ThreadUtils | A collection of classes that help with multithreading | Reflection library

by jbienzms C# Version: Current License: MIT

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kandi X-RAY | ThreadUtils Summary

ThreadUtils is a C# library typically used in Programming Style, Reflection applications. ThreadUtils has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

A collection of classes that help with multithreading.

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ThreadUtils has a low active ecosystem.

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The latest version of ThreadUtils is current.

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ThreadUtils has no bugs reported.

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ThreadUtils has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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ThreadUtils is licensed under the MIT License. This license is Permissive.

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Community Discussions

Trending Discussions on ThreadUtils

Spark non-descriptive error in DELTA MERGE

Spark executors fails to run on kubernetes cluster

Failed to send RPC XXXX in spark-shell Hadoop 3.2.1 and spark 3.0.0

Pyspark crashing for large datasets

Error including a column in a join between spark dataframes

Hadoop + Spark: There are 1 datanode(s) running and 1 node(s) are excluded in this operation

Spark Master Re-Submits endlessly after IOException on the Worker Node Application/Job Submitted

"A failure occurred while executing com.android.build.gradle.internal.tasks.Workers$ActionFacade" after adding firebase firestore dependency

Error with mvn test: java.lang.IllegalStateException: Cannot call methods on a stopped SparkContext

Too large frame error when running spark shell on standalone cluster

QUESTION

Spark non-descriptive error in DELTA MERGE

Asked 2021-Apr-12 at 07:56

I'm using Spark 3.1 in Databricks (Databricks Runtime 8) with a very large cluster (25 workers with 112 Gb of memory and 16 cores each) to replicate several SAP tables in an Azure Data Lake Storage (ADLS gen2). For doing this, a tool is writting the deltas of all these tables into an intermediate system (SQL Server) and then, if I have new data for a certain table, I execute a Databricks job to merge the new data with the existing data available in ADLS.

This process is working fine for most of the tables, but some of them (the biggest ones) take a lot of time to be merged (I merge the data using the PK of each table) and the biggest one has started failing since a week ago (When a big delta of the table was generated). Trace of the error that I can see in the job:

Py4JJavaError: An error occurred while calling o233.sql. : org.apache.spark.SparkException: Job aborted. at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:234) at com.databricks.sql.transaction.tahoe.files.TransactionalWriteEdge.$anonfun$writeFiles$5(TransactionalWriteEdge.scala:246) ... .. ............................................................................................................................................................................................................................................................................................................................................................................ Caused by: org.apache.spark.SparkException: Exception thrown in awaitResult: at org.apache.spark.util.ThreadUtils$.awaitResult(ThreadUtils.scala:428) at com.databricks.sql.transaction.tahoe.perf.DeltaOptimizedWriterExec.awaitShuffleMapStage$1(DeltaOptimizedWriterExec.scala:153) at com.databricks.sql.transaction.tahoe.perf.DeltaOptimizedWriterExec.getShuffleStats(DeltaOptimizedWriterExec.scala:158) at com.databricks.sql.transaction.tahoe.perf.DeltaOptimizedWriterExec.computeBins(DeltaOptimizedWriterExec.scala:106) at com.databricks.sql.transaction.tahoe.perf.DeltaOptimizedWriterExec.doExecute(DeltaOptimizedWriterExec.scala:174) at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:196) at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:240) at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:165) at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:236) at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:192) at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:180) ... 141 more Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: ShuffleMapStage 68 (execute at DeltaOptimizedWriterExec.scala:97) has failed the maximum allowable number of times: 4. Most recent failure reason: org.apache.spark.shuffle.FetchFailedException: Connection from /XXX.XX.XX.XX:4048 closed at org.apache.spark.storage.ShuffleBlockFetcherIterator.throwFetchFailedException(ShuffleBlockFetcherIterator.scala:769) at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:684) at org.apache.spark.storage.ShuffleBlockFetcherIterator.next(ShuffleBlockFetcherIterator.scala:69) at .................................................................................................................................................................................................................................................................................................................................... ... java.lang.Thread.run(Thread.java:748) Caused by: java.io.IOException: Connection from /XXX.XX.XX.XX:4048 closed at org.apache.spark.network.client.TransportResponseHandler.channelInactive(TransportResponseHandler.java:146) at org.apache.spark.network.server.TransportChannelHandler.channelInactive(TransportChannelHandler.java:117) at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:262) at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:248) at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:241) at io.netty.channel.ChannelInboundHandlerAdapter.channelInactive(ChannelInboundHandlerAdapter.java:81) at io.netty.handler.timeout.IdleStateHandler.channelInactive(IdleStateHandler.java:277) at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:262) at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:248) at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:241) at io.netty.channel.ChannelInboundHandlerAdapter.channelInactive(ChannelInboundHandlerAdapter.java:81) at org.apache.spark.network.util.TransportFrameDecoder.channelInactive(TransportFrameDecoder.java:225) at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:262) at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:248) at io.netty.channel.AbstractChannelHandlerContext.fireChannelInactive(AbstractChannelHandlerContext.java:241) at io.netty.channel.DefaultChannelPipeline$HeadContext.channelInactive(DefaultChannelPipeline.java:1405) at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:262) at io.netty.channel.AbstractChannelHandlerContext.invokeChannelInactive(AbstractChannelHandlerContext.java:248) at io.netty.channel.DefaultChannelPipeline.fireChannelInactive(DefaultChannelPipeline.java:901) at io.netty.channel.AbstractChannel$AbstractUnsafe$8.run(AbstractChannel.java:818) at io.netty.util.concurrent.AbstractEventExecutor.safeExecute(AbstractEventExecutor.java:164) at io.netty.util.concurrent.SingleThreadEventExecutor.runAllTasks(SingleThreadEventExecutor.java:472) at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:497) at io.netty.util.concurrent.SingleThreadEventExecutor$4.run(SingleThreadEventExecutor.java:989) at io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74) at io.netty.util.concurrent.FastThreadLocalRunnable.run(FastThreadLocalRunnable.java:30) ... 1 more

As the error is non descriptive, I have taken a look to each executor log and I have seen following message:

21/04/07 09:11:24 ERROR OneForOneBlockFetcher: Failed while starting block fetches java.io.IOException: Connection from /XXX.XX.XX.XX:4048 closed

And in the executor that seems to be unable to connect, I see the following error message:

21/04/06 09:30:46 ERROR SparkThreadLocalCapturingRunnable: Exception in thread Task reaper-7 org.apache.spark.SparkException: Killing executor JVM because killed task 5912 could not be stopped within 60000 ms. at org.apache.spark.executor.Executor$TaskReaper.run(Executor.scala:1119) at org.apache.spark.util.threads.SparkThreadLocalCapturingRunnable.$anonfun$run$1(SparkThreadLocalForwardingThreadPoolExecutor.scala:104) at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23) at org.apache.spark.util.threads.SparkThreadLocalCapturingHelper.runWithCaptured(SparkThreadLocalForwardingThreadPoolExecutor.scala:68) at org.apache.spark.util.threads.SparkThreadLocalCapturingHelper.runWithCaptured$(SparkThreadLocalForwardingThreadPoolExecutor.scala:54) at org.apache.spark.util.threads.SparkThreadLocalCapturingRunnable.runWithCaptured(SparkThreadLocalForwardingThreadPoolExecutor.scala:101) at org.apache.spark.util.threads.SparkThreadLocalCapturingRunnable.run(SparkThreadLocalForwardingThreadPoolExecutor.scala:104) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(

I have tried increasing the default shuffle parallelism (From 200 to 1200 as It's suggested here Spark application kills executor) and it seems that the job is more time in execution, but it fails again.

I have tried to monitor the SparkUI meanwhile the job is in execution:

But as you can see, the problem is the same: Some stages are failing because an executor its unreachable because a task has failed more than X times.

