Spark can't see hive schema updates partly because it stores the schema
in a weird way in hive metastore.
1. FROM SPARK: create a table
>>> spark.sql("select 1 col1, 2
>>> col2").write.format("parquet").saveAsTable("my_table")
>>> spark.table("my_table").printSchema()
root
|-- col1
> With IntelliJ you are OK with Spark & Scala.
also intelliJ as a nice python plugin that turns it into pycharm.
On Thu Sep 30, 2021 at 1:57 PM CEST, Jeff Zhang wrote:
> IIRC, you want an IDE for pyspark on yarn ?
>
> Mich Talebzadeh 于2021年9月30日周四
> 下午7:00写道:
>
> > Hi,
> >
> > This may look lik
as a workaround turn off pruning :
spark.sql.hive.metastorePartitionPruning false
spark.sql.hive.convertMetastoreParquet false
see
https://github.com/awslabs/aws-glue-data-catalog-client-for-apache-hive-metastore/issues/45
On Tue Aug 24, 2021 at 9:18 AM CEST, Gourav Sengupta wrote:
> Hi,
>
> I
ClassLoader.java:357)
>
>
> Thanks
>
>
>
> *Disclaimer:* Use it at your own risk. Any and all responsibility for any
> loss, damage or destruction of data or any other property which may arise
> from relying on this email's technical content is explicitly disclaimed.
&
ar file and replace it with the same
>>>> version but package* it works!
>>>>
>>>>
>>>> spark-submit --driver-class-path /home/hduser/jars/ddhybrid.jar --jars
>>>> /home/hduser/jars/spark-bigquery-latest.jar,/home/hduser/jars/ddhybrid.jar
>>>> *-**-packages com.github.samelamin:spark-bigquery_2.11:0.2.6*
>>>>
>>>>
>>>> I have read the write-ups about packages searching the maven
>>>> libraries etc. Not convinced why using the package should make so much
>>>> difference between a failure and success. In other words, when to use a
>>>> package rather than a jar.
>>>>
>>>>
>>>> Any ideas will be appreciated.
>>>>
>>>>
>>>> Thanks
>>>>
>>>>
>>>>
>>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>>>> any loss, damage or destruction of data or any other property which may
>>>> arise from relying on this email's technical content is explicitly
>>>> disclaimed. The author will in no case be liable for any monetary damages
>>>> arising from such loss, damage or destruction.
>>>>
>>>>
>>>>
>>> --
> Best Regards,
> Ayan Guha
--
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millions of partitions which results in millions of tasks.
> Perhaps the high memory usage is a side effect of caching the results of lots
> of tasks.
>
> On 10/19/20, 1:27 PM, "Nicolas Paris" wrote:
>
> CAUTION: This email originated from outside of the organiz
illion
> distinct values into the driver? Is countDistinct not recommended for data
> frames with large number of distinct values?
>
> What’s the solution? Should I use approx._count_distinct?
--
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to a dense
> format
> 4) GroupBy/aggregating ids by timestamp, converting to a sparse,
> frequency-based vector using CountVectorizer, and then expanding to a dense
> format
>
> Any other approaches we could try?
>
> Thanks!
> Sakshi
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Holden Karau writes:
> I work in emacs with ensime.
the ensime project was stoped and the project archived. its successor
"metals" works well for scala >=2.12
any good ressource to setup ensime with emacs ? can't wait overall spark
community goes on scala 2
>>> There is no dataframe spark API which writes/creates a single file
>>> instead of directory as a result of write operation.
>>>
>>> Below both options will create directory with a random file name.
>>>
>>&g
It's a memory-over-CPU trade-off.
>
> Enrico
>
>
> Am 17.02.20 um 22:06 schrieb Nicolas PARIS:
>>> .dropDuplicates() \ .cache() |
>>> Since df_actions is cached, you can count inserts and updates quickly
>>> with only that one join in df_actions:
ccessorImpl.java:0
>> => WholeStageCodegen/MapPartitionsRDD [75]count at
>> NativeMethodAccessorImpl.java:0 =>
>> InMemoryTableScan/MapPartitionsRDD [78]count at
>> NativeMethodAccessorImpl.java:0 => MapPartitionsRDD [79]count at
>> NativeMethodAccessorImpl.jav
Hi
Anyone has experience in ceph / lustre as a replacement of hdfs for
spark storage (parquet, orc..)?
