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Spark vs storm

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Spark vs storm

  1. 1. Spark vs Storm Trong-Ton PHAM trongton@gmail.com
  2. 2. Batch vs Streaming Spark • Batch & micro-batch processing Storm • Micro-batch & real-time stream processing Batch Streaming
  3. 3. Usability Spark Storm Production mode Since 2013 (UC Berkeley) Since 2011 (Twitter) Implemented in Scala (In-memory processing) Clojure API Language Java, Scala, Python Java, Scala, Clojure, others Library components SparkSQL Spark Streaming MLLib (Machine Learning) GraphX (graph) Stream Spouts (read data stream) Bolts (filters, joins) Topologies
  4. 4. Hadoop compatibility Spark Storm Data sources HDFS, Hbase, Cassandra HDFS, Hbase, Kafka Ressource Manager YARN, Mesos Mesos Latency Few seconds < 1 second Fault tolerance (every record processed) Exactly once At least once Reliability Improved reliability (Spark + YARN) Guarantees no data loss (Storm + Kafka)
  5. 5. Supported distribution N/A Manual configuration needed Supported
  6. 6. Performance • This is NOT an official benchmark in term of performance of Spark and Storm System Performance Storm (Twitter) 10,000 records/s/node Spark Streaming 400,000 records/s/node Apache S4 7,000 records/s/node Other Commericial Systems 100,000 records/s/node http://www.cs.duke.edu/~kmoses/cps516/dstream.html
  7. 7. References • http://xinhstechblog.blogspot.fr/2014/06/storm- vs-spark-streaming-side-by-side.html • https://www.linkedin.com/groups/Can-anyone- share-some-experience-4158686.S.235367680 • http://www.slideshare.net/ptgoetz/apache- storm-vs-spark-streaming • http://www.slideshare.net/nathanmarz/storm- distributed-and-faulttolerant-realtime- computation • Spark & Storm websites

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