Introduction

High-frequency trading and real-time risk management require ML prediction latencies under 100 milliseconds. Apache Kafka (message broker) and Apache Flink (stream processing) enable low-latency ML pipelines: Kafka ingests market data in real time, Flink applies pre-trained ML models instantly, predictions flow to trading systems. Designing efficient Kafka-Flink architectures is critical for competitive latency performance.

Kafka for Real-Time Data Ingestion

Kafka reliably ingests high-volume tick data (thousands of messages/second) from market data providers. Partitioning by symbol enables parallelization. Kafka guarantees exactly-once delivery and fault tolerance, ensuring no data loss in critical trading pipelines. Proper configuration (batch size, compression, replication) optimizes throughput.

Flink for Stream Processing

Flink applies pre-trained ML models to Kafka streams in microseconds. Stateful computations maintain rolling features (e.g., 20-minute moving average). Window operations enable aggregations over time periods. Flink's parallelism scales to millions of events/second. Connection to Kafka sources and sinks creates seamless streaming pipelines.

End-to-End Latency Optimization

Total latency: data generation → Kafka ingestion → Flink processing → prediction output → order execution. Bottlenecks: Kafka batching (configure for latency), Flink state updates (use efficient data structures), model inference (use optimized runtimes like ONNX). Benchmark end-to-end latencies; optimize slowest components.

Conclusion

Kafka-Flink architectures enable microsecond-latency ML predictions, essential for competitive high-frequency trading infrastructure.