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The Secrets of Building Realtime Big Data Systems
- 1. The Secrets ofBuilding Realtime Big Data Systems Nathan Marz @nathanmarz
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- 5. Who am I? (Upcomingbook)
- 6. BackType • >30 TBof data • Process 100M messages / day • Serve 300 requests / sec • 100 to 200 machine cluster • 3 full-time employees, 2 interns
- 7. Built on open-source Thrift Cascading Scribe ZeroMQ Zookeeper Pallet
- 8. What is adata system? View 1 Raw data View 2 View 3
- 9. What is adata system? # Tweets / URL Tweets Influence scores Trending topics
- 10. Everything else: schemas,databases, indexing, etc are implementation
- 11. Essential properties of a data system
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- 13. 1. Robust to machinefailure
- 14. 1. Robust to machinefailure and human error
- 15. 2. Low latencyreads and updates
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- 19. 6. Allows ad-hoc analysis
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- 22. Layered Architecture Speed Layer Batch Layer
- 23. Let’s pretend temporarilythat update latency doesn’t matter
- 24. Let’s pretend it’sOK for a view to lag by a few hours
- 25. Batch layer • Arbitrarycomputation • Horizontally scalable • High latency
- 26. Batch layer Not the end-all-be-all of batch computation, but the most general
- 27. Hadoop Distributed Distributed Filesystem Filesystem Input files Output files MapReduce Input files Output files Input files Output files
- 28. Hadoop • Express yourcomputation in terms of MapReduce • Get parallelism and scalability “for free”
- 29. Batch layer • Storemaster copy of dataset • Master dataset is append-only
- 30. Batch layer view =fn(master dataset)
- 31. Batch layer MapReduce Batch Master dataset View 1 MapReduce Batch View 2 Batch View 3 MapReduce
- 32. Batch layer • Inpractice, too expensive to fully recompute each view to get updates • A production batch workflow adds minimum amount of incrementalization necessary for performance
- 33. Incremental batch layer Batch View 1 New data Batch View Batch View 2 maintenance workflow Query Append Batch View 3 All data
- 34. Batch layer Robust andfault-tolerant to both machine and human error. Low latency reads. Low latency updates. Scalable to increases in data or traffic. Extensible to support new features or related services. Generalizes to diverse types of data and requests. Allows ad hoc queries. Minimal maintenance. Debuggable: can trace how any value in the system came to be.
- 35. Speed layer Compensate forhigh latency of updates to batch layer
- 36. Speed layer Key point:Only needs to compensate for data not yet absorbed in serving layer
- 37. Speed layer Key point:Only needs to compensate for data not yet absorbed in serving layer Hours of data instead of years of data
- 38. Application-level Queries Batch Layer Query Merge Speed Layer Query
- 39. Speed layer Once datais absorbed into batch layer, can discard speed layer results
- 40. Speed layer • Messagepassing • Incremental algorithms • Read/Write databases • Riak • Cassandra • HBase • etc.
- 41. Speed layer Significantly morecomplex than the batch layer
- 42. Speed layer But thebatch layer eventually overrides the speed layer
- 43. Speed layer So thatcomplexity is transient
- 44. Flexibility in layered architecture • Do slow and accurate algorithm in batch layer • Do fast but approximate algorithm in speed layer • “Eventual accuracy”
- 45. Data model Every recordis a single, discrete fact at a moment in time
- 46. Data model • Alicelives in San Francisco as of time 12345 • Bob and Gary are friends as of time 13723 • Alice lives in New York as of time 19827
- 47. Data model • Remember:master dataset is append-only • A person can have multiple location records • “Current location” is a view on this data: pick location with most recent timestamp
- 48. Data model • Extremelyuseful having the full history for each entity • Doing analytics • Recovering from mistakes (like writing bad data)
- 49. Data model Reshare: true Gender: female Property Tweet: 456 Property Reaction Reactor Reactor Tweet: 123 Alice Bob Property Property Content: RT @bob Content: Data is fun! Data is fun!
- 50. Questions? Twitter: @nathanmarz Email: nathan.marz@gmail.com Web: http://nathanmarz.com