Apache Druid
  • Technology
  • Use Cases
  • Powered By
  • Docs
  • Community
  • Apache
  • Download

โ€บHidden

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

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

Apache Druid vs SQL-on-Hadoop

SQL-on-Hadoop engines provide an execution engine for various data formats and data stores, and many can be made to push down computations down to Druid, while providing a SQL interface to Druid.

For a direct comparison between the technologies and when to only use one or the other, things basically comes down to your product requirements and what the systems were designed to do.

Druid was designed to

  1. be an always on service
  2. ingest data in real-time
  3. handle slice-n-dice style ad-hoc queries

SQL-on-Hadoop engines generally sidestep Map/Reduce, instead querying data directly from HDFS or, in some cases, other storage systems. Some of these engines (including Impala and Presto) can be co-located with HDFS data nodes and coordinate with them to achieve data locality for queries. What does this mean? We can talk about it in terms of three general areas

  1. Queries
  2. Data Ingestion
  3. Query Flexibility

Queries

Druid segments stores data in a custom column format. Segments are scanned directly as part of queries and each Druid server calculates a set of results that are eventually merged at the Broker level. This means the data that is transferred between servers are queries and results, and all computation is done internally as part of the Druid servers.

Most SQL-on-Hadoop engines are responsible for query planning and execution for underlying storage layers and storage formats. They are processes that stay on even if there is no query running (eliminating the JVM startup costs from Hadoop MapReduce). Some (Impala/Presto) SQL-on-Hadoop engines have daemon processes that can be run where the data is stored, virtually eliminating network transfer costs. There is still some latency overhead (e.g. serialization/deserialization time) associated with pulling data from the underlying storage layer into the computation layer. We are unaware of exactly how much of a performance impact this makes.

Data Ingestion

Druid is built to allow for real-time ingestion of data. You can ingest data and query it immediately upon ingestion, the latency between how quickly the event is reflected in the data is dominated by how long it takes to deliver the event to Druid.

SQL-on-Hadoop, being based on data in HDFS or some other backing store, are limited in their data ingestion rates by the rate at which that backing store can make data available. Generally, the backing store is the biggest bottleneck for how quickly data can become available.

Query Flexibility

Druid's query language is fairly low level and maps to how Druid operates internally. Although Druid can be combined with a high level query planner to support most SQL queries and analytic SQL queries (minus joins among large tables), base Druid is less flexible than SQL-on-Hadoop solutions for generic processing.

SQL-on-Hadoop support SQL style queries with full joins.

Druid vs Parquet

Parquet is a column storage format that is designed to work with SQL-on-Hadoop engines. Parquet doesn't have a query execution engine, and instead relies on external sources to pull data out of it.

Druid's storage format is highly optimized for linear scans. Although Druid has support for nested data, Parquet's storage format is much more hierarchical, and is more designed for binary chunking. In theory, this should lead to faster scans in Druid.

โ† Apache Druid vs SparkAuthentication and Authorization โ†’
  • Druid vs Parquet

Technologyโ€‚ยทโ€‚Use Casesโ€‚ยทโ€‚Powered by Druidโ€‚ยทโ€‚Docsโ€‚ยทโ€‚Communityโ€‚ยทโ€‚Downloadโ€‚ยทโ€‚FAQ

โ€‚ยทโ€‚โ€‚ยทโ€‚โ€‚ยทโ€‚
Copyright ยฉ 2022 Apache Software Foundation.
Except where otherwise noted, licensed under CC BY-SA 4.0.
Apache Druid, Druid, and the Druid logo are either registered trademarks or trademarks of The Apache Software Foundation in the United States and other countries.