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โ€บDruid SQL

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
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Design

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Ingestion

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    • Apache Kafka ingestion
    • Apache Kafka supervisor
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    Batch ingestion

    • Native batch
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    • Migrate from firehose
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    SQL-based ingestion ๐Ÿ†•

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Data management

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Querying

    Druid SQL

    • Overview and syntax
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    • Scalar functions
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    • All functions
    • Druid SQL API
    • JDBC driver API
    • SQL query context
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  • Troubleshooting
  • Concepts

    • Datasources
    • Joins
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    • Nested columns
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    • 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)
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Configuration

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Operations

  • Web console
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    Performance tuning

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    Monitoring

    • Request logging
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    • Alerts
  • API reference
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  • Working with different versions of Apache Hadoop
  • Misc

    • dump-segment tool
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Development

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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
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  • DataSketches extension
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  • Basic Security
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  • MySQL Metadata Store
  • ORC Extension
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  • Graphite Emitter
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  • GCE Extensions
  • Aliyun OSS
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  • Cardinality/HyperUnique aggregators
  • Select
  • Firehose (deprecated)
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  • Realtime Process
Edit

SQL JDBC driver API

Apache Druid supports two query languages: Druid SQL and native queries. This document describes the SQL language.

You can make Druid SQL queries using the Avatica JDBC driver. We recommend using Avatica JDBC driver version 1.17.0 or later. Note that as of the time of this writing, Avatica 1.17.0, the latest version, does not support passing connection string parameters from the URL to Druid, so you must pass them using a Properties object. Once you've downloaded the Avatica client jar, add it to your classpath and use the connect string jdbc:avatica:remote:url=http://BROKER:8082/druid/v2/sql/avatica/.

Example code:

// Connect to /druid/v2/sql/avatica/ on your Broker.
String url = "jdbc:avatica:remote:url=http://localhost:8082/druid/v2/sql/avatica/";

// Set any connection context parameters you need here
// Or leave empty for default behavior.
Properties connectionProperties = new Properties();

try (Connection connection = DriverManager.getConnection(url, connectionProperties)) {
  try (
      final Statement statement = connection.createStatement();
      final ResultSet resultSet = statement.executeQuery(query)
  ) {
    while (resultSet.next()) {
      // process result set
    }
  }
}

It is also possible to use a protocol buffers JDBC connection with Druid, this offer reduced bloat and potential performance improvements for larger result sets. To use it apply the following connection url instead, everything else remains the same

String url = "jdbc:avatica:remote:url=http://localhost:8082/druid/v2/sql/avatica-protobuf/;serialization=protobuf";

The protobuf endpoint is also known to work with the official Golang Avatica driver

Table metadata is available over JDBC using connection.getMetaData() or by querying the "INFORMATION_SCHEMA" tables.

Connection stickiness

Druid's JDBC server does not share connection state between Brokers. This means that if you're using JDBC and have multiple Druid Brokers, you should either connect to a specific Broker, or use a load balancer with sticky sessions enabled. The Druid Router process provides connection stickiness when balancing JDBC requests, and can be used to achieve the necessary stickiness even with a normal non-sticky load balancer. Please see the Router documentation for more details.

Note that the non-JDBC JSON over HTTP API is stateless and does not require stickiness.

Dynamic parameters

You can use parameterized queries in JDBC code, as in this example:

PreparedStatement statement = connection.prepareStatement("SELECT COUNT(*) AS cnt FROM druid.foo WHERE dim1 = ? OR dim1 = ?");
statement.setString(1, "abc");
statement.setString(2, "def");
final ResultSet resultSet = statement.executeQuery();
โ† Druid SQL APISQL query context โ†’
  • Connection stickiness
  • Dynamic parameters

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