Gwen Shapira on Realtime Data Processing at Facebook

Realtime data processing powers many use cases at Facebook, including realtime reporting of the aggregated, anonymized voice of Facebook users, analytics for mobile applications, and insights for Facebook page administrators. Many companies have developed their own systems; we have a realtime data processing ecosystem at Facebook that handles hundreds of Gigabytes per second across hundreds of data pipelines.

Many decisions must be made while designing a realtime stream processing system. In this paper, we identify five important design decisions that affect their ease of use, performance, fault tolerance, scalability, and correctness. We compare the alternative choices for each decision and contrast what we built at Facebook to other published systems.

Our main decision was targeting seconds of latency, not milliseconds. Seconds is fast enough for all of the use cases we support and it allows us to use a persistent message bus for data transport. This data transport mechanism then paved the way for fault tolerance, scalability, and multiple options for correctness in our stream processing systems Puma, Swift, and Stylus...

Transcript

1. 1 Papers We Love: Realtime Data Processing at Facebook Gwen Shapira Confluent Inc.
2. 2 Papers We Love: Realtime Data Processing at Facebook
3. 3 Published in 2016 (!)
4. 4 What kind of paper is this?
5. 5 This is NOT The one true architecture . Please don’t cargo-cult this paper
6. 6 Few real-time systems at Facebook • Chorus – aggregate trends • Realtime feedback for mobile app developers • Page analytics – likes, engagement… • Offload CPU-intensive dashboard queries
10. 10 Looking for trending topics in 5 minute windows
11. 11 The Tofu & Potatoes of the paper: Design Decisions
12. 12 / KafkaStreams + exactly once
13. 13 Decision #1 – Language Paradigm • Declarative (SQL) – easy & limited • Functional • Procedural (C++, Java, Python) – most flexibility, control, performance. Longer dev cycle.
14. 14 Decision #1 – Language Paradigm • Declarative (SQL) – easy & limited • Functional • Procedural (C++, Java, Python) – most flexibility, control, performance. Longer dev cycle.
15. 15 Decision #2: Data Transfer • RPC (Millwheel, Flink, SparkStreaming) • All about speed • Message-forwarding broker (Heron) • Applies back-pressure, multiplex • Persistent stream storage (Samza, Kafka’s Stream API) • Most reliable • Decouples processors
16. 16 Decision #2: Data Transfer
17. 17 Love Song to Scribe Independent stream processing nodes And storing inputs / outputs Made everything great
18. 18 Decision #3 – Processing Semantics
19. 19 Decision #3 – Processing Semantics Facebook Verdict: It depends on requirements • Ranker writes to idempotent system – at least once • Scuba can lose data, but not handle duplicates – at most once • …. Exactly once is REALLY HARD and requires transactions
20. 20 Don’t miss the side-note on side-effects • Exactly once means writing output + offsets to a transactional system • This takes time • Why just wait when you can deserialize? And maybe do other stateless stuff?
21. 21 Decision #4 – State Saving • In-memory state with replication (Old VoltDB) • Requires lots of hardware and network • Local database (Samza, Kafka Streams API) • Remote database (Millwheel) • Upstream (i.e. replay everything on failure) • Global consistent snapshot (Flink)
22. 22 Decision #4 – State Saving Facebook Verdict: It depends Rhode Island Alaska
23. 23 Best Part of the Paper – by far How to efficiently work with state in remote DB?
24. 24 Decision #5 - Reprocessing • Stream only – requires long retention in the stream store • Maintain both batch and stream systems • Develop systems that can run in streams and batch (Flink, Spark)
25. 25 Decision #5 - Reprocessing • Stream only – requires long retention in the stream store • Maintain both batch and stream systems • Develop systems that can run in streams and batch (Flink, Spark) Facebook Verdict: SQL runs everywhere And binary generation FTW
26. 26 Applications – Or a whirlwind tour of good patterns One example:
27. 27 Lessons Learned! The biggest win is pipelines composed of independent processors • Mixing multiple systems let us move fast • High level abstractions let us improve implementation • Ease of debugging – Independent nodes and ability to replay • Ease of deployment – Puma as-a-service • Ease of monitoring – Lag is the most important metric. Everything is instrumented out of the box. • In the future – auto-scale based on lag
28. 28 Thank You!