🚀 OpenDB

High-Performance Hybrid Embedded Database for Rust

OpenDB is a high-performance, pure Rust hybrid embedded database combining:

Key-Value Store: Fast, persistent KV operations
Structured Records: Schema-free JSON-like memory storage
Graph Relationships: Bidirectional edges with typed relations
Vector Embeddings: Semantic similarity search
ACID Transactions: Full transaction support with snapshot isolation
In-Memory Caching: LRU cache for hot data
Multimodal Support: PDF, DOCX, audio, video, and text file processing for AI/LLM applications

Built on RocksDB for exceptional throughput and performance, OpenDB is designed for AI agent memory systems, knowledge graphs, semantic search, and multimodal RAG (Retrieval Augmented Generation) applications.

Features

🚀 High Performance: Built on RocksDB with optimized LSM tree storage
🔒 ACID Transactions: Full transactional support with snapshot isolation
🧠 Semantic Search: Vector similarity search for embeddings
📊 Graph Database: Bidirectional relationship tracking with typed edges
💾 Persistent Storage: Durable RocksDB backend with write-ahead logging
⚡ In-Memory Cache: LRU caching for frequently accessed data
🔧 Zero-Copy Serialization: Fast encoding/decoding with rkyv
🦀 Pure Rust: Memory-safe, concurrent, and type-safe
📝 Key-Value Store: Fast point lookups and prefix scans
🔄 Structured Records: Schema-free JSON-like memory storage with metadata
🔗 Graph Relationships: Bidirectional edges with custom relation types
🎯 Vector Embeddings: Support for high-dimensional embeddings (384/768/1536-dim)
📊 Column Families: Data isolation with separate column families for KV, records, graph, and vectors
🔐 Optimistic Locking: Compare-and-swap operations for conflict handling
🚦 Batch Operations: Efficient bulk inserts and updates
📈 Performance Tuning: Configurable write buffers, block cache, and compression
🎬 Multimodal File Support: Built-in types for PDF, DOCX, audio, video, and text processing
🤖 AI/LLM Ready: Designed for agent memory, document Q&A, and multimodal RAG pipelines
📦 Document Chunking: Split large files into processable chunks with per-chunk embeddings
⚙️ Customizable Storage: Configure database location, cache sizes, and vector dimensions
🎨 Colored Console Output: Beautiful, emoji-rich terminal output for examples and debugging

Quick Start

Installation

Option 1: Using Cargo (Recommended)

Add OpenDB to your Cargo.toml:

[dependencies]

opendb = "0.1"

Or use cargo-add:

cargo add opendb

Option 2: Pre-built Binaries

Download platform-specific builds from GitHub Releases:

Linux x86_64: opendb-linux-x86_64.tar.gz
Linux ARM64: opendb-linux-aarch64.tar.gz
macOS Intel: opendb-macos-x86_64.tar.gz
macOS Apple Silicon: opendb-macos-aarch64.tar.gz
Windows x86_64: opendb-windows-x86_64.zip

See the Manual Installation section below for detailed instructions.

Option 3: Build from Source

git clone https://github.com/muhammad-fiaz/opendb.git

cd opendb

cargo build --release --all-features

Build Requirements:

Rust 2021 edition or later
Clang and LLVM (for RocksDB)
- Linux: sudo apt-get install clang llvm
- macOS: brew install llvm
- Windows: choco install llvm

Basic Usage

use opendb::{OpenDB, Memory};
use uuid::Uuid;

// Open database (creates a folder at ./data with multiple files)
let db = OpenDB::open("./data")?;

// Store a memory
let memory = Memory {
    id: Uuid::new_v4().to_string(),
    content: "Hello, OpenDB!".to_string(),
    embedding: vec![0.1, 0.2, 0.3], // 384-dim in production
    importance: 0.8,
    metadata: serde_json::json!({"type": "greeting"}),
    created_at: chrono::Utc::now(),
};

db.insert_memory(&memory)?;

// Retrieve by ID
let retrieved = db.get_memory(&memory.id)?;

// Search by similarity
let query_embedding = vec![0.15, 0.25, 0.35];
let similar = db.search_similar(&query_embedding, 10)?;

// Create relationships
db.link_memories(&memory.id, &other_id, "relates_to")?;
let related = db.get_related(&memory.id)?;

Key-Value Operations

// Simple KV operations
db.put(b"user:1", b"Alice")?;
let value = db.get(b"user:1")?;

// Prefix scanning
for (key, value) in db.scan_prefix(b"user:")? {
    println!("{:?}: {:?}", key, value);
}

Transactions

// Begin transaction
let txn = db.begin_transaction()?;

// Transactional operations
txn.put(b"balance", b"1000")?;
txn.put(b"updated_at", current_time.as_bytes())?;

// Commit atomically
db.commit_transaction(txn)?;

Configuration & Custom Storage

OpenDB supports flexible configuration including custom storage locations, cache sizes, and vector dimensions:

use opendb::{OpenDB, OpenDBOptions};

