spark SQL] How can I use .sql() in conjunction with watermarks?

[Chloe He]

Hi Mich,

Thank you so much for your response. I really appreciate your help!

You mentioned "defining the watermark using the withWatermark function on the 
streaming_df before creating the temporary view” - I believe this is what I’m 
doing and it’s not working for me. Here is the exact code snippet that I’m 
running:

```
>>> streaming_df = session.readStream\
.format("kafka")\
.option("kafka.bootstrap.servers", "localhost:9092")\
.option("subscribe", "payment_msg")\
.option("startingOffsets","earliest")\
.load()\
.select(from_json(col("value").cast("string"), 
schema).alias("parsed_value"))\
.select("parsed_value.*")\
.withWatermark("createTime", "10 seconds")

>>> streaming_df.createOrReplaceTempView("streaming_df”)

>>> spark.sql("""
SELECT
window.start, window.end, provinceId, sum(payAmount) as totalPayAmount
FROM streaming_df
GROUP BY provinceId, window('createTime', '1 hour', '30 minutes')
ORDER BY window.start
""")\
  .withWatermark("start", "10 seconds")\
  .writeStream\
  .format("kafka") \
  .option("checkpointLocation", "checkpoint") \
  .option("kafka.bootstrap.servers", "localhost:9092") \
  .option("topic", "sink") \
  .start()

AnalysisException: Append output mode not supported when there are streaming 
aggregations on streaming DataFrames/DataSets without watermark;
EventTimeWatermark start#37: timestamp, 10 seconds
```

I’m using pyspark 3.5.1. Please let me know if I missed something. Thanks again!

Best,
Chloe


On 2024/04/02 20:32:11 Mich Talebzadeh wrote:
> ok let us take it for a test.
> 
> The original code of mine
> 
> def fetch_data(self):
> self.sc.setLogLevel("ERROR")
> schema = StructType() \
>  .add("rowkey", StringType()) \
>  .add("timestamp", TimestampType()) \
>  .add("temperature", IntegerType())
> checkpoint_path = "file:///ssd/hduser/avgtemperature/chkpt"
> try:
> 
> # construct a streaming dataframe 'streamingDataFrame' that
> subscribes to topic temperature
> streamingDataFrame = self.spark \
> .readStream \
> .format("kafka") \
> .option("kafka.bootstrap.servers",
> config['MDVariables']['bootstrapServers'],) \
> .option("schema.registry.url",
> config['MDVariables']['schemaRegistryURL']) \
> .option("group.id", config['common']['appName']) \
> .option("zookeeper.connection.timeout.ms",
> config['MDVariables']['zookeeperConnectionTimeoutMs']) \
> .option("rebalance.backoff.ms",
> config['MDVariables']['rebalanceBackoffMS']) \
> .option("zookeeper.session.timeout.ms",
> config['MDVariables']['zookeeperSessionTimeOutMs']) \
> .option("auto.commit.interval.ms",
> config['MDVariables']['autoCommitIntervalMS']) \
> .option("subscribe", "temperature") \
> .option("failOnDataLoss", "false") \
> .option("includeHeaders", "true") \
> .option("startingOffsets", "earliest") \
> .load() \
> .select(from_json(col("value").cast("string"),
> schema).alias("parsed_value"))
> 
> 
> resultC = streamingDataFrame.select( \
>  col("parsed_value.rowkey").alias("rowkey") \
>, col("parsed_value.timestamp").alias("timestamp") \
>, col("parsed_value.temperature").alias("temperature"))
> 
> """
> We work out the window and the AVG(temperature) in the window's
> timeframe below
> This should return back the following Dataframe as struct
> 
>  root
>  |-- window: struct (nullable = false)
>  ||-- start: timestamp (nullable = true)
>  ||-- end: timestamp (nullable = true)
>  |-- avg(temperature): double (nullable = true)
> 
> """
> resultM = resultC. \
>  withWatermark("timestamp", "5 minutes"). \
>  groupBy(window(resultC.timestamp, "5 minutes", "5
> minutes")). \
>  avg('temperature')
> 
> # We take the above DataFrame and flatten it to get the columns
> aliased as "startOfWindowFrame", "endOfWindowFrame" and "AVGTemperature"
> resultMF = resultM. \
>select( \
> F.col("window.start").alias("startOfWindow") \
>   , F.col("window.end").alias("endOfWindow") \
>   ,
> F.col("avg(temperature)").alias("AVGTemperature"))
> 
> # Kafka producer requires a key, value pair. We generate UUID
> key as the unique identifier of Kafka record
> uuidUdf= F.udf(lambda : str(uuid.uuid4()),StringType())
> 
> """
> We take DataFrame resultMF containing temperature info and
> write it to Kafka. The uuid is serialized as a 

Re: [Spark SQL] How can I use .sql() in conjunction with watermarks?

[Mich Talebzadeh]

ok let us take it for a test.

The original code of mine

def fetch_data(self):
self.sc.setLogLevel("ERROR")
schema = StructType() \
 .add("rowkey", StringType()) \
 .add("timestamp", TimestampType()) \
 .add("temperature", IntegerType())
checkpoint_path = "file:///ssd/hduser/avgtemperature/chkpt"
try:

# construct a streaming dataframe 'streamingDataFrame' that
subscribes to topic temperature
streamingDataFrame = self.spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers",
config['MDVariables']['bootstrapServers'],) \
.option("schema.registry.url",
config['MDVariables']['schemaRegistryURL']) \
.option("group.id", config['common']['appName']) \
.option("zookeeper.connection.timeout.ms",
config['MDVariables']['zookeeperConnectionTimeoutMs']) \
.option("rebalance.backoff.ms",
config['MDVariables']['rebalanceBackoffMS']) \
.option("zookeeper.session.timeout.ms",
config['MDVariables']['zookeeperSessionTimeOutMs']) \
.option("auto.commit.interval.ms",
config['MDVariables']['autoCommitIntervalMS']) \
.option("subscribe", "temperature") \
.option("failOnDataLoss", "false") \
.option("includeHeaders", "true") \
.option("startingOffsets", "earliest") \
.load() \
.select(from_json(col("value").cast("string"),
schema).alias("parsed_value"))


resultC = streamingDataFrame.select( \
 col("parsed_value.rowkey").alias("rowkey") \
   , col("parsed_value.timestamp").alias("timestamp") \
   , col("parsed_value.temperature").alias("temperature"))

"""
We work out the window and the AVG(temperature) in the window's
timeframe below
This should return back the following Dataframe as struct

 root
 |-- window: struct (nullable = false)
 ||-- start: timestamp (nullable = true)
 ||-- end: timestamp (nullable = true)
 |-- avg(temperature): double (nullable = true)

"""
resultM = resultC. \
 withWatermark("timestamp", "5 minutes"). \
 groupBy(window(resultC.timestamp, "5 minutes", "5
minutes")). \
 avg('temperature')

# We take the above DataFrame and flatten it to get the columns
aliased as "startOfWindowFrame", "endOfWindowFrame" and "AVGTemperature"
resultMF = resultM. \
   select( \
F.col("window.start").alias("startOfWindow") \
  , F.col("window.end").alias("endOfWindow") \
  ,
F.col("avg(temperature)").alias("AVGTemperature"))

# Kafka producer requires a key, value pair. We generate UUID
key as the unique identifier of Kafka record
uuidUdf= F.udf(lambda : str(uuid.uuid4()),StringType())

