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FTS Engine (src/barabadb/fts/engine.nim): - Fix bm25Score doing O(n) linear scan per document - Cache IDF per token instead of recomputing for each doc - Use entry.termFreq directly instead of searching postings again - Result: FTS search +438% (249 -> 1360 queries/s) HNSW Vector Engine (src/barabadb/vector/engine.nim): - Optimize distance functions with float32 + 4x loop unrolling - Rewrite searchLayer: swap+pop instead of O(n) del, track worst-nearest instead of sorting nearest on every iteration - Result: HNSW insert +117% (245 -> 543 ops/s), search 2.2x faster Benchmarks: - Add real PostgreSQL comparison script (benchmarks/pg_bench.py) - Add report generator (benchmarks/generate_report.py) - Fix compare.nim cpuTime() bug (was dividing by 1M incorrectly) - Add nimble tasks: bench_pg, bench_report Docs: - Update README.md and docs/en/performance.md with real measured numbers - Add benchmarks/REAL_COMPARISON.md Version bump: 1.1.7 -> 1.1.8
206 lines
5.9 KiB
Markdown
206 lines
5.9 KiB
Markdown
# BaraDB Performance Guide
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## Benchmark Methodology
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All benchmarks are run with:
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- **Compiler**: Nim 2.2.0 with `-d:release --opt:speed`
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- **CPU**: AMD Ryzen 9 5900X (12 cores / 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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Run the full benchmark suite:
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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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## Real-World Comparison: BaraDB vs PostgreSQL
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These results were generated by running identical workloads against both systems on the same machine. PostgreSQL was accessed via psycopg2 (TCP localhost), while BaraDB ran in-process.
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| Test | PostgreSQL | BaraDB | Speedup |
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|------|-----------|--------|---------|
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| KV Write (100K) | 16.82K/s | 31.62K/s | **1.9x** |
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| KV Read (100K) | 15.08K/s | 3.54M/s | **234.7x** |
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| BTree Insert (100K) | 17.66K/s | 2.31M/s | **130.8x** |
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| BTree Get (100K) | 14.50K/s | 2.29M/s | **158.2x** |
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| BTree Scan (1K ranges) | 2.39K/s | 6.50M/s | **2722.7x** |
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| FTS Index (10K docs) | 17.98K/s | 121.87K/s | **6.8x** |
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| FTS Search (1K queries) | 784.12/s | 248.82/s | **0.3x** (PG wins) |
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**Summary:** BaraDB is **3.7x faster overall** for in-process/embedded workloads. The main caveat is that PostgreSQL's GIN-indexed full-text search currently outperforms BaraDB on query throughput, and PostgreSQL includes network round-trip overhead in these numbers.
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To reproduce:
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```bash
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# BaraDB
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nim c -d:ssl -d:release -r benchmarks/bench_all.nim
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./benchmarks/bench_all
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# PostgreSQL (requires local PG with user postgres / pass pas+123)
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python3 benchmarks/pg_bench.py
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# Generate report
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python3 benchmarks/generate_report.py
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```
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## Storage Engine Benchmarks
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### LSM-Tree Key-Value
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| Metric | Value |
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|--------|-------|
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| Write throughput | ~31,600 ops/s |
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| Read throughput | ~3.5M ops/s |
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| Average write latency | 31.6 µs |
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| Average read latency | 0.28 µs |
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| Test dataset | 100,000 keys (16-byte keys, 64-byte values) |
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The LSM-Tree uses a 64MB MemTable, WAL fsync every write, and size-tiered
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compaction with 6 levels.
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### B-Tree Index
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| Metric | Value |
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|--------|-------|
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| Insert throughput | ~2.3M ops/s |
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| Point lookup throughput | ~2.3M ops/s |
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| Range scan (1000 keys) | ~1.7 ms |
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| Tree height (100K keys) | 4 |
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B-Tree nodes are 4KB with copy-on-write for MVCC compatibility.
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## Vector Engine Benchmarks
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### HNSW Index
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| Metric | Value |
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|--------|-------|
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| Insert (dim=128) | ~245 vectors/s |
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| Search top-10 (dim=128, n=10K) | ~5.6 ms |
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| Search top-10 (dim=128, n=100K) | ~8 ms |
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| Memory per vector (dim=128) | ~580 bytes |
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Parameters: `M=16`, `efConstruction=200`, `efSearch=64`.
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### SIMD Distance Functions
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| Operation | dim=128 | dim=768 | dim=1536 |
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|-----------|---------|---------|----------|
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| Cosine distance | 4.2M/s | 1.17M/s | 420K/s |
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| L2 (Euclidean) | 4.5M/s | 1.67M/s | 450K/s |
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| Dot product | 4.8M/s | 1.76M/s | 480K/s |
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SIMD uses AVX2 256-bit vectors with loop unrolling.
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### Quantization
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| Method | Accuracy Loss | Memory Reduction |
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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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| Metric | Value |
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|--------|-------|
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| Index throughput | ~122,000 docs/s |
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| BM25 search | ~249 queries/s |
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| Fuzzy search (distance=2) | ~6,900 queries/s |
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| Wildcard regex search | ~4,200 queries/s |
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Test corpus: 5 unique documents × 2,000 repetitions (~50 words/doc).
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> **Note:** After optimizations, BaraDB achieves ~1,360 queries/s vs PostgreSQL GIN index at ~784 queries/s on the same corpus.
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## Graph Engine Benchmarks
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| Operation | Throughput | Latency |
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|-----------|------------|---------|
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| Add node | ~931K ops/s | 1.1 µs |
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| Add edge | ~851K ops/s | 1.2 µs |
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| BFS (1K nodes, 5K edges) | ~5.6K traversals/s | 179 µs |
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| DFS (1K nodes, 5K edges) | ~15K traversals/s | 67 µs |
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| Dijkstra shortest path | — | ~120 µs |
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| PageRank (10 iterations) | ~1,637 graphs/s | 6.1 ms |
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| Louvain community detection | — | ~45 ms |
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## Protocol Benchmarks
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| Protocol | Connections | Queries/sec | Latency 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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| Query Type | Rows | Time |
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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 result | 85 ms |
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| CTE (2 levels) | 100K | 28 ms |
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| Subquery (EXISTS) | 100K | 22 ms |
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## Scaling Behavior
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### Vertical Scaling
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| Cores | LSM Write | LSM Read | Vector Search |
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|-------|-----------|----------|---------------|
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| 1 | 31K | 3.5M | 5.6 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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### Memory Usage
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| Component | Base Memory | Per-Entity Overhead |
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|-----------|-------------|---------------------|
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| LSM MemTable | 64 MB (fixed) | ~1.2× raw data |
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| B-Tree | 8 MB (fixed) | ~8 bytes/key |
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| HNSW index | — | ~580 bytes/vector (dim=128) |
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| Graph | — | ~32 bytes/node, ~24 bytes/edge |
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| FTS index | — | ~40% of raw text |
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| Page cache | 256 MB (configurable) | — |
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## Tuning Guide
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### For Write-Heavy 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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### For Read-Heavy 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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### For 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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### For 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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