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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
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  • 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
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  • Native queries
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  • 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
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    • 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
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  • 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
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  • Apache Avro
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  • DataSketches extension
  • DataSketches HLL Sketch module
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  • Basic Security
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  • Firehose (deprecated)
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  • Load files natively
Edit

Durable storage for the multi-stage query engine

You can use durable storage to improve querying from deep storage and SQL-based ingestion.

Note that only S3 is supported as a durable storage location.

Durable storage for queries from deep storage provides a location where you can write the results of deep storage queries to. Durable storage for SQL-based ingestion is used to temporarily house intermediate files, which can improve reliability.

Enabling durable storage also enables the use of local disk to store temporary files, such as the intermediate files produced while sorting the data. Tasks will use whatever has been configured for their temporary usage as described in Configuring task storage sizes. If the configured limit is too low, Druid may throw the error, NotEnoughTemporaryStorageFault.

Enable durable storage

To enable durable storage, you need to set the following common service properties:

druid.msq.intermediate.storage.enable=true
druid.msq.intermediate.storage.type=s3
druid.msq.intermediate.storage.bucket=YOUR_BUCKET
druid.msq.intermediate.storage.prefix=YOUR_PREFIX
druid.msq.intermediate.storage.tempDir=/path/to/your/temp/dir

For detailed information about the settings related to durable storage, see Durable storage configurations.

Use durable storage for SQL-based ingestion queries

When you run a query, include the context parameter durableShuffleStorage and set it to true.

For queries where you want to use fault tolerance for workers, set faultTolerance to true, which automatically sets durableShuffleStorage to true.

Use durable storage for queries from deep storage

Depending on the size of the results you're expecting, saving the final results for queries from deep storage to durable storage might be needed.

By default, Druid saves the final results for queries from deep storage to task reports. Generally, this is acceptable for smaller result sets but may lead to timeouts for larger result sets.

When you run a query, include the context parameter selectDestination and set it to DURABLESTORAGE:

    "context":{
        ...
        "selectDestination": "DURABLESTORAGE"
    }

You can also write intermediate results to durable storage (durableShuffleStorage) for better reliability. The location where workers write intermediate results is different than the location where final results get stored. This means that durable storage for results can be enabled even if you don't write intermediate results to durable storage.

If you write the results for queries from deep storage to durable storage, the results are cleaned up when the task is removed from the metadata store.

Durable storage clean up

To prevent durable storage from getting filled up with temporary files in case the tasks fail to clean them up, a periodic cleaner can be scheduled to clean the directories corresponding to which there isn't a controller task running. It utilizes the storage connector to work upon the durable storage. The durable storage location should only be utilized to store the output for the cluster's MSQ tasks. If the location contains other files or directories, then they will get cleaned up as well.

Use druid.msq.intermediate.storage.cleaner.enabled and druid.msq.intermediate.storage.cleaner.delaySEconds to configure the cleaner. For more information, see Durable storage configurations.

Note that if you choose to write query results to durable storage,the results are cleaned up when the task is removed from the metadata store.

← Java runtimeSecurity overview →
  • Enable durable storage
  • Use durable storage for SQL-based ingestion queries
  • Use durable storage for queries from deep storage
  • Durable storage clean up

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