Configure for Spark

If you are using Hortonworks, additional configuration is required to enable integration with Spark. 

Steps:

1. You can apply this change through the Admin Settings Page (recommended) or trifacta-conf.json. For more information, see Platform Configuration Methods.
2. Locate the spark-job-service configuration area.

3. For the jvmOptions properties, specify the following values. Do not remove any other values that may be specified:

   "spark-job-service" : {
     ...
     "jvmOptions" : [
       "-Xmx128m",
       "-Dhdp.version=<HDP_version_number>"
     ]
     ...
   }

4. In the spark-job-service area, locate the SPARK_DIST_CLASSPATH configuration. Add a reference to your specific version of the local Hadoop client. The following applies to Hortonworks:

   "spark-job-service.env.SPARK_DIST_CLASSPATH" : "/usr/hdp/<HDP_version_number>/hadoop/client/*",

5. In the spark.props configuration area, add the following values. Do not remove any other values that may be set for these options:

    "spark": {
     ...
     "props": {
       ...
      "spark.yarn.am.extraJavaOptions": "-Dhdp.version=<HDP_version_number>",
      "spark.driver.extraJavaOptions": "-Dhdp.version=<HDP_version_number>"
     },

6. Save your changes.

Additional Configuration

Modify Spark version and Java JDK version

The Designer Cloud Powered by Trifacta platform defaults to using Spark 2.1.0. Depending on the version of your Hadoop distribution, you may need to modify the version of Spark that is used by the platform. 

In the following table, you can review the requirements for the Alteryx node hosting  Designer Cloud Enterprise Edition and (if applicable) the requirements for the EMR cluster.

• For platform instances connected to an EMR cluster, you must set the local version of Spark (spark.version property) to match the version of Spark that is used on the EMR cluster.
• For other platform instances that connect to non-EMR clusters, you must also specify the path on the Alteryx node to the Spark bundle JAR.

You can apply this change through the Admin Settings Page (recommended) or trifacta-conf.json. For more information, see Platform Configuration Methods.

"spark.version": "2.1.0",
"sparkBundleJar": "services/spark-job-server/build/bundle/spark21/*",

"spark.version": "2.1.0",

"spark.version": "2.2.0",
"sparkBundleJar": "services/spark-job-server/build/bundle/spark22/*",

"spark.version": "2.2.0",

"spark.version": "2.2.1",

Other Spark configuration topics

• For more information on executing jobs on Spark, see Configure Spark Running Environment.
• For more information on visual profiling using Spark, see Configure Spark Profiler.

Restart Platform

You can restart the platform now. See Start and Stop the Platform.

Verify Operations

At this point, you should be able to run a job in the platform, which launches a Spark execution job and a profiling. Results appear normally in the Designer Cloud application.

Steps:

To verify that the Spark running environment is working:

1. After you have applied changes to your configuration, you must restart services. See Start and Stop the Platform.
2. Run a simple job, including visual profiling, through the Designer Cloud application. 
3. The job should appear as normal in the Job Status page.
4. To verify that it ran on Spark, open the following file:
   /opt/trifacta/logs/batch-job-runnner.log
5. Search the log file for a SPARK JOB INFO block with a timestamp corresponding to your job execution. 
6. See below for information on how to check the job-specific logs. 
7. Review any errors.

For more information, see Verify Operations.

Logs

Service logs:

Logs for the Spark Job Service are located in the following location:

/opt/trifacta/logs/spark-job-service.log

Additional log information on the launching of profile jobs is located here: 

/opt/trifacta/logs/batch-job-runner.log

Job logs:

When profiling jobs fail, additional log information is written to the following:

/opt/trifacta/logs/jobs/<job-id>/spark-job.log

Troubleshooting

Below is a list of common errors in the log files and their likely causes.

Problem - Spark jobs fail with "Unknown message type: -22" error

Spark jobs may fail with the following error in the YARN application logs:

ExecutorLostFailure (executor 4 exited caused by one of the running tasks) Reason: Unable to create executor due to Unable to register with external shuffle server due to : java.lang.IllegalArgumentException: Unknown message type: -22
at org.apache.spark.network.shuffle.protocol.BlockTransferMessage$Decoder.fromByteBuffer(BlockTransferMessage.java:67)

Solution

This problem may occur if Spark authentication is disabled on the Hadoop cluster but enabled in the Designer Cloud Powered by Trifacta platform. Spark authentication must match on the cluster and the platform. 

Steps:

1. You can apply this change through the Admin Settings Page (recommended) or trifacta-conf.json. For more information, see Platform Configuration Methods.
2. Locate the spark.props entry. 

