Re: [Spark SQL]: Source code for PartitionedFile

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

Re: External Spark shuffle service for k8s

" 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

Re: External Spark shuffle service for k8s

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

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

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([

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

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

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

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

Re: [Spark SQL]: Source code for PartitionedFile

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

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

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

[Spark SQL]: Source code for PartitionedFile

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

Re: External Spark shuffle service for k8s

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

Re: External Spark shuffle service for k8s

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

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

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

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

Re: External Spark shuffle service for k8s

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,

Spark UDAF in examples fail with not serializable error

The type-safe example given at https://spark.apache.org/docs/latest/sql-ref-functions-udf-aggregate.html fails with a not serializable exception Is this a known issue?

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

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

Re: External Spark shuffle service for k8s

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

Re: External Spark shuffle service for k8s

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

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

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

Re: External Spark shuffle service for k8s

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]

Re: External Spark shuffle service for k8s

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 = {

Example UDAF fails with "not serializable" exception

https://spark.apache.org/docs/3.3.2/sql-ref-functions-udf-aggregate.html I'm trying to run this example on Databricks, and it fails with the stacktrace below. It's literally a copy-paste from the example, what am I missing? Job aborted due to stage failure: Task not serializable:

Re: External Spark shuffle service for k8s

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

Re: External Spark shuffle service for k8s

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") \

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

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

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

[[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

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External Spark shuffle service for k8s

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.

Clarification on what "[id=#]" refers to in Physical Plan Exchange hashpartitioning

Hello, While looking through spark physical plans generated by the spark history server log to find any bottle necks in my code, I stumbled across an ID that shows up in a partitioning stage. My goal is to use the history server log to provide meaningful analysis on my spark system

Clarification on what "[id=#]" refers to in Physical Plan Exchange hashpartitioning

Hello, While looking through spark physical plans generated by the spark history server log to find any bottle necks in my code, I stumbled across an ID that shows up in a partitioning stage. My goal is to use the history server log to provide meaningful analysis on my spark system

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

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

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

Participate in the ASF 25th Anniversary Campaign

Hi everyone, As part of The ASF’s 25th anniversary campaign[1], we will be celebrating projects and communities in multiple ways. We invite all projects and contributors to participate in the following ways: * Individuals - submit your first contribution:

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

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.

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

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

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

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

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

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())

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

Hello! I am attempting to write a streaming pipeline that would consume data from a Kafka source, manipulate the data, and then write results to a downstream sink (Kafka, Redis, etc). I want to write fully formed SQL instead of using the function API that Spark offers. I read a few guides on

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 From: Bjørn Jørgensen Sent: Wednesday, February 28, 2024 5:06:54 PM To: Chawla, Parul Cc: Sahni, Ashima ;

Apache Spark integration with Spring Boot 3.0.0+

Hello, Ive got a project which has to use newest versions of both Apache Spark and Spring Boot due to vulnerabilities issues. I build my project using Gradle. And when I try to run it i get : Unsatisfied dependecy exception about javax/servlet/Servlet. Ive tried to add jakarta servlet,

Community Over Code NA 2024 Travel Assistance Applications now open!

Hello to all users, contributors and Committers! [ You are receiving this email as a subscriber to one or more ASF project dev or user mailing lists and is not being sent to you directly. It is important that we reach all of our users and contributors/committers so that they may get a chance

Re: [DISCUSS] MySQL version support policy

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.

[DISCUSS] MySQL version support policy

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 one Spark partition mapped to one and only Spark Task ?

I am trying to understand the Spark Architecture for my upcoming certification, however there seems to be conflicting information available. https://stackoverflow.com/questions/47782099/what-is-the-relationship-between-tasks-and-partitions Does Spark assign a Spark partition to only a single

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

+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

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

+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 |

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

Sorry from this link Leveraging Generative AI with Apache Spark: Transforming Data Engineering | LinkedIn Mich Talebzadeh, Technologist | Data | Generative AI |

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

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

Re:

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

Bug in org.apache.spark.util.sketch.BloomFilter

Hi All, I believe that there is a bug that affects the Spark BloomFilter implementation when creating a bloom filter with large n. Since this implementation uses integer hash functions, it doesn’t work properly when the number of bits exceeds MAX_INT. I asked a question about this on

[no subject]

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",

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

+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

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

+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

Announcing the Community Over Code 2024 Streaming Track

Hi all, Community Over Code , the ASF conference, will be held in Denver, Colorado, October 7-10, 2024. The call for presentations is open

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

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

Spark-UI stages and other tabs not accessible in standalone mode when reverse-proxy is enabled

Hi Team, We're encountering an issue with Spark UI. I've documented the details here: https://issues.apache.org/jira/browse/SPARK-47232 When enabled reverse proxy in master and worker configOptions. We're not able to access different tabs available in spark UI e.g.(stages, environment, storage

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

+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

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

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.

