Organizations rely on real-time platforms for trading, logistics, personalization, and telemetry. This guide details the architecture patterns to achieve predictable, low-latency experiences at scale.
Latency budget and SLAs
- Define end-to-end p95 and p99 targets
- Allocate budgets across ingress, processing, storage, and egress
- Instrument every hop to enforce budgets
Data plane components
- Ingest: API Gateway, gRPC, WebSockets
- Stream: Kafka, Kinesis, Event Hubs
- Process: Flink, Kafka Streams, Spark Structured Streaming
- Serve: Redis, Elastic, bespoke read models
Backpressure and flow control
- Consumer lag monitoring and autoscaling
- Batching with micro-batch windows
- Adaptive sampling when overload is detected
Storage and read models
- Log-structured storage for append-only streams
- CQRS views optimized for queries
- TTL and compaction to control footprint
Consistency choices
- Choose eventual vs strong where it matters
- Use explicit reconciliation and idempotency
Testing real-time systems
- Load testing with variable burst profiles
- Failure injection for partitions and broker loss
Internal links
For event-driven designs, see: Internal Link: Serverless Platform Architecture: Building Event-Driven Systems. For performance at scale, read: Internal Link: Platform Performance Optimization: Scaling for High Traffic.
FAQs
How do we handle bursty traffic? Autoscale consumers and use queues to absorb spikes.
Is exactly-once worth it? Often not—prefer at-least-once with idempotency.
What’s a good p95 target? Align to domain; many interactive systems target <200ms end-to-end.
Conclusion
Real-time platforms require precise budgets, robust observability, and resilient stream processing. With the right patterns, you can deliver consistently low-latency experiences at scale.
PADISO designs and ships production platforms on AWS, Azure, and Google Cloud — see Platform Design & Engineering.