Apache Druid
  • Technology
  • Use Cases
  • Powered By
  • Docs
  • Community
  • Apache
  • Download

โ€บHidden

Getting started

  • Introduction to Apache Druid
  • Quickstart (local)
  • Single server deployment
  • Clustered deployment

Tutorials

  • Load files natively
  • Load files using SQL ๐Ÿ†•
  • Load from Apache Kafka
  • Load from Apache Hadoop
  • Querying data
  • Roll-up
  • Theta sketches
  • Configuring data retention
  • Updating existing data
  • Compacting segments
  • Deleting data
  • Writing an ingestion spec
  • Transforming input data
  • Tutorial: Run with Docker
  • Kerberized HDFS deep storage
  • Convert ingestion spec to SQL
  • Jupyter Notebook tutorials

Design

  • Design
  • Segments
  • Processes and servers
  • Deep storage
  • Metadata storage
  • ZooKeeper

Ingestion

  • Ingestion
  • Data formats
  • Data model
  • Data rollup
  • Partitioning
  • Ingestion spec
  • Schema design tips
  • Stream ingestion

    • Apache Kafka ingestion
    • Apache Kafka supervisor
    • Apache Kafka operations
    • Amazon Kinesis

    Batch ingestion

    • Native batch
    • Native batch: input sources
    • Migrate from firehose
    • Hadoop-based

    SQL-based ingestion ๐Ÿ†•

    • Overview
    • Key concepts
    • API
    • Security
    • Examples
    • Reference
    • Known issues
  • Task reference
  • Troubleshooting FAQ

Data management

  • Overview
  • Data updates
  • Data deletion
  • Schema changes
  • Compaction
  • Automatic compaction

Querying

    Druid SQL

    • Overview and syntax
    • SQL data types
    • Operators
    • Scalar functions
    • Aggregation functions
    • Multi-value string functions
    • JSON functions
    • All functions
    • Druid SQL API
    • JDBC driver API
    • SQL query context
    • SQL metadata tables
    • SQL query translation
  • Native queries
  • Query execution
  • Troubleshooting
  • Concepts

    • Datasources
    • Joins
    • Lookups
    • Multi-value dimensions
    • Nested columns
    • Multitenancy
    • Query caching
    • Using query caching
    • Query context

    Native query types

    • Timeseries
    • TopN
    • GroupBy
    • Scan
    • Search
    • TimeBoundary
    • SegmentMetadata
    • DatasourceMetadata

    Native query components

    • Filters
    • Granularities
    • Dimensions
    • Aggregations
    • Post-aggregations
    • Expressions
    • Having filters (groupBy)
    • Sorting and limiting (groupBy)
    • Sorting (topN)
    • String comparators
    • Virtual columns
    • Spatial filters

Configuration

  • Configuration reference
  • Extensions
  • Logging

Operations

  • Web console
  • Java runtime
  • Security

    • Security overview
    • User authentication and authorization
    • LDAP auth
    • Password providers
    • Dynamic Config Providers
    • TLS support

    Performance tuning

    • Basic cluster tuning
    • Segment size optimization
    • Mixed workloads
    • HTTP compression
    • Automated metadata cleanup

    Monitoring

    • Request logging
    • Metrics
    • Alerts
  • API reference
  • High availability
  • Rolling updates
  • Using rules to drop and retain data
  • Working with different versions of Apache Hadoop
  • Misc

    • dump-segment tool
    • reset-cluster tool
    • insert-segment-to-db tool
    • pull-deps tool
    • Deep storage migration
    • Export Metadata Tool
    • Metadata Migration
    • Content for build.sbt

Development

  • Developing on Druid
  • Creating extensions
  • JavaScript functionality
  • Build from source
  • Versioning
  • Experimental features

Misc

  • Papers

Hidden

  • Apache Druid vs Elasticsearch
  • Apache Druid vs. Key/Value Stores (HBase/Cassandra/OpenTSDB)
  • Apache Druid vs Kudu
  • Apache Druid vs Redshift
  • Apache Druid vs Spark
  • Apache Druid vs SQL-on-Hadoop
  • Authentication and Authorization
  • Broker
  • Coordinator Process
  • Historical Process
  • Indexer Process
  • Indexing Service
  • MiddleManager Process
  • Overlord Process
  • Router Process
  • Peons
  • Approximate Histogram aggregators
  • Apache Avro
  • Microsoft Azure
  • Bloom Filter
  • DataSketches extension
  • DataSketches HLL Sketch module
  • DataSketches Quantiles Sketch module
  • DataSketches Theta Sketch module
  • DataSketches Tuple Sketch module
  • Basic Security
  • Kerberos
  • Cached Lookup Module
  • Apache Ranger Security
  • Google Cloud Storage
  • HDFS
  • Apache Kafka Lookups
  • Globally Cached Lookups
  • MySQL Metadata Store
  • ORC Extension
  • Druid pac4j based Security extension
  • Apache Parquet Extension
  • PostgreSQL Metadata Store
  • Protobuf
  • S3-compatible
  • Simple SSLContext Provider Module
  • Stats aggregator
  • Test Stats Aggregators
  • Druid AWS RDS Module
  • Kubernetes
  • Ambari Metrics Emitter
  • Apache Cassandra
  • Rackspace Cloud Files
  • DistinctCount Aggregator
  • Graphite Emitter
  • InfluxDB Line Protocol Parser
  • InfluxDB Emitter
  • Kafka Emitter
  • Materialized View
  • Moment Sketches for Approximate Quantiles module
  • Moving Average Query
  • OpenTSDB Emitter
  • Druid Redis Cache
  • Microsoft SQLServer
  • StatsD Emitter
  • T-Digest Quantiles Sketch module
  • Thrift
  • Timestamp Min/Max aggregators
  • GCE Extensions
  • Aliyun OSS
  • Prometheus Emitter
  • kubernetes
  • Cardinality/HyperUnique aggregators
  • Select
  • Firehose (deprecated)
  • Native batch (simple)
  • Realtime Process
Edit