3. Insert the following property and value:
   
   

   "spark.authenticate": "false"

4. Save your changes and restart the platform.

Problem - Spark jobs fail when Spark Authentication is enabled on the Hadoop cluster

When Spark authentication is enabled on the Hadoop cluster, Spark jobs can fail. The YARN log file message looks something like the following:

17/09/22 16:55:42 INFO yarn.ApplicationMaster: Unregistering ApplicationMaster with FAILED (diag message: User class threw exception: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 (TID 3, example.com, executor 4): ExecutorLostFailure (executor 4 exited caused by one of the running tasks) Reason: Unable to create executor due to Unable to register with external shuffle server due to : java.lang.IllegalStateException: Expected SaslMessage, received something else (maybe your client does not have SASL enabled?)
at org.apache.spark.network.sasl.SaslMessage.decode(SaslMessage.java:69)

Solution

When Spark authentication is enabled on the Hadoop cluster, the Designer Cloud Powered by Trifacta platform must also be configured with Spark authentication enabled.

1. You can apply this change through the Admin Settings Page (recommended) or trifacta-conf.json. For more information, see Platform Configuration Methods.

2. Inside the spark.props entry, insert the following property value:

   "spark.authenticate": "true"

3. Save your changes and restart the platform.

Problem - Job fails with "Required executor memory MB is above the max threshold MB of this cluster" error

When executing a job through Spark, the job may fail with the following error in the spark-job-service.log:

Required executor memory (6144+614 MB) is above the max threshold (1615 MB) of this cluster! Please check the values of 'yarn.scheduler.maximum-allocation-mb' and/or 'yarn.nodemanager.resource.memory-mb'.

Solution

The per-container memory allocation in Spark (spark.executor.memory and spark.driver.memory) must not exceed the YARN thresholds. See Spark tuning properties above.

Problem - Job fails with ask timed out error

When executing a  job through Spark, the job may fail with the following error in the  spark-job-service.log  file:

Job submission failed
akka.pattern.AskTimeoutException: Ask timed out on [Actor[akka://SparkJobServer/user/ProfileLauncher#1213485950]] after [20000 ms]

There is a 20-second timeout on the attempt to submit a Profiling job to Yarn. If the initial upload of the spark libraries to the cluster takes longer than 20 seconds, the spark-job-service times out and returns an error to the UI. However, the libraries do finish uploading successfully to the cluster.

The library upload is a one-time operation for each install/upgrade. Despite the error, the libraries are uploaded successfully the first time. This error does not affect subsequent profiler job runs.

Solution:

Try running the job again.

Problem - Spark fails with ClassNotFound error in Spark job service log

When executing a job through Spark, the job may fail with the following error in the spark-job-service.log file:

java.lang.ClassNotFoundException: com.trifacta.jobserver.profiler.Profiler

By default, the Spark job service attempts to optimize the distribution of the Spark JAR files across the cluster. This optimization involves a one-time upload of the spark-assembly and profiler-bundle JAR files to HDFS. Then, YARN distributes these JARs to the worker nodes of the cluster, where they are cached for future use.

In some cases, the localized JAR files can get corrupted on the worker nodes, causing this ClassNotFound error to occur.

Solution:

The solution is to disable this optimization through platform configuration. 

Steps:

1. You can apply this change through the Admin Settings Page (recommended) or trifacta-conf.json. For more information, see Platform Configuration Methods.
2. Locate the spark-job-service configuration node.

3. Set the following property to false:
   

   "spark-job-service.optimizeLocalization" : true

    

4. Save your changes and restart the platform.

Problem - Spark fails with PermGen OutOfMemory error in the Spark job service log

Exception in thread "LeaseRenewer:trifacta@nameservice1" java.lang.OutOfMemoryError: PermGen space

The solution is to configure the PermGen space for the Spark driver: 

1. You can apply this change through the Admin Settings Page (recommended) or trifacta-conf.json. For more information, see Platform Configuration Methods.
2. Locate the spark configuration node.

3. Set the following property to the given value:

   "spark.props.spark.driver.extraJavaOptions" : "-XX:MaxPermSize=1024m -XX:PermSize=256m",

4. Save your changes and restart the platform. 
   
   

org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.security.token.SecretManager$InvalidToken): token (HDFS_DELEGATION_TOKEN token x for trifacta) can't be found in cache

"Job aborted due to stage failure: Total size of serialized results of 208 tasks (1025.4 MB) is bigger than spark.driver.maxResultSize (1024.0 MB)"

"java.lang.IllegalArgumentException: Error while instantiating 'org.apache.spark.sql.hive.HiveSessionState'"

java.io.FileNotFoundException: No Avro files found. Hadoop option "avro.mapred.ignore.inputs.without.extension" is set to true. Do all input files have ".avro" extension?