MODULE 03 // DATA STREAM INTEGRATION
MODULE 07 // OPTIMIZATION

Performance Tuning.

High-volume streams demand optimized performance. Learn techniques to maximize throughput while minimizing latency and resource consumption.

PERFORMANCE OPTIMIZATION

At scale, every optimization compounds. Batching reduces connection overhead by sending multiple events per request. Compression shrinks payloads by 70-90%. Connection pooling eliminates TCP handshake latency. Together, these techniques can increase throughput 10x while reducing costs.

Batching strategy: collect events until either a count threshold (e.g., 100 events) or time threshold (e.g., 2 seconds) is reached, whichever comes first. This balances latency (events don't wait too long) with efficiency (batches are reasonably sized).

Monitor key metrics: events per second (throughput), end-to-end latency (time from event creation to ABIS acknowledgment), error rate, and memory usage. Set alerts on anomalies—a sudden latency spike or error rate increase indicates problems before they become critical.

100
Batch Size
Events per batch. Balance between overhead reduction and latency. Tune based on event size and throughput.
2s
Max Batch Wait
Maximum time to wait before sending incomplete batch. Ensures low-volume periods don't introduce excessive latency.
70%
Compression Ratio
Typical gzip compression savings for JSON events. Reduces bandwidth costs and improves throughput.
KNOWLEDGE CHECK // Q07
Why should batching use both a count threshold AND a time threshold?