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
• Only partition by tags that always have a value
• Avoid over-partitioning

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.

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.

• Avoid using partition time intervals that are less than one day.

  The partition time interval should be balanced 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.