Event-Driven Architecture Masterclass: Engineering a Robust, High-performance EDA
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Discover how to avoid common pitfalls when shifting to an event-driven architecture (EDA) in order to boost system recovery and scalability. We cover Kafka Schema Registry, in-broker transformations, event sourcing, and more.
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Event-Driven Architecture Masterclass: Engineering a Robust, High-performance EDA
- 1. Engineering a Robust and High performance EDA with Redpanda Christina Lin
- 2. 2 About me 2 Christina Lin Developer Advocate, Redpanda SOA WebSphere DB2 Sybase Oracle MQ J2EE EJB DevOps Microservice EIP K8s Agile Integration Data Mesh Active MQ Live data stack Resilience - handle failures and scale gracefully Elasticity – infrastructure that can scale dynamically Decentralization - data ownership, empowering individual teams Performance - low latency and high throughput Autonomy – self service, define quality, and access Nimble - efficient data movement Distributed -distributed data processing for cloud native Agility – quickly respond to change in data
- 3. 3 Robust
- 4. Event Driven Architecture 4 Services Microservices Databases IoT Devices Applications System B Team A Department C Team D Group E Services Databases IoT Devices Applications System B Team A Department C Team D Group E Microservices Producer Consumer
- 5. Event Driven Architecture 5 Orders Health records Restock Signal CDC Event Streaming Table/ Materialize view Data Store Payroll Payment Shipment Signal Inventory
- 7. Schema Registry 7 Producer Data structure encoding - Avro, Protobuf and JSON Data structure - {name:type} Serialize Download the schema (version) Consumer Schema Registry Deserialize Value (Binary) Schema ID Key (Binary) Value (Binary)
- 8. Schema Registry 8 Server-side validation Value (Binary) Sche ma ID Key (Binar y) Value (Binary) Schema Registry Check if schema id is valid Schema Registry Producer • Backward • Forward • Full compatibility • None Schema Registry Version 1 Version 2 Version 3
- 9. Schema Registry in Redpanda 9 Service Registry Service Registry Restful Endpoint Restful Endpoint _schemas _schemas
- 10. Schema Registry • Assign a default value to the fields that you might remove in the future • Do not rename an existing field—add an alias instead • When using schema evolution, always provide a default value • Never delete a required field When not to use Schema registry • You’re certain the schema won’t change in the future • If hardware resources are limited and low latency is critical, it may impact performance (e.g., for IoT) • You want to serialize the data with an unsupported serialization scheme 10
- 11. Event validation & DLQ 11 DLQ Consumer Correction/ Remedy Validator DLQ Correction/ Remedy Validator DLQ Correction/ Remedy
- 12. In broker validation – how it works 12 Replicate across clusters customer partition 1 Load to cache Validate against schema Transform Write back to disk with DMA Customer validated partition 1 Example repo: https://github.com/redpanda-data/redpanda-labs/tree/main/data-transforms/to_avro
- 13. In broker validation & transformation • Firsthand processing, quick filtering • Simple rerouting determine on ingested data • Masking, schema validation • Stateless, functional processing When not to use in broker transformation? • When it requires external data dependencies • Windowing, complex processing, with multiple streams of input • When it requires to keep the state of the processes 13
- 16. 16 The Broker Infrastructure Storage – XFS,NVMe Network bandwidth Memory CPU Location (Multi-AZ) OS Disk I/O read_iops/bandwidth write_iops/bandwidth Broker # Brokers # Replicas # Partitions Log segment size
- 17. 17 Partitions Partitions Producer Consumer Consumer Consumer Group A • Round Robin • Hashing Key Partition • Custom Partitioner Overhead • File handler • Follower, heartbeat • Large Metadata quadratic (N2) Idempotency • Order guarantee in partition only Higher latency • Producer batch Consumer rebalance • RangeAssignor (SW) • RoundRobinAssignor(SW) • StickyAssignor(SW) • CooperativeStickyAssignor • Static (No Rebalance)
- 18. 18 Producer Producer fsync Acknowledgment from the leader Ack=all Ack=1 Majority of replicas acknowledge write_caching_default=true flush_bytes, flush_ms Ack=0 Doesn’t wait for acknowledgments and doesn’t retry sending messages Producer batch.size linger.ms compression
- 20. High Throughput • There is no on size fits all, there are many factor when it comes to performances. • More partition will allow more parallel processing, hence higher throughput, but it comes with cost.(Avoid over-partitioning or under-partitioning.) • Experiment with acks settings, Enable write caching, • Explore how the producer batches messages. Increasing the value of batch.size and linger.ms can increase throughput by making the producer add more messages into one batch • Explore consumer fetch frequency and message size. • Start with a baseline configuration and gradually make changes, measuring the impact of each change on performance. 20
- 21. 21 Robust for Stateful Processes
- 22. Beyond just streams of events 22 Databases / CDC Microservices Databases / CDC Databases / CDC Processor
- 23. Beyond just streams of events 23 Databases / CDC Microservices Databases / CDC Databases / CDC Processor
- 24. Limited disk space 24 Event Sourcing S3 Rehydrate
- 25. State Snapshot 25 Microservices Databases / CDC Databases / CDC Databases / CDC Processor Snapshot
- 26. Summary ■ Use schema to insure data shape for consumer ■ When designing, think about compatibility ■ Validation to ensures consumer always get the correct format. ■ In broker transform are great for simple, functions, stateless processes ■ Provision appropriate partition to your topics ■ Depends on your use case, for producer, always set the right Ack, and buffer ■ For stateful streams processing, use snapshot for fault tolerance 26
- 27. On demand example 27 Batch Every 10 mins CSV CSV Batch pipeline Batch Processing Batch pipeline Right away! Stream CSV
- 28. Keep in touch! Christina Lin Developer Advocate Redpanda Christina@redpanda.com