Unified Processing with Apache Beam

Talk at http://cdsea17.xnextcon.com/ on Apache Beam

Transcript

1. Unified Processing with Apache Beam Cloud+Data NEXTCon 2017
2. I am Sourabh Hello!
3. I am Sourabh Hello! I am a Software Engineer
4. I am Sourabh Hello! I am a Software Engineer I tweet at @sb2nov
5. What is Apache Beam?
6. Apache Beam is a unified programming model for expressing efficient and portable data processing pipelines
7. Big Data
8. https://commons.wikimedia.org/wiki/File:Globe_centered_in_the_Atlantic_Ocean_(green_and_grey_globe_scheme).svg LAUNCH!!
9. DATA CAN BE BIG
10. … REALLY BIG ... Tuesday Wednesday Thursday
11. UNBOUNDED, DELAYED, OUT OF ORDER 9:00 8:00 14:00 13:00 12:00 11:00 10:00 8:00 8:00 8:00
12. ORGANIZING THE STREAM 8:00 8:00 8:00
13. DATA PROCESSING TRADEOFFS Completeness Latency $$$ Cost
14. WHAT IS IMPORTANT? Completeness Low Latency Low Cost Important Not Important $$$
15. MONTHLY BILLING Completeness Low Latency Low Cost Important Not Important $$$
16. BILLING ESTIMATE Completeness Low Latency Low Cost Important Not Important $$$
17. FRAUD DETECTION Completeness Low Latency Low Cost Important Not Important $$$
18. Beam Model
19. GENERATIONS BEYOND MAP-REDUCE Clearly separates event time from processing time Improved abstractions let you focus on your application logic Batch and stream processing are both first-class citizens
20. Pipeline PTransform PCollection (bounded or unbounded)
21. EVENT TIME VS PROCESSING TIME
22. EVENT TIME VS PROCESSING TIME
23. EVENT TIME VS PROCESSING TIME Watermarks describe event time progress. "No timestamp earlier than the watermark will be seen" Often heuristic-based. Too Slow? Results are delayed. Too Fast? Some data is late.
24. ASKING THE RIGHT QUESTIONS When in processing time? What is being computed? Where in event time? How do refinements happen?
25. WHAT IS BEING COMPUTED? scores: PCollection[KV[str, int]] = (input | beam.CombinePerKey(sum))
26. WHAT IS BEING COMPUTED? Element-Wise Aggregating Composite
27. WHAT IS BEING COMPUTED?
28. WHERE IN EVENT TIME? scores: PCollection[KV[str, int]] = (input | beam.WindowInto(FixedWindows(2 * 60)) | beam.CombinePerKey(sum))
29. WHERE IN EVENT TIME?
30. WHERE IN EVENT TIME?
31. WHERE IN EVENT TIME? scores: PCollection[KV[str, int]] = (input | beam.WindowInto(FixedWindows(2 * 60)) | beam.CombinePerKey(sum)) The choice of windowing is retained through subsequent aggregations.
32. WHEN IN PROCESSING TIME? scores: PCollection[KV[str, int]] = (input | beam.WindowInto(FixedWindows(2 * 60), triggerfn=trigger.AfterWatermark()) | beam.CombinePerKey(sum))
33. WHEN IN PROCESSING TIME? Triggers control when results are emitted. Triggers are often relative to the watermark.
34. WHEN IN PROCESSING TIME?
35. HOW DO REFINEMENTS HAPPEN? scores: PCollection[KV[str, int]] = (input | beam.WindowInto(FixedWindows(2 * 60), triggerfn=trigger.AfterWatermark( early=trigger.AfterPeriod(1*60), late=trigger.AfterCount(1)), accumulation_mode=ACCUMULATING) | beam.CombinePerKey(sum))
36. HOW DO REFINEMENTS HAPPEN?
37. CUSTOMIZING WHAT WHERE WHEN HOW Classic Batch Windowed Batch Streaming Streaming + Accumulation For more information see https://cloud.google.com/dataflow/examples/gaming-example
38. Python SDK
39. 39 SIMPLE PIPELINE with beam.Pipeline() as p: Pipeline construction is deferred.
40. 40 SIMPLE PIPELINE with beam.Pipeline() as p: lines = p | beam.io.ReadTextFile('/path/to/files') lines is a PCollection, a deferred collection of all lines in the specified files.
41. 41 SIMPLE PIPELINE with beam.Pipeline() as p: lines = p | beam.io.ReadTextFile('/path/to/files') words = lines | beam.FlatMap(lambda line: re.findall('\w+', line)) The "pipe" operator applies a transformation (on the right) to a PCollection, reminiscent of bash. This will be applied to each line, resulting in a PCollection of words.
