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›Ingestion

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

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

    Batch ingestion

    • Native batch
    • Simple task indexing
    • Input sources
    • Firehose
    • Hadoop-based
  • Task reference
  • Troubleshooting FAQ

Querying

    Druid SQL

    • Overview and syntax
    • SQL data types
    • Operators
    • Scalar functions
    • Aggregation functions
    • Multi-value string 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
    • 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
  • Getting started with Apache Druid
  • 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
  • Retaining or automatically dropping data
  • Working with different versions of Apache Hadoop
  • Misc

    • Legacy Management UIs
    • 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

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
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  • 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
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  • Google Cloud Storage
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  • MySQL Metadata Store
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  • Apache Parquet Extension
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  • Protobuf
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  • Stats aggregator
  • Test Stats Aggregators
  • Druid AWS RDS Module
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  • Realtime Process
Edit

Druid data model

Druid stores data in datasources, which are similar to tables in a traditional relational database management system (RDBMS). Druid's data model shares similarities with both relational and timeseries data models.

Primary timestamp

Druid schemas must always include a primary timestamp. Druid uses the primary timestamp to partition and sort your data. Druid uses the primary timestamp to rapidly identify and retrieve data within the time range of queries. Druid also uses the primary timestamp column for time-based data management operations such as dropping time chunks, overwriting time chunks, and time-based retention rules.

Druid parses the primary timestamp based on the timestampSpec configuration at ingestion time. Regardless of the source field for the primary timestamp, Druid always stores the timestamp in the __time column in your Druid datasource.

You can control other important operations that are based on the primary timestamp in the granularitySpec. If you have more than one timestamp column, you can store the others as secondary timestamps.

Dimensions

Dimensions are columns that Druid stores "as-is". You can use dimensions for any purpose. For example, you can group, filter, or apply aggregators to dimensions at query time when necessary.

If you disable rollup, then Druid treats the set of dimensions like a set of columns to ingest. The dimensions behave exactly as you would expect from any database that does not support a rollup feature.

At ingestion time, you configure dimensions in the dimensionsSpec.

Metrics

Metrics are columns that Druid stores in an aggregated form. Metrics are most useful when you enable rollup. If you specify a metric, you can apply an aggregation function to each row during ingestion. This has the following benefits:

Rollup is a form of aggregation that collapses dimensions while aggregating the values in the metrics, that is, it collapses rows but retains its summary information."

  • Rollup is a form of aggregation that combines multiple rows with the same timestamp value and dimension values. For example, the rollup tutorial demonstrates using rollup to collapse netflow data to a single row per (minute, srcIP, dstIP) tuple, while retaining aggregate information about total packet and byte counts.
  • Druid can compute some aggregators, especially approximate ones, more quickly at query time if they are partially computed at ingestion time, including data that has not been rolled up.

At ingestion time, you configure Metrics in the metricsSpec.

← Data formatsData rollup →
  • Primary timestamp
  • Dimensions
  • Metrics

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