Is hdfs still far superior to the former ?
Thanks
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hi
we have many users on the spark on yarn cluster. most of them forget to
release their sparkcontext after analysis (spark-thrift or pyspark
jupyter kernels).
I wonder how to detect their is no activity on the sparkcontext to kill
them.
Thanks
--
nicolas
--
Hi
I have written spark udf and I am able to use them in spark scala /
pyspark by using the org.apache.spark.sql.api.java.UDFx API.
I d'like to use them in spark-sql thought thrift. I tried to create the
functions "create function as 'org.my.MyUdf'". however I get the below
error when using it:
apparently the "withColumn" issue only apply for hundred or thousand of
calls. This was not the case here (twenty calls)
On Fri, Dec 20, 2019 at 08:53:16AM +0100, Enrico Minack wrote:
> The issue is explained in depth here: https://medium.com/@manuzhang/
> the-hidden-cost-of-spark-withcolumn-8ffea
Hi Alfredo
my 2 cents:
To my knowlegde and reading the spark3 pre-release note, it will handle
hive metastore 2.3.5 - no mention of hive 3 metastore. I made several
tests on this in the past[1] and it seems to handle any hive metastore
version.
However spark cannot read hive managed table AKA tra
Hello spark users,
Spark-postgres is designed for reliable and performant ETL in big-data
workload and offer read/write/scd capability . The version 3 introduces
a datasource API and simplifies the usage. It outperforms sqoop by
factor 8 and the apache spark core jdbc by infinity.
Features:
- us
have you deactivated the spark.ui ?
I have read several thread explaining the ui can lead to OOM because it
stores 1000 dags by default
On Sun, Oct 20, 2019 at 03:18:20AM -0700, Paul Wais wrote:
> Dear List,
>
> I've observed some sort of memory leak when using pyspark to run ~100
> jobs in loca
> extends the proposed 3.0 changes in a compatible way, are active.
>
> Yrs,
>
> Alastair
>
>
> Alastair Green
>
> Query Languages Standards and Research
>
>
> Neo4j UK Ltd
>
> Union House
> 182-194 Union Street
> London, SE1 0LH
>
>
hi all
graphframes was intended to replace graphx.
however the former looks not maintained anymore while the latter is
still active.
any thought ?
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> I have to call Oracle sequence using spark.
You might use jdbc and write your own lib from scala
I did such thing for postgres
(https://framagit.org/parisni/spark-etl/tree/master/spark-postgres)
see sqlExecWithResultSet
On Thu, Aug 15, 2019 at 10:58:11PM +0530, rajat kumar wrote:
> Hi All,
> • Scala/Java APIs for DML commands - You can now modify data in Delta Lake
> tables using programmatic APIs for Delete, Update and Merge. These APIs
> mirror the syntax and semantics of their corresponding SQL commands and
> are
> great for many workloads, e.g., Slowly Changing Dim
Congrats
The read/write feature with hive3 is highly interesting
On Fri, Jul 26, 2019 at 06:07:55PM +0530, Abhishek Somani wrote:
> Hi All,
>
> We at Qubole have open sourced a datasource that will enable users to work on
> their Hive ACID Transactional Tables using Spark.
>
> Github: https://
gt; to delete this message and destroy any printed copies.
>
>
> -Original Message-
> From: Nicolas Paris
> Sent: Tuesday, July 23, 2019 6:56 PM
> To: user@spark.apache.org
> Subject: Avro large binary read memory problem
>
> Hi
>
> I have those avro file with t
Hi
I have those avro file with the schema id:Long, content:Binary
the binary are large image with a maximum of 2GB of size.
I d like to get a subset of row "where id in (...)"
Sadly I get memory errors even if the subset is 0 of size. It looks like
the reader stores the binary information until
hi
spark 2.4.1
The csv writer does not quote string columns when they contain the \r
carriage return character. It works as expected for both \n and \r\n
\r is considered as a newline by many parsers, and spark should consider
it as a newline marker.
thanks
--
nicolas
--
Hi
spark 2.4.1
hive 1.2.2
The orc files saved as tables from spark are not working correctly with
hive. A "timestampCol is not null" does not work as expected.
The parquet format works as expected for the same input.
