Manage data partitioning

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InfluxDB Clustered is currently only available to a limited group of InfluxData customers. If interested in being part of the limited access group, please contact the InfluxData Sales team.

When writing data to InfluxDB Clustered, the InfluxDB v3 storage engine stores data in the Object store in Apache Parquet format. Each Parquet file represents a partition–a logical grouping of data. By default, InfluxDB partitions each table by day. InfluxDB Clustered lets you customize the partitioning strategy and partition by tag values and different time intervals. Customize your partitioning strategy to optimize query performance for your specific schema and workload.


The primary advantage of custom partitioning is that it lets you customize your storage structure to improve query performance specific to your schema and workload.

  • Optimized storage for improved performance on specific types of queries. For example, if queries often select data with a specific tag value, you can partition by that tag to improve the performance of those queries.
  • Optimized storage for specific types of data. For example, if the data you store is sparse and the time ranges you query are often much larger than a day, you could partition your data by week instead of by day.


Using custom partitioning may increase the load on other parts of the InfluxDB v3 storage engine, but each can be scaled individually to address the added load.

The following disadvantages assume that your custom partitioning strategy includes additional tags to partition by or partition intervals smaller than a day.

  • Increased load on the Ingester as it groups data into smaller partitions and files.
  • Increased load on the Catalog as more references to partition Parquet file locations are stored and queried.
  • Increased load on the Compactor as more partition Parquet files need to be compacted.
  • Increased costs associated with Object storage as more partition Parquet files are created and stored.
  • Risk of decreased performance for queries that don’t use tags in the WHERE clause. These queries may end up reading many partitions and smaller files, degrading performance.


Custom partitioning has the following limitations:

  • Database and table partitions can only be defined on create. You cannot update the partition strategy of a database or table after it has been created.
  • You can partition by up to eight dimensions (seven tags and a time interval).

How partitioning works

Partition templates

A partition template defines the pattern used for partition keys and determines the time interval that data is partitioned by. Partition templates use tag values and Rust strftime date and time formatting syntax.

For more detailed information, see Partition templates.

Partition keys

A partition key uniquely identifies a partition. The structure of partition keys is defined by a partition template. Partition keys are composed of up to eight parts or dimensions (tags, tag buckets, and time). Each part is delimited by the partition key separator (|).

View example partition templates and keys

Partitions in the query life cycle

When querying data:

  1. The Catalog provides the v3 query engine (Querier) with the locations of partitions that contain the queried time series data.
  2. The query engine reads all rows in the returned partitions to identify what rows match the logic in the query and should be included in the query result.

The faster the query engine can identify what partitions to read and then read the data in those partitions, the more performant queries are.

For more information about the query lifecycle, see InfluxDB v3 query life cycle.

Query example

Consider the following query that selects everything in the production table where the line tag is A and the station tag is 1:

FROM production
  time >= now() - INTERVAL '1 week'
  AND line = 'A'
  AND station = 'cnc'

Using the default partitioning strategy (by day), the query engine reads eight separate partitions (one partition for today and one for each of the last seven days):

  • 2024-05-28
  • 2024-05-27
  • 2024-05-26
  • 2024-05-25
  • 2024-05-24
  • 2024-05-23
  • 2024-05-22
  • 2024-05-21

The query engine must scan all rows in the partitions to identify rows where line is A and station is cnc. This process takes valuable time and results in less performant queries.

However, if you partition by other tags, InfluxDB can identify partitions that contain only the tag values your query needs and spend less time scanning rows to see if they contain the tag values.

For example, if data is partitioned by line, station, and day, although there are more partition files, the query engine can quickly identify and read only those with data relevant to the query:

  • A | cnc | 2024-05-28

  • A | wld | 2024-05-28

  • B | cnc | 2024-05-28

  • B | wld | 2024-05-28

  • A | cnc | 2024-05-27

  • A | wld | 2024-05-27

  • B | cnc | 2024-05-27

  • B | wld | 2024-05-27

  • A | cnc | 2024-05-26

  • A | wld | 2024-05-26

  • B | cnc | 2024-05-26

  • B | wld | 2024-05-26

  • A | cnc | 2024-05-25

  • A | wld | 2024-05-25

  • B | cnc | 2024-05-25

  • B | wld | 2024-05-25

  • A | cnc | 2024-05-24

  • A | wld | 2024-05-24

  • B | cnc | 2024-05-24

  • B | wld | 2024-05-24

  • A | cnc | 2024-05-23

  • A | wld | 2024-05-23

  • B | cnc | 2024-05-23

  • B | wld | 2024-05-23

  • A | cnc | 2024-05-22

  • A | wld | 2024-05-22

  • B | cnc | 2024-05-22

  • B | wld | 2024-05-22

  • A | cnc | 2024-05-21

  • A | wld | 2024-05-21

  • B | cnc | 2024-05-21

  • B | wld | 2024-05-21

Partition guides

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The future of Flux

Flux is going into maintenance mode. You can continue using it as you currently are without any changes to your code.

Flux is going into maintenance mode and will not be supported in InfluxDB 3.0. This was a decision based on the broad demand for SQL and the continued growth and adoption of InfluxQL. We are continuing to support Flux for users in 1.x and 2.x so you can continue using it with no changes to your code. If you are interested in transitioning to InfluxDB 3.0 and want to future-proof your code, we suggest using InfluxQL.

For information about the future of Flux, see the following: