[REQ-PERF] Achieve Sub-50ms Response Times for Interactive Range Operations

[groldan]

Performance Requirement Summary

Achieve consistent sub-50ms response times for cached range operations to support real-time interactive applications like mapping and data visualization.

Performance Category

• Response Time / Latency
• Throughput / Bandwidth
• Resource Utilization (CPU, Memory, Network)
• Scalability / Concurrency
• Startup Time
• Cache Performance

Baseline & Target Metrics

Current Performance (if applicable)

• Metric: P95 response time for cached 64KB range reads
• Value: Not yet measured (initial baseline required)
• Measurement Method: JMH microbenchmarks + integration tests
• Environment: Local development with in-memory cache

Target Performance

• Metric: P95 response time for interactive operations
• Target Value: <50ms for cached reads, <200ms for uncached reads
• Acceptable Range: 40-60ms cached, 150-250ms uncached
• Measurement Method: JMH + realistic load testing with TestContainers

Comparison Benchmark

• Compared to: Direct AWS SDK/Azure SDK usage, local file operations
• Expected Improvement: Within 2x of direct SDK performance, 10x faster than naive implementations

Test Scenarios

Scenario 1: Interactive Map Tile Access

Description: Real-time map tile rendering with range requests for PMTiles

Test Parameters:

• Data Size: 64KB-256KB ranges from 100MB-1GB PMTiles files
• Request Pattern: Random access with 70% cache hit ratio
• Concurrency Level: 50 concurrent users, 200 requests/second
• Environment: Cloud storage (S3/Azure) with regional caching

Expected Results:

• Latency (P50/P95/P99): 25ms/45ms/80ms
• Throughput: 200+ requests/second sustained
• Resource Usage: <512MB heap, <50% CPU utilization

Scenario 2: Data Visualization Dashboard

Description: Interactive charts loading data ranges on demand

Test Parameters:

• Data Size: 4KB-32KB ranges from time-series data files
• Request Pattern: Sequential with temporal locality
• Concurrency Level: 10 concurrent dashboards
• Environment: HTTP backend with compression

Expected Results:

• Latency (P50/P95/P99): 15ms/35ms/60ms
• Throughput: 100+ requests/second per dashboard
• Resource Usage: <256MB heap, <30% CPU utilization

Scenario 3: Batch Analytics Preview

Description: Quick preview of large datasets for interactive exploration

Test Parameters:

• Data Size: 1MB-4MB ranges with block alignment
• Request Pattern: Large sequential reads with prefetching
• Concurrency Level: 5 concurrent analysis sessions
• Environment: Cloud storage with aggressive caching

Expected Results:

• Latency (P50/P95/P99): 100ms/180ms/300ms
• Throughput: 50GB/hour effective data access
• Resource Usage: <1GB heap, optimized for throughput

Performance Context

Use Case

• Interactive applications (real-time mapping)
• Batch processing (large dataset analysis)
• Server applications (high-concurrency web services)
• Embedded systems (resource-constrained environments)
• Edge computing (latency-sensitive operations)

Load Profile

• Sustained load
• Burst load
• Peak load
• Stress test conditions

Data Characteristics

• File Sizes: 100MB-10GB (PMTiles, scientific datasets)
• Range Sizes: 4KB-4MB (optimized for cache efficiency)
• Access Patterns: 70% random, 30% sequential with temporal locality
• Cache Hit Ratio: Target 80%+ for interactive workloads

System Requirements

Hardware Profile

• CPU: 4+ cores, modern x64 architecture
• Memory: 4GB+ available, 512MB-2GB heap
• Storage: SSD for disk caching (1GB-100GB cache size)
• Network: 100Mbps+ bandwidth, <50ms latency to cloud storage

Software Environment

• Java Version: 17+ (21+ for virtual thread optimization)
• Operating System: Linux, macOS, Windows (cross-platform)
• Cloud Provider: AWS S3, Azure Blob, Google Cloud Storage
• Network Conditions: Variable (3G to fiber, with offline scenarios)

Configuration

• Cache Settings: 256MB memory cache, 10GB disk cache
• Block Sizes: 64KB memory blocks, 1MB disk blocks
• Connection Pools: 20 HTTP connections, keep-alive enabled
• Other Settings: Compression enabled, retry logic configured

Measurement & Monitoring

Benchmarking Strategy

• JMH microbenchmarks
• Integration test benchmarks
• Load testing with realistic data
• Continuous performance monitoring

Key Performance Indicators (KPIs)

1. Primary KPI: P95 response time for cached operations <50ms
2. Secondary KPI: Cache hit ratio >80% under realistic load
3. Supporting KPI: Memory efficiency <10MB per 1000 cached ranges

