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How To Fix - "Py4JJavaError: An Error Occurred While Calling oxx.showString" in Spark ?


How To Fix - "Py4JJavaError: An Error Occurred While Calling oxx.showString" in Spark. This error can occur in various formats as shown below -


Py4JJavaError: An error occurred while calling oxx.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure:
java.lang.OutOfMemoryError: Java heap space



Py4JJavaError: An error occurred while calling oxx.showString.
: java.lang.IllegalArgumentException: java.net.UnknownHostException: xxxxxxxxxx



Py4JJavaError: An error occurred while calling oxxxx.collectToPython.
: org.apache.spark.SparkException
Based on the format, flavour and type, the base reason for this error can be multiple. Let's try to see possible causes and how we could solve this.  

Check :

  • First thing first, it is advisable to quickly browse through the logs if they hold any clue.
  • Also look at the executor logs - from Spark UI -> Failed Tasks -> View Details
  • Look at the various logs as per below location
    • Master logs - $SPARK_LOG_DIR/spark-userID-org.apache.spark.deploy.master.Master-instance-host.out
    • Worker logs - $SPARK_LOG_DIR/spark-userID-org.apache.spark.deploy.master.Worker-instance-host.out
    • Driver logs (if using edge node or client mode) - Can be seen on the command line by default
    • Driver logs (if using cluster mode)
      • stdout - $SPARK_WORKER_DIR/driverID/stdout
      • stderr - $SPARK_WORKER_DIR/driverID/stderr
    • Executor logs
      • stdout - $SPARK_WORKER_DIR/applID/executorID/stdout
      • stderr - $SPARK_WORKER_DIR/applID/executorID/stderr
  userID - user ID that started the master or worker instance - The master or worker instance number. host - host where master or worker is started driverID - ID of the driver from application web UI. executorID - executor id from web UI.  

Check :

  • As a next check, you could try increasing the various settings and flags related to memory allocation, task execution etc. As a reference, we would advise checking the below flags and their set values -
    • spark.memory.fraction — default 0.75
    • spark.memory.storageFraction — defaults 0.5
    • spark.executor.instances- No. executors for the application
    • spark.executor.memory - Allocated memory for each executor to run the task
    • spark.executor.cores - No. of concurrent tasks an executor can run.
    • spark.driver.memory - Allocated memory for the driver.
    • spark.driver.cores - No. of virtual cores for the driver process.
    • spark.sql.shuffle.partitions - No. of partitions while shuffling data for various operations.
    • spark.default.parallelism - Default count of partitions in RDD for transformations.
    • spark.driver.maxResultSizeSets - This directly impacts as the limit on the size of serialized results for operations like collect(). If the size of the results exceeds this value, job will fail.
 

Check :

If the above process doesn’t help fix the issue, then the root cause might lie somewhere else. As a matter of fact, the actual issue can be something completely different which causes the memory or other issues as a byproduct. Try the below steps and checks if they help you to figure out the root cause.  

  • Check the versions of various softwares & packages used e.g. Spark, Java, Kafka, Scala etc. Sometimes a small version incompatibility mistake can lead to issues or errors which are extremely difficult to trace back ,debug and fix. So make sure the versions used in the project are compatible with each other. Also, if possible, make sure to use the latest but Stable (not beta) version.
 

  • Check if you are using any operations like count() or show() to dump intermediate or final results. Such operations should be restrictive in terms of how many sets of results are to be displayed.
 

  • Check that hostname, ip addresses of all the nodes viz. master node, worker nodes etc. are defined correctly in etc\hosts file and other configurational entities. Ensure these nodes are up-and-running and accessible. This is applicable for both On-premise as well as if you are using cloud-based setup like AWS, GCP, Azure etc. where the Public or External IP definitions requires to be suitably defined and accessible. Cross-check the correctness of the below environment variables in the Spark configuration viz. conf/spark-env.sh
    • JAVA_HOME - Location where Java is installed
    • PYSPARK_PYTHON - Python executable binary used by driver and workers . However - spark.pyspark.python take precedence if it is set
    • PYSPARK_DRIVER_PYTHON - Python binary used by driver only (However default is PYSPARK_PYTHON). But spark.pyspark.driver.python overtakes this if set
    • SPARKR_DRIVER_R - R executable binary
    • SPARK_LOCAL_IP - IP address of the node to bind
    • SPARK_PUBLIC_DNS - It sets the public DNS name of the Spark master and workers.
    • SPARK_LOCAL_IP - Used to configure Spark processes to bind to a specific and consistent IP address when we create listening ports
    • SPARK_MASTER_HOST - If there are multiple network adaptors, Spark will try default setting but will stop if it fails. Hence to avoid that, set this SPARK_MASTER_HOST. Prior to Spark2.0, it was named as SPARK_MASTER_IP.
 

  • Null values are also a major culprit for many cases especially if you are doing data operation using spark-sql. Make sure data fields or columns are correctly attributed with null or not-null property as appropriate for the use case.
    • Check null values

df.filter("col1 is null").show

df.filter($"col1".isNotNull)

df.where(df.col("col1").isNull)
    • Replace the columns values with null to something else like 0, 1 or as appropriate for the dataset.

df.withColumn("col1", when($"col1".isNull, 0).otherwise(1)).show
 

 

  • Sometimes intermediate operations like joins etc. can cause too many nested sub-results which might be out-of-capacity for the cluster to manage\operate . Queries that are very big or very complex , might end-up taking too much time, cpu and memory to complete and eventually might show error. Check for such queries in the code.
  Hope this helps to solve the error.  

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