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Druid | Interactive Analytics at Scale

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Druid | Interactive Analytics at Scale Technology Use Cases Powered By Docs Community Download MENU MENU Apache Druid (incubating) is a upper performance analytics data store for event-driven data. Download GitHub Overview Druid is primarily used to store, query, and unriddle large event streams. Examples of event streams include user generated data such as clickstreams, using generated data such as performance metrics, and machine generated data such as network flows and server metrics. Druid is optimized for sub-second queries to slice-and-dice, drill down, search, filter, and volume this data. Druid is wontedly used to power interactive applications where performance, concurrency, and uptime are important. Druid was initially created to power a scalable, visual, multi-tenant using where users could not only rapidly slice and dice data to create ad-hoc reports, but moreover interactively explore data to quickly determine the root rationalization of patterns and anomalies. Druid is designed from the ground up for sub-second queries, which are hair-trigger in interactive applications as usability studies have shown that humans get distracted and lose their train of thought if responses take longer than a second.DiamondDruid’s cadre diamond combines ideas from OLAP/analytic databases, timeseries databases, and search systems to create a unified system for operational analytics.Cadredesign ideas include: Column-oriented storage Druid stores and compresses each post 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 wing to its native JSON based language, Druid speaks SQL over either HTTP or JDBC. Horizontally scalable Druid has been used in production to ingest millions of events/sec, retain years of data, and provide sub-second queries. Easy to operate Scale up or lanugo by just subtracting or removing servers, and Druid automatically rebalances. Fault-tolerant tracery routes virtually server failures. To learn more, read our Technology page. Use cases Druid is proven in production at the world’s leading companies, with the largest installations having increasingly than a thousand servers, ingesting over 10 million events per second, and supporting thousands of concurrent queries per second. Druid is used to:Unriddleperformance Create interactive dashboards with full drill lanugo capabilities.Unriddleperformance of digital products, track mobile app usage, or monitor site reliability. Diagnose problems Find the root rationalization of issues. Troubleshoot netflow bottlenecks, unriddle security threats, or diagnose software crashes. Find commonalities Find worldwide nature among events. Identify shared components in needing products, or determine patterns in top performing products. Increase efficiency Improve product engagement. Optimize ad-spend in digital marketing campaigns or increase user engagement in online products. To learn more, read our Use Cases page. Learn increasingly Quickstart Get started with Druid in minutes. Load your own data and query it. Visualize Visualize data in Druid with Pivot and Superset. FAQ Learn well-nigh some of the most worldwide questions well-nigh Druid. Latest releases Druid 0.12.3 Released Sep 18 2018 Druid 0.12.2 Released Aug 9 2018 Druid 0.12.1 Released Jun 8 2018 Druid 0.12.0 Released Mar 8 2018 Druid 0.11.0 Released Dec 4 2017 Druid 0.10.1 Released Aug 22 2017 Druid 0.10.0 Released Apr 18 2017 Upcoming Events Sep 26 ApacheCon 2018 Druid @ ApacheCon 2018 Oct 3 NANOG 74 New data architectures for upper performance netflow analytics Oct 12 DEVIEW 2018 Continuously Optimizing Storage of Stream Histories: Lessons Learned with Apache Druid Oct 16 Druid Seoul Meetup 8th Druid Seoul meetup Nov 14 Druid Bay Area Meetup Druid Bay Area Meetup @ Netflix Join a Druid Meetup! Featured Content Data Analytics and Processing at Snap Charles Allen - Snap, Inc. Sep 25 2018 Securing Druid Jon Wei - Imply Sep 13 2018 Streaming SQL and Druid Arup Malakar - Lyft Aug 30 2018 PayPal merchant ecosystem using Apache Spark, Hive, Druid, and HBase Deepika Khera & Kasi Natarajan - Paypal Jun 19 2018 Pushing druid metrics to Graphite Grafana with full metrics whitelist Vishnu Rao - DataSpark Jun 11 2018 Disclaimer: Apache Druid is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly wonted projects until a remoter review indicates that the infrastructure, communications, and visualization making process have stabilized in a manner resulting with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF. 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