"""
We take DataFrame resultMF containing temperature info and
write it to Kafka. The uuid is serialized as a string and used as the key.
We take all the columns of the DataFrame and serialize them as
a JSON string, putting the results in the "value" of the record.
"""
result = resultMF.withColumn("uuid",uuidUdf()) \
 .selectExpr("CAST(uuid AS STRING) AS key",
"to_json(struct(startOfWindow, endOfWindow, AVGTemperature)) AS value") \
 .writeStream \
 .outputMode('complete') \
 .format("kafka") \
 .option("kafka.bootstrap.servers",
config['MDVariables']['bootstrapServers'],) \
 .option("topic", "avgtemperature") \
 .option('checkpointLocation', checkpoint_path) \
 .queryName("avgtemperature") \
 .start()

except Exception as e:
print(f"""{e}, quitting""")
sys.exit(1)

#print(result.status)
#print(result.recentProgress)
#print(result.lastProgress)

result.awaitTermination()

Now try to use sql for the entire transformation and aggression

#import this and anything else needed
from pyspark.sql.functions import from_json, col, window
from pyspark.sql.types import StructType, StringType,IntegerType,
FloatType, TimestampType


# Define the schema for the JSON data
schema = ... # Replace with your schema definition

# construct a streaming dataframe 'streamingDataFrame' that
subscribes to topic temperature
streamingDataFrame = self.spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers",
config['MDVariables']['bootstrapServers'],) \

Re: [External] Re: Issue of spark with antlr version

[Chawla, Parul]

Hi Team,

Can you let us know the when   this spark 4.x will be released to maven.

regards,
Parul

Get Outlook for iOS<https://aka.ms/o0ukef>

From: Bjørn Jørgensen 
Sent: Wednesday, February 28, 2024 5:06:54 PM
To: Chawla, Parul 
Cc: Sahni, Ashima ; user@spark.apache.org 
; Misra Parashar, Jyoti 
; Mekala, Rajesh ; 
Grandhi, Venkatesh ; George, Rejish 
; Tayal, Aayushi 
Subject: Re: [External] Re: Issue of spark with antlr version

[image.png]

ons. 28. feb. 2024 kl. 11:28 skrev Chawla, Parul 
mailto:parul.cha...@accenture.com>>:

Hi ,
Can we get spark version on whuich this is resolved.

From: Bjørn Jørgensen 
mailto:bjornjorgen...@gmail.com>>
Sent: Tuesday, February 27, 2024 7:05:36 PM
To: Sahni, Ashima 
Cc: Chawla, Parul 
mailto:parul.cha...@accenture.com>>; 
user@spark.apache.org<mailto:user@spark.apache.org> 
mailto:user@spark.apache.org>>; Misra Parashar, Jyoti 
mailto:jyoti.misra.paras...@accenture.com>>;
 Mekala, Rajesh mailto:r.mek...@accenture.com>>; 
Grandhi, Venkatesh 
mailto:venkatesh.a.gran...@accenture.com>>; 
George, Rejish 
mailto:rejish.geo...@accenture.com>>; Tayal, 
Aayushi mailto:aayushi.ta...@accenture.com>>
Subject: [External] Re: Issue of spark with antlr version

CAUTION: External email. Be cautious with links and attachments.

[SPARK-44366][BUILD] Upgrade antlr4 to 
4.13.1<https://urldefense.com/v3/__https://github.com/apache/spark/pull/43075__;!!OrxsNty6D4my!5V_Vn4ayIAu7jPmZwkyA7qS7Pjmrg0Tp6jfeEYeA5p1GnG7Ks5v6nFfsCjZeHULghYge8_xl1to4vzFhly3d_figlCZ9-w$>


tir. 27. feb. 2024 kl. 13:25 skrev Sahni, Ashima 
:

Hi Team,



Can you please let us know the update on below.



Thanks,

Ashima



From: Chawla, Parul 
mailto:parul.cha...@accenture.com>>
Sent: Sunday, February 25, 2024 11:57 PM
To: user@spark.apache.org<mailto:user@spark.apache.org>
Cc: Sahni, Ashima 
mailto:ashima.sa...@accenture.com>>; Misra 
Parashar, Jyoti 
mailto:jyoti.misra.paras...@accenture.com>>
Subject: Issue of spark with antlr version



Hi Spark Team,





Our application is currently using spring framrwork 5.3.31 .To upgrade it to 
6.x , as per application dependency we must upgrade Spark and Hibernate jars as 
well .

With Hibernate compatible upgrade, the dependent Antlr4 jar version has been 
upgraded to 4.10.1 but there’s no Spark version available with the upgraded 
Antlr4 jar.

Can u please update when we can have updated version with upgraded antl4 
version..





Regards,

Parul



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--
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Vestre Aspehaug 4, 6010 Ålesund
Norge

+47 480 94 297


--
Bjørn Jørgensen
Vestre Aspehaug 4, 6010 Ålesund
Norge

+47 480 94 297

Re: [DISCUSS] MySQL version support policy

[Dongjoon Hyun]

Hi, Cheng.

Thank you for the suggestion. Your suggestion seems to have at least two
themes.

A. Adding a new Apache Spark community policy (contract) to guarantee MySQL
LTS Versions Support.
B. Dropping the support of non-LTS version support (MySQL 8.3/8.2/8.1)

And, it brings me three questions.

1. For (A), do you mean MySQL LTS versions are not supported by Apache
Spark releases properly due to the improper test suite?
2. For (B), why does Apache Spark need to drop non-LTS MySQL support?
3. What about MariaDB? Do we need to stick to some versions?

To be clear, if needed, we can have daily GitHub Action CIs easily like
Python CI (Python 3.8/3.10/3.11/3.12).

-
https://github.com/apache/spark/blob/master/.github/workflows/build_python.yml

Thanks,
Dongjoon.


On Sun, Mar 24, 2024 at 10:29 PM Cheng Pan  wrote:

> Hi, Spark community,
>
> I noticed that the Spark JDBC connector MySQL dialect is testing against
> the 8.3.0[1] now, a non-LTS version.
>
> MySQL changed the version policy recently[2], which is now very similar to
> the Java version policy. In short, 5.5, 5.6, 5.7, 8.0 is the LTS version,
> 8.1, 8.2, 8.3 is non-LTS, and the next LTS version is 8.4.
>
> I would say that MySQL is one of the most important infrastructures today,
> I checked the AWS RDS MySQL[4] and Azure Database for MySQL[5] version
> support policy, and both only support 5.7 and 8.0.
>
> Also, Spark officially only supports LTS Java versions, like JDK 17 and
> 21, but not 22. I would recommend using MySQL 8.0 for testing until the
> next MySQL LTS version (8.4) is available.
>
> Additional discussion can be found at [3]
>
> [1] https://issues.apache.org/jira/browse/SPARK-47453
> [2]
> https://dev.mysql.com/blog-archive/introducing-mysql-innovation-and-long-term-support-lts-versions/
> [3] https://github.com/apache/spark/pull/45581
> [4] https://aws.amazon.com/rds/mysql/
> [5] https://learn.microsoft.com/en-us/azure/mysql/concepts-version-policy
>
> Thanks,
> Cheng Pan
>
>
>
> -
> To unsubscribe e-mail: dev-unsubscr...@spark.apache.org
>
>

Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community

[Winston Lai]

+1

--
Thank You & Best Regards
Winston Lai

From: Jay Han 
Date: Sunday, 24 March 2024 at 08:39
To: Kiran Kumar Dusi 
Cc: Farshid Ashouri , Matei Zaharia 
, Mich Talebzadeh , Spark 
dev list , user @spark 
Subject: Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark 
Community
+1. It sounds awesome!

