Introduction to Storm
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Introduction to Storm

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    Introduction to Storm Introduction to Storm Presentation Transcript

    • StormDistributed 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 ?
    • Before Storm
    • Problems• Scale is painful• Poor fault-tolerance – Hadoop is stateful• Coding is tedious• Batch processing – Long latency – no realtime
    • Storm• Scalable and robust – No persistent layer• Guarantees no data loss• Fault-tolerant• Programming language agnostic• Use case – Stream processing – Distributed RPC – Continues computation
    • Components
    • 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
    • System architecture
    • System architecture• Nimbus – Like JobtTacker in hadoop• Supervisor – Manage workers• Zookeeper – Store meta data• UI – Web-UI
    • Topology
    • Topology• Tuples – ordered list of elements – (“user”, “link”, “event”, “10/3/12 17:50“)• Streams – unbounded sequence of tuples
    • Spouts• Source of streams• Example • Read from logs, API calls, event data, queues, …
    • Spout• Interface ISpout – BaseRichSpout, ClojureSpout, DRPCSpout, FeederSpout, FixedTupleSpout, MasterBatchCoordinator, NoOpSpout, RichShellSpout, RichSpoutBatchTriggerer, ShellS pout, SpoutTracker, TestPlannerSpout, TestWordSpout, TransactionalSpoutCoordinator
    • Topology• Bolts – Processes input streams and produces new streams – Example • Stream Joins, DBs, APIs, Filters, Aggregation, …
    • Bolts• Interface Ibolt – BaseRichBolt, BasicBoltExecutor, BatchBoltExecutor, BoltTracker, ClojureBolt, Coordinate dBolt, JoinResult, KeyedFairBolt, NonRichBoltTracker, ReturnResults, BaseShellBolt, ShellBolt, TestAggregatesCounter, TestGlobalCount, TestPlannerBolt, TransactionalSpout BatchExecutor,TridentBoltExecutor, TupleCaptureBolt
    • Topology• Topology – A directed graph of Spouts and Bolts
    • 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/
    • 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
    • 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);
    • 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);
    • Storm & DRPC
    • Distributed RPC
    • Multilang Protocol
    • Multilang protocol• Using ShellSpout/ShellBolt• Process using stand in/out to communicate• Massage are encoded as JSON/ lines of plain text
    • 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
    • Three steps• Start looping – storm_sync would expect torm_ack• Read or write tuples – Follow defined structure – Implement read_msg(), storm_emit() ,…
    • Experience
    • 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
    • Reference
    • 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
    • Q/A
    • Thanks