Apache Druid
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›Development

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

JavaScript programming guide

This page discusses how to use JavaScript to extend Apache Druid.

Examples

JavaScript can be used to extend Druid in a variety of ways:

  • Aggregators
  • Extraction functions
  • Filters
  • Post-aggregators
  • Input parsers
  • Router strategy
  • Worker select strategy

JavaScript can be injected dynamically at runtime, making it convenient to rapidly prototype new functionality without needing to write and deploy Druid extensions.

Druid uses the Mozilla Rhino engine at optimization level 9 to compile and execute JavaScript.

Security

Druid does not execute JavaScript functions in a sandbox, so they have full access to the machine. So JavaScript functions allow users to execute arbitrary code inside druid process. So, by default, JavaScript is disabled. However, on dev/staging environments or secured production environments you can enable those by setting the configuration property druid.javascript.enabled = true.

Global variables

Avoid using global variables. Druid may share the global scope between multiple threads, which can lead to unpredictable results if global variables are used.

Performance

Simple JavaScript functions typically have a slight performance penalty to native speed. More complex JavaScript functions can have steeper performance penalties. Druid compiles JavaScript functions once on each data process per query.

You may need to pay special attention to garbage collection when making heavy use of JavaScript functions, especially garbage collection of the compiled classes themselves. Be sure to use a garbage collector configuration that supports timely collection of unused classes (this is generally easier on JDK8 with the Metaspace than it is on JDK7).

JavaScript vs. Native Extensions

Generally we recommend using JavaScript when security is not an issue, and when speed of development is more important than performance or memory use. If security is an issue, or if performance and memory use are of the utmost importance, we recommend developing a native Druid extension.

In addition, native Druid extensions are more flexible than JavaScript functions. There are some kinds of extensions (like sketches) that must be written as native Druid extensions due to their need for custom data formats.

← Creating extensionsBuild from source →
  • Examples
  • Security
  • Global variables
  • Performance
  • JavaScript vs. Native Extensions

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