Is this a known issue ?
thanks
--
nicolas
-
; most likely have to set something in spark-defaults.conf like
>
> spark.master yarn
> spark.submit.deployMode client
>
> On Mon, May 20, 2019 at 3:14 PM Nicolas Paris
> wrote:
>
> Finally that was easy to connect to both hive/hdfs. I just had to copy
> th
ed" installs, so you
> could also look into that if you are not comfortable with running your own
> spark build.
>
> On Mon, May 20, 2019 at 2:24 PM Nicolas Paris
> wrote:
>
> > correct. note that you only need to install spark on the node you launch
>
the shared components between spark jobs on yarn are only really
> spark-shuffle-service in yarn and spark-history-server. i have found
> compatibility for these to be good. its best if these run latest version.
>
> On Mon, May 20, 2019 at 2:02 PM Nicolas Paris
> wrote:
>
ppily run multiple spark versions side-by-side
> you will need the spark version you intend to launch with on the machine you
> launch from and point to the correct spark-submit
>
> On Mon, May 20, 2019 at 1:50 PM Nicolas Paris
> wrote:
>
> Hi
>
> I am
Hi
I am wondering whether that's feasible to:
- build a spark application (with sbt/maven) based on spark2.4
- deploy that jar on yarn on a spark2.3 based installation
thanks by advance,
--
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That's all right, i manage to reduce the log level by removing the
logback dependency in the pom.xml
On Sat, May 11, 2019 at 02:54:49PM +0200, Nicolas Paris wrote:
> Hi
>
> I have a spark code source with tests that create sparkSessions.
>
> I am running spark testin
Hi
I have a spark code source with tests that create sparkSessions.
I am running spark testing framework.
My concern is I am not able to configure the log level to INFO.
I have large debug traces such:
> DEBUG org.spark_project.jetty.util.Jetty -
> java.lang.NumberFormatException: For input s
org/jira/browse/SPARK-23555. Also, pyarrow 0.10.0 or greater
> is require as you saw in the docs.
>
> Bryan
>
> On Thu, May 2, 2019 at 4:26 AM Nicolas Paris
> wrote:
>
> Hi all
>
> I am using pySpark 2.3.0 and pyArrow 0.10.0
>
&
Hi all
I am using pySpark 2.3.0 and pyArrow 0.10.0
I want to apply a pandas-udf on a dataframe with
I have the bellow error:
> Invalid returnType with grouped map Pandas UDFs:
> StructType(List(StructField(filename,StringType,true),StructField(contents,BinaryType,true)))
> is not supported
I
Hi Huon
Good catch. A 64 bit hash is definitely a useful function.
> the birthday paradox implies >50% chance of at least one for tables larger
> than 77000 rows
Do you know how many rows to have 50% chances for a 64 bit hash ?
About the seed column, to me there is no need for such an argume
Hi all
Do anybody knows if spark spark able to connect to hive metastore for
hive 3 (metastore v3)?
I know spark cannot deal with transactional tables, however I wonder if
at least it can read/write non-transactional tables from hive 3.
Thanks
--
nicolas
, Nicolas Paris wrote:
> Hi
>
> The spark postgres JDBC reader is limited because it relies on basic
> SELECT statements with fetchsize and crashes on large tables even if
> multiple partitions are setup with lower/upper bounds.
>
> I am about writing a new postgres JDBC reader bas
Hi
The spark postgres JDBC reader is limited because it relies on basic
SELECT statements with fetchsize and crashes on large tables even if
multiple partitions are setup with lower/upper bounds.
I am about writing a new postgres JDBC reader based on "COPY TO STDOUT".
It would stream the data and
into something like Kafka and then
> use Spark streaming against that.
> Taking that Kafka approach further, you can capture the delta upstream so
> that the processing that pushes it into the RDBMS can also push it to Kafka
> directly.
>
> On 12/27/18, 4:52 PM, "Nicolas
Hi
I have this living RDBMS and I d'like to apply a spark job on several
tables once new data get in.
I could run batch spark jobs thought cron jobs every minutes. But the
job takes time and resources to begin (sparkcontext, yarn)
I wonder if I could run one instance of a spark streaming job
source management (?)
On Mon, Nov 19, 2018 at 07:23:10AM +0100, Jörn Franke wrote:
> Why does it have to be a stream?
>
> > Am 18.11.2018 um 23:29 schrieb Nicolas Paris :
> >
> > Hi
> >
> > I have pdf to load into spark with at least
> > format. I have con
Hi
I have pdf to load into spark with at least
format. I have considered some options:
- spark streaming does not provide a native file stream for binary with
variable size (binaryRecordStream specifies a constant size) and I
would have to write my own receiver.
- Structured streaming allow
On Sat, Nov 03, 2018 at 02:04:01AM -0700, conner wrote:
> My solution is to find a good way to divide the spark cluster resource
> into two.
What about yarn and its queue management system ?
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Hi Joel
I built such pipeline to transform pdf-> text
https://github.com/EDS-APHP/SparkPdfExtractor
You can take a look
It transforms 20M pdfs in 2 hours on a 5 node spark cluster
Le 2018-10-10 23:56, Joel D a écrit :
> Hi,
>
> I need to process millions of PDFs in hdfs using spark. First I’m
Hi
yes, multiline would only use one thread in that case.
The csv parser used by spark is uniVocity
Le 18 août 2018 à 18:07, Nirav Patel écrivait :
> does enabling 'multiLine' option impact performance? how? would it run read
> entire file with just one thread?
>
> Thanks
>
>
>
> What's New
Hi Raymond
Spark works well on single machine too, since it benefits from multiple
core.
The csv parser is based on univocity and you might use the
"spark.read.csc" syntax instead of using the rdd api;
>From my experience, this will better than any other csv parser
2018-06-19 16:43 GMT+02:00 Ra
IMO your json cannot be read in parallell at all then spark only offers you
to play again with memory.
I d'say at one step it has to feet in both one executor and in the driver.
I d'try something like 20GB for both driver and executors and by using
dynamic amount of executor in order to then repa
have you played with driver/executor memory configuration ?
Increasing them should avoid OOM
2018-06-05 22:30 GMT+02:00 raksja :
> Agreed, gzip or non splittable, the question that i have and examples i
> have
> posted above all are referring to non compressed file. A single json file
> with Arr
hi corey
not familiar with arrow, plasma. However recently read an article about
spark on
a standalone machine (your case). Sounds like you could take benefit of
pyspark
"as-is"
https://databricks.com/blog/2018/05/03/benchmarking-apache-spark-on-a-single-node-machine.html
regars,
2018-05-23 22:
Hi
I would go for a regular mysql bulkload. I m saying writing an output
that mysql is able to load in one process. I d'say spark jdbc is ok for
small fetch/load. When comes large RDBMS call, it turns out using the
regular optimized API is better than jdbc
2018-05-16 16:18 GMT+02:00 Vadim Semenov
guys
here the illustration
https://github.com/parisni/SparkPdfExtractor
Please add issues if any questions or improvement ideas
Enjoy
Cheers
2018-04-23 20:42 GMT+02:00 unk1102 :
> Thanks much Nicolas really appreciate it.
>
>
>
> --
> Sent from: http://apache-spark-user-list.1001560.n3.nabble
sure then let me recap steps:
1. load pdfs in a local folder to hdfs avro
2. load avro in spark as a RDD
3. apply pdfbox to each csv and return content as string
4. write the result as a huge csv file
That's some work guys for me to push all that. Should find some time
however within 7 days
@unk1
2018-04-23 18:59 GMT+02:00 unk1102 :
> Hi Nicolas thanks much for the reply. Do you have any sample code
> somewhere?
>
I have some open-source code. I could find time to push on github if
needed.
> Do your just keep pdf in avro binary all the time?
yes, I store them. Actually, I did that
Hi
Problem is number of files on hadoop;
I deal with 50M pdf files. What I did is to put them in an avro table on
hdfs,
as a binary column.
Then I read it with spark and push that into pdfbox.
Transforming 50M pdfs into text took 2hours on a 5 computers clusters
About colors and formating, I
Hi
Sounds your configuration files are not well filed.
What does :
spark.sql("SHOW DATABASES").show();
outputs ?
If you only have default database, such investigation there should help
https://stackoverflow.com/questions/47257680/unable-to-get-existing-hive-tables-from-hivecontext-using-spark
> Most likely not as most of the effort is currently on GraphFrames - a great
> blog post on the what GraphFrames offers can be found at: https://
Is the graphframes package still active ? The github repository
indicates it's not extremelly active. Right now, there is no available
package for spa
t;
> Bryan
>
> On Wed, Jan 31, 2018 at 10:20 PM, Nicolas Paris wrote:
>
> Hey
>
> I am also interested in how to get those parameters.
> For example, the demo code spark-2.2.1-bin-hadoop2.7/examples/src/main/
> python/ml/estimator_transform
Hey
I am also interested in how to get those parameters.
