Kafka provides support for data compaction, which is a mechanism that allows organizations to store only the most recent version of each message key in a Kafka topic. This can be useful for use cases where only the latest state of each key is required, such as maintaining the latest account balance for each customer in a financial application. Here’s how Kafka handles data compaction:
1. Message keys: Kafka uses message keys to identify unique messages within a topic. Each message key is associated with a value, which represents the data stored in the message.
2. Log compaction: Kafka supports log compaction, which is a mechanism that allows organizations to store only the most recent version of each message key in a Kafka topic. Log compaction is enabled on a per-topic basis, and it can be configured with different settings for each topic.
3. Tombstone markers: When a new message with a key that already exists in the log is written to a compacted topic, Kafka marks the previous message with the same key as a “tombstone” marker. Tombstone markers have a null value and a special flag that indicates that they should be retained in the log until the next log compaction.
4. Log cleaning: Kafka periodically performs log cleaning, which removes old messages that are no longer needed. During log cleaning, Kafka removes messages with tombstone markers that are older than the latest message with the same key.
5. Retention policies: Kafka’s retention policies control the amount of time that messages are retained in the system. By setting appropriate retention policies, organizations can ensure that only the most recent version of each message key is retained in the log, while older messages are removed during log compaction.
Overall, Kafka’s support for log compaction enables organizations to store only the most recent version of each message key in a Kafka topic, while still retaining the full history of messages. By using tombstone markers, log cleaning, and retention policies, Kafka ensures that compacted topics are efficient, reliable, and scalable, enabling organizations to build powerful data processing pipelines that can handle large volumes of data with high efficiency and reliability.