Monitoring Tools

• JMH benchmark reports
• Application Performance Monitoring (APM)
• JVM profiling tools
• Custom metrics collection
• Cloud provider monitoring

Alerting Thresholds

• Warning Threshold: P95 latency >60ms for 5+ minutes
• Critical Threshold: P95 latency >100ms for 2+ minutes
• Recovery Threshold: P95 latency <50ms for 10+ minutes

Performance Requirements

Response Time Requirements

• Interactive Operations: <50ms (P95) for cached reads
• Batch Operations: <200ms (P95) for uncached reads
• Background Operations: <1s (P95) for prefetch operations

Throughput Requirements

• Minimum Sustained: 100 requests/second per core
• Target Peak: 500 requests/second per core
• Maximum Expected: 1000 requests/second per core (burst)

Resource Usage Limits

• Memory: <10MB per 1000 active ranges
• CPU: <50% utilization under normal load
• Network Bandwidth: <80% of available bandwidth
• Storage I/O: <100 IOPS per concurrent operation

Scalability Requirements

• Concurrent Users: 1000+ simultaneous users
• Concurrent Operations: 10,000+ concurrent range reads
• Data Volume: 1TB+ total cached data across all operations
• Geographic Distribution: Multi-region with <100ms cross-region latency

Performance Optimization

Optimization Strategies

• Caching optimizations
• Block alignment tuning
• Connection pooling
• Compression techniques
• Parallel processing
• Buffer management
• Network optimization

Implementation Approach

1. Baseline Measurement: Establish current performance characteristics
2. Bottleneck Identification: Profile CPU, memory, network, and I/O
3. Optimization Implementation: Apply caching, pooling, and alignment
4. Performance Validation: Verify improvements meet targets
5. Continuous Monitoring: Maintain performance regression detection

Validation Plan

• Automated performance tests in CI/CD pipeline
• Load testing with realistic data patterns
• Memory leak detection under sustained load
• Performance comparison with direct SDK usage
• Cross-platform performance validation

Risk Assessment

Performance Risks

• Risk: Cache thrashing under memory pressure

  • Impact: High
  • Mitigation: Adaptive cache sizing and LRU eviction policies

• Risk: Network latency spikes affecting response times

  • Impact: High
  • Mitigation: Aggressive prefetching and local caching strategies

• Risk: GC pauses affecting P99 latencies

  • Impact: Medium
  • Mitigation: G1GC tuning, object pooling, off-heap caching

Technical Constraints

• Memory Constraints: JVM heap limitations and GC overhead
• Network Constraints: Cloud provider bandwidth and latency limits
• Platform Constraints: Different performance characteristics across OS
• Compatibility Constraints: Performance vs compatibility trade-offs

Acceptance Criteria

• Target metrics achieved in benchmark tests
• Performance is consistent across test environments
• No performance regression in existing functionality
• Resource usage within acceptable limits
• Performance documented and reproducible

Regression Testing

• Automated performance tests in CI/CD
• Performance baseline established
• Regression detection thresholds defined
• Performance trend monitoring implemented

Documentation Requirements

• Performance benchmark results
• Optimization guide updates
• Configuration recommendations
• Troubleshooting guide for performance issues

Dependencies

Internal Dependencies

• Caching implementation
• Block alignment strategy
• Connection management
• Buffer management

External Dependencies

• Cloud provider performance characteristics
• Network infrastructure
• Hardware specifications
• Java runtime optimizations

Success Criteria

1. P95 response time <50ms for cached operations under realistic load
2. Cache hit ratio >80% with optimized access patterns
3. Memory usage <10MB per 1000 cached ranges
4. Performance meets or exceeds direct SDK usage within 2x margin
5. Consistent performance across all supported platforms and providers

Target Release

Version 1.1 (Q2 2025)

Related Issues

• Connected to ByteBuffer pool management for memory efficiency
• Related to virtual thread optimization for concurrency
• Dependencies on caching strategy optimization
• Integration with performance monitoring infrastructure

Additional Context

Response time performance is critical for user experience in interactive applications. Sub-50ms response times represent the threshold for "instant" user perception, while anything above 100ms begins to feel sluggish.

The optimization strategy focuses on:

1. Multi-level caching to maximize hit ratios
2. Block alignment to optimize I/O operations
3. Connection pooling to reduce connection overhead
4. Buffer management to minimize allocation overhead
5. Prefetching to anticipate user needs

Benchmarking should use realistic data patterns and network conditions to ensure results translate to production environments. The performance requirements balance ambitious targets with practical constraints of distributed systems and cloud storage latency.