Storm University of California, Berkeley（风暴的加州大学伯克利分校）.pdf

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Storm University of California, Berkeley（风暴的加州大学伯克利分校）.pdf

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Storm University of California, Berkeley（风暴的加州大学伯克利分校）

Storm Distributed and fault-tolerant realtime computation Nathan Marz Twitter Basic info • Open sourced September 19th • Implementation is 15,000 lines of code • Used by over 25 companies • 2400 watchers on Github (most watched JVM project) • Very active mailing list • 1800 messages • 560 members Before Storm Queues Workers Example (simpliﬁed) Example Workers schemify tweets and append to Hadoop Example Workers update statistics on URLs by incrementing counters in Cassandra Scaling Deploy Reconﬁgure/redeploy Problems • Scaling is painful • Poor fault-tolerance • Coding is tedious What we want • Guaranteed data processing • Horizontal scalability • Fault-tolerance • No intermediate message brokers! • Higher level abstraction than message passing • “Just works” Storm Guaranteed data processing Horizontal scalability Fault-tolerance No intermediate message brokers! Higher level abstraction than message passing “Just works” Use cases Stream Distributed Continuous processing RPC computation Storm Cluster Storm Cluster Master node (similar to Hadoop JobTracker) Storm Cluster Used for cluster coordination Storm Cluster Run worker processes Starting a topology Killing a topology Concepts • Streams • Spouts • Bolts • Topologies