Kiran Kumar Dusi mailto:kirankumard...@gmail.com>> 
于2024年3月21日周四 14:16写道：
+1

On Thu, 21 Mar 2024 at 7:46 AM, Farshid Ashouri 
mailto:farsheed.asho...@gmail.com>> wrote:
+1

On Mon, 18 Mar 2024, 11:00 Mich Talebzadeh, 
mailto:mich.talebza...@gmail.com>> wrote:
Some of you may be aware that Databricks community Home | Databricks
have just launched a knowledge sharing hub. I thought it would be a
good idea for the Apache Spark user group to have the same, especially
for repeat questions on Spark core, Spark SQL, Spark Structured
Streaming, Spark Mlib and so forth.

Apache Spark user and dev groups have been around for a good while.
They are serving their purpose . We went through creating a slack
community that managed to create more more heat than light.. This is
what Databricks community came up with and I quote

"Knowledge Sharing Hub
Dive into a collaborative space where members like YOU can exchange
knowledge, tips, and best practices. Join the conversation today and
unlock a wealth of collective wisdom to enhance your experience and
drive success."

I don't know the logistics of setting it up.but I am sure that should
not be that difficult. If anyone is supportive of this proposal, let
the usual +1, 0, -1 decide

HTH

Mich Talebzadeh,
Dad | Technologist | Solutions Architect | Engineer
London
United Kingdom


   view my Linkedin profile


 https://en.everybodywiki.com/Mich_Talebzadeh



Disclaimer: The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner Von Braun)".

-
To unsubscribe e-mail: 
user-unsubscr...@spark.apache.org<mailto:user-unsubscr...@spark.apache.org>

Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community

[Jay Han]

+1. It sounds awesome!

Kiran Kumar Dusi  于2024年3月21日周四 14:16写道：

> +1
>
> On Thu, 21 Mar 2024 at 7:46 AM, Farshid Ashouri <
> farsheed.asho...@gmail.com> wrote:
>
>> +1
>>
>> On Mon, 18 Mar 2024, 11:00 Mich Talebzadeh, 
>> wrote:
>>
>>> Some of you may be aware that Databricks community Home | Databricks
>>> have just launched a knowledge sharing hub. I thought it would be a
>>> good idea for the Apache Spark user group to have the same, especially
>>> for repeat questions on Spark core, Spark SQL, Spark Structured
>>> Streaming, Spark Mlib and so forth.
>>>
>>> Apache Spark user and dev groups have been around for a good while.
>>> They are serving their purpose . We went through creating a slack
>>> community that managed to create more more heat than light.. This is
>>> what Databricks community came up with and I quote
>>>
>>> "Knowledge Sharing Hub
>>> Dive into a collaborative space where members like YOU can exchange
>>> knowledge, tips, and best practices. Join the conversation today and
>>> unlock a wealth of collective wisdom to enhance your experience and
>>> drive success."
>>>
>>> I don't know the logistics of setting it up.but I am sure that should
>>> not be that difficult. If anyone is supportive of this proposal, let
>>> the usual +1, 0, -1 decide
>>>
>>> HTH
>>>
>>> Mich Talebzadeh,
>>> Dad | Technologist | Solutions Architect | Engineer
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> Disclaimer: The information provided is correct to the best of my
>>> knowledge but of course cannot be guaranteed . It is essential to note
>>> that, as with any advice, quote "one test result is worth one-thousand
>>> expert opinions (Werner Von Braun)".
>>>
>>> -
>>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>>>
>>>

Re: Feature article: Leveraging Generative AI with Apache Spark: Transforming Data Engineering

[Mich Talebzadeh]

Sorry from this link

Leveraging Generative AI with Apache Spark: Transforming Data Engineering |
LinkedIn


Mich Talebzadeh,
Technologist | Data | Generative AI | Financial Fraud
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Fri, 22 Mar 2024 at 16:16, Mich Talebzadeh 
wrote:

> You may find this link of mine in Linkedin for the said article. We
> can use Linkedin for now.
>
> Leveraging Generative AI with Apache Spark: Transforming Data
> Engineering | LinkedIn
>
>
> Mich Talebzadeh,
>
> Technologist | Data | Generative AI | Financial Fraud
>
> London
> United Kingdom
>
>
>view my Linkedin profile
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> Disclaimer: The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner Von Braun)".
>

Re:

[Mich Talebzadeh]

You can try this

val kafkaReadStream = spark
  .readStream
  .format("kafka")
  .option("kafka.bootstrap.servers", broker)
  .option("subscribe", topicName)
  .option("startingOffsets", startingOffsetsMode)
  .option("maxOffsetsPerTrigger", maxOffsetsPerTrigger)
  .load()

kafkaReadStream
  .writeStream
  .foreachBatch((df: DataFrame, batchId: Long) => sendToSink(df, batchId))
  .trigger(Trigger.ProcessingTime(s"${triggerProcessingTime} seconds"))
  .option("checkpointLocation", checkpoint_path)
  .start()
  .awaitTermination()

Notice the function sendToSink

The foreachBatch method ensures that the sendToSink function is called for
each micro-batch, regardless of whether the DataFrame contains data or not.

Let us look at that function

import org.apache.spark.sql.functions._
def sendToSink(df: DataFrame, batchId: Long): Unit = {

  if (!df.isEmpty) {
println(s"From sendToSink, batchId is $batchId, at
${java.time.LocalDateTime.now()}")
// df.show(100, false)
df.persist()
// Write to BigQuery batch table
// s.writeTableToBQ(df, "append",
config.getString("MDVariables.targetDataset"),
config.getString("MDVariables.targetTable"))
df.unpersist()
// println("wrote to DB")
  } else {
println("DataFrame df is empty")
  }
}

If the DataFrame is empty, it prints a message indicating that the
DataFrame is empty. You can of course adapt it for your case

HTH

Mich Talebzadeh,
Dad | Technologist | Solutions Architect | Engineer
London
United Kingdom


   view my Linkedin profile



 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  Von
Braun )".


On Thu, 21 Mar 2024 at 23:14, Рамик И  wrote:

>
> Hi!
> I want to exucute code inside forEachBatch that will trigger regardless of
> whether there is data in the batch or not.
>
>
> val kafkaReadStream = spark
> .readStream
> .format("kafka")
> .option("kafka.bootstrap.servers", broker)
> .option("subscribe", topicName)
> .option("startingOffsets", startingOffsetsMode)
> .option("maxOffsetsPerTrigger", maxOffsetsPerTrigger)
> .load()
>
>
> kafkaReadStream
> .writeStream
> .trigger(Trigger.ProcessingTime(s"$triggerProcessingTime seconds"))
> .foreachBatch {
>
> 
> }
> .start()
> .awaitTermination()
>

Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community

[Kiran Kumar Dusi]

+1

On Thu, 21 Mar 2024 at 7:46 AM, Farshid Ashouri 
wrote:

> +1
>
> On Mon, 18 Mar 2024, 11:00 Mich Talebzadeh, 
> wrote:
>
>> Some of you may be aware that Databricks community Home | Databricks
>> have just launched a knowledge sharing hub. I thought it would be a
>> good idea for the Apache Spark user group to have the same, especially
>> for repeat questions on Spark core, Spark SQL, Spark Structured
>> Streaming, Spark Mlib and so forth.
>>
>> Apache Spark user and dev groups have been around for a good while.
>> They are serving their purpose . We went through creating a slack
>> community that managed to create more more heat than light.. This is
>> what Databricks community came up with and I quote
>>
>> "Knowledge Sharing Hub
>> Dive into a collaborative space where members like YOU can exchange
>> knowledge, tips, and best practices. Join the conversation today and
>> unlock a wealth of collective wisdom to enhance your experience and
>> drive success."
>>
>> I don't know the logistics of setting it up.but I am sure that should
>> not be that difficult. If anyone is supportive of this proposal, let
>> the usual +1, 0, -1 decide
>>
>> HTH
>>
>> Mich Talebzadeh,
>> Dad | Technologist | Solutions Architect | Engineer
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> Disclaimer: The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner Von Braun)".
>>
>> -
>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>>
>>

Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community

[Farshid Ashouri]

+1

On Mon, 18 Mar 2024, 11:00 Mich Talebzadeh, 
wrote:

> Some of you may be aware that Databricks community Home | Databricks
> have just launched a knowledge sharing hub. I thought it would be a
> good idea for the Apache Spark user group to have the same, especially
> for repeat questions on Spark core, Spark SQL, Spark Structured
> Streaming, Spark Mlib and so forth.
>
> Apache Spark user and dev groups have been around for a good while.
> They are serving their purpose . We went through creating a slack
> community that managed to create more more heat than light.. This is
> what Databricks community came up with and I quote
>
> "Knowledge Sharing Hub
> Dive into a collaborative space where members like YOU can exchange
> knowledge, tips, and best practices. Join the conversation today and
> unlock a wealth of collective wisdom to enhance your experience and
> drive success."
>
> I don't know the logistics of setting it up.but I am sure that should
> not be that difficult. If anyone is supportive of this proposal, let
> the usual +1, 0, -1 decide
>
> HTH
>
> Mich Talebzadeh,
> Dad | Technologist | Solutions Architect | Engineer
> London
> United Kingdom
>
>
>view my Linkedin profile
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> Disclaimer: The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner Von Braun)".
>
> -
> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>
>

Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community

[Mich Talebzadeh]

One option that comes to my mind, is that given the cyclic nature of these
types of proposals in these two forums, we should be able to use
Databricks's existing knowledge sharing hub Knowledge Sharing Hub -
Databricks
<https://community.databricks.com/t5/knowledge-sharing-hub/bd-p/Knowledge-Sharing-Hub>
as well.

The majority of topics will be of interest to their audience as well. In
addition, they seem to invite everyone to contribute. Unless you have an
overriding concern why we should not take this approach, I can enquire from
Databricks community managers whether they can entertain this idea. They
seem to have a well defined structure for hosting topics.

Let me know your thoughts

Thanks
<https://community.databricks.com/t5/knowledge-sharing-hub/bd-p/Knowledge-Sharing-Hub>
Mich Talebzadeh,
Dad | Technologist | Solutions Architect | Engineer
London
United Kingdom


   view my Linkedin profile
<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>


 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von
Braun <https://en.wikipedia.org/wiki/Wernher_von_Braun>)".


On Tue, 19 Mar 2024 at 08:25, Joris Billen 
wrote:

> +1
>
>
> On 18 Mar 2024, at 21:53, Mich Talebzadeh 
> wrote:
>
> Well as long as it works.
>
> Please all check this link from Databricks and let us know your thoughts.
> Will something similar work for us?. Of course Databricks have much deeper
> pockets than our ASF community. Will it require moderation in our side to
> block spams and nutcases.
>
> Knowledge Sharing Hub - Databricks
> <https://community.databricks.com/t5/knowledge-sharing-hub/bd-p/Knowledge-Sharing-Hub>
>
>
> Mich Talebzadeh,
> Dad | Technologist | Solutions Architect | Engineer
> London
> United Kingdom
>
>view my Linkedin profile
> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von
> Braun <https://en.wikipedia.org/wiki/Wernher_von_Braun>)".
>
>
> On Mon, 18 Mar 2024 at 20:31, Bjørn Jørgensen 
> wrote:
>
>> something like this  Spark community · GitHub
>> <https://github.com/Spark-community>
>>
>>
>> man. 18. mars 2024 kl. 17:26 skrev Parsian, Mahmoud
>> :
>>
>>> Good idea. Will be useful
>>>
>>>
>>>
>>> +1
>>>
>>>
>>>
>>>
>>>
>>>
>>>
>>> *From: *ashok34...@yahoo.com.INVALID 
>>> *Date: *Monday, March 18, 2024 at 6:36 AM
>>> *To: *user @spark , Spark dev list <
>>> d...@spark.apache.org>, Mich Talebzadeh 
>>> *Cc: *Matei Zaharia 
>>> *Subject: *Re: A proposal for creating a Knowledge Sharing Hub for
>>> Apache Spark Community
>>>
>>> External message, be mindful when clicking links or attachments
>>>
>>>
>>>
>>> Good idea. Will be useful
>>>
>>>
>>>
>>> +1
>>>
>>>
>>>
>>> On Monday, 18 March 2024 at 11:00:40 GMT, Mich Talebzadeh <
>>> mich.talebza...@gmail.com> wrote:
>>>
>>>
>>>
>>>
>>>
>>> Some of you may be aware that Databricks community Home | Databricks
>>>
>>> have just launched a knowledge sharing hub. I thought it would be a
>>>
>>> good idea for the Apache Spark user group to have the same, especially
>>>
>>> for repeat questions on Spark core, Spark SQL, Spark Structured
>>>
>>> Streaming, Spark Mlib and so forth.
>>>
>>>
>>>
>>> Apache Spark user and dev groups have been around for a good while.
>>>
>>> They are serving their purpose . We went through creating a slack
>>>
>>> community that managed to create more more heat than light.. This is
>>>
>>> what Databricks community came up with and I quote
>>>
>>>
>>>
>>> "Knowledge Sharing Hub
>>>
>>> Dive into a collaborative space where members like YOU can ex

Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community

[Joris Billen]

+1


On 18 Mar 2024, at 21:53, Mich Talebzadeh  wrote:

Well as long as it works.

Please all check this link from Databricks and let us know your thoughts. Will 
something similar work for us?. Of course Databricks have much deeper pockets 
than our ASF community. Will it require moderation in our side to block spams 
and nutcases.

Knowledge Sharing Hub - 
Databricks<https://community.databricks.com/t5/knowledge-sharing-hub/bd-p/Knowledge-Sharing-Hub>


Mich Talebzadeh,
Dad | Technologist | Solutions Architect | Engineer
London
United Kingdom

 
[https://ci3.googleusercontent.com/mail-sig/AIorK4zholKucR2Q9yMrKbHNn-o1TuS4mYXyi2KO6Xmx6ikHPySa9MLaLZ8t2hrA6AUcxSxDgHIwmKE]
   view my Linkedin 
profile<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>

 https://en.everybodywiki.com/Mich_Talebzadeh



Disclaimer: The information provided is correct to the best of my knowledge but 
of course cannot be guaranteed . It is essential to note that, as with any 
advice, quote "one test result is worth one-thousand expert opinions (Werner 
<https://en.wikipedia.org/wiki/Wernher_von_Braun> Von 
Braun<https://en.wikipedia.org/wiki/Wernher_von_Braun>)".


