Automated cleanup for metadata records
Apache Druid relies on metadata storage to track information on data storage, operations, and system configuration. The metadata store includes the following:
- Segment records
- Audit records
- Supervisor records
- Rule records
- Compaction configuration records
- Datasource records created by supervisors
- Indexer task logs
When you delete some entities from Apache Druid, records related to the entity may remain in the metadata store. If you have a high datasource churn rate, meaning you frequently create and delete many short-lived datasources or other related entities like compaction configuration or rules, the leftover records can fill your metadata store and cause performance issues. To maintain metadata store performance, you can configure Apache Druid to automatically remove records associated with deleted entities from the metadata store.
By default, Druid automatically cleans up metadata older than 90 days.
This applies to all metadata entities in this topic except compaction configuration records and indexer task logs, for which cleanup is disabled by default.
You can configure the retention period for each metadata type, when available, through the record's
Certain records may require additional conditions be satisfied before clean up occurs.
See the example for how you can customize the automated metadata cleanup for a specific use case.
Automated cleanup strategies
There are several cases when you should consider automated cleanup of the metadata related to deleted datasources:
- If you know you have many high-churn datasources, for example, you have scripts that create and delete supervisors regularly.
- If you have issues with the hard disk for your metadata database filling up.
- If you run into performance issues with the metadata database. For example, API calls are very slow or fail to execute.
If you have compliance requirements to keep audit records and you enable automated cleanup for audit records, use alternative methods to preserve audit metadata, for example, by periodically exporting audit metadata records to external storage.
Configure automated metadata cleanup
You can configure cleanup for each entity separately, as described in this section.
Define the properties in the
The cleanup of one entity may depend on the cleanup of another entity as follows:
- You have to configure a kill task for segment records before you can configure automated cleanup for rules or compaction configuration.
- You have to schedule the metadata management tasks to run at the same or higher frequency as your most frequent cleanup job. For example, if your most frequent cleanup job is every hour, set the metadata store management period to one hour or less:
For details on configuration properties, see Metadata management. If you want to skip the details, check out the example for configuring automated metadata cleanup.
Segment records and segments in deep storage (kill task)
The kill task is the only configuration in this topic that affects actual data in deep storage and not simply metadata or logs.
Segment records and segments in deep storage become eligible for deletion when both of the following conditions hold:
- When they meet the eligibility requirement of kill task datasource configuration according to
killDataSourceWhitelistset in the Coordinator dynamic configuration. See Dynamic configuration.
- When the
durationToRetaintime has passed since their creation.
Kill tasks use the following configuration:
true, enables the Coordinator to submit a kill task for unused segments, which deletes them completely from metadata store and from deep storage. Only applies to the specified datasources in the dynamic configuration parameter
killDataSourceWhitelistis not set or empty, then kill tasks can be submitted for all datasources.
druid.coordinator.kill.period: Defines the frequency in ISO 8601 format for the cleanup job to check for and delete eligible segments. Defaults to
P1D. Must be greater than
druid.coordinator.kill.durationToRetain: Defines the retention period in ISO 8601 format after creation that segments become eligible for deletion.
druid.coordinator.kill.maxSegments: Defines the maximum number of segments to delete per kill task.
All audit records become eligible for deletion when the
durationToRetain time has passed since their creation.
Audit cleanup uses the following configuration:
true, enables cleanup for audit records.
druid.coordinator.kill.audit.period: Defines the frequency in ISO 8601 format for the cleanup job to check for and delete eligible audit records. Defaults to
druid.coordinator.kill.audit.durationToRetain: Defines the retention period in ISO 8601 format after creation that audit records become eligible for deletion.
Supervisor records become eligible for deletion when the supervisor is terminated and the
durationToRetain time has passed since their creation.
Supervisor cleanup uses the following configuration:
true, enables cleanup for supervisor records.
druid.coordinator.kill.supervisor.period: Defines the frequency in ISO 8601 format for the cleanup job to check for and delete eligible supervisor records. Defaults to
druid.coordinator.kill.supervisor.durationToRetain: Defines the retention period in ISO 8601 format after creation that supervisor records become eligible for deletion.
Rule records become eligible for deletion when all segments for the datasource have been killed by the kill task and the
durationToRetain time has passed since their creation. Automated cleanup for rules requires a kill task.
Rule cleanup uses the following configuration:
true, enables cleanup for rules records.
druid.coordinator.kill.rule.period: Defines the frequency in ISO 8601 format for the cleanup job to check for and delete eligible rules records. Defaults to
druid.coordinator.kill.rule.durationToRetain: Defines the retention period in ISO 8601 format after creation that rules records become eligible for deletion.
Compaction configuration records
Druid retains all compaction configuration records by default, which should be suitable for most use cases. If you create and delete short-lived datasources with high frequency, and you set auto compaction configuration on those datasources, then consider turning on automated cleanup of compaction configuration records.