For example, the demo code
spark-2.2.1-bin-hadoop2.7/examples/src/main/python/ml/estimator_transformer_param_example.py
return empty parameters when printing "lr.extractParamMap()"
That's weird
Thanks
Le 30 janv. 2018 à 23:10, Bryan Cu
Hi
Thanks for this work.
Will this affect both:
1) spark.read.format("orc").load("...")
2) spark.sql("select ... from my_orc_table_in_hive")
?
Le 10 janv. 2018 à 20:14, Dongjoon Hyun écrivait :
> Hi, All.
>
> Vectorized ORC Reader is now supported in Apache Spark 2.3.
>
> https://issues.
layer (hive/kerberos)
- direct partitionned lazy datasets versus complicated jdbc dataset management
- more robust for analytics with less memory (apparently)
However presto still makes sence for sub second analytics, and oltp like
queries and data discovery.
Le 05 nov. 2017 à 13:57, Nicolas Paris
Le 06 nov. 2017 à 19:56, Nicolas Paris écrivait :
> Can anyone clarify the driver memory aspects of pySpark?
> According to [1], spark.driver.memory limits JVM + python memory.
>
> In case:
> spark.driver.memory=2G
> Then does it mean the user won't be able to use more
hi there
Can anyone clarify the driver memory aspects of pySpark?
According to [1], spark.driver.memory limits JVM + python memory.
In case:
spark.driver.memory=2G
Then does it mean the user won't be able to use more than 2G, whatever
the python code + the RDD stuff he is using ?
Thanks,
[1]:
Le 05 nov. 2017 à 22:46, ayan guha écrivait :
> Thank you for the clarification. That was my understanding too. However how to
> provide the upper bound as it changes for every call in real life. For example
> it is not required for sqoop.
True. AFAIK sqoop begins with doing a
"select min(colu
Le 05 nov. 2017 à 22:02, ayan guha écrivait :
> Can you confirm if JDBC DF Reader actually loads all data from source to
> driver
> memory and then distributes to the executors?
apparently yes when not using partition column
> And this is true even when a
> partition column is provided?
No, in
egards,
> Gourav Sengupta
>
>
> On Sun, Nov 5, 2017 at 12:57 PM, Nicolas Paris wrote:
>
> Hi
>
> After some testing, I have been quite disapointed with hiveContext way of
> accessing hive tables.
>
> The main problem is resource allocation: I have
s,
> Gourav
>
> On Sun, Oct 15, 2017 at 3:43 PM, Nicolas Paris wrote:
>
> > I do not think that SPARK will automatically determine the partitions.
> Actually
> > it does not automatically determine the partitions. In case a table has
> a
> fe
> I do not think that SPARK will automatically determine the partitions.
> Actually
> it does not automatically determine the partitions. In case a table has a few
> million records, it all goes through the driver.
Hi Gourav
Actualy spark jdbc driver is able to deal direclty with partitions.
Spa
tire data?
This works for static datasets, or when new data is comming by batch
processes, the spark application should be reloaded to get the new files
in the folder
>> On Sun, Oct 15, 2017 at 12:55 PM, Nicolas Paris wrote:
>
> Le 03 oct. 2017 à 20:08, Nicolas Paris écriv
Le 03 oct. 2017 à 20:08, Nicolas Paris écrivait :
> I wonder the differences accessing HIVE tables in two different ways:
> - with jdbc access
> - with sparkContext
Well there is also a third way to access the hive data from spark:
- with direct file access (here ORC format)
For exam
> In case a table has a few
> million records, it all goes through the driver.
This sounds clear in JDBC mode, the driver get all the rows and then it
spreads the RDD over the executors.
I d'say that most use cases deal with SQL to aggregate huge datasets,
and retrieve small amount of rows to be
Hi
I wonder the differences accessing HIVE tables in two different ways:
- with jdbc access
- with sparkContext
I would say that jdbc is better since it uses HIVE that is based on
map-reduce / TEZ and then works on disk.
Using spark rdd can lead to memory errors on very huge datasets.
Anybody
Hi
I put million files into a har archive on hdfs. I d'like to iterate over
their file paths, and read them. (Basically they are pdf, and I want to
transform them into text with apache pdfbox)
My first attempts has been to list them with hadoop command
`hdfs dfs -ls har:///user//har/pdf.har` and
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