Metadata storage

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.

Metadata storage - 图1info

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. Note that your metadata store must be ACID-compliant. If it isn’t ACID-compliant, you can encounter issues, such as tasks failing sporadically.

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.

MySQL

See mysql-metadata-storage extension documentation.

PostgreSQL

See postgresql-metadata-storage.

Derby

Metadata storage - 图2info

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.

  1. druid.metadata.storage.type=derby
  2. druid.metadata.storage.connector.connectURI=jdbc:derby://localhost:1527//opt/var/druid_state/derby;create=true

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 druid.metadata.storage.connector.dbcp. prefix. For example:

  1. druid.metadata.storage.connector.dbcp.maxConnLifetimeMillis=1200000
  2. druid.metadata.storage.connector.dbcp.defaultQueryTimeout=30000

Certain properties cannot be set through druid.metadata.storage.connector.dbcp. and must be set with the prefix druid.metadata.storage.connector.:

  • username
  • password
  • connectURI
  • validationQuery
  • testOnBorrow

See BasicDataSource Configuration for a full list of configurable properties.

Metadata storage tables

This section describes the various tables in metadata storage.

Segments table

This is dictated by the druid.metadata.storage.tables.segments property.

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 when the 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).

The 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:

  1. {
  2.  "dataSource":"wikipedia",
  3.  "interval":"2012-05-23T00:00:00.000Z/2012-05-24T00:00:00.000Z",
  4.  "version":"2012-05-24T00:10:00.046Z",
  5.  "loadSpec":{
  6.     "type":"s3_zip",
  7.     "bucket":"bucket_for_segment",
  8.     "key":"path/to/segment/on/s3"
  9.  },
  10.  "dimensions":"comma-delimited-list-of-dimension-names",
  11.  "metrics":"comma-delimited-list-of-metric-names",
  12.  "shardSpec":{"type":"none"},
  13.  "binaryVersion":9,
  14.  "size":size_of_segment,
  15.  "identifier":"wikipedia_2012-05-23T00:00:00.000Z_2012-05-24T00:00:00.000Z_2012-05-23T00:10:00.046Z"
  16. }

Rule table

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.

Config table

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.

Task-related tables are created and used by the Overlord and MiddleManager when managing tasks.

Audit table

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:

  1. Indexing service processes (if any)
  2. Realtime processes (if any)
  3. Coordinator processes

Thus you need to give permissions (e.g., in AWS security groups) for only these machines to access the metadata storage.

Learn more

See the following topics for more information: