![]() A new segment file is created and opened in read-write mode, becoming the active segment.įrom the example, you can see that the old segment was closed when it reached 16314 byes in size. When the active segment becomes full it is rolled, which means it is closed and re-opened in read-only mode. Non-active segments are read-only, and are accessed by consumers reading older records. It is the segment where new incoming records are appended. The active segment is the only file open for read and write operations. The second segment 00000000000000000109.log contains records starting from offset 109 and is called the active segment. 1 ppatiern ppatiern 8 Nov 14 16:24 leader-epoch-checkpointįrom the output, you can see that the first log segment 00000000000000000000.log contains records from offset 0 to offset 108. It is used when, after a new leader is elected, the preferred one comes back and needs such a state in order to become leader again.Ĭontinuing with the Strimzi Canary component as an example, here’s a more detailed view of the previous topic partition directory.ĭrwxrwxr-x. snapshot file contains a snapshot of the producer state regarding sequence IDs used to avoid duplicate records. timeindex file is another index used for accessing records by timestamp in the log. It is used for accessing records at specified offsets in the log without having to scan the whole. index file contains an index that maps a logical offset (in effect the record’s id) to the byte offset of the record within the. The name of the file defines the starting offset of the records in that log. log file is an actual segment containing records up to a specific offset. The directory contains the following files: The example shows partition 0 of the _strimzi_canary topic used by the Strimzi Canary component. Using the Strimzi Canary component with its producer and consumer as an example, here’s a sample of what the directory looks like. Looking at the broker disk, each topic partition is a directory containing the corresponding segment files and other files. When records are deleted on disk or a consumer starts to consume from a specific offset, a big, unsegmented file is slower and more error prone. Splitting into segments can really help with performance. The topic partition structure on the diskĪn Apache Kafka topic is split into partitions where records are appended to.Įach partition can be defined as a unit of work, rather than unit of storage, because it is used by clients to exchange records.Ī partition is further split into segments, which are the actual files on the disk. You can read more on compaction in the Strimzi documentation for removing log data with cleanup policies. Earlier messages that have the same key are discarded. Using keys to identify messages, Kafka compaction keeps the latest message (with the highest offset) for a specific message key. Log retention is handled differently when you use a compact policy instead of a delete policy. When you know more about how it works, you might want to adjust your log configuration. In this blog post, we will dig more into how log segmentation and record retention impacts broker performance when your log cleanup policy is set to delete. ![]() Not everyone is aware of how these parameters have an impact on broker behavior.įor instance, they determine how long records are stored and made available to consumers. You can use configuration to control the rolling of segments, record retention, and so on. ![]() So, the log is a logical sequence of records that’s composed of segments (files) and segments store a sub-sequence of records.īroker configuration allows you to tweak parameters related to logs. Segments help with deletion of older records, improving performance, and much more. ![]() Records are appended at the end of each log one after the other and each log is also split in segments. Apache Kafka behaves as a commit-log when it comes to dealing with storing records.
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