Скачать или смотреть Resolving java.io.EOFException in PyFlink UDF Processing

Learn how to fix the `java.io.EOFException` error when using user-defined functions (UDF) in PyFlink by ensuring the correct data types in your UDF output in this comprehensive guide.
---
This video is based on the question https://stackoverflow.com/q/66687797/ asked by the user 'yiksanchan' ( https://stackoverflow.com/u/7550592/ ) and on the answer https://stackoverflow.com/a/66705988/ provided by the user 'yiksanchan' ( https://stackoverflow.com/u/7550592/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: PyFlink java.io.EOFException at java.io.DataInputStream.readFully

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Resolving java.io.EOFException in PyFlink UDF Processing: A Step-by-Step Guide

When you're working with Apache Flink, particularly with its Python API known as PyFlink, you might run into various types of exceptions. One particularly puzzling error is the java.io.EOFException, which can occur when running a PyFlink job involving user-defined functions (UDFs). This guide aims to break down this error, its causes, particularly regarding UDFs, and provide a simple solution to fix it.

Understanding the Problem

In your PyFlink job, you might have encountered the following error when executing a script intended to read from a file, filter data, and then print results. The root of the issue lies in the way your UDF returns its data.

For example, the original user-defined function (UDF) in your PyFlink job looks like this:

[[See Video to Reveal this Text or Code Snippet]]

The problem arises because the result_type specified in the UDF declares that the return type is a Map of strings (DataTypes.MAP(DataTypes.STRING(), DataTypes.STRING())), but the item_id key in the dictionary res is actually an integer. This mismatch in data types causes an exception during job execution, leading to the java.io.EOFException error.

Diagnosing the Cause

The traceback provided indicates that the root cause is related to how the data is being processed:

Your UDF returns a result containing an integer, but the declared output type signifies that all values must be strings.

When Flink tries to deserialize this result, it expects a string value but encounters an integer instead, resulting in the EOFException.

Solution: Fixing the Issue

To resolve the java.io.EOFException, the solution is quite straightforward. Modify your UDF to ensure that all returned values are strings. Here’s how you can change your parse function:

Updated UDF

[[See Video to Reveal this Text or Code Snippet]]

Key Changes Made

The updated function iterates through the dictionary res and converts every key and value to string format using a dictionary comprehension. This ensures that the UDF output aligns with the declared type, thus preventing the EOFException during execution.

Conclusion

When working with PyFlink, paying close attention to data types is crucial, especially when returning data from UDFs. By ensuring that the types in your output match what you declare, you can effectively avoid common errors like java.io.EOFException.

If you encounter this error in your PyFlink jobs, remember to validate your UDFs and make adjustments to align with the expected data types.

Happy Flink-ing!