Apache Druid
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Getting started

  • Introduction to Apache Druid
  • Quickstart
  • Docker
  • Single server deployment
  • Clustered deployment

Tutorials

  • Loading files natively
  • Load from Apache Kafka
  • Load from Apache Hadoop
  • Querying data
  • Roll-up
  • Configuring data retention
  • Updating existing data
  • Compacting segments
  • Deleting data
  • Writing an ingestion spec
  • Transforming input data
  • Kerberized HDFS deep storage

Design

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

Ingestion

  • Ingestion
  • Data formats
  • Schema design tips
  • Data management
  • Stream ingestion

    • Apache Kafka
    • Amazon Kinesis
    • Tranquility

    Batch ingestion

    • Native batch
    • Hadoop-based
  • Task reference
  • Troubleshooting FAQ

Querying

  • Druid SQL
  • Native queries
  • Query execution
  • Concepts

    • Datasources
    • Joins
    • Lookups
    • Multi-value dimensions
    • Multitenancy
    • Query caching
    • Context parameters

    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
  • Getting started with Apache Druid
  • Basic cluster tuning
  • API reference
  • High availability
  • Rolling updates
  • Retaining or automatically dropping data
  • Metrics
  • Alerts
  • Working with different versions of Apache Hadoop
  • HTTP compression
  • TLS support
  • Password providers
  • dump-segment tool
  • reset-cluster tool
  • insert-segment-to-db tool
  • pull-deps tool
  • Misc

    • Legacy Management UIs
    • Deep storage migration
    • Export Metadata Tool
    • Metadata Migration
    • Segment Size Optimization
    • 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
  • 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
  • Cardinality/HyperUnique aggregators
  • Select
  • Realtime Process
Edit

Deep storage

Deep storage is where segments are stored. It is a storage mechanism that Apache Druid does not provide. This deep storage infrastructure defines the level of durability of your data, as long as Druid processes can see this storage infrastructure and get at the segments stored on it, you will not lose data no matter how many Druid nodes you lose. If segments disappear from this storage layer, then you will lose whatever data those segments represented.

Local Mount

A local mount can be used for storage of segments as well. This allows you to use just your local file system or anything else that can be mount locally like NFS, Ceph, etc. This is the default deep storage implementation.

In order to use a local mount for deep storage, you need to set the following configuration in your common configs.

PropertyPossible ValuesDescriptionDefault
druid.storage.typelocalMust be set.
druid.storage.storageDirectoryDirectory for storing segments.Must be set.

Note that you should generally set druid.storage.storageDirectory to something different from druid.segmentCache.locations and druid.segmentCache.infoDir.

If you are using the Hadoop indexer in local mode, then just give it a local file as your output directory and it will work.

S3-compatible

See druid-s3-extensions extension documentation.

HDFS

See druid-hdfs-storage extension documentation.

Additional Deep Stores

For additional deep stores, please see our extensions list.

← Processes and serversMetadata storage →
  • Local Mount
  • S3-compatible
  • HDFS
  • Additional Deep Stores

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