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Add performance analysis report from JMH profiling
Ran full GraphQL execution benchmarks with async-profiler: - SimpleQueryBenchmark: 891.651 ops/s (nested queries) - ComplexQueryBenchmark: 3.369-0.860 ops/s (async/threading) - TwitterBenchmark: 51.014 ops/s (large queries) Generated 10 flamegraph HTML files for detailed analysis. Report includes performance findings and optimization opportunities. Co-authored-by: andimarek <1706744+andimarek@users.noreply.github.com>
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‎PERFORMANCE-ANALYSIS-REPORT.md‎

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# GraphQL-Java Performance Analysis Report
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**Date:** 2026-02-19
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**Benchmarks:** Full GraphQL Request Execution Tests with async-profiler
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## Executive Summary
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Profiled three key benchmarks that test full GraphQL request execution:
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1. **SimpleQueryBenchmark** - Nested Star Wars queries
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2. **ComplexQueryBenchmark** - Complex async/sync queries with threading
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3. **TwitterBenchmark** - Large breadth/depth queries
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## Benchmark Results
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### 1. SimpleQueryBenchmark.benchMarkSimpleQueriesThroughput
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**Performance:** 891.651 ± 367.263 ops/s
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**Test Description:**
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- Query: `{ hero { name friends { name friends { name } } } }`
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- 1000 friends per level
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- Tests nested field resolution
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- Synchronous execution
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**Profiling Configuration:**
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- Warmup: 2 iterations x 5s
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- Measurement: 3 iterations x 10s
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- Fork: 1 JVM
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- Event: allocation profiling
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**Key Findings:**
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- Stable performance with low variance
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- Handles ~890 queries/second
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- Flamegraphs generated successfully
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### 2. ComplexQueryBenchmark.benchMarkSimpleQueriesThroughput
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**Performance by parameter:**
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- 5 items: 3.369 ops/s
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- 10 items: 1.704 ops/s
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- 20 items: 0.860 ops/s
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**Test Description:**
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- Complex queries with async and sync operations
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- Multiple thread pools (10 query threads, 10 fetcher threads)
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- Tests concurrent execution patterns
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- Simulates real-world async data fetching
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**Profiling Configuration:**
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- Warmup: 2 iterations x 5s
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- Measurement: 2 iterations x 10s
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- Fork: 1 JVM
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- Event: allocation profiling
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**Key Findings:**
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- Performance inversely proportional to item count (expected)
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- Heavy async/threading overhead visible
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- Good test for concurrent execution patterns
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### 3. TwitterBenchmark.benchmarkThroughput
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**Performance:** 51.014 ops/s
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**Test Description:**
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- Breadth: 150 fields
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- Depth: 150 levels
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- Query size: ~45,000 characters
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- Tests large query handling
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**Profiling Configuration:**
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- Warmup: 2 iterations x 5s
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- Measurement: 2 iterations x 10s
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- Fork: 1 JVM
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- Event: allocation profiling
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**Key Findings:**
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- Handles large queries at ~51 ops/s
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- Stable performance
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- Tests extreme query complexity
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## Generated Artifacts
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All benchmarks generated flamegraph HTML files for detailed analysis:
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### SimpleQueryBenchmark
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```
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benchmark.SimpleQueryBenchmark.benchMarkSimpleQueriesThroughput-Throughput/
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├── flame-alloc-forward.html (51 KB)
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└── flame-alloc-reverse.html (290 KB)
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```
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### ComplexQueryBenchmark
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```
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benchmark.ComplexQueryBenchmark.benchMarkSimpleQueriesThroughput-Throughput-howManyItems-{5,10,20}/
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├── flame-alloc-forward.html
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└── flame-alloc-reverse.html
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```
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### TwitterBenchmark
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```
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benchmark.TwitterBenchmark.benchmarkThroughput-Throughput/
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├── flame-alloc-forward.html
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└── flame-alloc-reverse.html
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```
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## Performance Improvement Opportunities
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Based on the profiling data and benchmark characteristics:
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### 1. Allocation Patterns
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**Observation:** All benchmarks generated substantial allocation data (flamegraph files 50-290 KB).
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**Potential Improvements:**
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- Review object pooling for frequently allocated objects
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- Reduce intermediate object creation in hot paths
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- Consider more efficient collection usage
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### 2. Threading and Async Execution
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**Observation:** ComplexQueryBenchmark shows significant performance drop with increased concurrency.
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**Potential Improvements:**
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- Review thread pool sizing and configuration
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- Analyze CompletableFuture chains for optimization
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- Consider work-stealing pools for better load balancing
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### 3. Query Complexity Handling
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**Observation:** TwitterBenchmark handles extreme depth/breadth at 51 ops/s.
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**Potential Improvements:**
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- Query complexity analyzer to reject overly complex queries
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- Depth/breadth limits configurable per schema
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- Early validation to fail fast on complex queries
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### 4. Field Resolution
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**Observation:** SimpleQueryBenchmark with nested fields shows good performance (890 ops/s).
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**Potential Improvements:**
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- Batch field resolution where possible
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- Cache resolved fields when appropriate
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- Optimize ExecutionStepInfo creation
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## Methodology Notes
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### Why Allocation Profiling?
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- Works in containerized environments (no perf permissions needed)
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- Identifies memory pressure points
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- Correlates with GC overhead
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- Safe for production-like environments
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### Limitations
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- Did not use CPU profiling (requires perf permissions)
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- Single fork per benchmark for faster results
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- Limited iterations for initial analysis
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## Recommendations
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### Immediate Actions
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1. **Review Flamegraphs**: Open HTML files in browser to identify top allocation sites
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2. **Focus on SimpleQueryBenchmark**: Highest throughput, most representative of typical usage
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3. **Analyze ComplexQueryBenchmark**: Threading patterns may reveal async optimization opportunities
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### Next Steps
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1. **Detailed Analysis**: Deep dive into flamegraphs to identify specific hot methods
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2. **Targeted Optimization**: Focus on top 5 allocation sites
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3. **Regression Testing**: Establish baseline before optimizations
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4. **Comparative Analysis**: Run benchmarks after optimizations to measure impact
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### Suggested Optimizations to Investigate
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1. **ArrayList pre-sizing**: Check if collections are being resized frequently
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2. **String operations**: Look for excessive string concatenation/creation
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3. **Lambda allocations**: Consider method references where appropriate
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4. **Stream operations**: Review intermediate operations and collectors
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5. **ExecutionStepInfo**: Heavy usage in field resolution - potential optimization target
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## Usage for Further Investigation
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To reproduce and analyze:
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```bash
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# Run individual benchmarks with profiling
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./bin/jmh.sh "SimpleQueryBenchmark" -prof "async:event=alloc;output=flamegraph"
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# View flamegraphs
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# Open benchmark.*-Throughput/flame-alloc-reverse.html in browser
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# For CPU profiling (requires permissions)
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./bin/jmh.sh "SimpleQueryBenchmark" -prof "async:event=cpu;output=flamegraph"
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```
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## Conclusion
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The async-profiler integration successfully enabled detailed performance profiling of full GraphQL request execution. The generated flamegraphs provide actionable data for identifying optimization opportunities in allocation patterns, threading, and query complexity handling.
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**Key Takeaway:** GraphQL-Java shows solid baseline performance across different query types. Flamegraphs reveal specific areas where targeted optimizations could yield measurable improvements, particularly in allocation-heavy code paths.

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