Apache Druid relies on an external dependency for metadata storage. Druid uses the metadata store to house various metadata about the system, but not to store the actual data. The metadata store retains all metadata essential for a Druid cluster to work.
The metadata store includes the following:
- Segments records
- Rule records
- Configuration records
- Task-related tables
- Audit records
Derby is the default metadata store for Druid, however, it is not suitable for production. MySQL and PostgreSQL are more production suitable metadata stores. See Metadata storage configuration for the default configuration settings.
We also recommend you set up a high availability environment because there is no way to restore lost metadata.
Available metadata stores
Druid supports Derby, MySQL, and PostgreSQL for storing metadata.
To avoid issues with upgrades that require schema changes to a large metadata table, consider a metadata store version that supports instant ADD COLUMN semantics. See the database-specific docs for guidance on versions.
For production clusters, consider using MySQL or PostgreSQL instead of Derby.
Configure metadata storage with Derby by setting the following properties in your Druid configuration.
Adding custom DBCP properties
You can add custom properties to customize the database connection pool (DBCP) for connecting to the metadata store.
Define these properties with a
Certain properties cannot be set through
druid.metadata.storage.connector.dbcp. and must be set with the prefix
See BasicDataSource Configuration for a full list of configurable properties.
Metadata storage tables
This section describes the various tables in metadata storage.
This is dictated by the
This table stores metadata about the segments that should be available in the system. (This set of segments is called "used segments" elsewhere in the documentation and throughout the project.) The table is polled by the Coordinator to determine the set of segments that should be available for querying in the system. The table has two main functional columns, the other columns are for indexing purposes.
Value 1 in the
used column means that the segment should be "used" by the cluster (i.e., it should be loaded and
available for requests). Value 0 means that the segment should not be loaded into the cluster. We do this as a means of
unloading segments from the cluster without actually removing their metadata (which allows for simpler rolling back if
that is ever an issue). The
used column has a corresponding
used_status_last_updated column which denotes the time
used status of the segment was last updated. This information can be used by the Coordinator to determine if
a segment is a candidate for deletion (if automated segment killing is enabled).
payload column stores a JSON blob that has all of the metadata for the segment.
Some of the data in the
payload column intentionally duplicates data from other columns in the segments table.
As an example, the
payload column may take the following form:
The rule table stores the various rules about where segments should land. These rules are used by the Coordinator when making segment (re-)allocation decisions about the cluster.
The config table stores runtime configuration objects. We do not have many of these yet and we are not sure if we will keep this mechanism going forward, but it is the beginnings of a method of changing some configuration parameters across the cluster at runtime.
The audit table stores the audit history for configuration changes such as rule changes done by Coordinator and other config changes.
Metadata storage access
Only the following processes access the metadata storage:
- Indexing service processes (if any)
- Realtime processes (if any)
- Coordinator processes
Thus you need to give permissions (e.g., in AWS security groups) for only these machines to access the metadata storage.
See the following topics for more information: