![Topology
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Storm
- 1. Storm Distributed and fault-tolerant realtime computation Nathan Marz Twitter
- 2. Basic info • Open sourced September 19th • Implementation is 12,000 lines of code • Used by over 25 companies • >2280 watchers on Github (most watched JVM project) • Very active mailing list • >1700 messages • >520 members
- 3. Hadoop Batch computation Distributed Fault-tolerant
- 4. Storm Realtime computation Distributed Fault-tolerant
- 5. Hadoop • Large, finite jobs • Process a lot of data at once • High latency
- 6. Storm • Infinite computations called topologies • Process infinite streams of data • Tuple-at-a-time computational model • Low latency
- 7. Before Storm Queues Workers
- 9. Example Workers schemify tweets and append to Hadoop
- 10. Example Workers update statistics on URLs by incrementing counters in Cassandra
- 11. Problems • Scaling is painful • Poor fault-tolerance • Coding is tedious
- 12. What we want • Guaranteed data processing • Horizontal scalability • Fault-tolerance • No intermediate message brokers! • Higher level abstraction than message passing • “Just works”
- 13. Storm Guaranteed data processing Horizontal scalability Fault-tolerance No intermediate message brokers! Higher level abstraction than message passing “Just works”
- 14. Use cases Stream Distributed Continuous processing RPC computation
- 15. Storm Cluster
- 16. Storm Cluster Master node (similar to Hadoop JobTracker)
- 17. Storm Cluster Used for cluster coordination
- 18. Storm Cluster Run worker processes
- 21. Concepts • Streams • Spouts • Bolts • Topologies
- 22. Streams Tuple Tuple Tuple Tuple Tuple Tuple Tuple Unbounded sequence of tuples
- 24. Spout examples • Read from Kestrel queue • Read from Twitter streaming API
- 25. Bolts Processes input streams and produces new streams
- 26. Bolts • Functions • Filters • Aggregation • Joins • Talk to databases
- 27. Topology Network of spouts and bolts
- 28. Tasks Spouts and bolts execute as many tasks across the cluster
- 29. Task execution Tasks are spread across the cluster
- 30. Task execution Tasks are spread across the cluster
- 31. Stream grouping When a tuple is emitted, which task does it go to?
- 32. Stream grouping • Shuffle grouping: pick a random task • Fields grouping: mod hashing on a subset of tuple fields • All grouping: send to all tasks • Global grouping: pick task with lowest id
- 33. Topology shuffle [“id1”, “id2”] shuffle [“url”] shuffle all
- 34. Streaming word count TopologyBuilder is used to construct topologies in Java
- 35. Streaming word count Define a spout in the topology with parallelism of 5 tasks
- 36. Streaming word count Split sentences into words with parallelism of 8 tasks
- 37. Streaming word count Consumer decides what data it receives and how it gets grouped Split sentences into words with parallelism of 8 tasks
- 38. Streaming word count Create a word count stream
- 39. Streaming word count splitsentence.py
- 41. Streaming word count Submitting topology to a cluster
- 42. Streaming word count Running topology in local mode
- 43. Demo
- 44. Distributed RPC Data flow for Distributed RPC
- 45. DRPC Example Computing “reach” of a URL on the fly
- 46. Reach Reach is the number of unique people exposed to a URL on Twitter
- 47. Computing reach Follower Distinct Tweeter Follower follower Follower Distinct URL Tweeter follower Count Reach Follower Follower Distinct Tweeter follower Follower
- 48. Reach topology
- 49. Reach topology
- 50. Reach topology
- 51. Reach topology Keep set of followers for each request id in memory
- 52. Reach topology Update followers set when receive a new follower
- 53. Reach topology Emit partial count after receiving all followers for a request id
- 54. Demo
- 55. Guaranteeing message processing “Tuple tree”
- 56. Guaranteeing message processing • A spout tuple is not fully processed until all tuples in the tree have been completed
- 57. Guaranteeing message processing • If the tuple tree is not completed within a specified timeout, the spout tuple is replayed
- 58. Guaranteeing message processing Reliability API
- 59. Guaranteeing message processing “Anchoring” creates a new edge in the tuple tree
- 60. Guaranteeing message processing Marks a single node in the tree as complete
- 61. Guaranteeing message processing • Storm tracks tuple trees for you in an extremely efficient way
- 62. Transactional topologies How do you do idempotent counting with an at least once delivery guarantee?
- 63. Transactional topologies Won’t you overcount?
- 64. Transactional topologies Transactional topologies solve this problem
- 65. Transactional topologies Built completely on top of Storm’s primitives of streams, spouts, and bolts
- 66. Transactional topologies Batch 1 Batch 2 Batch 3 Process small batches of tuples
- 67. Transactional topologies Batch 1 Batch 2 Batch 3 If a batch fails, replay the whole batch
- 68. Transactional topologies Batch 1 Batch 2 Batch 3 Once a batch is completed, commit the batch
- 69. Transactional topologies Batch 1 Batch 2 Batch 3 Bolts can optionally implement “commit” method
- 70. Transactional topologies Commit 1 Commit 1 Commit 2 Commit 3 Commit 4 Commit 4 Commits are ordered. If there’s a failure during commit, the whole batch + commit is retried
- 71. Example
- 72. Example New instance of this object for every transaction attempt
- 73. Example Aggregate the count for this batch
- 74. Example Only update database if transaction ids differ
- 75. Example This enables idempotency since commits are ordered
- 76. Example (Credit goes to Kafka guys for figuring out this trick)
- 77. Transactional topologies Multiple batches can be processed in parallel, but commits are guaranteed to be ordered
- 78. Transactional topologies • Will be available in next version of Storm (0.7.0) • Requires a source queue that can replay identical batches of messages • Aiming for first TransactionalSpout implementation to use Kafka
- 79. Storm UI
- 80. Storm on EC2 https://github.com/nathanmarz/storm-deploy One-click deploy tool
- 81. Starter code https://github.com/nathanmarz/storm-starter Example topologies
- 82. Documentation
- 83. Ecosystem • Scala, JRuby, and Clojure DSL’s • Kestrel, AMQP, JMS, and other spout adapters • Serializers • Multilang adapters • Cassandra, MongoDB integration