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›Druid SQL

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
  • Data updates
  • 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
    • 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

API reference

  • Overview
  • HTTP APIs

    • Druid SQL
    • SQL-based ingestion
    • JSON querying
    • Tasks
    • Supervisors
    • Retention rules
    • Data management
    • Automatic compaction
    • Lookups
    • 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
  • Contribute to Druid docs
  • 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
  • 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
  • HDFS
  • Apache Kafka Lookups
  • Globally Cached Lookups
  • MySQL Metadata Store
  • ORC Extension
  • Druid pac4j based Security extension
  • Apache Parquet Extension
  • PostgreSQL Metadata Store
  • Protobuf
  • S3-compatible
  • Simple SSLContext Provider Module
  • 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
  • Firehose (deprecated)
  • JSON-based batch (simple)
  • Realtime Process
  • kubernetes
  • Cardinality/HyperUnique aggregators
  • Select
  • Load files natively
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SQL JSON functions

Druid supports nested columns, which provide optimized storage and indexes for nested data structures. See Nested columns for more information.

You can use the following JSON functions to extract, transform, and create COMPLEX<json> values.

FunctionNotes
JSON_KEYS(expr, path)Returns an array of field names from expr at the specified path.
JSON_OBJECT(KEY expr1 VALUE expr2[, KEY expr3 VALUE expr4, ...])Constructs a new COMPLEX<json> object. The KEY expressions must evaluate to string types. The VALUE expressions can be composed of any input type, including other COMPLEX<json> values. JSON_OBJECT can accept colon-separated key-value pairs. The following syntax is equivalent: JSON_OBJECT(expr1:expr2[, expr3:expr4, ...]).
JSON_PATHS(expr)Returns an array of all paths which refer to literal values in expr in JSONPath format.
JSON_QUERY(expr, path)Extracts a COMPLEX<json> value from expr, at the specified path.
JSON_VALUE(expr, path [RETURNING sqlType])Extracts a literal value from expr at the specified path. If you specify RETURNING and an SQL type name (such as VARCHAR, BIGINT, DOUBLE, etc) the function plans the query using the suggested type. Otherwise, it attempts to infer the type based on the context. If it can't infer the type, it defaults to VARCHAR.
PARSE_JSON(expr)Parses expr into a COMPLEX<json> object. This operator deserializes JSON values when processing them, translating stringified JSON into a nested structure. If the input is not a VARCHAR or it is invalid JSON, this function will result in an error.
TRY_PARSE_JSON(expr)Parses expr into a COMPLEX<json> object. This operator deserializes JSON values when processing them, translating stringified JSON into a nested structure. If the input is not a VARCHAR or it is invalid JSON, this function will result in a NULL value.
TO_JSON_STRING(expr)Serializes expr into a JSON string.

JSONPath syntax

Druid supports a subset of the JSONPath syntax operators, primarily limited to extracting individual values from nested data structures.

OperatorDescription
$Root element. All JSONPath expressions start with this operator.
.<name>Child element in dot notation.
['<name>']Child element in bracket notation.
[<number>]Array index.

Consider the following example input JSON:

{"x":1, "y":[1, 2, 3]}
  • To return the entire JSON object:
    $ -> {"x":1, "y":[1, 2, 3]}
  • To return the value of the key "x":
    $.x -> 1
  • For a key that contains an array, to return the entire array:
    $['y'] -> [1, 2, 3]
  • For a key that contains an array, to return an item in the array:
    $.y[1] -> 2
← Multi-value string functionsAll functions →

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