Скачать или смотреть How to Solve NotSerializableException in Apache Spark When Handling JSON Strings

Discover how to effectively resolve the `NotSerializableException` encountered in Apache Spark while converting JSON strings to HashMaps in Scala.
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Understanding NotSerializableException in Apache Spark

When integrating Scala with Apache Spark, developers often encounter various exceptions that can disrupt their workflow. One of the notable issues is the NotSerializableException, particularly when handling JSON strings. This problem can be especially frustrating when it appears in a distributed environment, such as during a Spark submit, while everything seems to work perfectly in the local environment.

In this post, we will explore this exception more closely and discuss effective solutions to avoid it.

The Problem

An exception occurs when Spark tries to convert a JSON string into a HashMap. The following outline highlights the situation:

Error Message: Task not serializable

Specific Cause: java.io.NotSerializableException : com.fasterxml.jackson.module.scala.modifiers.ScalaTypeModifier

Context: The problem arises when serializing transformations on an RDD (Resilient Distributed Dataset).

Here is a simplified version of the problematic code:

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As you can see, it attempts to parse JSON strings within a transformation. However, the libraries for JSON parsing (jsonParser and objectMapper) are created on the driver and are not serializable, leading to the exception.

The Solution

Let's break down how we can address the NotSerializableException.

Understanding Serialization in Spark

Apache Spark runs operations in a distributed manner. This means that the code executed on the driver that operates on RDD transformations must be serializable since it gets sent to various executors. If your code or any objects it references are not serializable, you’ll encounter this exception.

Example of Serialization Failure

To better understand serialization, let’s consider a simple example:

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Fixing the Serialization Issue

To resolve the problem, it’s best to create non-serializable objects inside the transformation rather than defining them on the driver. Here are a couple of ways to do this:

Create Object Inside Transformation

This technique involves instantiating the object within the RDD transformation:

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However, re-instantiating the object for each record can be inefficient.

Using mapPartitions for Efficiency

To enhance performance, consider using mapPartitions, which allows you to instantiate the object just once per partition:

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This way, you create a single instance of Stuff per partition, minimizing object creation and boosting performance.

Conclusion

Handling JSON strings in Apache Spark while ensuring all tasks are serializable can be challenging. By understanding the nature of serialization and leveraging techniques like creating objects within transformations or using mapPartitions, you can effectively bypass NotSerializableException.

Next time you encounter this exception, remember this guide to streamline your Spark development process. Happy coding!