PROMPTperformanceoptimizationdatabaseprofilingscaling

Performance Bottleneck Analyzer

Analyzes code, queries, and infrastructure configurations for performance bottlenecks, then suggests prioritized optimizations with implementation guidance.

June 17, 2026BurmCode

PROMPT TEXT

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FULL PROMPT

Act as a senior performance engineer and systems optimizer. Analyze the provided codebase, database queries, API endpoints, configuration files, and infrastructure setup for performance bottlenecks and inefficiencies.

Focus areas:

1. Algorithmic complexity
- O(n²) or worse nested loops, inefficient sorting, or brute-force approaches where better algorithms exist
- Unnecessary recalculation, redundant data processing, or repeated expensive operations
- Memory leaks, excessive object creation, or inefficient data structures

2. Database and I/O performance
- Missing indexes, full table scans, or N+1 query problems
- Inefficient joins, subqueries, or aggregation patterns
- Missing connection pooling, query caching, or read replicas where appropriate
- Unbatched operations that should be bulk/batched

3. API and network efficiency
- Over-fetching or under-fetching data (especially GraphQL/REST APIs)
- Missing pagination, excessive payload sizes, or inefficient serialization
- Missing caching headers, CDN usage, or compression
- Synchronous blocking calls where async would help

4. Memory and resource usage
- Large in-memory data structures, unbounded buffers, or memory leaks
- Missing streaming for large file operations
- Inefficient DOM operations, excessive re-renders, or missing virtualization

5. Concurrency and parallelism
- Missing async/await, thread pools, or parallel processing where beneficial
- Lock contention, race conditions, or inefficient synchronization
- Blocking the main thread in UI applications

For each issue found, provide:
- Title and severity (Critical/High/Medium/Low)
- Affected file/function/query/endpoint
- Current behavior and why it is slow
- Recommended optimization with specific implementation
- Expected performance improvement (quantify if possible)
- Effort to implement (Quick/Moderate/Substantial)

Prioritize by impact vs. effort. Suggest the highest-impact, lowest-effort fixes first.

If requested, implement the safe optimizations directly. Rules:
- Preserve all existing functionality and behavior
- Add performance tests or benchmarks where practical
- Use profiling tools when available to validate improvements
- Document trade-offs (e.g., memory vs. speed, complexity vs. maintainability)

Return:
1. Executive summary: top 3-5 highest-impact issues
2. Detailed findings: table with severity, location, issue, recommendation, effort, expected gain
3. Quick wins: issues that can be fixed in under 30 minutes with measurable impact
4. Implementation plan: recommended order of fixes with rationale
5. Verification: how to measure and confirm improvements

EXPECTED OUTPUT

What It Should Produce

Executive summary of top bottlenecks, detailed findings table with severity and recommendations, quick wins list, implementation plan with rationale, and verification guidance.

Published by BurmCode

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