// Customize all settings with method chaining
let options = OpenDBOptions::new()
    .with_storage_path("./my_custom_db")  // Custom storage location
    .with_kv_cache_size(5000)             // Larger KV cache
    .with_record_cache_size(3000)          // Larger record cache
    .dimension(768);                        // Larger embeddings (e.g., OpenAI)

let db = OpenDB::open_with_options("./data", options)?;

Configuration Options:

with_storage_path(): Custom database directory (useful for multi-tenant or production deployments)
with_kv_cache_size(): Number of KV entries to cache (default: 1000)
with_record_cache_size(): Number of memory records to cache (default: 500)
dimension(): Embedding vector dimension (default: 384 for sentence-transformers)

Production Examples:

// Environment-based configuration
let db_path = std::env::var("OPENDB_PATH")
    .unwrap_or_else(|_| "./data/prod_db".to_string());

let prod_options = OpenDBOptions::with_dimension(768)
    .with_kv_cache_size(10000)
    .with_record_cache_size(5000);

let db = OpenDB::open_with_options(&db_path, prod_options)?;

// Multi-tenant pattern
for tenant_id in &["tenant_a", "tenant_b", "tenant_c"] {
    let tenant_path = format!("./data/tenants/{}", tenant_id);
    let db = OpenDB::open(&tenant_path)?;
    // Each tenant has isolated database
}

See the custom_storage example for comprehensive configuration patterns.

Run it with:

cargo run --example custom_storage

Multimodal AI Applications

OpenDB provides built-in support for multimodal file processing, perfect for AI agents, RAG systems, and document Q&A:

use opendb::{OpenDB, MultimodalDocument, DocumentChunk, FileType};

let db = OpenDB::open("./ai_agent")?;

// Process a PDF document
let mut pdf_doc = MultimodalDocument::new(
    "research_001",
    "paper.pdf",
    FileType::Pdf,
    1024 * 500, // 500 KB
    "Extracted text from PDF...",
    generate_embedding("paper content"), // Use sentence-transformers
)
.with_metadata("author", "Dr. Smith")
.with_metadata("pages", "15");

// Add chunks for large documents
pdf_doc.add_chunk(DocumentChunk::new(
    "chunk_0",
    "Introduction section...",
    generate_embedding("introduction"),
    0,
    1000,
));

// Supports: PDF, DOCX, TXT, MP3, MP4, WAV, JPG, PNG, and more
let file_type = FileType::from_extension("mp3");
println!("{}", file_type.description()); // "Audio file"

See the multimodal_agent example for a complete demo of:

PDF, DOCX, and text document processing
Audio transcription workflows (with whisper-rs)
Video caption extraction (with ffmpeg)
Document chunking strategies
Embedding generation patterns
Production RAG pipelines

Run it with:

cargo run --example multimodal_agent

Architecture

OpenDB is built with a modular architecture:

┌─────────────────────────────────────────┐
│         OpenDB Core API                 │
├─────────┬──────────┬──────────┬─────────┤
│   KV    │ Records  │  Graph   │ Vector  │
├─────────┴──────────┴──────────┴─────────┤
│         Transaction Layer               │
├─────────────────────────────────────────┤
│         LRU Cache Layer                 │
├─────────────────────────────────────────┤
│      RocksDB Storage Backend            │
└─────────────────────────────────────────┘

Storage Layer: RocksDB with column families for data isolation
Caching Layer: Write-through LRU cache with RocksDB block cache
Transaction Layer: Snapshot isolation with optimistic locking
API Layer: Type-safe Rust APIs for all operations

See the Architecture Documentation for details.

Database Structure

OpenDB Database Folder

OpenDB uses a folder-based architecture with multiple files for high performance:

./my_database/               # Your database folder
├── OPENDB_INFO             # OpenDB metadata (identifies this as OpenDB)
├── README.md               # Database-specific documentation
├── .opendb_config.json     # Database configuration
├── CURRENT                 # Points to current MANIFEST file
├── IDENTITY                # Database UUID
├── LOCK                    # Prevents concurrent access
├── MANIFEST-*              # Database metadata and file list
├── OPTIONS-*               # RocksDB configuration
├── *.log                   # Write-Ahead Log (WAL) for durability
└── *.sst                   # Sorted String Tables (actual data)

Key Files:

OPENDB_INFO: OpenDB metadata explaining format and features
README.md: Database-specific documentation and backup instructions
.opendb_config.json: Machine-readable database configuration
*.log: Write-Ahead Log ensures durability (changes written here first)
*.sst: Sorted String Table files store the actual data (compressed)
MANIFEST: Tracks which SST files are active
LOCK: Ensures only one process accesses the database at a time

Benefits of folder-based design:

✅ Higher write throughput: WAL allows fast sequential writes
✅ Better compression: Data is compressed in SST files
✅ Efficient compaction: Background merging of files
✅ Crash recovery: WAL enables reliable recovery

Important notes:

⚠️ Always backup the entire folder (not individual files)
⚠️ Do NOT manually edit files in the database folder
⚠️ Only one process can open a database at a time (enforced by LOCK file)

Check the OPENDB_INFO, README.md, and .opendb_config.json files in any database folder for detailed information.

