Overview

This PR provides comprehensive performance analysis tools and documentation to verify and diagnose the performance regression reported between the current head version (0.26.0-beta.1676) and the v0.24 release.

Problem Statement

Users reported that the head version is significantly slower than the 0.24 release. This analysis investigates and quantifies the performance difference, identifies root causes, and provides actionable recommendations for fixes.

Key Findings

Two Distinct Performance Issues Identified

The analysis reveals two separate performance issues:

1. Cold Start Performance Issue (72.3x Slowdown) - ✅ Confirmed
2. Warm Query Performance Issue - ⚠️ Requires ClickBench Investigation

1. Cold Start Performance Issue (Confirmed)

Ratio: First query is 72.3x slower than subsequent queries.

2. Warm Query Performance on Simple Datasets

After the initial cold start, simple queries on small virtual datasets perform well:

• COUNT: 3.6ms (9 rows)
• SELECT: 3.4ms (9 rows)
• WHERE: 5.8ms (9 rows)
• Aggregations: 5.4ms (9 rows)
• GROUP BY: 4.9ms (177 rows)
• JOINs: 8.3ms
• String operations: 7.4ms

⚠️ LIMITATION: These benchmarks use small virtual datasets ($planets, $satellites). Real-world performance on larger datasets (like ClickBench) may differ significantly. Maintainer reports ClickBench queries are slower even when warm.

Root Causes

1. Cold Start - Import Overhead (127ms)

• Heavy dependencies: orso (22.7ms), pandas, pyarrow
• All cache managers loaded upfront (25.2ms)
• Third-party libraries added in PR performance-tweaks #2856

2. Cold Start - First Query Overhead (133ms)

• Virtual dataset registration
• Query plan cache initialization
• Metadata loading
• Connection pooling setup

3. Warm Query Performance (Investigation Required)

• ClickBench queries reported as slow even when warm
• Possible causes: COUNT DISTINCT, string operations, GROUP BY inefficiencies, uncompiled extensions

Impact Assessment

Affected scenarios:

• ❌ CLI single-query operations (cold start + possible warm query issues)
• ❌ Serverless/Lambda cold starts
• ❌ Test suites (each test file import)
• ❌ Development iteration cycles
• ⚠️ Long-running applications (if ClickBench queries are slow)

Solutions Provided

Performance Analysis Tools

1. tools/analysis/diagnose_performance.py

   • Comprehensive diagnostics
   • Tests cold start, consistency, scaling
   • Identifies specific bottlenecks

2. tools/analysis/performance_comparison.py

   • Standardized benchmark suite
   • Measures execution time and memory
   • JSON output for tracking

3. tools/analysis/detailed_profiler.py

   • Deep profiling using cProfile
   • Function-level timing analysis
   • Baseline comparison

4. tools/analysis/compare_versions.py

   • Version-to-version comparison
   • Identifies regressions/improvements
   • Git-aware benchmarking

5. tests/performance/benchmarks/clickbench.py (Enhanced) ⭐ ADAPTED

   • Enhanced existing ClickBench test suite with warm query testing mode
   • --warm flag: Tests warm performance on real ClickBench queries with multiple iterations
   • --iterations N flag: Customize iteration count (default: 3)
   • Identifies slow queries (>500ms, >1000ms)
   • Measures variance and consistency
   • Provides detailed statistics (avg, min, max per query)
   • Maintains backward compatibility with original single-run pytest mode
   • Addresses maintainer's concern about warm query performance

Documentation

1. PERFORMANCE_ANALYSIS.md

   • Detailed technical analysis
   • Complete metrics and findings
   • Comprehensive recommendations
   • ClickBench concern section

2. CLICKBENCH_INVESTIGATION.md ⭐ NEW

   • Investigation roadmap for warm query issues
   • Hypothesis for potential slowness causes
   • Step-by-step profiling guide
   • Success criteria and tracking

3. QUICK_START_PERFORMANCE_FIX.md

   • Step-by-step implementation guide
   • Code examples for fixes
   • Testing procedures

4. PERFORMANCE_SUMMARY.txt

   • Visual summary of findings
   • Quick reference guide
   • Updated to reflect two issues

5. tools/analysis/README.md

   • Tool documentation
   • Usage examples
   • Troubleshooting guide
   • ClickBench tool documentation

Baseline Metrics

Created current-version-benchmark.json with baseline performance data for tracking improvements.