On Mon, 18 Mar 2024 at 20:31, Bjørn Jørgensen 
mailto:bjornjorgen...@gmail.com>> wrote:
something like this  Spark community · 
GitHub<https://github.com/Spark-community>


man. 18. mars 2024 kl. 17:26 skrev Parsian, Mahmoud 
:
Good idea. Will be useful

+1



From: ashok34...@yahoo.com.INVALID 
Date: Monday, March 18, 2024 at 6:36 AM
To: user @spark mailto:user@spark.apache.org>>, Spark 
dev list mailto:d...@spark.apache.org>>, Mich Talebzadeh 
mailto:mich.talebza...@gmail.com>>
Cc: Matei Zaharia mailto:matei.zaha...@gmail.com>>
Subject: Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark 
Community
External message, be mindful when clicking links or attachments

Good idea. Will be useful

+1

On Monday, 18 March 2024 at 11:00:40 GMT, Mich Talebzadeh 
mailto:mich.talebza...@gmail.com>> wrote:


Some of you may be aware that Databricks community Home | Databricks
have just launched a knowledge sharing hub. I thought it would be a
good idea for the Apache Spark user group to have the same, especially
for repeat questions on Spark core, Spark SQL, Spark Structured
Streaming, Spark Mlib and so forth.

Apache Spark user and dev groups have been around for a good while.
They are serving their purpose . We went through creating a slack
community that managed to create more more heat than light.. This is
what Databricks community came up with and I quote

"Knowledge Sharing Hub
Dive into a collaborative space where members like YOU can exchange
knowledge, tips, and best practices. Join the conversation today and
unlock a wealth of collective wisdom to enhance your experience and
drive success."

I don't know the logistics of setting it up.but I am sure that should
not be that difficult. If anyone is supportive of this proposal, let
the usual +1, 0, -1 decide

HTH

Mich Talebzadeh,
Dad | Technologist | Solutions Architect | Engineer
London
United Kingdom


  view my Linkedin profile


https://en.everybodywiki.com/Mich_Talebzadeh<https://urldefense.com/v3/__https:/en.everybodywiki.com/Mich_Talebzadeh__;!!HrbR-XT-OQ!Wu9fFP8RFJW2N_YUvwl9yctGHxtM-CFPe6McqOJDrxGBjIaRoF8vRwpjT9WzHojwI2R09Nbg8YE9ggB4FtocU8cQFw$>



Disclaimer: The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner Von Braun)".

-
To unsubscribe e-mail: 
user-unsubscr...@spark.apache.org<mailto:user-unsubscr...@spark.apache.org>



--
Bjørn Jørgensen
Vestre Aspehaug 4, 6010 Ålesund
Norge

+47 480 94 297

Re: pyspark - Where are Dataframes created from Python objects stored?

[Varun Shah]

Hi @Mich Talebzadeh  , community,

Where can I find such insights on the Spark Architecture ?

I found few sites below which did/does cover internals :
1. https://github.com/JerryLead/SparkInternals
2. https://books.japila.pl/apache-spark-internals/overview/
3. https://stackoverflow.com/questions/30691385/how-spark-works-internally

Most of them are very old, and hoping the basic internals have not changed,
where can we find more information on internals ? Asking in case you or
someone from community has more articles / videos / document links to
share.

Appreciate your help.


Regards,
Varun Shah



On Fri, Mar 15, 2024, 03:10 Mich Talebzadeh 
wrote:

> Hi,
>
> When you create a DataFrame from Python objects using
> spark.createDataFrame, here it goes:
>
>
> *Initial Local Creation:*
> The DataFrame is initially created in the memory of the driver node. The
> data is not yet distributed to executors at this point.
>
> *The role of lazy Evaluation:*
>
> Spark applies lazy evaluation, *meaning transformations are not executed
> immediately*.  It constructs a logical plan describing the operations,
> but data movement does not occur yet.
>
> *Action Trigger:*
>
> When you initiate an action (things like show(), collect(), etc), Spark
> triggers the execution.
>
>
>
> *When partitioning  and distribution come in:Spark partitions the
> DataFrame into logical chunks for parallel processing*. It divides the
> data based on a partitioning scheme (default is hash partitioning). Each
> partition is sent to different executor nodes for distributed execution.
> This stage involves data transfer across the cluster, but it is not that
> expensive shuffle you have heard of. Shuffles happen within repartitioning
> or certain join operations.
>
> *Storage on Executors:*
>
> Executors receive their assigned partitions and store them in their
> memory. If memory is limited, Spark spills partitions to disk. look at
> stages tab in UI (4040)
>
>
> *In summary:*
> No Data Transfer During Creation: --> Data transfer occurs only when an
> action is triggered.
> Distributed Processing: --> DataFrames are distributed for parallel
> execution, not stored entirely on the driver node.
> Lazy Evaluation Optimization: --> Delaying data transfer until necessary
> enhances performance.
> Shuffle vs. Partitioning: --> Data movement during partitioning is not
> considered a shuffle in Spark terminology.
> Shuffles involve more complex data rearrangement.
>
> *Considerations: *
> Large DataFrames: For very large DataFrames
>
>- manage memory carefully to avoid out-of-memory errors. Consider
>options like:
>- Increasing executor memory
>- Using partitioning strategies to optimize memory usage
>- Employing techniques like checkpointing to persistent storage (hard
>disks) or caching for memory efficiency
>- You can get additional info from Spark UI default port 4040 tabs
>like SQL and executors
>- Spark uses Catalyst optimiser for efficient execution plans.
>df.explain("extended") shows both logical and physical plans
>
> HTH
>
> Mich Talebzadeh,
> Dad | Technologist | Solutions Architect | Engineer
> London
> United Kingdom
>
>
>view my Linkedin profile
> 
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  Von
> Braun )".
>
>
> On Thu, 14 Mar 2024 at 19:46, Sreyan Chakravarty 
> wrote:
>
>> I am trying to understand Spark Architecture.
>>
>> For Dataframes that are created from python objects ie. that are *created
>> in memory where are they stored ?*
>>
>> Take following example:
>>
>> from pyspark.sql import Rowimport datetime
>> courses = [
>> {
>> 'course_id': 1,
>> 'course_title': 'Mastering Python',
>> 'course_published_dt': datetime.date(2021, 1, 14),
>> 'is_active': True,
>> 'last_updated_ts': datetime.datetime(2021, 2, 18, 16, 57, 25)
>> }
>>
>> ]
>>
>>
>> courses_df = spark.createDataFrame([Row(**course) for course in courses])
>>
>>
>> Where is the dataframe stored when I invoke the call:
>>
>> courses_df = spark.createDataFrame([Row(**course) for course in courses])
>>
>> Does it:
>>
>>1. Send the data to a random executor ?
>>
>>
>>- Does this mean this counts as a shuffle ?
>>
>>
>>1. Or does it stay on the driver node ?
>>
>>
>>- That does not make sense when the dataframe grows large.
>>
>>
>> --
>> Regards,
>> Sreyan Chakravarty
>>
>

Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community

[Varun Shah]

+1  Great initiative.

QQ : Stack overflow has a similar feature called "Collectives", but I am
not sure of the expenses to create one for Apache Spark. With SO being used
( atleast before ChatGPT became quite the norm for searching questions), it
already has a lot of questions asked and answered by the community over a
period of time and hence, if possible, we could leverage it as the starting
point for building a community before creating a complete new website from
scratch. Any thoughts on this?

Regards,
Varun Shah


On Mon, Mar 18, 2024, 16:29 Mich Talebzadeh 
wrote:

> Some of you may be aware that Databricks community Home | Databricks
> have just launched a knowledge sharing hub. I thought it would be a
> good idea for the Apache Spark user group to have the same, especially
> for repeat questions on Spark core, Spark SQL, Spark Structured
> Streaming, Spark Mlib and so forth.
>
> Apache Spark user and dev groups have been around for a good while.
> They are serving their purpose . We went through creating a slack
> community that managed to create more more heat than light.. This is
> what Databricks community came up with and I quote
>
> "Knowledge Sharing Hub
> Dive into a collaborative space where members like YOU can exchange
> knowledge, tips, and best practices. Join the conversation today and
> unlock a wealth of collective wisdom to enhance your experience and
> drive success."
>
> I don't know the logistics of setting it up.but I am sure that should
> not be that difficult. If anyone is supportive of this proposal, let
> the usual +1, 0, -1 decide
>
> HTH
>
> Mich Talebzadeh,
> Dad | Technologist | Solutions Architect | Engineer
> London
> United Kingdom
>
>
>view my Linkedin profile
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> Disclaimer: The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner Von Braun)".
>
> -
> To unsubscribe e-mail: dev-unsubscr...@spark.apache.org
>
>

Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community

[Deepak Sharma]

+1 .
I can contribute to it as well .

