docs-v2/content/shared/v3-distributed-admin-custom-partitions/best-practices.md

Use the following best practices when defining custom partitioning strategies for your data stored in {{< product-name >}}.

• Partition by tags that you commonly query for a specific value
• Only partition by tags that always have a value
• Avoid over-partitioning
• Limit the number of partition files
  • Estimate the total partition count

Partition by tags that you commonly query for a specific value

Custom partitioning primarily benefits single series 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.

{{% note %}}

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. {{% /note %}}

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.

Limit the number of partition files

Avoid exceeding 10,000 total partition files. Limiting the total partition count can help manage system performance and costs.

While planning your strategy include the following steps to keep the total partition count below 10,000 files over the next few years:

• Estimate the total partition count for the lifespan of your data

• Take the following steps to limit the total partition count:

  • Set a database retention period to prevent the number of files from growing unbounded.
  • Partition by month or year to avoid over-partitioning and creating too many partition files.
  • Don't partition on high cardinality tags unless you also use tag buckets

Estimate the total partition count

Use the following formula to estimate the total partition file count over the lifetime of the database (or retention period):

total_partition_count = (cardinality_of_partitioned_tag) * (data_lifespan / partition_duration)

• total_partition_count: The number of partition files in Object storage
• cardinality_of_partitioned_tag: The number of distinct values for a tag
• data_lifespan: The database retention period, if set, or the expected lifetime of the database
• partition_duration: The partition time interval, defined by the tine part template