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Getting started

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

Tutorials

  • Load files using SQL
  • Load from Apache Kafka
  • Load from Apache Hadoop
  • Query data
  • Aggregate data with rollup
  • Theta sketches
  • Configure data retention
  • Update existing data
  • Compact segments
  • Deleting data
  • Write an ingestion spec
  • Transform input data
  • Convert ingestion spec to SQL
  • Run with Docker
  • Kerberized HDFS deep storage
  • Get to know Query view
  • Unnesting arrays
  • Query from deep storage
  • Jupyter Notebook tutorials
  • Docker for tutorials
  • JDBC connector

Design

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

Ingestion

  • Overview
  • Ingestion concepts

    • Source input formats
    • Input sources
    • Schema model
    • Rollup
    • Partitioning
    • Task reference

    SQL-based batch

    • SQL-based ingestion
    • Key concepts
    • Security
    • Examples
    • Reference
    • Known issues

    Streaming

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

    Classic batch

    • JSON-based batch
    • Hadoop-based
  • Ingestion spec reference
  • Schema design tips
  • Troubleshooting FAQ

Data management

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

Querying

    Druid SQL

    • Overview and syntax
    • Query from deep storage
    • SQL data types
    • Operators
    • Scalar functions
    • Aggregation functions
    • Array functions
    • Multi-value string functions
    • JSON functions
    • All functions
    • 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

API reference

  • Overview
  • HTTP APIs

    • Druid SQL
    • SQL-based ingestion
    • JSON querying
    • Tasks
    • Supervisors
    • Retention rules
    • Data management
    • Automatic compaction
    • Lookups
    • Service status
    • Dynamic configuration
    • Legacy metadata

    Java APIs

    • SQL JDBC driver

Configuration

  • Configuration reference
  • Extensions
  • Logging

Operations

  • Web console
  • Java runtime
  • Durable storage
  • 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
  • High availability
  • Rolling updates
  • Using rules to drop and retain data
  • Migrate from firehose
  • 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
  • Contribute to Druid docs
  • 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
  • Firehose (deprecated)
  • JSON-based batch (simple)
  • Realtime Process
  • kubernetes
  • Cardinality/HyperUnique aggregators
  • Select
  • Load files natively
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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.

In addition to being the backing store for segments, you can use query from deep storage and run queries against segments stored primarily in deep storage. The load rules you configure determine whether segments exist primarily in deep storage or in a combination of deep storage and Historical processes.

Deep storage options

Druid supports multiple options for deep storage, including blob storage from major cloud providers. Select the one that fits your environment.

Local

Local storage is intended for use in the following situations:

  • You have just one server.
  • Or, you have multiple servers, and they all have access to a shared filesystem (for example: NFS).

In multi-server production clusters, rather than local storage with a shared filesystem, it is instead recommended to use cloud-based deep storage (Amazon S3, Google Cloud Storage, or Azure Blob Storage), S3-compatible storage (like Minio), or HDFS. These options are generally more convenient, more scalable, and more robust than setting up a shared filesystem.

The following configurations in common.runtime.properties apply to local storage:

PropertyPossible ValuesDescriptionDefault
druid.storage.typelocalMust be set.
druid.storage.storageDirectoryany local directoryDirectory for storing segments. Must be different from druid.segmentCache.locations and druid.segmentCache.infoDir./tmp/druid/localStorage
druid.storage.ziptrue, falseWhether segments in druid.storage.storageDirectory are written as directories (false) or zip files (true).false

For example:

druid.storage.type=local
druid.storage.storageDirectory=/tmp/druid/localStorage

The druid.storage.storageDirectory must be set to a different path than druid.segmentCache.locations or druid.segmentCache.infoDir.

Amazon S3 or S3-compatible

See druid-s3-extensions.

Google Cloud Storage

See druid-google-extensions.

Azure Blob Storage

See druid-azure-extensions.

HDFS

See druid-hdfs-storage extension documentation.

Additional options

For additional deep storage options, please see our extensions list.

Querying from deep storage

Although not as performant as querying segments stored on disk for Historical processes, you can query from deep storage to access segments that you may not need frequently or with the extreme low latency Druid queries traditionally provide. You trade some performance for a total lower storage cost because you can access more of your data without the need to increase the number or capacity of your Historical processes.

For information about how to run queries, see Query from deep storage.

← Processes and serversMetadata storage →
  • Deep storage options
    • Local
    • Amazon S3 or S3-compatible
    • Google Cloud Storage
    • Azure Blob Storage
    • HDFS
    • Additional options
  • Querying from deep storage

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