Resolving the Task Not Serializable error in PySpark : org.apache.spark.SparkException: Job aborted due to stage failure – Resolution

PySpark @

When we use PySpark to run operations on a distributed cluster, it divides the tasks across multiple nodes. In order to do this, the tasks and any associated data have to be serialized (converted into a format that can be easily stored or transferred) and then deserialized at the executor nodes.

The error “Task not serializable” arises when PySpark is unable to serialize the task to be sent to the executors.

Common causes

Local objects or functions: When you reference objects or functions defined in the main script or non-serializable objects within a Spark transformation, the entire object gets pulled in and PySpark tries to serialize it.

External libraries: If you use an external library inside a Spark transformation or action, and if this library is not serializable, you’ll get this error.

Nested functions: While the outer function might be serializable, the nested functions or objects it references might not be.

Spark Context (sc): The SparkContext object itself is not serializable. If you use or reference it inside a transformation, you’ll encounter this error.


a. Use broadcast variables: If you need to use a large read-only variable in your tasks, rather than sending this variable with every task, you can send it once to all workers. Use the broadcast function for this.

large_variable = [...]
broadcast_var = sc.broadcast(large_variable)

b. Use lambdas and global scope: Instead of defining functions within functions, you can define them at the global scope. Instead of:

def function1():
    def function2():


def function2():

c. Avoid using SparkContext inside transformations. Instead of: x: sc.parallelize(x))

You should parallelize outside of transformations.

d. Use PySpark’s built-in functions: Whenever possible, use functions from PySpark’s libraries since they are guaranteed to be serializable.

e. Kryo Serialization: Spark provides Kryo serialization as an alternative which is more efficient than Java serialization. Enable it with:

Debugging and Identifying the Problem

Stack trace: Always check the stack trace. The non-serializable object is usually mentioned in the error details.

Minimal code: If you can’t identify the problem, try to reduce your code to the minimal version that produces the error. This can often help pinpoint the exact issue.

Logging: Use logging libraries to check the flow of your program. See which part of your code is causing the serialization issue.

Serializable the class

If you have control over the class that is being labeled as ‘NotSerializable’, the most direct way to solve the issue is to make the class serializable. In Java, this can be achieved by implementing the Serializable interface.

For Java

public class MyNonSerializableClass {
    private int data;

    // ... rest of the class ...

need to changed to 

public class MySerializableClass implements Serializable {
    private int data;

    // ... rest of the class ...

Declare the instance only within the lambda function passed in map

This is a common practice in PySpark when dealing with objects that are not serializable. By defining and using the object strictly within the lambda function, you ensure that it’s locally scoped and doesn’t need to be serialized and sent to worker nodes. x: MyNonSerializableClass().process(x))

Make the NotSerializable object as static and create it once per machine

By making an object static, you’re creating a single instance that’s shared among all instances of the class, rather than each instance having its own copy. This can help when dealing with non-serializable objects by ensuring that the object is instantiated only once per JVM, thus reducing serialization issues.

public class MyClass {
    private static MyNonSerializableClass instance = new MyNonSerializableClass();

    // ... rest of the class ...

Call rdd.forEachPartition and create the NotSerializable object in there

This is a more advanced approach for dealing with non-serializable objects. Using forEachPartition, you can initialize non-serializable objects once for each partition, rather than for each element in the RDD. This is useful for resources like database connections, which are expensive to initialize and can’t be serialized.

rdd.foreachPartition(partition => {
    MyNonSerializableClass obj = new MyNonSerializableClass();
    partition.forEach(item => obj.process(item));

Spark important urls to refer

  1. Spark Examples
  2. PySpark Blogs
  3. Bigdata Blogs
  4. Spark Interview Questions
  5. Official Page


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