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Getting started

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

Tutorials

  • Load files natively
  • Load files using SQL ๐Ÿ†•
  • Load from Apache Kafka
  • Load from Apache Hadoop
  • Querying data
  • Roll-up
  • Theta sketches
  • Configuring data retention
  • Updating existing data
  • Compacting segments
  • Deleting data
  • Writing an ingestion spec
  • Transforming input data
  • Tutorial: Run with Docker
  • Kerberized HDFS deep storage
  • Convert ingestion spec to SQL
  • Jupyter Notebook tutorials

Design

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

Ingestion

  • Ingestion
  • Data formats
  • Data model
  • Data rollup
  • Partitioning
  • Ingestion spec
  • Schema design tips
  • Stream ingestion

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

    Batch ingestion

    • Native batch
    • Native batch: input sources
    • Migrate from firehose
    • Hadoop-based

    SQL-based ingestion ๐Ÿ†•

    • Overview
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    • API
    • Security
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    • Known issues
  • Task reference
  • Troubleshooting FAQ

Data management

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

Querying

    Druid SQL

    • Overview and syntax
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    • Aggregation functions
    • Multi-value string functions
    • JSON functions
    • All functions
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    • Datasources
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    Native query types

    • Timeseries
    • TopN
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    • Scan
    • Search
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    • 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
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Operations

  • Web console
  • Java runtime
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    • Security overview
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    Performance tuning

    • Basic cluster tuning
    • Segment size optimization
    • Mixed workloads
    • HTTP compression
    • Automated metadata cleanup

    Monitoring

    • Request logging
    • Metrics
    • Alerts
  • API reference
  • High availability
  • Rolling updates
  • Using rules to drop and retain data
  • 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
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    • Content for build.sbt

Development

  • Developing on Druid
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Misc

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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
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  • Select
  • Firehose (deprecated)
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Edit

Rolling updates

For rolling Apache Druid cluster updates with no downtime, we recommend updating Druid processes in the following order:

  1. Historical
  2. *Overlord (if any)
  3. *Middle Manager/Indexers (if any)
  4. Standalone Real-time (if any)
  5. Broker
  6. Coordinator ( or merged Coordinator+Overlord )

For information about the latest release, see Druid releases.

* In 0.12.0, there are protocol changes between the Kafka supervisor and Kafka Indexing task and also some changes to the metadata formats persisted on disk. Therefore, to support rolling upgrade, all the Middle Managers will need to be upgraded first before the Overlord. Note that this ordering is different from the standard order of upgrade, also note that this ordering is only necessary when using the Kafka Indexing Service. If one is not using Kafka Indexing Service or can handle down time for Kafka Supervisor then one can upgrade in any order.

Historical

Historical processes can be updated one at a time. Each Historical process has a startup time to memory map all the segments it was serving before the update. The startup time typically takes a few seconds to a few minutes, depending on the hardware of the host. As long as each Historical process is updated with a sufficient delay (greater than the time required to start a single process), you can rolling update the entire Historical cluster.

Overlord

Overlord processes can be updated one at a time in a rolling fashion.

Middle Managers/Indexers

Middle Managers or Indexer nodes run both batch and real-time indexing tasks. Generally you want to update Middle Managers in such a way that real-time indexing tasks do not fail. There are three strategies for doing that.

Rolling restart (restore-based)

Middle Managers can be updated one at a time in a rolling fashion when you set druid.indexer.task.restoreTasksOnRestart=true. In this case, indexing tasks that support restoring will restore their state on Middle Manager restart, and will not fail.

Currently, only realtime tasks support restoring, so non-realtime indexing tasks will fail and will need to be resubmitted.

Rolling restart (graceful-termination-based)

Middle Managers can be gracefully terminated using the "disable" API. This works for all task types, even tasks that are not restorable.

To prepare a Middle Manager for update, send a POST request to <MiddleManager_IP:PORT>/druid/worker/v1/disable. The Overlord will now no longer send tasks to this Middle Manager. Tasks that have already started will run to completion. Current state can be checked using <MiddleManager_IP:PORT>/druid/worker/v1/enabled .

To view all existing tasks, send a GET request to <MiddleManager_IP:PORT>/druid/worker/v1/tasks. When this list is empty, you can safely update the Middle Manager. After the Middle Manager starts back up, it is automatically enabled again. You can also manually enable Middle Managers by POSTing to <MiddleManager_IP:PORT>/druid/worker/v1/enable.

Autoscaling-based replacement

If autoscaling is enabled on your Overlord, then Overlord processes can launch new Middle Manager processes en masse and then gracefully terminate old ones as their tasks finish. This process is configured by setting druid.indexer.runner.minWorkerVersion=#{VERSION}. Each time you update your Overlord process, the VERSION value should be increased, which will trigger a mass launch of new Middle Managers.

The config druid.indexer.autoscale.workerVersion=#{VERSION} also needs to be set.

Standalone Real-time

Standalone real-time processes can be updated one at a time in a rolling fashion.

Broker

Broker processes can be updated one at a time in a rolling fashion. There needs to be some delay between updating each process as Brokers must load the entire state of the cluster before they return valid results.

Coordinator

Coordinator processes can be updated one at a time in a rolling fashion.

โ† High availabilityUsing rules to drop and retain data โ†’
  • Historical
  • Overlord
  • Middle Managers/Indexers
    • Rolling restart (restore-based)
    • Rolling restart (graceful-termination-based)
    • Autoscaling-based replacement
  • Standalone Real-time
  • Broker
  • Coordinator

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