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Partitioning best practices

Use the following best practices when defining custom partitioning strategies for your data stored in InfluxDB Clustered.

Partition by tags that you commonly query for a specific value

Custom partitioning primarily benefits queries that look for a specific tag value in the WHERE clause. For example, if you often query data related to a specific ID, partitioning by the tag that stores the ID helps the InfluxDB query engine to more quickly identify what partitions contain the relevant data.

Use tag buckets for high-cardinality tags

Partitioning using distinct values of tags with many (10K+) unique values can actually hurt query performance as partitions are created for each unique tag value. Instead, use tag buckets to partition by high-cardinality tags. This method of partitioning groups tag values into “buckets” and partitions by bucket.

Only partition by tags that always have a value

You should only partition by tags that always have a value. If points don’t have a value for the tag, InfluxDB can’t store them in the correct partitions and, at query time, must read all the partitions.

Avoid over-partitioning

As you plan your partitioning strategy, keep in mind that data can be “over-partitioned”–meaning partitions are so granular that queries end up having to retrieve and read many partitions from the object store, which hurts query performance.

  • Balance the partition time interval with the actual amount of data written during each interval. If a single interval doesn’t contain a lot of data, it is better to partition by larger time intervals.
  • Don’t partition by tags that you typically don’t use in your query workload.
  • Don’t partition by distinct values of high-cardinality tags. Instead, use tag buckets to partition by these tags.

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