42. 42 SIMPLE PIPELINE with beam.Pipeline() as p: lines = p | beam.io.ReadTextFile('/path/to/files') words = lines | beam.FlatMap(lambda line: re.findall('\w+', line)) totals = (words | beam.Map(lambda w: (w, 1)) | beam.CombinePerKey(sum)) Operations can be chained.
43. 43 SIMPLE PIPELINE with beam.Pipeline() as p: lines = p | beam.io.ReadTextFile('/path/to/files') words = lines | beam.FlatMap(lambda line: re.findall('\w+', line)) totals = words | Count() Composite operations easily defined.
44. 44 SIMPLE PIPELINE with beam.Pipeline() as p: lines = p | beam.io.ReadTextFile('/path/to/files') words = lines | beam.FlatMap(lambda line: re.findall('\w+', line)) totals = words | Count() totals | beam.io.WriteTextFile('/path/to/output') (totals | beam.CombinePerKey(Largest(100)) | beam.io.WriteTextFile('/path/to/another/output') Finally, write the results somewhere. The pipeline actually executes on exiting its context. Pipelines are DAGs in general.
45. 45 SIMPLE BATCH PIPELINE with beam.Pipeline() as p: lines = p | beam.io.ReadTextFile('/path/to/files') words = lines | beam.FlatMap(lambda line: re.findall('\w+', line)) totals = words | Count() totals | beam.io.WriteTextFile('/path/to/output') (totals | beam.CombinePerKey(Largest(100)) | beam.io.WriteTextFile('/path/to/another/output')
46. 46 WHAT ABOUT STREAMING?
47. 47 SIMPLE STREAMING PIPELINE with beam.Pipeline() as p: lines = p | beam.io.ReadPubSub(...) | WindowInto(...) words = lines | beam.FlatMap(lambda line: re.findall('\w+', line)) totals = words | Count() totals | beam.io.WriteTextFile('/path/to/output') (totals | beam.CombinePerKey(Largest(100)) | beam.io.WriteTextFile('/path/to/another/output')
48. Portability & Vision Google Cloud Dataflow
49. WHAT DOES APACHE BEAM PROVIDE? Runners for Existing Distributed Processing Backends The Beam Model: What / Where / When / How API (SDKs) for writing Beam pipelines Apache Apex Apache Flink InProcess / Local Apache Spark Google Cloud Dataflow Apache GearPump
50. Other Languages Beam Java Beam Python Pipeline SDK User facing SDK, defines a language specific API for the end user to specify the pipeline computation DAG.
51. Runner API Other Languages Beam Java Beam Python Runner API Runner and language agnostic representation of the user’s pipeline graph. It only contains nodes of Beam model primitives that all runners understand to maintain portability across runners.
52. Runner API Other Languages Beam Java Beam Python Execution Execution Execution SDK Harness Docker based execution environments that are shared by all runners for running the user code in a consistent environment.
53. Fn API Runner API Other Languages Beam Java Beam Python Execution Execution Execution Fn API API which the execution environments use to send and receive data, report metrics around execution of the user code with the Runner.
54. Fn API Apache Flink Apache Spark Runner API Other Languages Beam Java Beam Python Execution Execution Cloud Dataflow Execution Apache Gear- pump Apache Apex Runner Distributed processing environments that understand the runner API graph and how to execute the Beam model primitives.
55. BEAM RUNNER CAPABILITIES https://beam.apache.org/capability-matrix/
56. MORE BEAM? Issue tracker (https://issues.apache.org/jira/projects/BEAM) Beam website (https://beam.apache.org/) Source code (https://github.com/apache/beam) Developers mailing list (dev-subscribe@beam.apache.org) Users mailing list (user-subscribe@beam.apache.org) Follow @ApacheBeam on Twitter
57. SUMMARY • Beam helps you tackle big data that is: ◦ Unbounded in volume ◦ Out of order ◦ Arbitrarily delayed • The Beam model separates concerns of: ◦ What is being computed? ◦ Where in event time? ◦ When in processing time? ◦ How do refinements happen?
58. Thanks! You can find me at: @sb2nov Questions?