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Data ingest lifecycle best practices

Data ingested into InfluxDB must conform to the retention period of the database in which it is stored. Points with timestamps outside of the retention period are no longer queryable, but may still have references maintained in Object storage or the Catalog, resulting in an increase in operational overhead and cost. To reduce these factors, it is important to manage the lifecycle of ingested data.

Use the following best practices to manage the lifecycle of data in your InfluxDB cluster:

Use appropriate retention periods

When creating or updating a database, use a retention period that is appropriate for your requirements. Storing data longer than is required adds unnecessary operational cost to your InfluxDB cluster.

Tune garbage collection

Once data falls outside of a database’s retention period, the garbage collection service can remove all artifacts associated with the data from the Catalog store and Object store. Tune the garbage collector cutoff period to ensure that data is removed in a timely manner.

Use the following environment variables to tune the garbage collector:

  • INFLUXDB_IOX_GC_OBJECTSTORE_CUTOFF: the age at which Parquet files not referenced in the Catalog store become eligible for deletion from Object storage. The default is 30d.
  • INFLUXDB_IOX_GC_PARQUETFILE_CUTOFF: how long to retain rows in the Catalog store that reference Parquet files marked for deletion. The default is 30d.

These values tune how aggressive the garbage collector can be. A shorter duration value means that files can be removed at a faster pace.

To ensure there is a grace period before files and references are removed, the minimum garbage collector (GC) object store and Parquet file cutoff time is three hours (3h).

We recommend setting these options to a value aligned to your organization’s backup and recovery strategy. For example, a value of 6h (6 hours) would be appropriate for running a lean Catalog that only maintains references to recent data and does not require backups.

Use case examples

Use the following scenarios as a guide for different use cases:

Leading edge data with no backups

Custom backup window with object storage versioning

Custom backup window without object storage versioning


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New in InfluxDB 3.5

Key enhancements in InfluxDB 3.5 and the InfluxDB 3 Explorer 1.3.

See the Blog Post

InfluxDB 3.5 is now available for both Core and Enterprise, introducing custom plugin repository support, enhanced operational visibility with queryable CLI parameters and manual node management, stronger security controls, and general performance improvements.

InfluxDB 3 Explorer 1.3 brings powerful new capabilities including Dashboards (beta) for saving and organizing your favorite queries, and cache querying for instant access to Last Value and Distinct Value caches—making Explorer a more comprehensive workspace for time series monitoring and analysis.

For more information, check out:

InfluxDB Docker latest tag changing to InfluxDB 3 Core

On February 3, 2026, the latest tag for InfluxDB Docker images will point to InfluxDB 3 Core. To avoid unexpected upgrades, use specific version tags in your Docker deployments.

If using Docker to install and run InfluxDB, the latest tag will point to InfluxDB 3 Core. To avoid unexpected upgrades, use specific version tags in your Docker deployments. For example, if using Docker to run InfluxDB v2, replace the latest version tag with a specific version tag in your Docker pull command–for example:

docker pull influxdb:2