PERFORMANCE_INDEX

Performance Documentation Index

Complete Performance Analysis & Benchmark Results

Stand: 22. Dezember 2025
Version: v1.3.0
Kategorie: ⚡ Performance
Status: 🟢 Production Ready

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📑 Table of Contents

• Quick Navigation
• Core Documents
• Results

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📋 Quick Navigation

Core Performance Documents

Document	Purpose	Last Updated
BENCHMARK_RESULTS_COMPLETE_2025.md	NEW - Complete benchmark suite results	2025-12-04
performance_benchmarks.md	Main performance & benchmarking guide	2025-12-04
performance_hardware.md	Hardware characteristics & optimization	Active
performance_compression_benchmarks.md	Compression algorithm analysis	Active
performance_tbb.md	Threading & parallelization	Active

Specialized Performance Topics

Document	Purpose
performance_memory.md	Memory optimization & profiling
performance_multi_cpu.md	Multi-CPU scaling & NUMA
performance_cuda.md	GPU acceleration (CUDA)
performance_vulkan.md	GPU acceleration (Vulkan)
performance_gpu_plan.md	GPU roadmap & planning
performance_enterprise_strategy.md	Enterprise deployment

Research & Scientific Findings

Document	Purpose
🔬 Wissenschaftliche Performance-Optimierungen	NEW - Research-based optimization strategies from top conferences (SIGMOD, VLDB, OSDI)

NEW: Umfassende Analyse von 25+ wissenschaftlichen Papers mit konkreten Implementierungs-Empfehlungen für ThemisDB. Erwartete Performance-Gewinne: +50-500% je nach Workload und Implementierungs-Phase.

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📊 Current Benchmark Summary

Test Configuration

• Hardware: 10 cores @ 3.7 GHz, 64 GB RAM, SSD (100K IOPS)
• Test Date: 2025-12-04
• Repetitions: 10 per test
• Warmup Runs: 5 per test

Key Performance Metrics

Random Operations

Read:       1,200,000 ops/sec  (vs RocksDB: 60%)
Write:        450,000 ops/sec  (vs RocksDB: 90%)
Scan:       1,800 MB/sec       (vs RocksDB: 90%)

Workload Performance

YCSB:       1,116,667 ops/sec  (mixed workload)
TPC-C:         50,000 TPMC     (OLTP benchmark)
TPC-H:         15,840 QPhH     (OLAP benchmark)
Sysbench:      21,000 TPS      (multi-workload)

Quality Metrics

Read Latency P50:   6.0 µs
Read Latency P99:   55.0 µs
Read Latency P999:  550.0 µs

Write Latency P50:  11.0 µs
Write Latency P99:  110.0 µs
Write Latency P999: 1,100.0 µs

Scaling & Efficiency

Scaling Efficiency (10 cores):  85.7%  (Expected: 75%)
Overall Compliance Rating:      75%    (Grade: B)
Primary Bottleneck:             Memory Bandwidth

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🎯 Performance Grades

Overall Assessment

Category	Score	Grade	Status
Read Performance	60%	🟠 C	Optimization Needed
Write Performance	90%	🟡 B+	Good
Scan Performance	90%	🟡 B+	Good
Scaling Efficiency	85.7%	🟡 B	Excellent
Overall	75%	🟡 B	Production Ready

Compliance Ratings

• 🟢 Grade A (≥95%): Exceeds expectations
• 🟡 Grade B (75-85%): Good performance ← ThemisDB
• 🟠 Grade C (60-75%): Acceptable
• 🔴 Grade D (40-60%): Needs work
• 🔴 Grade F (<40%): Critical issues

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💡 Key Findings

1. Excellent Scaling Efficiency

85.7% efficiency on 10 cores exceeds the expected 75% from Amdahl's Law. This indicates:

• ✅ Minimal lock contention
• ✅ Good cache coherency
• ✅ Effective load balancing

2. Strong Write Performance

90% of RocksDB on write operations shows:

• ✅ Well-optimized LSM tree write path
• ✅ Effective batching & compaction
• ✅ Good I/O scheduling

3. Read Optimization Opportunity

60% of RocksDB on random reads suggests:

• ⚠️ Memory bandwidth not fully utilized
• ⚠️ Random access patterns causing cache misses
• ⚠️ Potential for SIMD vectorization

4. Workload-Dependent Performance

• Read-Heavy (YCSB-C): 2,000,000 ops/sec ✅ Excellent
• Mixed (YCSB-A): 500,000 ops/sec - Acceptable
• Scan (YCSB-E): 800,000 ops/sec - Good
• OLTP (TPC-C): 50,000 TPMC - Production Ready
• OLAP (TPC-H): 15,840 QPhH - Competitive

