Engineering Update: Scaling Streaming Ingestion for 10M Events/Sec

At LineEquation, high-throughput streaming data pipelines power our real-time fraud detection and AI grounding engines. As client data volumes grew past 10 million events per second, our core ingestion cluster faced challenges around GC pauses, partition rebalancing latency, and memory pressure.

In this technical post, our core infrastructure engineering team outlines the optimizations made to our Apache Kafka and Flink streaming architecture.

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Technical Highlights

1. Zero-Copy Serialization: Implemented Apache Arrow memory format across consumer threads to eliminate JSON deserialization overhead.
2. Dynamic Partition Allocation: Auto-balancing Kafka topic partitions using custom consumer group metrics.
3. Sub-10ms End-to-End Latency: Reduced P99 processing latency from 45ms to 8.2ms under peak load.

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Stay tuned for more deep dives from the LineEquation engineering blog.