Handling High-Velocity Data: Real-Time Moving Averages at Scale

Processing 200,000 events per second (EPS) requires a system designed for high throughput, low latency, and horizontal scalability. To calculate a 5-minute moving average in real-time, we must balance memory usage with computational speed.

1. The Architectural Stack

• Ingestion: Apache Kafka or AWS Kinesis to buffer the incoming 200k EPS.
• Stream Processing: Apache Flink or Spark Streaming for windowed calculations.
• Storage: Redis for sub-millisecond retrieval of the current average.
• Visualization: Grafana or a custom dashboard to display results.

2. Technical Design Strategy

Data Ingestion

• Partition the Kafka topic by a relevant key (e.g., sensor_id) to allow parallel processing.
• Use a minimum of 20–30 partitions to spread the load across consumers.

Sliding Window Logic

• Define a Sliding Window of 5 minutes with a "slide" interval (e.g., every 1 or 5 seconds).
• Apache Flink is preferred here because it handles "event-time" processing and late-arriving data natively.

State Management

• With 200k EPS, a 5-minute window holds 60 million events.
• Instead of storing raw events in memory, store partial aggregates (Sum and Count) for smaller slices (e.g., 1-second buckets).
• The final average is simply: Sum(all buckets) / Count(all buckets).

3. Optimizing for Performance

• Pre-Aggregation: Aggregate data at the producer level or ingestion layer to reduce the number of individual records.
• Backpressure: Ensure the system can signal the producers to slow down if the processing pipeline lags.
• Checkpointing: Enable incremental state snapshots to recover quickly from node failures without re-processing 5 minutes of data.

4. Why This Works

By using a bucketed approach within a stream processor, we reduce the memory footprint from millions of individual points to just 300 aggregate pairs (one for each second in 5 minutes). This ensures the system remains responsive even as data volume spikes.