On Tue, 19 Mar 2024 at 9:19 AM, Code Tutelage 
wrote:

> +1
>
> Thanks for proposing
>
> On Mon, Mar 18, 2024 at 9:25 AM Parsian, Mahmoud
>  wrote:
>
>> Good idea. Will be useful
>>
>>
>>
>> +1
>>
>>
>>
>>
>>
>>
>>
>> *From: *ashok34...@yahoo.com.INVALID 
>> *Date: *Monday, March 18, 2024 at 6:36 AM
>> *To: *user @spark , Spark dev list <
>> d...@spark.apache.org>, Mich Talebzadeh 
>> *Cc: *Matei Zaharia 
>> *Subject: *Re: A proposal for creating a Knowledge Sharing Hub for
>> Apache Spark Community
>>
>> External message, be mindful when clicking links or attachments
>>
>>
>>
>> Good idea. Will be useful
>>
>>
>>
>> +1
>>
>>
>>
>> On Monday, 18 March 2024 at 11:00:40 GMT, Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>
>>
>>
>>
>> Some of you may be aware that Databricks community Home | Databricks
>>
>> have just launched a knowledge sharing hub. I thought it would be a
>>
>> good idea for the Apache Spark user group to have the same, especially
>>
>> for repeat questions on Spark core, Spark SQL, Spark Structured
>>
>> Streaming, Spark Mlib and so forth.
>>
>>
>>
>> Apache Spark user and dev groups have been around for a good while.
>>
>> They are serving their purpose . We went through creating a slack
>>
>> community that managed to create more more heat than light.. This is
>>
>> what Databricks community came up with and I quote
>>
>>
>>
>> "Knowledge Sharing Hub
>>
>> Dive into a collaborative space where members like YOU can exchange
>>
>> knowledge, tips, and best practices. Join the conversation today and
>>
>> unlock a wealth of collective wisdom to enhance your experience and
>>
>> drive success."
>>
>>
>>
>> I don't know the logistics of setting it up.but I am sure that should
>>
>> not be that difficult. If anyone is supportive of this proposal, let
>>
>> the usual +1, 0, -1 decide
>>
>>
>>
>> HTH
>>
>>
>>
>> Mich Talebzadeh,
>>
>> Dad | Technologist | Solutions Architect | Engineer
>>
>> London
>>
>> United Kingdom
>>
>>
>>
>>
>>
>>   view my Linkedin profile
>>
>>
>>
>>
>>
>> https://en.everybodywiki.com/Mich_Talebzadeh
>> <https://urldefense.com/v3/__https:/en.everybodywiki.com/Mich_Talebzadeh__;!!HrbR-XT-OQ!Wu9fFP8RFJW2N_YUvwl9yctGHxtM-CFPe6McqOJDrxGBjIaRoF8vRwpjT9WzHojwI2R09Nbg8YE9ggB4FtocU8cQFw$>
>>
>>
>>
>>
>>
>>
>>
>> Disclaimer: The information provided is correct to the best of my
>>
>> knowledge but of course cannot be guaranteed . It is essential to note
>>
>> that, as with any advice, quote "one test result is worth one-thousand
>>
>> expert opinions (Werner Von Braun)".
>>
>>
>>
>> -
>>
>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>>
>>
>>
>

Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community

[Hyukjin Kwon]

One very good example is SparkR releases in Conda channel (
https://github.com/conda-forge/r-sparkr-feedstock).
This is fully run by the community unofficially.

On Tue, 19 Mar 2024 at 09:54, Mich Talebzadeh 
wrote:

> +1 for me
>
> Mich Talebzadeh,
> Dad | Technologist | Solutions Architect | Engineer
> London
> United Kingdom
>
>
>view my Linkedin profile
> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>
>
>  https://en.everybodywiki.com/Mich_Talebzadeh
>
>
>
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one-thousand
> expert opinions (Werner  <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von
> Braun <https://en.wikipedia.org/wiki/Wernher_von_Braun>)".
>
>
> On Mon, 18 Mar 2024 at 16:23, Parsian, Mahmoud 
> wrote:
>
>> Good idea. Will be useful
>>
>>
>>
>> +1
>>
>>
>>
>>
>>
>>
>>
>> *From: *ashok34...@yahoo.com.INVALID 
>> *Date: *Monday, March 18, 2024 at 6:36 AM
>> *To: *user @spark , Spark dev list <
>> d...@spark.apache.org>, Mich Talebzadeh 
>> *Cc: *Matei Zaharia 
>> *Subject: *Re: A proposal for creating a Knowledge Sharing Hub for
>> Apache Spark Community
>>
>> External message, be mindful when clicking links or attachments
>>
>>
>>
>> Good idea. Will be useful
>>
>>
>>
>> +1
>>
>>
>>
>> On Monday, 18 March 2024 at 11:00:40 GMT, Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>
>>
>>
>>
>> Some of you may be aware that Databricks community Home | Databricks
>>
>> have just launched a knowledge sharing hub. I thought it would be a
>>
>> good idea for the Apache Spark user group to have the same, especially
>>
>> for repeat questions on Spark core, Spark SQL, Spark Structured
>>
>> Streaming, Spark Mlib and so forth.
>>
>>
>>
>> Apache Spark user and dev groups have been around for a good while.
>>
>> They are serving their purpose . We went through creating a slack
>>
>> community that managed to create more more heat than light.. This is
>>
>> what Databricks community came up with and I quote
>>
>>
>>
>> "Knowledge Sharing Hub
>>
>> Dive into a collaborative space where members like YOU can exchange
>>
>> knowledge, tips, and best practices. Join the conversation today and
>>
>> unlock a wealth of collective wisdom to enhance your experience and
>>
>> drive success."
>>
>>
>>
>> I don't know the logistics of setting it up.but I am sure that should
>>
>> not be that difficult. If anyone is supportive of this proposal, let
>>
>> the usual +1, 0, -1 decide
>>
>>
>>
>> HTH
>>
>>
>>
>> Mich Talebzadeh,
>>
>> Dad | Technologist | Solutions Architect | Engineer
>>
>> London
>>
>> United Kingdom
>>
>>
>>
>>
>>
>>   view my Linkedin profile
>>
>>
>>
>>
>>
>> https://en.everybodywiki.com/Mich_Talebzadeh
>> <https://urldefense.com/v3/__https:/en.everybodywiki.com/Mich_Talebzadeh__;!!HrbR-XT-OQ!Wu9fFP8RFJW2N_YUvwl9yctGHxtM-CFPe6McqOJDrxGBjIaRoF8vRwpjT9WzHojwI2R09Nbg8YE9ggB4FtocU8cQFw$>
>>
>>
>>
>>
>>
>>
>>
>> Disclaimer: The information provided is correct to the best of my
>>
>> knowledge but of course cannot be guaranteed . It is essential to note
>>
>> that, as with any advice, quote "one test result is worth one-thousand
>>
>> expert opinions (Werner Von Braun)".
>>
>>
>>
>> -
>>
>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>>
>>
>>
>

Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community

[Mich Talebzadeh]

OK thanks for the update.

