Baradb/docs/en/performance.md

BaraDB Performance Guide

Benchmark Methodology

All benchmarks are run with:

• Compiler: Nim 2.2.0 with -d:release --opt:speed
• CPU: AMD Ryzen 9 5900X (12 cores / 24 threads)
• Memory: 64 GB DDR4-3600
• Storage: Samsung 980 Pro NVMe SSD
• OS: Ubuntu 24.04 LTS

Run the full benchmark suite:

nim c -d:ssl -d:release -r benchmarks/bench_all.nim

Real-World Comparison: BaraDB vs PostgreSQL

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.

Test	PostgreSQL	BaraDB	Speedup
KV Write (100K)	16.82K/s	31.62K/s	1.9x
KV Read (100K)	15.08K/s	3.54M/s	234.7x
BTree Insert (100K)	17.66K/s	2.31M/s	130.8x
BTree Get (100K)	14.50K/s	2.29M/s	158.2x
BTree Scan (1K ranges)	2.39K/s	6.50M/s	2722.7x
FTS Index (10K docs)	17.98K/s	121.87K/s	6.8x
FTS Search (1K queries)	784.12/s	248.82/s	0.3x (PG wins)

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.

To reproduce:

# BaraDB
nim c -d:ssl -d:release -r benchmarks/bench_all.nim
./benchmarks/bench_all

# PostgreSQL (requires local PG with user postgres / pass pas+123)
python3 benchmarks/pg_bench.py

# Generate report
python3 benchmarks/generate_report.py

Storage Engine Benchmarks

LSM-Tree Key-Value

Metric	Value
Write throughput	~31,600 ops/s
Read throughput	~3.5M ops/s
Average write latency	31.6 µs
Average read latency	0.28 µs
Test dataset	100,000 keys (16-byte keys, 64-byte values)

The LSM-Tree uses a 64MB MemTable, WAL fsync every write, and size-tiered compaction with 6 levels.

B-Tree Index

Metric	Value
Insert throughput	~2.3M ops/s
Point lookup throughput	~2.3M ops/s
Range scan (1000 keys)	~1.7 ms
Tree height (100K keys)	4

B-Tree nodes are 4KB with copy-on-write for MVCC compatibility.

Vector Engine Benchmarks

HNSW Index

Metric	Value
Insert (dim=128)	~245 vectors/s
Search top-10 (dim=128, n=10K)	~5.6 ms
Search top-10 (dim=128, n=100K)	~8 ms
Memory per vector (dim=128)	~580 bytes

Parameters: M=16, efConstruction=200, efSearch=64.

SIMD Distance Functions

Operation	dim=128	dim=768	dim=1536
Cosine distance	4.2M/s	1.17M/s	420K/s
L2 (Euclidean)	4.5M/s	1.67M/s	450K/s
Dot product	4.8M/s	1.76M/s	480K/s

SIMD uses AVX2 256-bit vectors with loop unrolling.

Quantization

Method	Accuracy Loss	Memory Reduction
Scalar 8-bit	<1%	4×
Scalar 4-bit	~3%	8×
Product Quantization (PQ16)	~5%	16×
Binary	~15%	32×

Full-Text Search Benchmarks

Metric	Value
Index throughput	~122,000 docs/s
BM25 search	~249 queries/s
Fuzzy search (distance=2)	~6,900 queries/s
Wildcard regex search	~4,200 queries/s

Test corpus: 5 unique documents × 2,000 repetitions (~50 words/doc).

Note: After optimizations, BaraDB achieves ~1,360 queries/s vs PostgreSQL GIN index at ~784 queries/s on the same corpus.

Graph Engine Benchmarks

Operation	Throughput	Latency
Add node	~931K ops/s	1.1 µs
Add edge	~851K ops/s	1.2 µs
BFS (1K nodes, 5K edges)	~5.6K traversals/s	179 µs
DFS (1K nodes, 5K edges)	~15K traversals/s	67 µs
Dijkstra shortest path	—	~120 µs
PageRank (10 iterations)	~1,637 graphs/s	6.1 ms
Louvain community detection	—	~45 ms

Protocol Benchmarks

Protocol	Connections	Queries/sec	Latency p99
Binary (localhost)	1	45,000	0.4 ms
Binary (localhost)	100	380,000	1.2 ms
HTTP/REST	1	12,000	2.1 ms
HTTP/REST	100	95,000	5.8 ms
WebSocket	1	18,000	1.8 ms

Query Engine Benchmarks

Query Type	Rows	Time
Simple SELECT	100K	12 ms
SELECT + WHERE	100K	18 ms
SELECT + ORDER BY	100K	35 ms
GROUP BY + aggregates	100K	42 ms
INNER JOIN (1K × 1K)	1M result	85 ms
CTE (2 levels)	100K	28 ms
Subquery (EXISTS)	100K	22 ms

Scaling Behavior

Vertical Scaling

Cores	LSM Write	LSM Read	Vector Search
1	31K	3.5M	5.6 ms
4	1.9M	2.6M	1.1 ms
8	3.4M	4.8M	0.7 ms
16	5.8M	7.2M	0.5 ms

Memory Usage

Component	Base Memory	Per-Entity Overhead
LSM MemTable	64 MB (fixed)	~1.2× raw data
B-Tree	8 MB (fixed)	~8 bytes/key
HNSW index	—	~580 bytes/vector (dim=128)
Graph	—	~32 bytes/node, ~24 bytes/edge
FTS index	—	~40% of raw text
Page cache	256 MB (configurable)	—

Tuning Guide

For Write-Heavy Workloads

export BARADB_MEMTABLE_SIZE_MB=256
export BARADB_WAL_SYNC_INTERVAL_MS=10
export BARADB_COMPACTION_INTERVAL_MS=30000

For Read-Heavy Workloads

export BARADB_CACHE_SIZE_MB=1024
export BARADB_BLOOM_BITS_PER_KEY=10
export BARADB_COMPACTION_INTERVAL_MS=120000

For Vector Search

export BARADB_VECTOR_EF_CONSTRUCTION=200
export BARADB_VECTOR_EF_SEARCH=128
export BARADB_VECTOR_M=32

For Graph Analytics

export BARADB_GRAPH_PAGE_RANK_ITERATIONS=20
export BARADB_GRAPH_LOUVAIN_RESOLUTION=1.0