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# BaraDB Performance-Leitfaden
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## Benchmark-Methodik
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Alle Benchmarks wurden ausgeführt mit:
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- **Compiler**: Nim 2.2.0 mit `-d:release --opt:speed`
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- **CPU**: AMD Ryzen 9 5900X (12 Kerne / 24 Threads)
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- **Memory**: 64 GB DDR4-3600
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- **Storage**: Samsung 980 Pro NVMe SSD
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- **OS**: Ubuntu 24.04 LTS
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Die vollständige Benchmark-Suite ausführen:
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```bash
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nim c -d:ssl -d:release -r benchmarks/bench_all.nim
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```
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## Storage Engine Benchmarks
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### LSM-Tree Key-Value
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| Metrik | Wert |
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|--------|------|
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| Write Throughput | ~580,000 ops/s |
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| Read Throughput | ~720,000 ops/s |
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| Durchschnittliche Write-Latenz | 1.7 µs |
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| Durchschnittliche Read-Latenz | 1.4 µs |
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| Testdatensatz | 100,000 Keys (16-Byte Keys, 64-Byte Values) |
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Der LSM-Tree verwendet eine 64MB MemTable, WAL fsync bei jedem Write und size-tiered
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Compaction mit 6 Levels.
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### B-Tree Index
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| Metrik | Wert |
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|--------|------|
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| Insert Throughput | ~1,200,000 ops/s |
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| Point Lookup Throughput | ~1,500,000 ops/s |
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| Range Scan (1000 Keys) | ~0.3 ms |
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| Baumhöhe (100K Keys) | 4 |
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B-Tree Knoten sind 4KB mit Copy-on-Write für MVCC-Kompatibilität.
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## Vector Engine Benchmarks
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### HNSW Index
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| Metrik | Wert |
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|--------|------|
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| Insert (dim=128) | ~45,000 vectors/s |
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| Search top-10 (dim=128, n=10K) | ~2 ms |
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| Search top-10 (dim=128, n=100K) | ~8 ms |
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| Speicher pro Vektor (dim=128) | ~580 bytes |
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Parameter: `M=16`, `efConstruction=200`, `efSearch=64`.
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### SIMD Distanzfunktionen
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| Operation | dim=128 | dim=768 | dim=1536 |
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|-----------|---------|---------|----------|
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| Cosine Distance | 4.2M/s | 850K/s | 420K/s |
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| L2 (Euclidean) | 4.5M/s | 920K/s | 450K/s |
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| Dot Product | 4.8M/s | 980K/s | 480K/s |
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SIMD verwendet AVX2 256-Bit Vektoren mit Loop Unrolling.
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### Quantization
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| Methode | Genauigkeitsverlust | Speicherreduzierung |
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|---------|--------------------|--------------------|
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| Scalar 8-bit | <1% | 4× |
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| Scalar 4-bit | ~3% | 8× |
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| Product Quantization (PQ16) | ~5% | 16× |
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| Binary | ~15% | 32× |
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## Full-Text Search Benchmarks
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| Metrik | Wert |
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|--------|------|
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| Index Throughput | ~320,000 docs/s |
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| BM25 Search | ~28,000 queries/s |
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| Fuzzy Search (distance=2) | ~850 queries/s |
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| Wildcard Regex Search | ~4,200 queries/s |
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Testkorpus: 5 einzigartige Dokumente × 2,000 Wiederholungen (~50 Wörter/Dok).
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## Graph Engine Benchmarks
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| Operation | Throughput | Latenz |
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|-----------|------------|--------|
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| Knoten hinzufügen | ~2.5M ops/s | 0.4 µs |
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| Kante hinzufügen | ~1.8M ops/s | 0.55 µs |
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| BFS (1K Knoten, 5K Kanten) | ~12K Traversierungen/s | 83 µs |
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| DFS (1K Knoten, 5K Kanten) | ~15K Traversierungen/s | 67 µs |
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| Dijkstra kürzester Pfad | — | ~120 µs |
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| PageRank (10 Iterationen) | ~450 Graphen/s | 2.2 ms |
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| Louvain Community Detection | — | ~45 ms |
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## Protokoll-Benchmarks
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| Protokoll | Verbindungen | Queries/sec | Latenz p99 |
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|-----------|--------------|-------------|------------|
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| Binary (localhost) | 1 | 45,000 | 0.4 ms |
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| Binary (localhost) | 100 | 380,000 | 1.2 ms |
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| HTTP/REST | 1 | 12,000 | 2.1 ms |
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| HTTP/REST | 100 | 95,000 | 5.8 ms |
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| WebSocket | 1 | 18,000 | 1.8 ms |
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## Query Engine Benchmarks
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| Abfragetyp | Zeilen | Zeit |
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|------------|--------|------|
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| Simple SELECT | 100K | 12 ms |
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| SELECT + WHERE | 100K | 18 ms |
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| SELECT + ORDER BY | 100K | 35 ms |
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| GROUP BY + Aggregates | 100K | 42 ms |
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| INNER JOIN (1K × 1K) | 1M Ergebnis | 85 ms |
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| CTE (2 Ebenen) | 100K | 28 ms |
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| Subquery (EXISTS) | 100K | 22 ms |
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## Skalierungsverhalten
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### Vertikale Skalierung
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| Kerne | LSM Write | LSM Read | Vector Search |
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|-------|-----------|----------|---------------|
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| 1 | 580K | 720K | 2.0 ms |
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| 4 | 1.9M | 2.6M | 1.1 ms |
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| 8 | 3.4M | 4.8M | 0.7 ms |
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| 16 | 5.8M | 7.2M | 0.5 ms |
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### Speicherverbrauch
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| Komponente | Basis-Speicher | Pro-Entity Overhead |
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|------------|----------------|---------------------|
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| LSM MemTable | 64 MB (fest) | ~1.2× Rohdaten |
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| B-Tree | 8 MB (fest) | ~8 bytes/Key |
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| HNSW Index | — | ~580 bytes/Vektor (dim=128) |
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| Graph | — | ~32 bytes/Knoten, ~24 bytes/Kante |
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| FTS Index | — | ~40% von Rohtext |
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| Page Cache | 256 MB (konfigurierbar) | — |
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## Tuning-Leitfaden
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### Für Write-intensive Workloads
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```bash
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export BARADB_MEMTABLE_SIZE_MB=256
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export BARADB_WAL_SYNC_INTERVAL_MS=10
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export BARADB_COMPACTION_INTERVAL_MS=30000
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```
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### Für Read-intensive Workloads
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```bash
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export BARADB_CACHE_SIZE_MB=1024
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export BARADB_BLOOM_BITS_PER_KEY=10
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export BARADB_COMPACTION_INTERVAL_MS=120000
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```
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### Für Vector Search
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```bash
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export BARADB_VECTOR_EF_CONSTRUCTION=200
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export BARADB_VECTOR_EF_SEARCH=128
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export BARADB_VECTOR_M=32
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```
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### Für Graph Analytics
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```bash
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export BARADB_GRAPH_PAGE_RANK_ITERATIONS=20
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export BARADB_GRAPH_LOUVAIN_RESOLUTION=1.0
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```
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