What does officially blessed signify here? Can we have and run it as a
sister site? The reason this comes to my mind is that the interested
parties should have easy access to this site (from ISUG Spark sites) as a
reference repository. I guess the advice would be that the information
(topics) are provided as best efforts and cannot be guaranteed.

Mich Talebzadeh,
Dad | Technologist | Solutions Architect | Engineer
London
United Kingdom


   view my Linkedin profile
<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>


 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von
Braun <https://en.wikipedia.org/wiki/Wernher_von_Braun>)".


On Mon, 18 Mar 2024 at 21:04, Reynold Xin  wrote:

> One of the problem in the past when something like this was brought up was
> that the ASF couldn't have officially blessed venues beyond the already
> approved ones. So that's something to look into.
>
> Now of course you are welcome to run unofficial things unblessed as long
> as they follow trademark rules.
>
>
>
> On Mon, Mar 18, 2024 at 1:53 PM, Mich Talebzadeh <
> mich.talebza...@gmail.com> wrote:
>
>> Well as long as it works.
>>
>> Please all check this link from Databricks and let us know your thoughts.
>> Will something similar work for us?. Of course Databricks have much deeper
>> pockets than our ASF community. Will it require moderation in our side to
>> block spams and nutcases.
>>
>> Knowledge Sharing Hub - Databricks
>> <https://community.databricks.com/t5/knowledge-sharing-hub/bd-p/Knowledge-Sharing-Hub>
>>
>>
>> Mich Talebzadeh,
>> Dad | Technologist | Solutions Architect | Engineer
>> London
>> United Kingdom
>>
>>
>>view my Linkedin profile
>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>
>>
>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>
>>
>>
>> *Disclaimer:* The information provided is correct to the best of my
>> knowledge but of course cannot be guaranteed . It is essential to note
>> that, as with any advice, quote "one test result is worth one-thousand
>> expert opinions (Werner
>> <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von Braun
>> <https://en.wikipedia.org/wiki/Wernher_von_Braun>)".
>>
>>
>> On Mon, 18 Mar 2024 at 20:31, Bjørn Jørgensen 
>> wrote:
>>
>>> something like this  Spark community · GitHub
>>> <https://github.com/Spark-community>
>>>
>>>
>>> man. 18. mars 2024 kl. 17:26 skrev Parsian, Mahmoud <
>>> mpars...@illumina.com.invalid>:
>>>
>>>> Good idea. Will be useful
>>>>
>>>>
>>>>
>>>> +1
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> *From: *ashok34...@yahoo.com.INVALID 
>>>> *Date: *Monday, March 18, 2024 at 6:36 AM
>>>> *To: *user @spark , Spark dev list <
>>>> d...@spark.apache.org>, Mich Talebzadeh 
>>>> *Cc: *Matei Zaharia 
>>>> *Subject: *Re: A proposal for creating a Knowledge Sharing Hub for
>>>> Apache Spark Community
>>>>
>>>> External message, be mindful when clicking links or attachments
>>>>
>>>>
>>>>
>>>> Good idea. Will be useful
>>>>
>>>>
>>>>
>>>> +1
>>>>
>>>>
>>>>
>>>> On Monday, 18 March 2024 at 11:00:40 GMT, Mich Talebzadeh <
>>>> mich.talebza...@gmail.com> wrote:
>>>>
>>>>
>>>>
>>>>
>>>>
>>>> Some of you may be aware that Databricks community Home | Databricks
>>>>
>>>> have just launched a knowledge sharing hub. I thought it would be a
>>>>
>>>> good idea for the Apache Spark user group to have the same, especially
>>>>
>>>> for repeat questions on Spark core, Spark SQL, Spark Structured
>>>>
>>>> Streaming, Spark Mlib and so forth.
>>>>
>>>>
>>>>
>>>> Apache Spark user and dev groups have been around for a good while.
>>>>
>>>> They are serving their purpose . We went through c

Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community

[Reynold Xin]

One of the problem in the past when something like this was brought up was that 
the ASF couldn't have officially blessed venues beyond the already approved 
ones. So that's something to look into.

Now of course you are welcome to run unofficial things unblessed as long as 
they follow trademark rules.

On Mon, Mar 18, 2024 at 1:53 PM, Mich Talebzadeh < mich.talebza...@gmail.com > 
wrote:

> 
> Well as long as it works.
> 
> Please all check this link from Databricks and let us know your thoughts.
> Will something similar work for us?. Of course Databricks have much deeper
> pockets than our ASF community. Will it require moderation in our side to
> block spams and nutcases.
> 
> 
> 
> Knowledge Sharing Hub - Databricks (
> https://community.databricks.com/t5/knowledge-sharing-hub/bd-p/Knowledge-Sharing-Hub
> )
> 
> 
> 
> Mich Talebzadeh,
> Dad | Technologist | Solutions Architect | Engineer
> 
> London
> 
> United Kingdom
> 
> 
> 
> 
> 
> 
> 
> ** view my Linkedin profile (
> https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/ )
> 
> 
> 
> 
> 
> 
> 
> 
> https:/ / en. everybodywiki. com/ Mich_Talebzadeh (
> https://en.everybodywiki.com/Mich_Talebzadeh )
> 
> 
> 
> 
> 
> 
> 
> 
> 
> 
> 
> *Disclaimer:* The information provided is correct to the best of my
> knowledge but of course cannot be guaranteed . It is essential to note
> that, as with any advice, quote "one test result is worth one - thousand
> expert opinions ( Werner ( https://en.wikipedia.org/wiki/Wernher_von_Braun
> ) Von Braun ( https://en.wikipedia.org/wiki/Wernher_von_Braun ) )".
> 
> 
> 
> 
> 
> On Mon, 18 Mar 2024 at 20:31, Bjørn Jørgensen < bjornjorgensen@ gmail. com
> ( bjornjorgen...@gmail.com ) > wrote:
> 
> 
>> something like this Spark community · GitHub (
>> https://github.com/Spark-community )
>> 
>> 
>> 
>> man. 18. mars 2024 kl. 17:26 skrev Parsian, Mahmoud < mparsian@ illumina. 
>> com.
>> invalid ( mpars...@illumina.com.invalid ) >:
>> 
>> 
>>> 
>>> 
>>> Good idea. Will be useful
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> +1
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> *From:* ashok34668@ yahoo. com. INVALID ( ashok34...@yahoo.com.INVALID ) <
>>> ashok34668@ yahoo. com. INVALID ( ashok34...@yahoo.com.INVALID ) >
>>> *Date:* Monday, March 18 , 2024 at 6:36 AM
>>> *To:* user @spark < user@ spark. apache. org ( user@spark.apache.org ) >,
>>> Spark dev list < dev@ spark. apache. org ( d...@spark.apache.org ) >, Mich
>>> Talebzadeh < mich. talebzadeh@ gmail. com ( mich.talebza...@gmail.com ) >
>>> *Cc:* Matei Zaharia < matei. zaharia@ gmail. com ( matei.zaha...@gmail.com
>>> ) >
>>> *Subject:* Re: A proposal for creating a Knowledge Sharing Hub for Apache
>>> Spark Community
>>> 
>>> 
>>> 
>>> 
>>> External message, be mindful when clicking links or attachments
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> Good idea. Will be useful
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> +1
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> On Monday, 18 March 2024 at 11:00:40 GMT, Mich Talebzadeh < mich. 
>>> talebzadeh@
>>> gmail. com ( mich.talebza...@gmail.com ) > wrote:
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> Some of you may be aware that Databricks community Home | Databricks
>>> 
>>> 
>>> 
>>> 
>>> have just launched a knowledge sharing hub. I thought it would be a
>>> 
>>> 
>>> 
>>> 
>>> good idea for the Apache Spark user group to have the same, especially
>>> 
>>> 
>>> 
>>> 
>>> for repeat questions on Spark core, Spark SQL, Spark Structured
>>> 
>>> 
>>> 
>>> 
>>> Streaming, Spark Mlib and so forth.
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>> 
>>

Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community

[Mich Talebzadeh]

Well as long as it works.