Materialized View

To use this Apache Druid feature, make sure to load materialized-view-selection and materialized-view-maintenance. In addition, this feature currently requires a Hadoop cluster.

This feature enables Druid to greatly improve the query performance, especially when the query dataSource has a very large number of dimensions but the query only required several dimensions. This feature includes two parts. One is materialized-view-maintenance, and the other is materialized-view-selection.

Materialized-view-maintenance

In materialized-view-maintenance, dataSources user ingested are called "base-dataSource". For each base-dataSource, we can submit derivativeDataSource supervisors to create and maintain other dataSources which we called "derived-dataSource". The dimensions and metrics of derived-dataSources are the subset of base-dataSource's. The derivativeDataSource supervisor is used to keep the timeline of derived-dataSource consistent with base-dataSource. Each derivativeDataSource supervisor is responsible for one derived-dataSource.

A sample derivativeDataSource supervisor spec is shown below:

   {
       "type": "derivativeDataSource",
       "baseDataSource": "wikiticker",
       "dimensionsSpec": {
           "dimensions": [
               "isUnpatrolled",
               "metroCode",
               "namespace",
               "page",
               "regionIsoCode",
               "regionName",
               "user"
           ]
       },
       "metricsSpec": [
           {
               "name": "count",
               "type": "count"
           },
           {
               "name": "added",
               "type": "longSum",
               "fieldName": "added"
           }
       ],
       "tuningConfig": {
           "type": "hadoop"
       }
   }

Supervisor Configuration

FieldDescriptionRequired
TypeThe supervisor type. This should always be derivativeDataSource.yes
baseDataSourceThe name of base dataSource. This dataSource data should be already stored inside Druid, and the dataSource will be used as input data.yes
dimensionsSpecSpecifies the dimensions of the data. These dimensions must be the subset of baseDataSource's dimensions.yes
metricsSpecA list of aggregators. These metrics must be the subset of baseDataSource's metrics. See aggregations.yes
tuningConfigTuningConfig must be HadoopTuningConfig. See Hadoop tuning config.yes
dataSourceThe name of this derived dataSource.no(default=baseDataSource-hashCode of supervisor)
hadoopDependencyCoordinatesA JSON array of Hadoop dependency coordinates that Druid will use, this property will override the default Hadoop coordinates. Once specified, Druid will look for those Hadoop dependencies from the location specified by druid.extensions.hadoopDependenciesDirno
classpathPrefixClasspath that will be prepended for the Peon process.no
contextSee below.no

Context

FieldDescriptionRequired
maxTaskCountThe max number of tasks the supervisor can submit simultaneously.no(default=1)

Materialized-view-selection

In materialized-view-selection, we implement a new query type view. When we request a view query, Druid will try its best to optimize the query based on query dataSource and intervals.

A sample view query spec is shown below:

   {
       "queryType": "view",
       "query": {
           "queryType": "groupBy",
           "dataSource": "wikiticker",
           "granularity": "all",
           "dimensions": [
               "user"
           ],
           "limitSpec": {
               "type": "default",
               "limit": 1,
               "columns": [
                   {
                       "dimension": "added",
                       "direction": "descending",
                       "dimensionOrder": "numeric"
                   }
               ]
           },
           "aggregations": [
               {
                   "type": "longSum",
                   "name": "added",
                   "fieldName": "added"
               }
           ],
           "intervals": [
               "2015-09-12/2015-09-13"
           ]
       }
   }

There are 2 parts in a view query:

FieldDescriptionRequired
queryTypeThe query type. This should always be viewyes
queryThe real query of this view query. The real query must be groupBy, topN, or timeseries type.yes

Note that Materialized View is currently designated as experimental. Please make sure the time of all processes are the same and increase monotonically. Otherwise, some unexpected errors may happen on query results.

โ† Kafka EmitterMoment Sketches for Approximate Quantiles module โ†’
  • Materialized-view-maintenance
  • Materialized-view-selection

Technologyโ€‚ยทโ€‚Use Casesโ€‚ยทโ€‚Powered by Druidโ€‚ยทโ€‚Docsโ€‚ยทโ€‚Communityโ€‚ยทโ€‚Downloadโ€‚ยทโ€‚FAQ

โ€‚ยทโ€‚โ€‚ยทโ€‚โ€‚ยทโ€‚
Copyright ยฉ 2022 Apache Software Foundation.
Except where otherwise noted, licensed under CC BY-SA 4.0.
Apache Druid, Druid, and the Druid logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.