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Скачать или смотреть Understanding Kafka Offsets: Do They Start at 0 or 1000 After Cleanup?

  • vlogize
  • 2025-09-06
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Understanding Kafka Offsets: Do They Start at 0 or 1000 After Cleanup?
Does Kafka change offset to 0 after cleaning up partitions?apache kafkamessagebrokerconsumer
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Описание к видео Understanding Kafka Offsets: Do They Start at 0 or 1000 After Cleanup?

Discover how Kafka manages offsets and understand the implications for consumers, especially after partitions cleanup.
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This video is based on the question https://stackoverflow.com/q/63248552/ asked by the user 'Radosław Kopeć' ( https://stackoverflow.com/u/14048277/ ) and on the answer https://stackoverflow.com/a/63248894/ provided by the user 'aballaci' ( https://stackoverflow.com/u/11319016/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

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Understanding Kafka Offsets: Do They Start at 0 or 1000 After Cleanup?

Apache Kafka is a powerful message broker used for building real-time data pipelines and streaming applications. However, many users often face questions concerning its internal mechanisms, particularly around message offsets. One common query is: Does Kafka reset message offsets back to 0 after cleaning up partitions?

In this post, we’ll clarify how message offsets work in Kafka, especially in relation to partitions that have undergone cleanup.

The Concept of Offsets in Kafka

Offsets are a fundamental aspect of Kafka's architecture. Each message in a Kafka partition has a unique offset, which acts as a permanent identifier that denotes the position of that message within the log of the partition.

Key Points about Kafka Offsets:

Unique and Permanent: Each message has a distinct offset that does not change after its creation.

Incremental Structure: When new messages are produced, they are assigned the next sequential offset, which means they build on the highest existing offset.

Offset Management: Consumers in Kafka utilize these offsets to track which messages they have read.

Partition Cleanup in Kafka

Kafka is designed to handle large volumes of data, and to manage this efficiently, it sometimes cleans up partitions to remove older messages. This cleanup process is governed by configuration settings, such as retaining messages for a certain time or a specific number of messages.

What Happens During Cleanup?

When partitions are cleaned up, older messages are deleted based on the retention strategy you’ve configured, for instance:

Time-Based Cleanup: Messages older than a defined age are removed.

Count-Based Cleanup: Messages exceeding a defined number (like 1000 events) are purged.

Addressing the Misconception: Do Offsets Reset to 0?

Now, let’s tackle the main question: If Kafka deletes older messages and retains only the latest ones, does the offset get reset to 0 after this cleanup?

The Answer

No, the offset for a message never changes. This is crucial to understand:

After Kafka cleans up a partition, the offset for the next new message will continue incrementally. Therefore, if the cleanup removed messages up to 1000, the next message produced will be assigned an offset of 1000 — not 0.

Implications for Consumers

This understanding has practical implications for consumers who utilize the seek() method. If you want a consumer to start processing messages right after cleanup, here’s how it works:

Using the seek() Method: If a consumer intends to start reading after the cleanup, it should set the start offset to 1000, not 0. This ensures the consumer picks up from the latest available message.

Conclusion

Understanding how Kafka manages offsets and the impact of partition cleanup is essential for effectively using this powerful tool. Remember that offsets are permanent markers for messages, and they will not reset to 0 even after older data is purged. Instead, they will continue from their last assigned value, allowing for seamless message processing and reduced confusion over message tracking.

By grasping these concepts, you can better leverage Kafka's capabilities in your data streaming and processing applications.

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