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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
    • Key concepts
    • API
    • Security
    • Examples
    • Reference
    • Known issues
  • Task reference
  • Troubleshooting FAQ

Data management

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

Querying

    Druid SQL

    • Overview and syntax
    • SQL data types
    • Operators
    • Scalar functions
    • Aggregation functions
    • Multi-value string functions
    • JSON functions
    • All functions
    • Druid SQL API
    • JDBC driver API
    • 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

Configuration

  • Configuration reference
  • Extensions
  • Logging

Operations

  • Web console
  • Java runtime
  • 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
  • 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
    • Export Metadata Tool
    • Metadata Migration
    • Content for build.sbt

Development

  • Developing on Druid
  • Creating extensions
  • JavaScript functionality
  • Build from source
  • Versioning
  • Experimental features

Misc

  • Papers

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  • 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
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  • Protobuf
  • S3-compatible
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  • 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
  • kubernetes
  • Cardinality/HyperUnique aggregators
  • Select
  • Firehose (deprecated)
  • Native batch (simple)
  • Realtime Process
Edit

Historical Process

Configuration

For Apache Druid Historical Process Configuration, see Historical Configuration.

For basic tuning guidance for the Historical process, see Basic cluster tuning.

HTTP endpoints

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

Running

org.apache.druid.cli.Main server historical

Loading and serving segments

Each Historical process copies or "pulls" segment files from Deep Storage to local disk in an area called the segment cache. Set the druid.segmentCache.locations to configure the size and location of the segment cache on each Historical process. See Historical general configuration.

See the Tuning Guide for more information.

The Coordinator controls the assignment of segments to Historicals and the balance of segments between Historicals. Historical processes do not communicate directly with each other, nor do they communicate directly with the Coordinator. Instead, the Coordinator creates ephemeral entries in Zookeeper in a load queue path. Each Historical process maintains a connection to Zookeeper, watching those paths for segment information.

For more information about how the Coordinator assigns segments to Historical processes, see Coordinator.

When a Historical process detects a new entry in the Zookeeper load queue, it checks its own segment cache. If no information about the segment exists there, the Historical process first retrieves metadata from Zookeeper about the segment, including where the segment is located in Deep Storage and how it needs to decompress and process it.

For more information about segment metadata and Druid segments in general, see Segments.

After a Historical process pulls down and processes a segment from Deep Storage, Druid advertises the segment as being available for queries from the Broker. This announcement by the Historical is made via Zookeeper, in a served segments path.

For more information about how the Broker determines what data is available for queries, please see Broker.

To make data from the segment cache available for querying as soon as possible, Historical services search the local segment cache upon startup and advertise the segments found there.

Loading and serving segments from cache

The segment cache uses memory mapping. The cache consumes memory from the underlying operating system so Historicals can hold parts of segment files in memory to increase query performance at the data level. The in-memory segment cache is affected by the size of the Historical JVM, heap / direct memory buffers, and other processes on the operating system itself.

At query time, if the required part of a segment file is available in the memory mapped cache or "page cache", the Historical re-uses it and reads it directly from memory. If it is not in the memory-mapped cache, the Historical reads that part of the segment from disk. In this case, there is potential for new data to flush other segment data from memory. This means that if free operating system memory is close to druid.server.maxSize, the more likely that segment data will be available in memory and reduce query times. Conversely, the lower the free operating system memory, the more likely a Historical is to read segments from disk.

Note that this memory-mapped segment cache is in addition to other query-level caches.

Querying segments

Please see Querying for more information on querying Historical processes.

A Historical can be configured to log and report metrics for every query it services.

โ† Coordinator ProcessIndexer Process โ†’

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