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Скачать или смотреть When Does Greenplum GPSs Commit to the Kafka Topic?

  • vlogize
  • 2025-08-26
  • 0
When Does Greenplum GPSs Commit to the Kafka Topic?
When greenplum gpss commit to kafka topicapache kafkagreenplum
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Описание к видео When Does Greenplum GPSs Commit to the Kafka Topic?

Discover how `Greenplum Stream Server (GPSs)` interacts with `Kafka`, learns about the commit behavior, and addresses common configuration questions.
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This video is based on the question https://stackoverflow.com/q/65700830/ asked by the user 'Anton Manevskiy' ( https://stackoverflow.com/u/4743269/ ) and on the answer https://stackoverflow.com/a/67775825/ provided by the user 'Li Yang' ( https://stackoverflow.com/u/11003787/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Understanding Greenplum GPSs and Kafka Commit Behavior

In the world of data processing, seamlessly integrating tools can significantly enhance performance and reliability. One such integration is between Greenplum Database and Apache Kafka, facilitated by the Greenplum Stream Server (GPSs). In this guide, we’ll tackle a specific challenge regarding the commit behavior of GPSs when loading data from Kafka, addressing common user concerns along the way.

The Problem

When utilizing Greenplum Stream Server (GPSs) to load data from Kafka into GreenplumDB, many users wonder about the conditions under which GPSs commits offsets to Kafka topics. Specifically, a user raised the question of why their GPSs instance does not commit messages to Kafka despite it handling the current offsets correctly in the GreenplumDB service table.

The Expected Process

The expected behavior for many users involves several sequential steps:

Using a Provided Group ID and Topic: Set the Kafka topic and group ID, essential for tracking consumer offsets.

Consuming Data: Begin reading data from the specified Kafka topic.

Tracking Offsets: Monitor the highest offsets by partition.

Commit Condition: Wait for a defined COMMIT condition to be met.

Batch Writing: Write the retrieved data from Kafka to an external table using gpfdist.

Offset Commit: Commit the maximum offset by partition back to Kafka.

Repeat the Process: Continue the cycle for processing new messages.

The user, however, noted that their process does not include the critical step of committing offsets back to Kafka, leading to confusion and potential data issues.

The Solution

The behavior observed by the user can be traced to the configuration settings used in GPSs. Let's break down the solution to understand how to ensure that offsets are committed correctly:

Enabling Offset Commit in GPSs

Setting the group.id: Ensure the group.id is set in the YAML configuration file for GPSs. This has been a requirement since version 1.6.0 if you want your instance to commit offsets to Kafka. Without it, offsets will only be recorded in the Greenplum tracking table.

Using the PROPERTIES Block: In your GPSs job configuration, look for the PROPERTIES block, which corresponds to the Kafka consumer settings. Within this block, double-check that the group.id is properly specified.

Example Configuration

Here is a simple example of what your YAML configuration might need to look like:

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

Key Takeaways

Greenplum GPSs will only commit consumed offsets to Kafka if the group.id is correctly set in the configuration file.

Before version 1.6.0, GPSs only handled offset tracking internally without committing them back to Kafka, so it’s crucial to ensure you’re using an updated version if you want this functionality.

If you're experiencing issues with commits, always verify your YAML configuration for potential oversights.

Conclusion

Connecting Greenplum Stream Server with Kafka can be a powerful setup for handling data streams effectively. Understanding the configuration necessary for GPSs to commit offsets back to Kafka is crucial for maintaining accurate data tracking and ensuring smooth processing. By following the outlined steps and ensuring your settings are correct, you can achieve the expected behavior and fully leverage the capabilities of both technologies.

By implementing the suggested configurations, you'll not only improve your system’s reliability but also enhance your data processing workflows.

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