Event Sourcing, Stream Processing and Serverless (Ben Stopford, Confluent) Kafka Summit SF 2019
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In this talk we'll look at the relationship between three of the most disruptive software engineering paradigms: event sourcing, stream processing and serverless. We'll debunk some of the myths around event sourcing. We'll look at the inevitability of event-driven programming in the serverless space and we'll see how stream processing links these two concepts together with a single 'database for events'. As the story unfolds we'll dive into some use cases, examine the practicalities of each approach-particularly the stateful elements-and finally extrapolate how their future relationship is likely to unfold. Key takeaways include: The different flavors of event sourcing and where their value lies. The difference between stream processing at application- and infrastructure-levels. The relationship between stream processors and serverless functions. The practical limits of storing data in Kafka and stream processors like KSQL.
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Event Sourcing, Stream Processing and Serverless (Ben Stopford, Confluent) Kafka Summit SF 2019
- 1. Event Sourcing, Stream Processing & Serverless Ben Stopford Office of the CTO, Confluent
- 2. What we’re going to talk about • Event Sourcing • What it is and how does it relate to Event Streaming? • Stream Processing as a kind of “Database” • What does this mean? • Serverless Functions • How do this relate?
- 3. Can you do event sourcing with Kafka?
- 5. Popular example: Shopping Cart DB Apps Search Apps Apps Database Table matches what the user sees.
- 6. 12.42 12.44 12.49 12.50 12.59 Event Sourcing stores events, then derives the ‘current state view’ Apps Apps DERIVE Chronological Reduce Event Timeseries of user activity
- 7. Traditional Event Sourcing (Store immutable events in a database in time order) Apps Search NoSQL Monitoring Security Apps Apps S T R E A M I N G P L AT F O R MTable of events Persist events Apps Apps
- 8. Traditional Event Sourcing (Read) Apps Search NoSQL Monitoring Security Apps Apps S T R E A M I N G P L AT F O R M Apps Search Monitoring Apps Apps Chronological Reduce on read (done inside the app) Query by customer Id (+session?) - No schema migration - Similar to ’schema on read’
- 9. 3 Benefits
- 10. Evidentiary Accountants don’t use erasers (e.g. audit, ledger, git)
- 11. Replayability Recover corrupted data after a programmatic bug
- 12. Analytics Keep the data needed to extract trends and behaviors i.e. non-lossy (e.g. insight, metrics, ML)
- 13. Traditional Event Sourcing • Use a database (any one will do) • Create a table and insert events as they occur • Query all the events associated with your problem* • Reduce them chronologically to get the current state *Aggregate ID in DDD parlance
- 14. Traditional Event Sourcing with Kafka • Use a database Kafka • Create a table topic insert events as they occur • Query all the events associated with your problem* • Reduce them chronologically to get the current state *Aggregate ID in DDD parlance
- 15. Confusion: You can’t query Kafka by say Customer Id* *Aggregate ID in DDD parlance
- 16. If we can’t query by Customer ID then what do we do?
- 17. CQRS is a tonic: Cache the projection in a ‘View’ Apps Search Monitoring Apps Apps S T R E A M I N G P L AT F O R M Query by customer Id Apps Search NoSQL Apps Apps DWH Hadoop S T R E A M I N G P L AT F O R M View Events/Command Events are the Storage Model Stream Processor Cache/DB/Ktable etc. Regenerate the view rather than doing schema migration
- 18. CQRS provides the benefits of event sourcing using a “Materialized View”
- 19. Even with CQRS, Event Sourcing is Hard CQRS helps, but it’s still quite hard if you’re a CRUD app
- 20. What’s the problem? Harder: • Eventually Consistent • Multi-model (Complexity ∝ #Schemas in the log) • More moving parts Apps Search NoSQL Monitoring Security Apps Apps S T R E A M I N G P L A T F O R M CRUD System CQRS
- 21. New York Times Website Source of Truth Every article since 1851 https://www.confluent.io/blog/publishing-apache-kafka-new-york-times/ Normalized assets (images, articles, bylines, tags all separate messages) Denormalized into “Content View”
- 22. If CRUD makes sense there are other ways: audit tables, CDC, etc. Trigger Evidentiary Replayable N/A to web app Analytics CDC
- 23. More advanced: Use a Bi-Temporal Database
- 24. Events make most sense where data has to move
- 25. This is where CQRS comes into its own!
- 26. Online Transaction Processing: e.g. a Flight Booking System - Flight price served 10,000 x #bookings - Consistency required only at booking time
- 27. CQRS with event movement Apps Search Monitoring Apps Apps S T R E A M I N G P L AT F O R M Apps Search NoSQL Apps Apps DWH Hadoop S T R E A M I N G P L AT F O R M View Book Flight Apps Search Apps S T R E A M I N G P L A View Apps Search NoSQL Apps DWH S T R E A M I N G P L A View Get Flights Get Flights Get Flights Global Read Central Write
- 28. The exact same logic applies to microservices
- 29. Event Sourcing for Microservices Basket Service Fraud Service Billing Service Email ServiceBasket Events
- 30. Event Sourcing for Microservices Basket Service Fraud Service Billing Service Email ServiceBasket Events Events are the storage model Each microservice creates a view that suits its use case
- 31. Event Sourcing “with a DB” for monoliths. Event Streaming for Microservices & Scale. (Often via. CQRS)
- 32. Event Streaming
- 33. Event Streaming is a more general form of Event Sourcing/CQRS Event Streaming • Events as shared data model • Many microservices • Polyglot persistence • Event-Driven processing Traditional Event Sourcing • Events as a storage model • Single microservice • Single DB • data-at-rest
- 34. Event Streams is about many event sources (Join, Filter, Transform and Summarize) Fraud Service Orders Service Payment Service Customer Service Event Log Projection created in Kafka Streams API
- 35. KStreams & KSQL have different positioning •KStreams is a library for Dataflow programming: • App Logic & Stream Processor (including state) are combined. • Apps are stateful. • JVM only. •KSQL is a ‘database’ for event preparation: • App sends SQL to a separate process • Apps are stateless • Connect from any language
- 36. This difference makes most sense if we we look to the future.
