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Databus: LinkedIn's Change Data Capture Pipeline SOCC 2012

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SOCC 2012 Databus Presentation

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  • @Shashank Narayan The point of having a database was to support the compaction of the log and the snapshot to make bootstrap fast, since you only care about the latest value of a given key, not all the values. The separate database allows us to isolate the bootstrap workloads (which can be abusive and unpredictable) from the primary OLTP database so that online reads and writes are not affected. Hope this helps!
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  • Why did you use another Database instead of *MQ for bootstrap service. You can do same with primary OLTP even when new consumers arrives. Slow consumers can easily be tracked by MQ only. Thoughts ?
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Databus: LinkedIn's Change Data Capture Pipeline SOCC 2012

  1. 1. All Aboard the Databus!LinkedIn’s Change Data Capture Pipeline ACM SOCC 2012 Oct 16thDatabus Team @ LinkedInShirshanka Dashttp://www.linkedin.com/in/shirshankadas@shirshanka Recruiting Solutions
  2. 2. The Consequence of Specialization in Data SystemsData Flow is essentialData Consistency is critical !!!
  3. 3. The Timeline Consistent Data Flow problem
  4. 4. Two Ways Application code dual Extract changes from writes to database and database commit log pub-sub system Easy on the surface Tough but possible Consistent? Consistent!!!
  5. 5. The Result: Databus Standar Standar Standar Standar Standar Standar Standar Standar Updates Standar dization Search dization Graph dization Read dization dization dization dization Index dization Index dization Replicas Primary DB Data Change Events Databus 5
  6. 6. Key Design Decisions : Semantics Logical clocks attached to the source – Physical offsets are only used for internal transport – Simplifies data portability Pull model – Restarts are simple – Derived State = f (Source state, Clock) – + Idempotence = Timeline Consistent! 6
  7. 7. Key Design Decisions : Systems Isolate fast consumers from slow consumers – Workload separation between online, catch-up, bootstrap Isolate sources from consumers – Schema changes – Physical layout changes – Speed mismatch Schema-aware – Filtering, Projections – Typically network-bound  can burn more CPU 7
  8. 8. Databus: First attempt (2007) Issues  Source database pressure caused by slow consumers  Brittle serialization
  9. 9. Current Architecture (2011) Four Logical Components  Fetcher – Fetch from db, relay…  Log Store – Store log snippet  Snapshot Store – Store moving data snapshot  Subscription Client – Orchestrate pull across these
  10. 10. The Relay Change event buffering (~ 2 – 7 days) Low latency (10-15 ms) Filtering, Projection Hundreds of consumers per relay Scale-out, High-availability through redundancy Option 1: Peered Deployment Option 2: Clustered Deployment
  11. 11. The Bootstrap Service Catch-all for slow / new consumers Isolate source OLTP instance from large scans Log Store + Snapshot Store Optimizations – Periodic merge – Predicate push-down – Catch-up versus full bootstrap Guaranteed progress for consumers via chunking Implementations – Database (MySQL) – Raw Files Bridges the continuum between stream and batch systems
  12. 12. The Consumer Client Library Glue between Databus infra and business logic in the consumer Switches between relay and bootstrap as needed API – Callback with transactions – Iterators over windows
  13. 13. Fetcher Implementations Oracle – Trigger-based (see paper for details) MySQL – Custom-storage-engine based (see paper for details) In Labs – Alternative implementations for Oracle – OpenReplicator integration for MySQL
  14. 14. Meta-data Management Event definition, serialization and transport – Avro Oracle, MySQL – Table schema generates Avro definition Schema evolution – Only backwards-compatible changes allowed Isolation between upgrades on producer and consumer
  15. 15. Partitioning the Stream Server-side filtering – Range, mod, hash – Allows client to control partitioning function Consumer groups – Distribute partitions evenly across a group – Move partitions to available consumers on failure – Minimize re-processing
  16. 16. Experience in Production: The Good Source isolation: Bootstrap benefits – Typically, data extracted from sources just once – Bootstrap service routinely used to satisfy new or slow consumers Common Data Format – Early versions used hand-written Java classes for schema  Too brittle – Java classes also meant many different serializations for versions of the classes – Avro offers ease-of-use flexibility & performance improvements (no re-marshaling) Rich Subscription Support – Example: Search, Relevance
  17. 17. Experience in Production: The Bad Oracle Fetcher Performance Bottlenecks – Complex joins – BLOBS and CLOBS – High update rate driven contention on trigger table Bootstrap: Snapshot store seeding – Consistent snapshot extraction from large sources – Complex joins hurt when trying to create exactly the same results
  18. 18. What’s Next? Open-source: Q4 2012 Internal replication tier for Espresso Reduce latency further, scale to thousands of consumers per relay – Poll  Streaming Investigate alternate Oracle implementations Externalize joins outside the source User-defined functions Eventually-consistent systems
  19. 19. Three Takeaways Specialization in Data Systems – CDC pipeline is a first class infrastructure citizen up there with your stores and indexes Bootstrap Service – Isolates the source from abusive scans – Serves both streaming and batch use-cases Pull and External clock – Makes client application development simple – Fewer things can go wrong inside the pipeline 19
  20. 20. Recruiting Solutions ‹#›

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