Performance Optimization

Overview

Performance optimization improves response time, throughput, and resource efficiency through measurement-driven changes.

Optimization Workflow

flowchart LR
    A[Measure Baseline] --> B[Find Bottleneck]
    B --> C[Implement Change]
    C --> D[Load Test]
    D --> E[Compare Metrics]
    E --> A

Practical Baseline Metrics

• p50, p95, p99 latency
• Throughput (requests per second)
• Error rate
• CPU, memory, and I/O saturation

Production Example

A search endpoint showed p95 latency above 900 ms. Profiling identified repeated database joins. Introducing a read model and targeted cache reduced p95 to 220 ms under the same load profile.

# Example load test
k6 run search-load-test.js

Optimization Areas

• Database index tuning
• Efficient query shape and pagination
• Caching hot read paths
• Asynchronous processing for non-critical tasks
• Connection pooling and keep-alive tuning

Common Pitfalls

• Optimizing before establishing baseline data
• Tuning one layer while ignoring downstream limits
• Measuring only average latency and missing tail behavior

Key Takeaways

• Performance work must start with reliable measurement.
• Tail latency matters more than averages for user experience.
• Sustained gains come from end-to-end bottleneck analysis.