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
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Development

  • Developing on Druid
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Misc

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  • Authentication and Authorization
  • Broker
  • Coordinator Process
  • Historical Process
  • Indexer Process
  • Indexing Service
  • MiddleManager Process
  • Overlord Process
  • Router Process
  • Peons
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Overlord Process

Configuration

For Apache Druid Overlord Process Configuration, see Overlord Configuration.

HTTP endpoints

For a list of API endpoints supported by the Overlord, please see the API reference.

Overview

The Overlord process is responsible for accepting tasks, coordinating task distribution, creating locks around tasks, and returning statuses to callers. Overlord can be configured to run in one of two modes - local or remote (local being default). In local mode Overlord is also responsible for creating Peons for executing tasks. When running the Overlord in local mode, all MiddleManager and Peon configurations must be provided as well. Local mode is typically used for simple workflows. In remote mode, the Overlord and MiddleManager are run in separate processes and you can run each on a different server. This mode is recommended if you intend to use the indexing service as the single endpoint for all Druid indexing.

Overlord console

The Overlord provides a UI for managing tasks and workers. For more details, please see overlord console.

Blacklisted workers

If a MiddleManager has task failures above a threshold, the Overlord will blacklist these MiddleManagers. No more than 20% of the MiddleManagers can be blacklisted. Blacklisted MiddleManagers will be periodically whitelisted.

The following variables can be used to set the threshold and blacklist timeouts.

druid.indexer.runner.maxRetriesBeforeBlacklist
druid.indexer.runner.workerBlackListBackoffTime
druid.indexer.runner.workerBlackListCleanupPeriod
druid.indexer.runner.maxPercentageBlacklistWorkers

Autoscaling

The Autoscaling mechanisms currently in place are tightly coupled with our deployment infrastructure but the framework should be in place for other implementations. We are highly open to new implementations or extensions of the existing mechanisms. In our own deployments, MiddleManager processes are Amazon AWS EC2 nodes and they are provisioned to register themselves in a galaxy environment.

If autoscaling is enabled, new MiddleManagers may be added when a task has been in pending state for too long. MiddleManagers may be terminated if they have not run any tasks for a period of time.

← MiddleManager ProcessRouter Process →

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