Script Performance Optimization

Overview

Performance optimization reduces execution time and resource usage for automation scripts, especially in CI pipelines and large-scale operations.

Optimization Areas

• Avoid unnecessary subprocess calls
• Batch I/O operations
• Reuse API sessions and connections
• Parallelize independent steps carefully

flowchart LR
    A[Measure Baseline] --> B[Find Hotspots]
    B --> C[Optimize Bottleneck]
    C --> D[Benchmark Again]

Practical Example

Instead of processing files one by one with separate command invocations, batch operations reduce overhead.

from pathlib import Path
files = list(Path('logs').glob('*.json'))

Production Example

A nightly report script reduced runtime from 40 minutes to 9 minutes by replacing repeated API calls with paginated bulk queries and local caching.

Common Pitfalls

• Optimizing before collecting baseline metrics
• Parallelizing tasks that compete for same resources
• Ignoring API rate limits and retry backoff

Key Takeaways

• Measure first, then optimize targeted bottlenecks.
• I/O and network patterns often dominate script runtime.
• Performance work should preserve correctness and observability.