1. D s config

2. Verify that the Spark master property is set accordingly:
   
   

   Code Block
   "spark.master": "yarn",

   
   

3. Review and set the following parameter based on your Hadoop distribution:

   Hadoop Distribution	Parameter Value	Value is required?
   CDH 6.x	"spark.useVendorSparkLibraries": true,	Yes. Additional configuration is in the next section.
   HDP 3.x	"spark.useVendorSparkLibraries": true,	Yes. Additional configuration is in the next section.
   CDH 5.x and earlier	"spark.useVendorSparkLibraries": false,	N
   HDP 2.x and earlier	"spark.useVendorSparkLibraries": false,	N

   
   

4. Locate the following setting:

    

   Code Block
   "spark.version"

   
   

5. Set the above value based on your Hadoop distribution in use:
   
   

   Hadoop Distribution	spark.version	Notes
   CDH 6.1 - CDH 6.3	2.4.4	Platform must use native Hadoop libraries from the cluster. Additional configuration is required. See below.
   CDH 5.16.x	A supported version of Spark
   HDP 3.1	2.3.0	Platform must use native Hadoop libraries from the cluster. Additional configuration is required. See below.
   HDP 3.0	2.3.0	Platform must use native Hadoop libraries from the cluster. Additional configuration is required. See below.
   HDP 2.6.x	A supported version of Spark
   HDP 2.5.x	A supported version of Spark

   
   

Acquire native libraries from the cluster

You must acquire native Hadoop libraries from the cluster if you are using any of the following versions:

For more information on acquiring these libraries, please see the documentation provided with your Hadoop distribution.

Use native libraries on Cloudera 6.0 and later

To integrate with CDH 6.x, the platform must use the native Spark libraries. Please add the following properties to your configuration. 

Steps:

1. D s config

2. Set sparkBundleJar to the following:
   
   

   Code Block
   "sparkBundleJar":"/opt/cloudera/parcels/CDH/lib/spark/jars/*:/opt/cloudera/parcels/CDH/lib/spark/hive/*"

   
   

3. For the Spark Job Service, the Spark bundle JAR must be added to the classpath:

   Info
   NOTE: The key modification is to remove the topOfTree element from the sparkBundleJar entry.

   
   

   Code Block
   "spark-job-service": { "classpath": "%(topOfTree)s/services/spark-job-server/server/build/libsinstall/spark-job-server-bundle.jarserver/lib/*:%(topOfTree)s/conf/hadoop-site/:/usr/lib/hdinsight-datalake/*:%(sparkBundleJar)s:%(topOfTree)s/%(hadoopBundleJar)s" },

   
   

4. In the spark.props section, add the following property:
   
   

   Code Block
   "spark.yarn.jars":"local:/opt/cloudera/parcels/CDH/lib/spark/jars/*,local:/opt/cloudera/parcels/CDH/lib/spark/hive/*",

   
   

5. Save your changes.

Use native libraries on Hortonworks 3.0 and later

To integrate with HDP 3.x, the platform must use the native Spark libraries. Please add the following properties to your configuration. 

Steps:

1. Set the Hadoop bundle JAR to point to the one provided with your distribution. The example below points to HDP 3.0:

   Code Block
   "hadoopBundleJar": "hadoop-deps/hdp-3.0/build/libs/hdp-3.0-bundle.jar"

   
   

2. Enable use of native libraries:

    

   Code Block
   "spark.useVendorSparkLibraries": true,

   
   

3. Set the path to the Spark bundle JAR:

   Code Block
   "sparkBundleJar": "/usr/hdp/current/spark2-client/jars/*"

   
   

4. Add the Spark bundle JAR to the Spark Job Service classpath (spark-job-service.classpath). Example:

   Code Block
   classpath: "%(topOfTree)s/services/spark-job-server/server/build/install/server/lib/*:%(topOfTree)s/conf/hadoop-site/:%(topOfTree)s/%(sparkBundleJar)s:%(topOfTree)s/%(hadoopBundleJar)s"

   
   

5. The following property needs to be added or updated in spark.props. If there are other values in this property, the following value must appear first in the list:

   Code Block
   "spark.executor.extraClassPath": "/usr/hdp/current/spark2-client/jars/guava-14.0.1.jar"

   
   

6. The following property needs to be added or updated to spark.props. These do not need to be listed in a specific order:
   
   

   Code Block
   "spark.yarn.jars": "local:/usr/hdp/current/spark2-client/jars/*"

   
   

7. Save your changes and restart the platform.

Modify Spark version and Java JDK version

The

In the following table, you can review the Spark/Java version requirements for the

To change the version of Spark in use by the

Additional requirements:

• The supported cluster must use Java JDK 1.8.

