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| <h1 id="segment-size-optimization">Segment size optimization</h1> |
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| <p>In Druid, it's important to optimize the segment size because</p> |
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| <ol> |
| <li>Druid stores data in segments. If you're using the <a href="../design/index.html#roll-up-modes">best-effort roll-up</a> mode, |
| increasing the segment size might introduce further aggregation which reduces the dataSource size.</li> |
| <li>When a query is submitted, that query is distributed to all historicals and realtimes |
| which hold the input segments of the query. Each node has a processing threads pool and use one thread per segment to |
| process it. If the segment size is too large, data might not be well distributed over the |
| whole cluster, thereby decreasing the degree of parallelism. If the segment size is too small, |
| each processing thread processes too small data. This might reduce the processing speed of other queries as well as |
| the input query itself because the processing threads are shared for executing all queries.</li> |
| </ol> |
| |
| <p>It would be best if you can optimize the segment size at ingestion time, but sometimes it's not easy |
| especially for the streaming ingestion because the amount of data ingested might vary over time. In this case, |
| you can roughly set the segment size at ingestion time and optimize it later. You have two options:</p> |
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| <ul> |
| <li>Turning on the <a href="../design/coordinator.html#compacting-segments">automatic compaction of coordinators</a>. |
| The coordinator periodically submits <a href="../ingestion/tasks.html#compaction-task">compaction tasks</a> to re-index small segments.</li> |
| <li>Running periodic Hadoop batch ingestion jobs and using a <code>dataSource</code> |
| inputSpec to read from the segments generated by the Kafka indexing tasks. This might be helpful if you want to compact a lot of segments in parallel. |
| Details on how to do this can be found under <a href="../ingestion/update-existing-data.html">'Updating Existing Data'</a>.</li> |
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