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| <h1>About Druid</h1> |
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| <p>Apache Druid is an open-source data store designed for sub-second queries on real-time |
| and historical data. It is primarily used for business intelligence |
| (<a href="http://en.wikipedia.org/wiki/Online_analytical_processing">OLAP</a>) queries on |
| event data. Druid provides low latency (real-time) data ingestion, flexible |
| data exploration, and fast data aggregation. Existing Druid deployments have |
| scaled to trillions of events and petabytes of data. Druid is most commonly |
| used to power user-facing analytic applications.</p> |
| |
| <h2 id="key-features">Key Features</h2> |
| |
| <p><strong>Sub-second OLAP Queries</strong> Druid’s unique architecture enables rapid |
| multi-dimensional filtering, ad-hoc attribute groupings, and extremely fast |
| aggregations.</p> |
| |
| <p><strong>Real-time Streaming Ingestion</strong> Druid employs lock-free ingestion to allow |
| for simultaneous ingestion and querying of high dimensional, high volume data |
| sets. Explore events immediately after they occur.</p> |
| |
| <p><strong>Power Analytic Applications</strong> Druid has numerous features built for |
| multi-tenancy. Power user-facing analytic applications designed to be used by |
| thousands of concurrent users.</p> |
| |
| <p><strong>Cost Effective</strong> Druid is extremely cost effective at scale and has numerous |
| features built in for cost reduction. Trade off cost and performance with |
| simple configuration knobs.</p> |
| |
| <p><strong>Highly Available</strong> Druid is used to back SaaS implementations that need to be |
| up all the time. Druid supports rolling updates so your data is still available |
| and queryable during software updates. Scale up or down without data loss.</p> |
| |
| <p><strong>Scalable</strong> Existing Druid deployments handle trillions of events, petabytes |
| of data, and thousands of queries every second.</p> |
| |
| <h2 id="is-druid-right-for-me">Is Druid Right for Me?</h2> |
| |
| <p>Organizations have deployed Druid to analyze user, server, and marketplace |
| events across a variety of industries including media, telecommunications, |
| security, banking, health care, and retail. Druid is a good fit if you have the |
| following requirements:</p> |
| |
| <ul> |
| <li>You are building an application that requires fast aggregations and |
| OLAP queries</li> |
| <li>You want to do real-time analysis</li> |
| <li>You have lots of data (trillions of events, petabytes of data)</li> |
| <li>You need a data store that is always available with no single point of |
| failure</li> |
| </ul> |
| |
| <h2 id="high-level-architecture">High Level Architecture</h2> |
| |
| <p>Druid is partially inspired by existing analytic data stores such as Google's |
| <a href="http://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/36632.pdf">BigQuery/Dremel</a>, |
| Google's |
| <a href="http://vldb.org/pvldb/vol5/p1436_alexanderhall_vldb2012.pdf">PowerDrill</a>, and |
| search infrastructure. Druid indexes all ingested data in a custom column |
| format optimized for aggregations and filters. A Druid cluster is composed of |
| various types of processes (called nodes), each designed to do a small set of |
| things very well.</p> |
| |
| <h2 id="comprehensive-architecture">Comprehensive Architecture</h2> |
| |
| <p>For a comprehensive look at Druid architecture, please read our <a href="http://static.druid.io/docs/druid.pdf">white paper</a> from 2014.</p> |
| |
| <h2 id="comparisons">Comparisons</h2> |
| |
| <ul> |
| <li><a href="/docs/latest/comparisons/druid-vs-elasticsearch.html">Druid vs Elasticsearch</a></li> |
| <li><a href="/docs/latest/comparisons/druid-vs-key-value.html">Druid vs Key/Value Stores (HBase/Cassandra)</a></li> |
| <li><a href="/docs/latest/comparisons/druid-vs-redshift.html">Druid vs Redshift</a></li> |
| <li><a href="/docs/latest/comparisons/druid-vs-spark.html">Druid vs Spark</a></li> |
| <li><a href="/docs/latest/comparisons/druid-vs-sql-on-hadoop.html">Druid vs SQL-on-Hadoop (Hive/Impala/Drill/Spark SQL/Presto)</a></li> |
| </ul> |
| |
| <h2 id="scale">Scale</h2> |
| |
| <p><a href="http://www.marketwired.com/press-release/metamarkets-clients-analyzing-100-billion-programmatic-events-daily-on-track-surpass-2061596.htm">Existing production Druid clusters</a> have scaled to the following:</p> |
| |
| <ul> |
| <li>3+ trillion events/month</li> |
| <li>3M+ events/sec through Druid's real-time ingestion</li> |
| <li>100+ PB of raw data</li> |
| <li>50+ trillion events</li> |
| <li>Thousands of queries per second for applications used by thousands of users</li> |
| <li>Tens of thousands of cores</li> |
| </ul> |
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