• If the platform is 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. 

Spark 2.2.0

"spark.version": "2.2.0",

With EMR:

"spark.version": "2.2.0",

Spark 2.2.1

With EMR:

"spark.version": "2.2.1",

Spark 2.3.0

"spark.version": "2.3.0",

With EMR:

"spark.version": "2.3.0",

Spark 2.4.4

"spark.version": "2.4.4",

"spark.useVendorSparkLibraries": true,

With EMR:

Additional Configuration

• For more information on executing jobs on Spark, see Configure Spark Running Environment.
• For more information on visual profiling, see Overview of Visual Profiling.

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

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. Through the application, run a simple job, including visual profiling. Be sure to select Spark as the running environment.
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 job succeeds on the cluster but is reported as failed in the application. Spark Job Service is constantly dying.

Whenever a Spark job is executed, it is reported back as having failed. On the cluster, the job appears to have succeeded. However, in the Spark Job Service logs, the Spark Job Service cannot find any of the applications that it has submitted to resource manager.

In this case, the root problem is that Spark is unable to delete temporary files after the job has completed execution. During job execution, a set of ephemeral files may be written to the designated temporary directory on the cluster, which is typically /trifacta/tempfiles. In most cases, these files are removed transparent to the user. 

• This location is defined in the hdfs.pathsConfig.tempFiles parameter in 

  D s triconf

  .

In some cases, those files may be left behind. To account for this accumulation in the directory, the 

• The age in days is defined in the the job.tempfiles.cleanup.age parameter in 

  D s triconf

  .

This cleanup operation can fail if HDFS is configured to send Trash to an encrypted zone. The HDFS API does not support the skipTrash option, which is available through the HDFS CLI. In this scenario, the temp files are not successfully removed, and the files continue to accumulate without limit in the temporary directory. Eventually, this accumulation of files can cause the Spark Job Service to crash with Out of Memory errors.

Solution

The following are possible solutions:

1. Solution 1: Configure HDFS to use an unencrypted zone for Trash files.
2. Solution 2: 

   1. Disable temp file cleanup in 

      D s triconf

      :
      
      

      Code Block
      "job.tempfiles.cleanup.age": 0,

      
      

   2. Clean up the tempfiles directory using an external process.

Problem - Spark Job Service fails to start with a "Exception in thread "main" com.fasterxml.jackson.databind.JsonMappingException: Jackson version is too old 2.5.3" error.

Spark job service fails to start with an error similar to the following in the spark-job-service.log file:

Exception in thread "main" com.fasterxml.jackson.databind.JsonMappingException: Jackson version is too old 2.5.3

Solution

Some versions of the hadoopBundleJar contain older versions of the Jackson dependencies, which break the spark-job-service.

To ensure that the spark-job-service is provided the correct Jackson dependency versions, the sparkBundleJar must be listed before the hadoopBundleJar in the spark-job-service.classpath, which is inserted as a parameter in

"spark-job-service.classpath": "%(topOfTree)s/services/spark-job-server/server/build/install/server/lib/*:%(topOfTree)s/conf/hadoop-site/:%(topOfTree)s/%(sparkBundleJar)s:%(topOfTree)s/%(hadoopBundleJar)s"

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 

Steps:

1. D s config

2. Locate the spark.props entry. 

3. Insert the following property and value:
   
   

   Code Block
   "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 

1. D s config

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

   Code Block
   "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 Sparktuningproperties 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. D s config

2. Locate the spark-job-service configuration node.

3. Set the following property to false:
   

   Code Block
   "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. D s config

2. Locate the spark configuration node.

3. Set the following property to the given value:

   Code Block
   "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?