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

+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

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

+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 >>

[ANNOUNCE] Apache Kyuubi released 1.9.0

Hi all, The Apache Kyuubi community is pleased to announce that Apache Kyuubi 1.9.0 has been released! Apache Kyuubi is a distributed and multi-tenant gateway to provide serverless SQL on data warehouses and lakehouses. Kyuubi provides a pure SQL gateway through Thrift JDBC/ODBC interface for

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

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 |

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

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

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

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

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

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

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

something like this Spark community · GitHub 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 ,

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

+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

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

On Mon, Mar 18, 2024 at 1:16 PM Mich Talebzadeh wrote: > > "I may need something like that for synthetic data for testing. Any way to > do that ?" > > Have a look at this. > > https://github.com/joke2k/faker > No I was not actually referring to data that can be faked. I want data to actually

pyspark - Use Spark to generate a large dataset on the fly

Hi, I have a specific problem where I have to get the data from REST APIs and store it, and then do some transformations on it and then write to a RDBMS table. I am wondering if Spark will help in this regard. I am confused as to how do I store the data while I actually acquire it on the driver

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

+1 for me 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

pyspark - Use Spark to generate a large dataset on the fly

Hi, I have a specific problem where I have to get the data from REST APIs and store it, and then do some transformations on it and then write to a RDBMS table. I am wondering if Spark will help in this regard. I am confused as to how do I store the data while I actually acquire it on the driver

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

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 , Mich Talebzadeh Cc: Matei Zaharia Subject: Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community External message, be mindful

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

Good idea. Will be useful +1 On Monday, 18 March 2024 at 11:00:40 GMT, 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

Re: [GraphX]: Prevent recomputation of DAG

Hi, I must admit I don't know much about this Fruchterman-Reingold (call it FR) visualization using GraphX and Kubernetes..But you are suggesting this slowdown issue starts after the second iteration, and caching/persisting the graph after each iteration does not help. FR involves many

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

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

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

Yes, transformations are indeed executed on the worker nodes, but they are only performed when necessary, usually when an action is called. This lazy evaluation helps in optimizing the execution of Spark jobs by allowing Spark to optimize the execution plan and perform optimizations such as

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

On Fri, Mar 15, 2024 at 3:10 AM Mich Talebzadeh wrote: > > 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

[GraphX]: Prevent recomputation of DAG

Dear community, for my diploma thesis, we are implementing a distributed version of Fruchterman-Reingold visualization algorithm, using GraphX and Kubernetes. Our solution is a backend that continously computes new positions of vertices in a graph and sends them via RabbitMQ to a consumer.

Re: [External] Re: [GraphFrames Spark Package]: Why is there not a distribution for Spark 3.3?

Just to add - the latest version is 0.8.3, it seems to support 3.3: "Support Spark 3.3 / Scala 2.12 , Spark 3.4 / Scala 2.12 and Scala 2.13, Spark 3.5 / Scala 2.12 and Scala 2.13" Releases · graphframes/graphframes (github.com) Ofir

Python library that generates fake data using Faker

I came across this a few weeks ago. II a nutshell you can use it for generating test data and other scenarios where you need realistic-looking but not necessarily real data. With so many regulations and copyrights etc it is a viable alternative. I used it to generate 1000 lines of mixed true and

Re: [GraphFrames Spark Package]: Why is there not a distribution for Spark 3.3?

There is an implementation for Spark 3, but GraphFrames isn't released often enough to match every point version. It supports Spark 3.4. Try it - it will probably work. https://spark-packages.org/package/graphframes/graphframes Thanks, Russell Jurney @rjurney

Requesting further assistance with Spark Scala code coverage

I have sent out an email regarding Spark coverage, but haven't received any response. I'm hoping someone could provide an answer on whether there is currently any code coverage statistics available for Scala code in Spark. https://lists.apache.org/thread/hob7x42gk3q244t9b0d8phwjtxjk2plt

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

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

pyspark - Where are Dataframes created from Python objects stored?

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':

Re: Bug in How to Monitor Streaming Queries in PySpark

Thanks for the clarification. That makes sense.. In the code below, we can see def onQueryProgress(self, event): print("onQueryProgress") # Access micro-batch data microbatch_data = event.progress #print("microbatch_data received") # Check if data is received

Re: Bug in How to Monitor Streaming Queries in PySpark

Oh I see why the confusion. microbatch_data = event.progress means that microbatch_data is a StreamingQueryProgress instance, it's not a dictionary, so you should use ` microbatch_data.processedRowsPerSecond`, instead of the `get` method which is used for dictionaries. But weirdly, for

Data ingestion into elastic failing using pyspark

Hi @all, I am using pyspark program to write the data into elastic index by using upsert operation (sample code snippet below). def writeDataToES(final_df): write_options = { "es.nodes": elastic_host, "es.net.ssl": "false", "es.nodes.wan.only": "true",

Re: Bugs with joins and SQL in Structured Streaming

Hi, Do you think there is any chance for this issue to get resolved? Should I create another bug report? As mentioned in my message, there is one open already: https://issues.apache.org/jira/browse/SPARK-45637 but it covers only one of the problems. Andrzej wt., 27 lut 2024 o 09:58 Andrzej Zera

Re: Bug in How to Monitor Streaming Queries in PySpark

Hi, Thank you for your advice This is the amended code def onQueryProgress(self, event): print("onQueryProgress") # Access micro-batch data microbatch_data = event.progress #print("microbatch_data received") # Check if data is received

Re: Bug in How to Monitor Streaming Queries in PySpark

*now -> not 刘唯 于2024年3月10日周日 22:04写道： > Have you tried using microbatch_data.get("processedRowsPerSecond")? > Camel case now snake case > > Mich Talebzadeh 于2024年3月10日周日 11:46写道： > >> >> There is a paper from Databricks on this subject >> >> >>

Re: Bug in How to Monitor Streaming Queries in PySpark

Have you tried using microbatch_data.get("processedRowsPerSecond")? Camel case now snake case Mich Talebzadeh 于2024年3月10日周日 11:46写道： > > There is a paper from Databricks on this subject > > > https://www.databricks.com/blog/2022/05/27/how-to-monitor-streaming-queries-in-pyspark.html > > But

Bug in How to Monitor Streaming Queries in PySpark

There is a paper from Databricks on this subject https://www.databricks.com/blog/2022/05/27/how-to-monitor-streaming-queries-in-pyspark.html But having tested it, there seems to be a bug there that I reported to Databricks forum as well (in answer to a user question) I have come to a conclusion

Spark on Kubenets, execute dataset.show raise exceptions

Hi, I encountered a strange issue. I run spark-shell with client mode in kubernets. as below command: val data=spark.read.parquet("datapath") When I run: "data.show", it may raise exceptions, the stacktrace like below: DEBUG BlockManagerMasterEndpoint: Updating block info on master taskresult_3

Spark-UI stages and other tabs not accessible in standalone mode when reverse-proxy is enabled

Hi Team, We're encountering an issue with Spark UI. When enabled reverse proxy in master and worker configOptions. We're not able to access different tabs available in spark UI e.g.(stages, environment, storage etc.) We're deploying spark through bitnami helm chart :

Re: Creating remote tables using PySpark

The error message shows a mismatch between the configured warehouse directory and the actual location accessible by the Spark application running in the container.. You have configured the SparkSession with spark.sql.warehouse.dir="file:/data/hive/warehouse". This tells Spark where to store

Re: Creating remote tables using PySpark

Okay that was some caching issue. Now there is a shared mount point between the place the pyspark code is executed and the spark nodes it runs. Hrmph, I was hoping that wouldn't be the case. Fair enough! On Thu, Mar 7, 2024 at 11:23 PM Tom Barber wrote: > Okay interesting, maybe my assumption

Re: Creating remote tables using PySpark

Okay interesting, maybe my assumption was incorrect, although I'm still confused. I tried to mount a central mount point that would be the same on my local machine and the container. Same error although I moved the path to /tmp/hive/data/hive/ but when I rerun the test code to save a table,

Creating remote tables using PySpark

Wonder if anyone can just sort my brain out here as to whats possible or not. I have a container running Spark, with Hive and a ThriftServer. I want to run code against it remotely. If I take something simple like this from pyspark.sql import SparkSession from pyspark.sql.types import

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