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Performance Documentation Index

Complete Performance Analysis & Benchmark Results

Stand: 6. April 2026
Version: v1.3.0
Kategorie: ⚡ Performance
Status: 🟢 Production Ready

📑 Table of Contents

📋 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.

📊 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

🎯 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

💡 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

🔧 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%)

  1. Write Path Optimization

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

  2. Scan Performance

    • Index optimizations
    • Predicate pushdown
    • Columnar storage hints

Low Priority (Polish)

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

📈 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

🔍 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

📚 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

🚀 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

📞 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

🔗 Related Documentation

Document Version: 1.0
Last Updated: 2026-04-06
Status: ✅ Production Ready