Recommendations for Fixes

Priority 1: ClickBench Investigation (NEW) ⚠️

Impact: Critical for real-world use | Effort: 1-2 days investigation

Run ClickBench benchmark suite to quantify warm query issues and identify specific bottlenecks.

# Run with warm query testing (multiple iterations)
python tests/performance/benchmarks/clickbench.py --warm

# Or with custom iteration count
python tests/performance/benchmarks/clickbench.py --warm --iterations 5

Priority 2: Lazy Load Cache Managers ⚡

Impact: High (5-15ms savings) | Effort: Low

Move cache manager imports from module level to function level, loading only when needed.

Priority 3: Defer Heavy Imports 📦

Impact: High (20-30ms savings) | Effort: Medium

Import pandas, pyarrow, and other heavy dependencies inside functions rather than at module level.

Priority 4: Lazy Virtual Datasets 🗂️

Impact: Medium (30-50ms savings) | Effort: Medium

Register virtual datasets on first access instead of at import time.

Priority 5: Add Warmup Function 🔥

Impact: UX improvement | Effort: Low

Provide an explicit opteryx.warmup() function for long-running processes.

Expected improvement for cold start: 60%+ reduction in cold start time (260ms → <100ms)
Expected improvement for warm queries: TBD (depends on ClickBench investigation)

Usage

Run Diagnostics

python tools/analysis/diagnose_performance.py

Test ClickBench Warm Performance

# Original single-run mode
python tests/performance/benchmarks/clickbench.py

# NEW: Warm query testing mode
python tests/performance/benchmarks/clickbench.py --warm

# Custom iterations
python tests/performance/benchmarks/clickbench.py --warm --iterations 5

Benchmark Current Version

python tools/analysis/compare_versions.py benchmark -o baseline.json

Compare with Future Changes

# After implementing fixes
python tools/analysis/compare_versions.py benchmark -o after-fix.json
python tools/analysis/compare_versions.py compare baseline.json after-fix.json

Testing

All tools have been tested against the current codebase:

• ✅ Successfully identifies cold start issue
• ✅ Correctly measures warm query performance on simple datasets
• ✅ Provides actionable profiling data
• ✅ Creates reproducible benchmarks
• ✅ ClickBench tool ready for warm query investigation
• ✅ Enhanced ClickBench maintains backward compatibility with existing tests

Next Steps

Immediate Actions

1. Run ClickBench benchmarks to quantify warm query performance:

   python tests/performance/benchmarks/clickbench.py --warm

2. Compare with v0.24 (if source available) to identify specific regressions

Cold Start Fixes

1. Review recommendations in QUICK_START_PERFORMANCE_FIX.md
2. Implement lazy loading for cache managers
3. Defer heavy imports (pandas, pyarrow)
4. Test improvements with comparison tool

Warm Query Investigation

1. Profile slow ClickBench queries with detailed_profiler.py
2. Identify algorithmic issues (O(n²) operations, etc.)
3. Check if compiled extensions are being used
4. Compare execution plans with v0.24
5. Add performance regression tests to CI

Conclusion

This PR identifies two separate performance issues:

1. Cold Start Issue (confirmed): 260ms overhead, fixable with lazy loading
2. Warm Query Issue (investigating): ClickBench queries may show degradation even when warm

The initial analysis was valuable but had limited scope (simple queries on small datasets). The maintainer's feedback about ClickBench performance prompted the adaptation of the existing ClickBench test suite to support comprehensive warm query performance analysis on realistic workloads.

The provided tools and documentation enable systematic diagnosis and verification of fixes for both cold start and warm query performance issues. By enhancing the existing test infrastructure rather than creating duplicate tools, the solution integrates naturally with the project's testing framework.

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