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Overview

Druid is an open source distributed data store. Druid’s core design combines ideas from OLAP/analytic databases, timeseries databases, and search systems to create a unified system for a broad range of use cases. Druid merges key characteristics of each of the 3 systems into its ingestion layer, storage format, querying layer, and core architecture.

Key Features

  • Column-oriented storage

    Druid stores and compresses each column individually, and only needs to read the ones needed for a particular query, which supports fast scans, rankings, and groupBys.

  • Native search indexes

    Druid creates inverted indexes for string values for fast search and filter.

  • Streaming and batch ingest

    Out-of-the-box connectors for Apache Kafka, HDFS, AWS S3, stream processors, and more.

  • Flexible schemas

    Druid gracefully handles evolving schemas and nested data.

  • Time-optimized partitioning

    Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases.

  • SQL support

    In addition to its native JSON based language, Druid speaks SQL over either HTTP or JDBC.

  • Horizontal scalability

    Druid has been used in production to ingest millions of events/sec, retain years of data, and provide sub-second queries.

  • Easy operation

    Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures.

Architecture

Druid Architecture

  • 历史节点（Historical Node） ：历史数据 Segment 的加载和查询
  • 中间管理节点（MiddleManager Node）：实时及离线数据摄入，生成 Segment
  • 协调节点（Coordinator Node）：对历史服务数据负载均衡
  • 控制节点（Overlord Node）：控制数据摄入分配，协调发布 Segment
  • 查询节点（Broker Node）：对外提供查询服务
  • 路由节点（Router Node）：统一API网关

Reference