The big delta that I mentioned above has more or less 4-5 billion rows and the big dump that I want to merge has, more or less, 100 million rows. The table is not partitioned (yet) so the process is very work-intensive. What is failing is the merge part, not the process to copy the data from SQL Server to ADLS, so the merge is being done once the data to be merge is already in Parquet format.

Any idea of what is happening or what can I do in order to finish this merge?

Thanks in advance.

...

ANSWER

Answered 2021-Apr-12 at 07:56

Finally, I reviewed the cluster and I changed the spark.sql.shuffle.partitions property to 1600 in the code of the job that I wanted to execute with this configuration (Instead than changing this directly on the cluster). In my cluster I have 400 cores so I chose a multiple (1600) of that number.

After that, the execution finished in two hours. I came to this conclusion because, in my logs and Spark UI I observed a lot of disk spilling so I thought that the partitions wasn't fitting in the worker nodes.

Source https://stackoverflow.com/questions/67019196

QUESTION

Spark executors fails to run on kubernetes cluster

Asked 2021-Feb-02 at 13:31

I am trying to run a simple spark job on a kubernetes cluster. I deployed a pod that starts a pyspark shell and in that shell I am changing the spark configuration as specified below:

...

ANSWER

Answered 2021-Feb-01 at 09:42

I don't have much experience with PySpark but I once setup Java Spark to run on a Kubernetes cluster in client mode, like you are trying now.. and I believe the configuration should mostly be the same.

First of all, you should check if the headless service is working as expected or not. First with a:

Source https://stackoverflow.com/questions/65980391

QUESTION

Failed to send RPC XXXX in spark-shell Hadoop 3.2.1 and spark 3.0.0

Asked 2020-Dec-26 at 20:08

I am trying to run spark shell in psuedodistributed mode on my windows 10 pc having 8 Gigs of ram. I am able to submit and run a mapreduce wordcount on yarn ,but when i try to initialize a spark shell or spark submit any program with master as yarn it fails with failed to send RPC error. The error is given below.

Below is my yarn-site.xml config

...

ANSWER

Answered 2020-Dec-26 at 20:08

Caused by: java.lang.NoSuchMethodError: org.apache.spark.network.util.AbstractFileRegion.transferred()J
    at org.apache.spark.network.util.AbstractFileRegion.transfered(AbstractFileRegion.java:28)
    at io.netty.channel.nio.AbstractNioByteChannel.doWrite(AbstractNioByteChannel.java:228)
    at io.netty.channel.socket.nio.NioSocketChannel.doWrite(NioSocketChannel.java:282)
    at io.netty.channel.AbstractChannel$AbstractUnsafe.flush0(AbstractChannel.java:879)
    ... 21 more

Source https://stackoverflow.com/questions/65457195

QUESTION

Pyspark crashing for large datasets

Asked 2020-Oct-15 at 01:03

I am learning pyspark but came across this error. I have been stuck at this for a few hours now. I have seen many questions on StackOverflow but most of them either increase the driver memory or executor memory. I also tried this but doesn't seem to get it working. Anybody here if has experienced such an error, your help is much appreciated.

The same code is working if I have a smaller dataset, but when I use a large dataset this error comes up again.

My laptop configurations:

...

ANSWER

Answered 2020-Oct-13 at 14:04

This is your culprit: sorted_df.coalesce(1).write.json(output, mode='overwrite') . You have to understand repercussions that arise when repartitioning or coalescing to a single partition. Now, all your data will have to be transferred to a single worker in order to write it to a single file. You could try it with repartition(1) instead of coalesce(1). Plus, not sure why you need print(sorted_df.show()).

Source https://stackoverflow.com/questions/64327763

QUESTION

Error including a column in a join between spark dataframes

Asked 2020-May-26 at 01:04

I have a join between cleanDF and sentiment_df using array_contains that works fine (from solution 61687997). And I need include in the Result df a new column ('Year') from cleanDF.

