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Introduction to Storm

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Storm
Distributed and fault-tolerant realtime computation system




                                               Chandler@PyHug
                                            previa [at] gmail.com
Outline
•   Background
•   Why Strom
•   Component
•   Topology
•   Storm & DRPC
•   Multilang Protocol
•   Experience
Background
Background
• Creates by Nathan Marz @ BackType/Twitter
  – Analyze twits, links, users on Twitter

• Opensourced at Sep 2011
  – Eclipse Public License 1.0
  – Storm 0.5.2
  – 16k java and 7k Clojure Loc
  – Current stable release 0.8.2
     • 0.9.0 major core improvement
Background
• Active user group
  – https://groups.google.com/group/storm-user
  – https://github.com/nathanmarz/storm

  – Most watched java repo at GitHub (>4k watcher)
  – Used by over 30 companies
     • Twitter, Groupon, Alibaba, GumGum, ..
Why Storm ?
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Introduction to Storm

  • 1. Storm Distributed and fault-tolerant realtime computation system Chandler@PyHug previa [at] gmail.com
  • 2. Outline • Background • Why Strom • Component • Topology • Storm & DRPC • Multilang Protocol • Experience
  • 4. Background • Creates by Nathan Marz @ BackType/Twitter – Analyze twits, links, users on Twitter • Opensourced at Sep 2011 – Eclipse Public License 1.0 – Storm 0.5.2 – 16k java and 7k Clojure Loc – Current stable release 0.8.2 • 0.9.0 major core improvement
  • 5. Background • Active user group – https://groups.google.com/group/storm-user – https://github.com/nathanmarz/storm – Most watched java repo at GitHub (>4k watcher) – Used by over 30 companies • Twitter, Groupon, Alibaba, GumGum, ..
  • 8. Problems • Scale is painful • Poor fault-tolerance – Hadoop is stateful • Coding is tedious • Batch processing – Long latency – no realtime
  • 9. Storm • Scalable and robust – No persistent layer • Guarantees no data loss • Fault-tolerant • Programming language agnostic • Use case – Stream processing – Distributed RPC – Continues computation
  • 11. Base on • Apache Zookeeper – Distributed system, used to store metadata • ØMQ – Asynchronous message transport layer • Apache Thrift – Cross-language bridge, RPC • LMAX Disruptor – High performance queue shared by threads • Kryo – Serialization framework
  • 13. System architecture • Nimbus – Like JobtTacker in hadoop • Supervisor – Manage workers • Zookeeper – Store meta data • UI – Web-UI
  • 15. Topology • Tuples – ordered list of elements – (“user”, “link”, “event”, “10/3/12 17:50“) • Streams – unbounded sequence of tuples
  • 16. Spouts • Source of streams • Example • Read from logs, API calls, event data, queues, …
  • 17. Spout • Interface ISpout – BaseRichSpout, ClojureSpout, DRPCSpout, FeederSpout, FixedTupleSpout, MasterBatchCoordinator, NoOpSpout, RichShellSpout, RichSpoutBatchTriggerer, ShellS pout, SpoutTracker, TestPlannerSpout, TestWordSpout, TransactionalSpoutCoordinator
  • 18. Topology • Bolts – Processes input streams and produces new streams – Example • Stream Joins, DBs, APIs, Filters, Aggregation, …
  • 19. Bolts • Interface Ibolt – BaseRichBolt, BasicBoltExecutor, BatchBoltExecutor, BoltTracker, ClojureBolt, Coordinate dBolt, JoinResult, KeyedFairBolt, NonRichBoltTracker, ReturnResults, BaseShellBolt, ShellBolt, TestAggregatesCounter, TestGlobalCount, TestPlannerBolt, TransactionalSpout BatchExecutor,TridentBoltExecutor, TupleCaptureBolt
  • 20. Topology • Topology – A directed graph of Spouts and Bolts
  • 21. Tasks • Instances of Spouts and Blots • Managed by Supervisor – http://www.michael-noll.com/blog/2012/10/16/understanding-the-parallelism-of-a-storm-topology/
  • 22. Stream grouping • All grouping – Send to all tasks • Global grouping – Pick task with lowest id • Shuffle grouping – Pick a random task • Fields grouping – Consistent hashing on a subset of tuple fields
  • 23. Storm fault-tolerance • Reliability API – Spout tuple creation • colloctor.emit(values, msgID); – Child tuple creation (Bolts) • colloctor.emit(parentTuples, values); – Tuple end of processing • collector.ack(tuple); – Tuple failed to process • collector.fail(tuple);
  • 24. Storm fault-tolerance • Disable reliability API – Globally • Config.TOPOLOGY_ACKER_EXECUTORS = 0 – On topology level • Collector.emit(values, msgID); – For a single tuple • Collector.emit(paranetTuples, values);
  • 28. Multilang protocol • Using ShellSpout/ShellBolt • Process using stand in/out to communicate • Massage are encoded as JSON/ lines of plain text
  • 29. Three steps • Initiate a handshake – Keep track with process id – Send a json object to standard input while start – Contains • Storm configuration, topology, context, PID directory
  • 30. Three steps • Start looping – storm_sync would expect torm_ack • Read or write tuples – Follow defined structure – Implement read_msg(), storm_emit() ,…
  • 32. Experience • Not hard to setup, but – Beware of certain version of Zookeeper – Wait a while after topology deployed • Fast, – Better use fabric • Stable – But beware of memory leak
  • 34. Reference • “Getting started with Storm”, O’REILLY • Twitter Storm – Sergey Lukjanov@slideshare – http://www.slideshare.net/lukjanovsv/twitter-storm • Storm – nathanmarz@slideshare – http://www.slideshare.net/nathanmarz/storm-11164672 • Realtime Analytics with Storm and Hadoop – Hadoop_Summit@slideshare – http://www.slideshare.net/Hadoop_Summit/realtime-analytics-with- storm
  • 35. Q/A