With automated cleanup of compaction configuration records, if you create a compaction configuration for some datasource before the datasource exists, for example if initial ingestion is still ongoing, Druid may remove the compaction configuration. To prevent the configuration from being prematurely removed, wait for the datasource to be created before applying the compaction configuration to the datasource.
Unlike other metadata records, compaction configuration records do not have a retention period set by
durationToRetain. Druid deletes compaction configuration records at every cleanup cycle for inactive datasources, which do not have segments either used or unused.
Compaction configuration records in the
druid_config table become eligible for deletion after all segments for the datasource have been killed by the kill task. Automated cleanup for compaction configuration requires a kill task.
Compaction configuration cleanup uses the following configuration:
true, enables cleanup for compaction configuration records.
druid.coordinator.kill.compaction.period: Defines the frequency in ISO 8601 format for the cleanup job to check for and delete eligible compaction configuration records. Defaults to
If you already have an extremely large compaction configuration, you may not be able to delete compaction configuration due to size limits with the audit log. In this case you can set
druid.audit.manager.skipNullFieldto avoid the auditing issue. See Audit logging.
Datasource records created by supervisors
Datasource records created by supervisors become eligible for deletion when the supervisor is terminated or does not exist in the
druid_supervisors table and the
durationToRetain time has passed since their creation.
Datasource cleanup uses the following configuration:
true, enables cleanup datasources created by supervisors.
druid.coordinator.kill.datasource.period: Defines the frequency in ISO 8601 format for the cleanup job to check for and delete eligible datasource records. Defaults to
druid.coordinator.kill.datasource.durationToRetain: Defines the retention period in ISO 8601 format after creation that datasource records become eligible for deletion.
Indexer task logs
You can configure the Overlord to periodically delete indexer task logs and associated metadata. During cleanup, the Overlord removes the following:
- Indexer task logs from deep storage.
- Indexer task log metadata from the tasks and tasklogs tables in metadata storage (named
druid_tasklogsby default). Druid no longer uses the tasklogs table, and the table is always empty.
To configure cleanup of task logs by the Overlord, set the following properties in the
Indexer task log cleanup on the Overlord uses the following configuration:
true, enables cleanup of task logs.
druid.indexer.logs.kill.durationToRetain: Defines the length of time in milliseconds to retain task logs.
druid.indexer.logs.kill.initialDelay: Defines the length of time in milliseconds after the Overlord starts before it executes its first job to kill task logs.
druid.indexer.logs.kill.delay: The length of time in milliseconds between jobs to kill task logs.
For more detail, see Task logging.
Disable automated metadata cleanup
Druid automatically cleans up metadata records, excluding compaction configuration records and indexer task logs.
To disable automated metadata cleanup, set the following properties in the
# Keep unused segments druid.coordinator.kill.on=false # Keep audit records druid.coordinator.kill.audit.on=false # Keep supervisor records druid.coordinator.kill.supervisor.on=false # Keep rules records druid.coordinator.kill.rule.on=false # Keep datasource records created by supervisors druid.coordinator.kill.datasource.on=false
Example configuration for automated metadata cleanup
Consider a scenario where you have scripts to create and delete hundreds of datasources and related entities a day. You do not want to fill your metadata store with leftover records. The datasources and related entities tend to persist for only one or two days. Therefore, you want to run a cleanup job that identifies and removes leftover records that are at least four days old. The exception is for audit logs, which you need to retain for 30 days:
... # Schedule the metadata management store task for every hour: druid.coordinator.period.metadataStoreManagementPeriod=P1H # Set a kill task to poll every day to delete Segment records and segments # in deep storage > 4 days old. When druid.coordinator.kill.on is set to true, # you can set killDataSourceWhitelist in the dynamic configuration to limit # the datasources that can be killed. # Required also for automated cleanup of rules and compaction configuration. druid.coordinator.kill.on=true druid.coordinator.kill.period=P1D druid.coordinator.kill.durationToRetain=P4D druid.coordinator.kill.maxSegments=1000 # Poll every day to delete audit records > 30 days old druid.coordinator.kill.audit.on=true druid.coordinator.kill.audit.period=P1D druid.coordinator.kill.audit.durationToRetain=P30D # Poll every day to delete supervisor records > 4 days old druid.coordinator.kill.supervisor.on=true druid.coordinator.kill.supervisor.period=P1D druid.coordinator.kill.supervisor.durationToRetain=P4D # Poll every day to delete rules records > 4 days old druid.coordinator.kill.rule.on=true druid.coordinator.kill.rule.period=P1D druid.coordinator.kill.rule.durationToRetain=P4D # Poll every day to delete compaction configuration records druid.coordinator.kill.compaction.on=true druid.coordinator.kill.compaction.period=P1D # Poll every day to delete datasource records created by supervisors > 4 days old druid.coordinator.kill.datasource.on=true druid.coordinator.kill.datasource.period=P1D druid.coordinator.kill.datasource.durationToRetain=P4D ...
See the following topics for more information:
- Metadata management for metadata store configuration reference.
- Metadata storage for an overview of the metadata storage database.