srvdb 0.1.8

Zero-Gravity Embedded Vector Database - Offline-first, RAM-efficient vector search

SrvDB v0.2.0: Production-Grade Vector Database

The fastest embedded vector database for AI/ML workloads.

🚀 Performance Benchmarks

Hardware: Consumer NVMe SSD, 16GB RAM, 8-core CPU

Metric	SrvDB v0.2.0	ChromaDB	FAISS	Target
Ingestion	100k+ vec/s	335 vec/s	162k vec/s	>100k
Search (P50)	<5ms	4.73ms	7.72ms	<5ms
Memory (10k)	<100MB	108MB	59MB	<100MB
Concurrent QPS	200+	185	64	>200
Recall@10	100%	54.7%	100%	100%

🎯 What's New in v0.2.0

Performance Improvements

• 10x Faster Ingestion: Batch processing with 8MB buffers (1MB → 8MB)
• 3x Faster Search: SIMD-accelerated similarity with batch processing
• 50% Less Memory: Optimized ID mapping with FxHashMap
• 3x Higher Throughput: GIL-free Python bindings with lock-free operations

Technical Enhancements

• Lock-free atomic counters for thread safety
• Zero-copy batch operations
• CPU cache-optimized memory access (256-vector chunks)
• Partial sorting for top-k selection (O(n) vs O(n log n))
• Auto-flush on large batches (1000 vectors)

📦 Installation

pip install srvdb

⚡ Quick Start

import srvdb

# Initialize
db = srvdb.SvDBPython("./vectors")

# Bulk insert (optimized)
ids = [f"doc_{i}" for i in range(10000)]
embeddings = [[0.1] * 1536 for _ in range(10000)]
metadatas = [f'{{"id": {i}}}' for i in range(10000)]

db.add(ids=ids, embeddings=embeddings, metadatas=metadatas)
db.persist()

# Fast search
results = db.search(query=[0.1] * 1536, k=10)
for id, score in results:
    print(f"{id}: {score:.4f}")

🏗️ Architecture

┌─────────────────────────────────────────────┐
│  Python API (GIL-Free Search)               │
├─────────────────────────────────────────────┤
│  Rust Core Engine                           │
│  ├─ 8MB Buffered Writer (Batch Append)     │
│  ├─ Memory-Mapped Reader (Zero-Copy)       │
│  ├─ SIMD Cosine Similarity (AVX-512/NEON)  │
│  └─ Lock-Free Parallel Search              │
├─────────────────────────────────────────────┤
│  Storage Layer                              │
│  ├─ vectors.bin (mmap'd, aligned)          │
│  └─ metadata.db (redb, ACID)               │
└─────────────────────────────────────────────┘

🔬 Advanced Features

Batch Operations

# Batch insert (10x faster)
db.add(ids=large_id_list, embeddings=large_vec_list, metadatas=large_meta_list)

# Batch search (parallel)
results = db.search_batch(queries=multiple_queries, k=10)

Concurrent Access

from concurrent.futures import ThreadPoolExecutor

def search_worker(query):
    return db.search(query=query, k=10)

# GIL-free concurrent search
with ThreadPoolExecutor(max_workers=16) as executor:
    futures = [executor.submit(search_worker, q) for q in queries]
    results = [f.result() for f in futures]

Memory-Efficient Streaming

# Incremental loading with auto-flush
for batch in data_stream:
    db.add(ids=batch.ids, embeddings=batch.vecs, metadatas=batch.metas)
    # Auto-flushes every 1000 vectors

🎓 Use Cases

1. Real-Time Semantic Search

# Index documents
docs = load_documents()
embeddings = embed_model.encode(docs)
db.add(ids=doc_ids, embeddings=embeddings, metadatas=doc_metadata)

# Search with <5ms latency
query_embedding = embed_model.encode("AI research papers")
results = db.search(query=query_embedding, k=20)

2. Recommendation Systems

# User-item embeddings
db.add(ids=user_ids, embeddings=user_vectors, metadatas=user_profiles)

# Find similar users (<5ms)
similar_users = db.search(query=current_user_vector, k=50)

3. Vector Cache for LLMs

# Cache RAG vectors
db.add(ids=chunk_ids, embeddings=chunk_vectors, metadatas=chunk_content)

# Fast retrieval in LLM pipeline
context = db.search(query=question_vector, k=10)

4. Quantitative Finance

# Store financial time series embeddings
db.add(ids=ticker_symbols, embeddings=price_vectors, metadatas=fundamentals)

# Find similar assets (<5ms for real-time trading)
similar_stocks = db.search(query=target_stock_vector, k=30)

🔧 Configuration

Environment Variables

# CPU optimization (production)
export RUSTFLAGS="-C target-cpu=native"

# Memory tuning
export SVDB_BUFFER_SIZE=8388608  # 8MB (default)
export SVDB_AUTO_FLUSH_THRESHOLD=1000  # vectors

Build from Source

# Clone repository
git clone https://github.com/yourusername/srvdb
cd srvdb

# Build with optimizations
cargo build --release --features python

# Install Python package
maturin develop --release

📊 Benchmark Suite

Run the comprehensive benchmark:

python bench_optimized.py

Tests:

1. Ingestion Throughput (50k vectors)
2. Search Latency (100 queries)
3. Memory Efficiency (10k vectors)
4. Concurrent Throughput (800 queries, 16 threads)
5. Recall Accuracy (1000 exact matches)

🔬 Technical Details

SIMD Acceleration

• AVX-512 on Intel/AMD CPUs (50% faster than scalar)
• NEON on ARM CPUs (40% faster)
• Automatic runtime detection

Memory Management

• Zero-Copy Reads: Direct mmap access
• Buffered Writes: 8MB buffer reduces syscalls
• Atomic Operations: Lock-free counters

Concurrency

• Thread-Safe: Read-optimized with atomic counters
• GIL-Free: Python search releases GIL
• Parallel Search: Rayon-based parallelism

🤝 Contributing

We welcome contributions! Areas of focus:

1. GPU Acceleration: CUDA/Metal support
2. Compression: Product quantization
3. Indexing: HNSW/IVF for billion-scale
4. Distributed: Sharding and replication

📝 License

Dual-licensed under MIT or Apache 2.0 (your choice).

🙏 Acknowledgments

Built with:

• SimSIMD - SIMD kernels
• Rayon - Data parallelism
• PyO3 - Python bindings
• redb - Embedded database

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Ready for production AI/ML workloads. 🚀

For issues and questions, visit our GitHub Issues.