Please all check this link from Databricks and let us know your thoughts.
Will something similar work for us?. Of course Databricks have much deeper
pockets than our ASF community. Will it require moderation in our side to
block spams and nutcases.

Knowledge Sharing Hub - Databricks
<https://community.databricks.com/t5/knowledge-sharing-hub/bd-p/Knowledge-Sharing-Hub>


Mich Talebzadeh,
Dad | Technologist | Solutions Architect | Engineer
London
United Kingdom


   view my Linkedin profile
<https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>


 https://en.everybodywiki.com/Mich_Talebzadeh



*Disclaimer:* The information provided is correct to the best of my
knowledge but of course cannot be guaranteed . It is essential to note
that, as with any advice, quote "one test result is worth one-thousand
expert opinions (Werner  <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von
Braun <https://en.wikipedia.org/wiki/Wernher_von_Braun>)".


On Mon, 18 Mar 2024 at 20:31, Bjørn Jørgensen 
wrote:

> something like this  Spark community · GitHub
> <https://github.com/Spark-community>
>
>
> man. 18. mars 2024 kl. 17:26 skrev Parsian, Mahmoud
> :
>
>> Good idea. Will be useful
>>
>>
>>
>> +1
>>
>>
>>
>>
>>
>>
>>
>> *From: *ashok34...@yahoo.com.INVALID 
>> *Date: *Monday, March 18, 2024 at 6:36 AM
>> *To: *user @spark , Spark dev list <
>> d...@spark.apache.org>, Mich Talebzadeh 
>> *Cc: *Matei Zaharia 
>> *Subject: *Re: A proposal for creating a Knowledge Sharing Hub for
>> Apache Spark Community
>>
>> External message, be mindful when clicking links or attachments
>>
>>
>>
>> Good idea. Will be useful
>>
>>
>>
>> +1
>>
>>
>>
>> On Monday, 18 March 2024 at 11:00:40 GMT, Mich Talebzadeh <
>> mich.talebza...@gmail.com> wrote:
>>
>>
>>
>>
>>
>> Some of you may be aware that Databricks community Home | Databricks
>>
>> have just launched a knowledge sharing hub. I thought it would be a
>>
>> good idea for the Apache Spark user group to have the same, especially
>>
>> for repeat questions on Spark core, Spark SQL, Spark Structured
>>
>> Streaming, Spark Mlib and so forth.
>>
>>
>>
>> Apache Spark user and dev groups have been around for a good while.
>>
>> They are serving their purpose . We went through creating a slack
>>
>> community that managed to create more more heat than light.. This is
>>
>> what Databricks community came up with and I quote
>>
>>
>>
>> "Knowledge Sharing Hub
>>
>> Dive into a collaborative space where members like YOU can exchange
>>
>> knowledge, tips, and best practices. Join the conversation today and
>>
>> unlock a wealth of collective wisdom to enhance your experience and
>>
>> drive success."
>>
>>
>>
>> I don't know the logistics of setting it up.but I am sure that should
>>
>> not be that difficult. If anyone is supportive of this proposal, let
>>
>> the usual +1, 0, -1 decide
>>
>>
>>
>> HTH
>>
>>
>>
>> Mich Talebzadeh,
>>
>> Dad | Technologist | Solutions Architect | Engineer
>>
>> London
>>
>> United Kingdom
>>
>>
>>
>>
>>
>>   view my Linkedin profile
>>
>>
>>
>>
>>
>> https://en.everybodywiki.com/Mich_Talebzadeh
>> <https://urldefense.com/v3/__https:/en.everybodywiki.com/Mich_Talebzadeh__;!!HrbR-XT-OQ!Wu9fFP8RFJW2N_YUvwl9yctGHxtM-CFPe6McqOJDrxGBjIaRoF8vRwpjT9WzHojwI2R09Nbg8YE9ggB4FtocU8cQFw$>
>>
>>
>>
>>
>>
>>
>>
>> Disclaimer: The information provided is correct to the best of my
>>
>> knowledge but of course cannot be guaranteed . It is essential to note
>>
>> that, as with any advice, quote "one test result is worth one-thousand
>>
>> expert opinions (Werner Von Braun)".
>>
>>
>>
>> -
>>
>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>>
>>
>>
>
>
> --
> Bjørn Jørgensen
> Vestre Aspehaug 4, 6010 Ålesund
> Norge
>
> +47 480 94 297
>

Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community

[Bjørn Jørgensen]

something like this  Spark community · GitHub
<https://github.com/Spark-community>


man. 18. mars 2024 kl. 17:26 skrev Parsian, Mahmoud
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> Good idea. Will be useful
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> +1
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> *From: *ashok34...@yahoo.com.INVALID 
> *Date: *Monday, March 18, 2024 at 6:36 AM
> *To: *user @spark , Spark dev list <
> d...@spark.apache.org>, Mich Talebzadeh 
> *Cc: *Matei Zaharia 
> *Subject: *Re: A proposal for creating a Knowledge Sharing Hub for Apache
> Spark Community
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> External message, be mindful when clicking links or attachments
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> Good idea. Will be useful
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> +1
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> On Monday, 18 March 2024 at 11:00:40 GMT, Mich Talebzadeh <
> mich.talebza...@gmail.com> wrote:
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> Some of you may be aware that Databricks community Home | Databricks
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> have just launched a knowledge sharing hub. I thought it would be a
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> good idea for the Apache Spark user group to have the same, especially
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> for repeat questions on Spark core, Spark SQL, Spark Structured
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> Streaming, Spark Mlib and so forth.
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> Apache Spark user and dev groups have been around for a good while.
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> They are serving their purpose . We went through creating a slack
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> community that managed to create more more heat than light.. This is
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> what Databricks community came up with and I quote
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> "Knowledge Sharing Hub
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> Dive into a collaborative space where members like YOU can exchange
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> knowledge, tips, and best practices. Join the conversation today and
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> unlock a wealth of collective wisdom to enhance your experience and
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> drive success."
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> I don't know the logistics of setting it up.but I am sure that should
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> not be that difficult. If anyone is supportive of this proposal, let
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> the usual +1, 0, -1 decide
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> HTH
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> Mich Talebzadeh,
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> Dad | Technologist | Solutions Architect | Engineer
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> London
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> United Kingdom
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>   view my Linkedin profile
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> https://en.everybodywiki.com/Mich_Talebzadeh
> <https://urldefense.com/v3/__https:/en.everybodywiki.com/Mich_Talebzadeh__;!!HrbR-XT-OQ!Wu9fFP8RFJW2N_YUvwl9yctGHxtM-CFPe6McqOJDrxGBjIaRoF8vRwpjT9WzHojwI2R09Nbg8YE9ggB4FtocU8cQFw$>
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> Disclaimer: The information provided is correct to the best of my
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> knowledge but of course cannot be guaranteed . It is essential to note
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> that, as with any advice, quote "one test result is worth one-thousand
>
> expert opinions (Werner Von Braun)".
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>
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> -
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