- 38. Thesis • Serverless provides event-driven infrastructure • KSQL is the corollary: an event-driven database
- 39. Serverless Functions (FaaS) • Write a function • Upload • Configure a trigger (HTTP, Messaging, Object Store, Database, Timer etc.) Request Respond Event Source
- 40. FaaS in a Nutshell • Fully managed (Runs in a container pool) • Pay for execution time (not resources used) • Auto-scales with load • 0-1000+ concurrent functions • Stateless • Short lived (limit 5-15 mins) • Weak ordering guarantees • Cold start’s can be (very) slow: 100ms – 45s (AWS 250ms-7s)
- 41. Where is FaaS useful? • Spikey workloads and ‘occasional’ use cases • Use cases that don’t typically warrant massive parallelism e.g. CI systems. • General purpose programming paradigm?
- 42. But there are open questions
- 43. Serverless Developer Ecosystem • Runtime diagnostics • Monitoring • Deploy loop • Testing • IDE integration Currently quite poor
- 44. Harder than current approaches Easier than current approaches Amazon Google Microsoft Serverless programming will likely become prevalent
- 45. In the future it seems unlikely we’ll manage our own infrastructure. But where will we manage our data?
- 47. Event-Streaming approaches this from a different angle
- 48. FaaS is event-driven But it isn’t streaming
- 49. Complex, Timing issues, Scaling limits Customers Event Source Orders Event Source Payments Event Source Serverless functions handle only one event source FaaS/μS FaaS/μS FaaS/μS
- 50. A slightly more complex example: Send email only to platinum customers
- 51. Payments Event Source Event is received by serverless function FaaS/μS
- 52. Payments Event Source Block and calls the database to get customer+order FaaS/μS Get customer Get order
- 53. Payments Event Source Is it a ‘Platinum’ customer? FaaS/μS Get customer Get order Is the customer platinum?
- 54. Payments Event Source Send email if ‘Platinum’ FaaS/μS Get customer Get order Maybe send email
- 55. Payments Event Source Increase Load: 100 concurrant functions doing IO. FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS
- 56. Payments Event Source Only send 2 emails. FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS FaaS/μS
- 57. Send SQL Process boundary Orders Payments KSQL Customers Table Customers KSQL simplifies: App Logic CREATE STREAM foo AS SELECT * FROM orders, payments, customers LEFT JOIN… WHERE customer.type = ‘PLATINUM’ Order Payment Customer KSQL - Handle timing issues - No “per-event” IO. - Price efficient
- 58. Functions have no additional data dependencies: Everything is in the event!
- 59. Queries filter out the events you need (much like you filter rows in a database query)
- 60. FaaSFaaSFaaSKSQL Customers Table KSQL as a “Database” for Event-Driven Infrastructure FaaSFaaS Stateless, elastic compute Prepare the events we need (Sateful) Orders Payments Customers Autoscale with load
- 61. FaaS Traditional Application Event-Driven Application Application Database KSQL Stateful Data Layer FaaS FaaS FaaS FaaS FaaS Streaming Stateless Stateless Stateless Compute Layer Massive linear scalability with elasticity
- 62. Event-Driven vs. Event Streaming Event Driven Event Streaming Multiple Event Sources Use Database + ETL + Code Handles automatically Efficiency Extract data from DB in the FaaS (IO) Only the data you need Logic-driven data requests. Call DB from the FaaS (IO) DB/KStreams KqlDB?
- 65. Summary • Event Streaming provides the benefits of Event Sourcing to microservices and data pipelines. • Events are the data model. • Projections are the serving model: matching to each specific use case • Serving layer can be regenerated from the log (CQRS) • KSQL provides the same benefits for event-driven programs: e.g. preparing the event streams each FaaS application’s specific needs • In serverless architectures this drives efficiency: a ‘database- equivalent’ for event-driven infrastructure.
- 66. FaaSFaaSFaaSKSQL Can I Build This? FaaSFaaS AWS Lambda / Azure Functions Connectors (in Preview) Hosted KSQL In Preview Confluent Cloud
- 67. Things I didn’t tell you • Tools like KSQL provide data provisioning, not state mutation. • Use single writers. Try KSQL DB? • Can KSQL handle large state? • Unintended rebalance can stall processing • Static membership (KIP-345) – name the list of stream processors • Increase the timeout for rebalance after node removal (group.max.session.timeout.ms) • Worst case reload: RocksDB ~GbE speed • Can Kafka be used for long term storage? • Log files are immutable once they roll (unless compacted) • Jun spent a decade working on DB2 • Careful: • Historical reads can stall real-time requests (cached) • ZFS has several page cache optimizations • Tiered storage will help
- 68. Find out More • Peeking Behind the Curtains of Serverless Platforms, Wang et al. • Cloud Programming Simplified: A Berkeley View on Serverless Compute • Neil Avery’s Journey to Event Driven Part 3. The Affinity Between Events, Streams and Serverless. • Designing Event Driven Systems, Ben Stopford