CASE STUDY // 02Real-Time Systems · Distributed Backend

StreamAlpha

A real-time distributed market analytics platform built around event streaming, low-latency state, and WebSocket delivery capable of 10,000 events/second.

Real-Time Event Stream & Backpressure Pipeline

Producer → Kafka/Redpanda → Stream Processor → Redis/PostgreSQL → WebSocket Clients

01. INGESTION
Tick Producer

Financial market feeds (AAPL, NVDA, MSFT) publishing live tick events.

ACTIVE_STREAM
02. BROKER
Kafka / Redpanda

Partitioned topic logs guaranteeing per-symbol total order under load.

LAG: 0 TICKS
03. ROUTER
FastAPI Gateway

Asynchronous ring buffer manager dispatching frames & handling backpressure.

p50: 2.57 ms
04. STORAGE
Redis + Postgres

Redis for sub-1ms state snapshot cache; PostgreSQL for 1s/1m OHLCV batch aggregations.

BATCH COPY: 1s
05. CLIENTS
WASM Clients

Perspective WebAssembly rendering in Web Worker at 60 FPS without freezing UI.

BUFFER CAPACITY: 98%
LIVE STREAM TELEMETRY
THROUGHPUT10,000 evt/s
TOTAL TICKS STREAMED1,420,500
SLOW CLIENT EVICTIONS0 (Healthy)
BROWSER RENDER FPS60.0 FPS

01. Problem Statement & Motivation

High-frequency financial market updates overwhelm typical web architectures: WebSocket broadcast storms freeze browser client renderers, slow consumers cause catastrophic memory leaks in the backend, and database write throughput collapses when persisting sub-second tick streams.

WHY THIS MATTERS IN PRODUCTION:Traders and quantitative models need reliable, sub-10ms market telemetry. If a slow client forces backpressure onto the event broker or if duplicate ticks trigger incorrect trade execution, financial losses compound rapidly.

02. System Architecture Design

Engineered an event-driven decoupled pipeline. Market tick producers broadcast to Kafka/Redpanda partitions. An independent FastAPI WebSocket gateway subscribes to partitioned topics, manages per-client bounded ring buffers, drops slow-consumer frames under configurable backpressure, and caches the latest market snapshot in Redis. An independent analytics consumer aggregates 1-second and 1-minute OHLCV candles for batch persistence into PostgreSQL.

Enforced Reliability & Security Invariants
  • Slow-Client Message Eviction: Drops non-critical market ticks when client buffer reaches capacity, sending an explicit lag warning packet.
  • Symbol-Based Topic Partitioning: Ensures tick ordering per equity symbol across Kafka consumer groups.
  • Prometheus Telemetry Instrumentation: Exposes live metrics for queue buffer depth, drop rates, Kafka consumer lag, and end-to-end latency.
  • Automated Load Testing Harness: Configurable benchmark suite verifying throughput from 100 to 10,000 events/second.

03. Architectural Decisions & Tradeoffs

Per-Client Bounded Asynchronous Ring Queues

Each WebSocket client connection receives an isolated bounded queue. If a client stalls, oldest non-critical ticks are evicted without blocking the shared event ingestion pipeline.

Tradeoff: Stalled clients may drop intermediate ticks, but server memory usage remains strictly O(1) bounded and other clients experience zero lag.

FINOS Perspective WebAssembly Engine in Web Worker

Processed 10,000 updates/second entirely in a Web Worker running C++ compiled to WebAssembly. Only viewport diffs are sent to the DOM, keeping main thread UI rendering rock solid at 60 FPS.

Tradeoff: Increased initial bundle payload by ~420 KB for WebAssembly binaries.

Independent Analytics Consumer with Micro-Batching

Decoupled live WebSocket delivery from relational persistence. Tick events are aggregated in-memory over 1-second sliding windows before executing PostgreSQL COPY batch inserts.

Tradeoff: Database analytics lag real-time ticks by up to 1,000 ms, while eliminating 98% of relational write operations.

04. Verified Empirical Outcomes

Metric DimensionGuarded PlatformSignificance
100 evt/s Throughputp50: 2.70 ms | p95: 7.10 msBaseline low-frequency streaming with pristine delivery
1,000 evt/s Throughputp50: 2.57 ms | p95: 6.87 msStandard market hours trading load
5,000 evt/s Throughputp50: 5.83 ms | p95: 10.79 msHigh volatility surge simulation
10,000 evt/s Burst Loadp50: 12.56 ms | p95: 17.09 msSustained peak market opening stress test at 60 FPS UI

05. Production Roadmap & Next Iterations

  • >Implement zero-copy serialization using Apache Arrow Flight for cross-service analytics streaming.
  • >Add kernel-bypass DPDK networking for sub-microsecond tick capture on bare metal.