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lightning-fast unified analytics engine toggle navigation download libraries sql and dataframes spark streaming mllib (machine learning) graphx (graph) third-party projects documentation latest release (spark 2.3.1) older versions and other resources frequently asked questions examples community mailing lists & resources contributing to spark improvement proposals (spip) issue tracker powered by project committers project history developers useful developer tools versioning policy release process security apache software foundation apache homepage license sponsorship thanks security latest news spark 2.2.2 released (jul 02, 2018) spark 2.1.3 released (jun 29, 2018) spark 2.3.1 released (jun 08, 2018) spark+ai summit (june 4-6th, 2018, san francisco) agenda posted (mar 01, 2018) archive download spark built-in libraries: sql and dataframes spark streaming mllib (machine learning) graphx (graph) third-party projects apache spark™ is a unified analytics engine for large-scale data processing. speed run workloads 100x faster. apache spark achieves high performance for both batch and streaming data, using a state-of-the-art dag scheduler, a query optimizer, and a physical execution engine. logistic regression in hadoop and spark ease of use write applications quickly in java, scala, python, r, and sql. spark offers over 80 high-level operators that make it easy to build parallel apps. and you can use it interactively from the scala, python, r, and sql shells. df = spark. read . json ( "logs.json" ) df. where ( "age > 21" ) . select ( "name.first" ) . show ( ) spark's python dataframe api read json files with automatic schema inference text_file = spark.textfile( "hdfs://..." ) text_file. filter ( lambda line: "error" in line ) . count () -- word count in spark -- generality combine sql, streaming, and complex analytics. spark powers a stack of libraries including sql and dataframes , mllib for machine learning, graphx , and spark streaming . you can combine these libraries seamlessly in the same application. runs everywhere spark runs on hadoop, apache mesos, kubernetes, standalone, or in the cloud. it can access diverse data sources. you can run spark using its standalone cluster mode , on ec2 , on hadoop yarn , on mesos , or on kubernetes . access data in hdfs , apache cassandra , apache hbase , apache hive , and hundreds of other data sources. community spark is used at a wide range of organizations to process large datasets. you can find many example use cases on the powered by page. there are many ways to reach the community: use the mailing lists to ask questions. in-person events include numerous meetup groups and conferences . we use jira for issue tracking. contributors apache spark is built by a wide set of developers from over 300 companies. since 2009, more than 1200 developers have contributed to spark! the project's committers come from more than 25 organizations. if you'd like to participate in spark, or contribute to the libraries on top of it, learn how to contribute . getting started learning apache spark is easy whether you come from a java, scala, python, r, or sql background: download the latest release: you can run spark locally on your laptop. read the quick start guide . learn how to deploy spark on a cluster. apache spark, spark, apache, the apache feather logo, and the apache spark project logo are either registered trademarks or trademarks of the apache software foundation in the united states and other countries. see guidance on use of apache spark trademarks . all other marks mentioned may be trademarks or registered trademarks of their respective owners. copyright © 2018 the apache software foundation, licensed under the apache license, version 2.0 .
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