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Introduction to Storm: Distributed, Fault-tolerant Real-time Computation

Storm is a distributed, fault-tolerant real-time computation framework that can be used with any programming language. It integrates with various technologies, works on HDFS for batch processing, and allows for the creation of topologies using spouts and bolts. Storm includes concepts like tuples, streams, stream grouping, and parallelism levels, making it a powerful tool for processing data at scale.

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Introduction to Storm: Distributed, Fault-tolerant Real-time Computation

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  1. STORM Chrystalla Tsoutsouki Chrysovalantis Anastasiou

  2. PROBLEM 1

  3. SOLUTIONS 2

  4. • Distributed • Fault-tolerant • Any programming language • Distributed • Fault-tolerant • Any programming language • Integrates with all technologies • Real-time Computation • Works on HDFS • Batch Processing 3

  5. STORM Introduction to Storm Concept 4

  6. Tuples Ordered set of elements [“the”, 14], [“boy”, 2] 5

  7. Streams Sequence of tuples Tuple Tuple Tuple Tuple Tuple 6

  8. Storm Topology • Spout: Source of streams • Bolt: Computation unit 7

  9. Stream Grouping • Shuffle Grouping • Fields Grouping • All Grouping • Global Grouping • None Grouping • Direct Grouping • Local Grouping 8

  10. Parallelism • Level 1: o Different Bolts can make different computations Bolt Spout Bolt 9

  11. Parallelism • Level 2: o Worker Processes o Executors (Threads) o Task 10

  12. Storm Cluster 11

  13. Storm UI 12

  14. Wordcount Topology Random Sentence Spout Sentence Split Bolt Word Count Bolt 13

  15. Example 14

  16. Example 15

  17. Example 16

  18. Spitfire Distributed AkNN computation 17

  19. 18

  20. Step 1 - Partitioning 90.0 -90.0 19 180.0 -180.0

  21. Step 2 - Replication 20

  22. Step 3 - Refinement Each node performs a local kNN using internal geographic grouping and bulk processing 21

  23. Our approach – Step 1 SyncBolt Sort CellBounds UsersSpout 22

  24. Our approach – Step 2 Distribute ECs Neighbors ECands 23

  25. Our approach – Step 3 Local kNN 24

  26. 25

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