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🔧 Optimization Opportunities

High Priority (Potential +20-30%)

1. Improve Random Read Performance

   • Implement SIMD vectorization
   • Optimize cache line usage
   • Use memory prefetching

2. Reduce P999 Latency

   • Profile with Intel VTune
   • Implement predictive prefetching
   • Thread pinning for NUMA

Medium Priority (Potential +10-15%)

3. Write Path Optimization

   • Increase memtable sizing
   • Concurrent compaction tuning
   • SSD-specific optimizations

4. Scan Performance

   • Index optimizations
   • Predicate pushdown
   • Columnar storage hints

Low Priority (Polish)

5. Lock Contention
   • Fine-grained locking
   • Lock-free algorithms
   • Profiling & tuning

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📈 Performance Trends

Workload Comparison

YCSB Workloads:
┌─────────────────────────────────────┐
│ C: 2,000,000 ops/sec  ████████████ │
│ B: 1,500,000 ops/sec  █████████    │
│ D: 1,200,000 ops/sec  ███████      │
│ E:   800,000 ops/sec  ████         │
│ A:   500,000 ops/sec  ██           │
│ F:   400,000 ops/sec  ██           │
└─────────────────────────────────────┘

Latency Distribution

Read Operations:
├─ P50:  6.0 µs   ✅ Excellent
├─ P99:  55.0 µs  ✅ Good
└─ P999: 550.0 µs ⚠️ Needs work

Write Operations:
├─ P50:  11.0 µs  ✅ Good
├─ P99:  110.0 µs ✅ Acceptable
└─ P999: 1,100.0 µs ⚠️ Optimization needed

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🔍 Hardware Constraints Analysis

CPU-Bound Limits

Theoretical Max:  ~14.8 Giga ops/sec
Actual Performance: 1.2 Giga ops/sec (reads)
Hardware Efficiency: 8.1%

This is EXPECTED because:

• Database workloads are memory-intensive
• Pure CPU-bound throughput is theoretical
• Real-world performance depends on data access patterns

Memory-Bound Limits

System Bandwidth: 100 GB/sec
Typical Throughput: 1.2M ops/sec with 100B/op
Utilization: ~12 MB/sec (0.012% of bandwidth)

Implication:

• Memory bandwidth NOT the bottleneck
• Random access patterns causing stalls
• Optimization through better prefetching & algorithms

Storage-Bound Limits

Storage IOPS: 100,000
Scan Bandwidth: 1,800 MB/sec (OK for SSD)
Write Operations: Below IOPS ceiling

Implication:

• Storage NOT bottleneck for this workload
• I/O operations well-handled
• Room for increased concurrency

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📚 Full Benchmark Details

Complete analysis available in: 👉 BENCHMARK_RESULTS_COMPLETE_2025.md

Includes:

• ✅ Detailed scientific benchmark results
• ✅ All YCSB workload comparisons
• ✅ TPC-C transaction analysis
• ✅ TPC-H query performance
• ✅ Sysbench multi-workload results
• ✅ Hardware constraint impact analysis
• ✅ RocksDB baseline comparisons
• ✅ Optimization recommendations
• ✅ Next steps & roadmap

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🚀 Running Benchmarks

Quick Start

# Run complete benchmark suite
cd benchmarks
python run_complete_benchmarks.py

# Results saved to: benchmark_results/complete_benchmark_latest.json

Individual Benchmarks

# Scientific benchmarks (warmup, repetitions, stats)
python themis_complete_with_constraints.py --mode scientific

# Standard benchmarks (YCSB, TPC-C, TPC-H, Sysbench)
python themis_complete_with_constraints.py --mode standards

# Hardware constraint analysis
python themis_complete_with_constraints.py --mode hardware-analyze

# Full compliance check
python themis_complete_with_constraints.py --mode compliance

# Everything
python themis_complete_with_constraints.py --mode full

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📞 Performance Support

For Questions About

• Benchmark Results: See BENCHMARK_RESULTS_COMPLETE_2025.md
• Hardware Limits: See performance_hardware.md
• Compression: See performance_compression_benchmarks.md
• Threading: See performance_tbb.md
• Memory: See performance_memory.md
• GPU Acceleration: See performance_cuda.md or performance_vulkan.md

Performance Tuning Checklist

• Review current benchmark results
• Identify primary bottleneck
• Check optimization recommendations
• Profile with Intel VTune or Linux perf
• Implement optimization
• Re-run benchmarks
• Document improvements

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🔗 Related Documentation

• Architecture Guide
• Development Guide
• Deployment Guide
• Monitoring & Observability
• Tuning Guide

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Document Version: 1.0
Last Updated: 2025-12-04
Status: ✅ Production Ready