Project Name
Micro-Batch Latency Eliminated for a Tier-1 Trading Firm With Apache Flink and Kafka
A Tier-1 institutional quantitative trading firm and brokerage provider managing high-volume algorithmic strategies and direct market access execution for institutional participants needed a platform capable of two simultaneous demands: continuously calculating complex mathematical price signals while concurrently enforcing margin, balance, and risk-gate boundaries across thousands of client accounts in real time. Traditional micro-batch architectures introduced processing delays that produced stale data analysis, trading slippage, and missed execution windows. Applying its AI-First approach, Ksolves built a decoupled, event-driven streaming infrastructure on Apache Flink, Kafka, and Redis, achieving deterministic sub-millisecond execution with exactly-once financial state mutation guarantees and zero UI freeze under full market load.
- The Latency Trap and Trading Slippage: Traditional analytical engines operating on micro-batch principles introduced processing delays that produced stale data analysis, resulting in trading slippage, missed execution windows, and degraded signal accuracy in fast-moving markets.
- Stream Asymmetry Conflict: The architecture had to seamlessly blend two completely asymmetric data structures: a massive, continuous torrent of high-frequency market ticks and unpredictable, transaction-critical user order intents - both requiring different processing semantics from the same pipeline.
- Browser Rendering and Network Saturation: Pumping raw streams directly to user dashboards overloaded single-threaded client-side JavaScript execution engines - causing browser UI freeze-ups and destabilising active network links during peak market activity.
- State Mutation Integrity Under Cluster Failure: Financial balances require absolute accuracy. The platform had to handle continuous high-throughput mutations while guaranteeing exactly-once processing semantics to prevent balance corruption or duplicate order routing during sudden cluster infrastructure failures.
Ksolves implemented a decoupled, event-driven streaming infrastructure. Apache Kafka acts as the immutable transaction highway, Apache Flink processes events via a RocksDB out-of-core state model with zero GC overhead, a Redis presentation buffer handles client-facing caching and Pub/Sub, and a FastAPI WebSocket pipeline delivers smooth data frames to browser UIs without exposing the internal cluster.
- Apache Kafka as the Immutable Transaction Highway: Kafka safely queues both high-frequency market tick feeds and irregular user order intents in a single durable, replayable event log, decoupling ingestion from processing and eliminating data loss risk at the ingestion boundary regardless of downstream processing speed.
- Apache Flink With RocksDB Out-of-Core State Model: Flink processes every market event deterministically in sub-millisecond windows using RocksDB as the embedded state backend, mutating raw binary bytes in memory with zero application-level GC overhead and no micro-batch latency accumulation.
- Redis Presentation Buffer for UI Decoupling: An ultra-fast Redis Pub/Sub and cache layer intercepts all client-facing data before it reaches browser connections - throttling and buffering the stream to prevent UI saturation and decouple web delivery from core processing throughput entirely.
- FastAPI WebSocket Delivery Pipeline: A lightweight FastAPI WebSocket engine reads directly from Redis to push smooth, throttled data frames to browser UIs maintaining live client handshakes without exposing the internal Flink processing cluster to external connection instability.
- Asynchronous Chandy-Lamport Distributed Snapshotting: Exactly-once processing semantics and zero data loss under cluster failure achieved by replacing synchronous pipeline stalls with an asynchronous variant of the Chandy-Lamport distributed snapshotting algorithm maintaining financial ledger integrity across any infrastructure failure scenario.
Technology Stack
| Category | Technology |
|---|---|
| Stream Processing | Apache Flink |
| Message Queue | Apache Kafka |
| Cache / Pub-Sub | Redis (Pub/Sub + Cache) |
| API / Delivery | FastAPI + WebSockets |
| Database | PostgreSQL |
| State Backend | RocksDB |
- Sub-Millisecond Deterministic Execution: Deterministic event-by-event processing paths under 1 millisecond achieved, eliminating latency slippage from micro-batch architectures and ensuring price signal calculations and risk gate enforcement operate on current market state.
- Zero UI Freeze Under Full Market Load: Decoupling web delivery from the processing cluster via the Redis presentation buffer enables high-performance rendering of live stock tickers and algorithmic signals without freezing the browser UI or destabilising network links.
- Zero Data Loss Under Cluster Failure: Asynchronous Chandy-Lamport snapshotting maintains exactly-once semantics and flawless financial ledger persistence, replacing synchronous pipeline stalls with fault-tolerant snapshotting at zero throughput cost.
- Exactly-Once Financial State Guarantee: RocksDB-backed Flink state management prevents balance corruption and duplicate order routing under any cluster failure scenario, the financial ledger remains consistent across all infrastructure failure modes.
- Hybrid Workflow Maximising Execution Throughput: Pairing Apache Flink's distributed stream processing with a FastAPI delivery layer maximises mathematical execution throughput while keeping backend integration clean and independently scalable.
“The platform handles thousands of simultaneous account risk calculations at sub-millisecond latency with no UI degradation under peak market load. The Flink and Redis architecture gave us the performance and fault tolerance we needed to run a production-grade HFT operation.”
CTO / Head of Quantitative Engineering.
A Tier-1 institutional quantitative trading firm requiring sub-millisecond price signal calculation and real-time pre-trade risk enforcement across thousands of accounts was delivered a production-grade HFT platform through Ksolves Big Data consulting services. Apache Flink with RocksDB achieves deterministic sub-millisecond processing with exactly-once financial state guarantees. Kafka provides a durable, asymmetry-tolerant transaction highway. Redis decouples UI delivery from the processing cluster, eliminating browser saturation. The result: continuous high-frequency risk enforcement with zero data loss, zero UI freeze, and full fault tolerance under any cluster failure scenario.
Build a High-Frequency Trading or Real-Time Risk Platform That Cannot Tolerate Latency or Data Loss?
