* Reconcile terminology and method naming to 'used/unused segments'; Don't use terms 'enable/disable data source'; Rename MetadataSegmentManager to MetadataSegments; Make REST API methods which mark segments as used/unused to return server error instead of an empty response in case of error * Fix brace * Import order * Rename withKillDataSourceWhitelist to withSpecificDataSourcesToKill * Fix tests * Fix tests by adding proper methods without interval parameters to IndexerMetadataStorageCoordinator instead of hacking with Intervals.ETERNITY * More aligned names of DruidCoordinatorHelpers, rename several CoordinatorDynamicConfig parameters * Rename ClientCompactTaskQuery to ClientCompactionTaskQuery for consistency with CompactionTask; ClientCompactQueryTuningConfig to ClientCompactionTaskQueryTuningConfig * More variable and method renames * Rename MetadataSegments to SegmentsMetadata * Javadoc update * Simplify SegmentsMetadata.getUnusedSegmentIntervals(), more javadocs * Update Javadoc of VersionedIntervalTimeline.iterateAllObjects() * Reorder imports * Rename SegmentsMetadata.tryMark... methods to mark... and make them to return boolean and the numbers of segments changed and relay exceptions to callers * Complete merge * Add CollectionUtils.newTreeSet(); Refactor DruidCoordinatorRuntimeParams creation in tests * Remove MetadataSegmentManager * Rename millisLagSinceCoordinatorBecomesLeaderBeforeCanMarkAsUnusedOvershadowedSegments to leadingTimeMillisBeforeCanMarkAsUnusedOvershadowedSegments * Fix tests, refactor DruidCluster creation in tests into DruidClusterBuilder * Fix inspections * Fix SQLMetadataSegmentManagerEmptyTest and rename it to SqlSegmentsMetadataEmptyTest * Rename SegmentsAndMetadata to SegmentsAndCommitMetadata to reduce the similarity with SegmentsMetadata; Rename some methods * Rename DruidCoordinatorHelper to CoordinatorDuty, refactor DruidCoordinator * Unused import * Optimize imports * Rename IndexerSQLMetadataStorageCoordinator.getDataSourceMetadata() to retrieveDataSourceMetadata() * Unused import * Update terminology in datasource-view.tsx * Fix label in datasource-view.spec.tsx.snap * Fix lint errors in datasource-view.tsx * Doc improvements * Another attempt to please TSLint * Another attempt to please TSLint * Style fixes * Fix IndexerSQLMetadataStorageCoordinator.createUsedSegmentsSqlQueryForIntervals() (wrong merge) * Try to fix docs build issue * Javadoc and spelling fixes * Rename SegmentsMetadata to SegmentsMetadataManager, address other comments * Address more comments
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id | title |
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metadata-storage | Metadata storage |
The Metadata Storage is an external dependency of Apache Druid. Druid uses it to store various metadata about the system, but not to store the actual data. There are a number of tables used for various purposes described below.
Derby is the default metadata store for Druid, however, it is not suitable for production. MySQL and PostgreSQL are more production suitable metadata stores.
The Metadata Storage stores the entire metadata which is essential for a Druid cluster to work. For production clusters, consider using MySQL or PostgreSQL instead of Derby. Also, it's highly recommended to set up a high availability environment because there is no way to restore if you lose any metadata.
Using Derby
Add the following to your Druid configuration.
druid.metadata.storage.type=derby
druid.metadata.storage.connector.connectURI=jdbc:derby://localhost:1527//opt/var/druid_state/derby;create=true
MySQL
See mysql-metadata-storage extension documentation.
PostgreSQL
See postgresql-metadata-storage.
Adding custom dbcp properties
NOTE: These properties are not settable through the druid.metadata.storage.connector.dbcp properties
: username
, password
, connectURI
, validationQuery
, testOnBorrow
. These must be set through druid.metadata.storage.connector
properties.
Example supported properties:
druid.metadata.storage.connector.dbcp.maxConnLifetimeMillis=1200000
druid.metadata.storage.connector.dbcp.defaultQueryTimeout=30000
See BasicDataSource Configuration for full list.
Metadata storage tables
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 payload
column stores a JSON blob that has all of the metadata for the segment (some of the data stored in this payload is redundant with some of the columns in the table, that is intentional). This looks something like
{
"dataSource":"wikipedia",
"interval":"2012-05-23T00:00:00.000Z/2012-05-24T00:00:00.000Z",
"version":"2012-05-24T00:10:00.046Z",
"loadSpec":{
"type":"s3_zip",
"bucket":"bucket_for_segment",
"key":"path/to/segment/on/s3"
},
"dimensions":"comma-delimited-list-of-dimension-names",
"metrics":"comma-delimited-list-of-metric-names",
"shardSpec":{"type":"none"},
"binaryVersion":9,
"size":size_of_segment,
"identifier":"wikipedia_2012-05-23T00:00:00.000Z_2012-05-24T00:00:00.000Z_2012-05-23T00:10:00.046Z"
}
Note that the format of this blob can and will change from time-to-time.
Rule table
The rule table is used to store 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 is used to store 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
There are also a number of tables created and used by the Overlord and MiddleManager when managing tasks.
Audit table
The Audit table is used to store the audit history for configuration changes e.g rule changes done by Coordinator and other config changes.
##Accessed by: ##
The Metadata Storage is accessed only by:
- Indexing Service Processes (if any)
- Realtime Processes (if any)
- Coordinator Processes
Thus you need to give permissions (e.g., in AWS Security Groups) only for these machines to access the Metadata storage.