This is the join:

...

ANSWER

Answered 2020-May-26 at 01:04

Year column might be having null values & because of that it is failing with Caused by: java.lang.NullPointerException exception. Filter all null values from Year column.

Source https://stackoverflow.com/questions/62008988

QUESTION

Hadoop + Spark: There are 1 datanode(s) running and 1 node(s) are excluded in this operation

Asked 2020-May-05 at 05:14

I am currently experimenting with Apache Spark. Everything seems to be working fine in that all the various components are up and running (i.e. HDFS, Spark, Yarn, etc). There do not appear to be any errors during the startup of any of these. I am running this in a Vagrant VM and Spark/HDFS/Yarn are dockerized.

tl;dr: Submitting an job via Yarn results in There are 1 datanode(s) running and 1 node(s) are excluded in this operation.

Submitting my application with: $ spark-submit --master yarn --class org.apache.spark.examples.SparkPi --driver-memory 512m --executor-memory 512m --executor-cores 1 /Users/foobar/Downloads/spark-3.0.0-preview2-bin-hadoop3.2/examples/jars/spark-examples_2.12-3.0.0-preview2.jar 10

Which results in the following:

...

ANSWER

Answered 2020-May-05 at 05:14

Turns out it was a networking issue. If you look closely at what was originally posted in the question you will see the following error in the log, one that I originally missed:

Source https://stackoverflow.com/questions/61432807

QUESTION

Spark Master Re-Submits endlessly after IOException on the Worker Node Application/Job Submitted

Asked 2020-Apr-29 at 13:12

I am quite new to Spark world. In our application we have an in-built Spark standalone cluster(Version 2.4.3) which takes in submitted jobs by our main data engine loader application via spark submit master URL.

We have 3 worker slave nodes on different VMs. Interestingly because of some IOException which I am posting in a very limited and cryptic format to limit system internals. The Master assumes it needs to Re-Submit the same job/application to the same worker over and over again(10s of thousands of time)

Worker App/Job Logs which is the same for every Job RE-Submission

...

ANSWER

Answered 2020-Apr-28 at 16:55

Try setting below config

Source https://stackoverflow.com/questions/61484866

QUESTION

"A failure occurred while executing com.android.build.gradle.internal.tasks.Workers$ActionFacade" after adding firebase firestore dependency

Asked 2020-Apr-24 at 16:12

I got the error after adding the Firebase Firestore dependency to my app.

implementation 'com.google.firebase:firebase-firestore:21.2.1'

Compiling after adding this dependency gives the following error:

...

ANSWER

Answered 2019-Oct-31 at 14:51

Turns out the problem wasn't caused by the Firebase Firestore dependency. It worked after I increased the maximum heap size for the daemon in the global gradle.properties file. I wasn't aware this had precedence over the project-level properties.

Source https://stackoverflow.com/questions/58636032

QUESTION

Error with mvn test: java.lang.IllegalStateException: Cannot call methods on a stopped SparkContext

Asked 2020-Apr-20 at 15:07

I am getting the following error only when running all the test cases using mvn test. This doesn't happen if I run each of the test classes independently from IDE. I am using https://github.com/MrPowers/spark-fast-tests framework.

Trait

...

ANSWER

Answered 2020-Apr-20 at 15:07

I checked your git repo

after entering in to this test case

Source https://stackoverflow.com/questions/61313019

QUESTION

Too large frame error when running spark shell on standalone cluster

Asked 2020-Apr-06 at 05:02

I am having troubles starting spark shell against my local running spark standalone cluster. Any ideas? I'm running this on spark 3.1.0-SNAPSHOT.

Starting the shell or regular app works fine in local mode, but both fail with below command.

...

ANSWER

Answered 2020-Apr-06 at 05:02

The problem was that the incorrect port was being used.

This line appeared in the standalone master log:

Source https://stackoverflow.com/questions/61051316

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