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›Development

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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Versioning

This page discusses how we do versioning and provides information on our stable releases.

Versioning Strategy

We generally follow semantic versioning. The general idea is

  • "Major" version (leftmost): backwards incompatible, no guarantees exist about APIs between the versions
  • "Minor" version (middle number): you can move forward from a smaller number to a larger number, but moving backwards might be incompatible.
  • "bug-fix" version ("patch" or the rightmost): Interchangeable. The higher the number, the more things are fixed (hopefully), but the programming interfaces are completely compatible and you should be able to just drop in a new jar and have it work.

Note that this is defined in terms of programming API, not in terms of functionality. It is possible that a brand new awesome way of doing something is introduced in a "bug-fix" release version if it doesn’t add to the public API or change it.

One exception for right now, while we are still in major version 0, we are considering the APIs to be in beta and are conflating "major" and "minor" so a minor version increase could be backwards incompatible for as long as we are at major version 0. These will be communicated via email on the group.

For external deployments, we recommend running the stable release tag. Releases are considered stable after we have deployed them into our production environment and they have operated bug-free for some time.

Tagging strategy

Tags of the codebase are equivalent to release candidates. We tag the code every time we want to take it through our release process, which includes some QA cycles and deployments. So, it is not safe to assume that a tag is a stable release, it is a solidification of the code as it goes through our production QA cycle and deployment. Tags will never change, but we often go through a number of iterations of tags before actually getting a stable release onto production. So, it is recommended that if you are not aware of what is on a tag, to stick to the stable releases listed on the Release page.

← Build from sourceContribute to Druid docs →
  • Versioning Strategy
  • Tagging strategy

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