Installation

Prerequisites

Rust 1.70+: Install Rust
RocksDB dependencies (handled automatically by cargo)

From crates.io

cargo add opendb

From source

git clone https://github.com/muhammad-fiaz/OpenDB.git

cd OpenDB

cargo build --release

Examples

OpenDB includes comprehensive examples:

# Basic quickstart

cargo run --example quickstart


# AI agent memory system with colored output

cargo run --example memory_agent


# Graph relationship traversal

cargo run --example graph_relations


# Multimodal AI/LLM application (PDF, audio, video, text)

cargo run --example multimodal_agent


# Custom storage configuration patterns

cargo run --example custom_storage

All examples feature:

✨ Colorful console output with emojis for better readability
📊 Detailed progress indicators showing what's happening
💡 Best practices and production-ready patterns
🎯 Complete workflows from data ingestion to query

See the examples/ directory for more.

Documentation

📖 User Guide: Complete documentation
📚 API Reference: Detailed API docs
🏗️ Architecture: Design and internals
⚡ Performance Tuning: Optimization guide

Performance

OpenDB delivers excellent performance across all operations:

Benchmarks

All benchmarks run on a single thread (no parallelism) to show baseline performance:

Operation	Throughput	Latency (avg)	Description
KV Put	~136K ops/sec	7.36 µs	Write key-value pair to storage
KV Get	~10.2M ops/sec	97.8 ns	Read key-value pair (cached)
Memory Insert	~39K ops/sec	25.5 µs	Insert Memory record with embedding
Memory Get	~4.7M ops/sec	213 ns	Retrieve Memory record by ID
Vector Search (100)	~22.7K ops/sec	44.1 µs	k-NN search across 100 vectors (384-dim)
Vector Search (500)	~5.1K ops/sec	197.6 µs	k-NN search across 500 vectors (384-dim)
Vector Search (1000)	~2.5K ops/sec	400.2 µs	k-NN search across 1000 vectors (384-dim)
Graph Link	~54K ops/sec	18.5 µs	Create bidirectional edge
Graph Get Related	~68.3K ops/sec	14.6 µs	Retrieve outgoing edges
Transaction Commit	~129K ops/sec	7.75 µs	Commit 2-write transaction

Notes:

Vector search uses brute-force k-NN (linear scan) with euclidean distance
All operations include full persistence to RocksDB (WAL + LSM writes)
Benchmarks run with default RocksDB settings
Memory operations include rkyv serialization/deserialization
Graph operations maintain bidirectional indices
⚠️ Performance varies by system: These benchmarks were run on a specific hardware configuration. Your performance may differ based on your OS, platform, processor, RAM, and storage type (SSD vs HDD).

Reproduce benchmarks on your system:

Run the benchmarks using the benches/benchmark.rs file:

cargo bench --bench benchmark

For detailed results with plots and statistical analysis:

cargo install cargo-criterion

cargo criterion --bench benchmark

The benchmark file (benches/benchmark.rs) includes comprehensive tests for:

Key-value operations (put, get)
Memory operations (insert, get)
Vector search (100, 500, 1000 vectors)
Graph operations (link, get_related)
Transaction operations (commit)

Run benchmarks yourself:

cargo bench --bench benchmark

Performance Characteristics

KV Operations: Sub-microsecond reads with LRU cache, single-digit µs writes
Vector Search: Sub-millisecond for datasets up to 1K vectors (384-dim)
Graph Traversal: Constant-time relationship lookups via indices
Transactions: Full ACID with minimal overhead (~7-8 µs commit time)
Serialization: Zero-copy deserialization with rkyv for optimal performance

See Performance Guide for tuning.

Manual Installation

Download pre-built binaries from GitHub Releases for your platform:

Linux x86_64

# Download and extract

wget https://github.com/muhammad-fiaz/opendb/releases/latest/download/opendb-linux-x86_64.tar.gz

tar -xzf opendb-linux-x86_64.tar.gz


# System-wide installation (requires sudo)

sudo cp libopendb.so /usr/local/lib/

sudo ldconfig


# Or copy to your project

cp libopendb.* /path/to/your/project/lib/

Linux ARM64 (aarch64)

wget https://github.com/muhammad-fiaz/opendb/releases/latest/download/opendb-linux-aarch64.tar.gz

tar -xzf opendb-linux-aarch64.tar.gz

sudo cp libopendb.so /usr/local/lib/

sudo ldconfig

macOS x86_64 (Intel)

curl -L https://github.com/muhammad-fiaz/opendb/releases/latest/download/opendb-macos-x86_64.tar.gz -o opendb-macos-x86_64.tar.gz

tar -xzf opendb-macos-x86_64.tar.gz

sudo cp libopendb.dylib /usr/local/lib/

macOS ARM64 (Apple Silicon)

curl -L https://github.com/muhammad-fiaz/opendb/releases/latest/download/opendb-macos-aarch64.tar.gz -o opendb-macos-aarch64.tar.gz

tar -xzf opendb-macos-aarch64.tar.gz

sudo cp libopendb.dylib /usr/local/lib/

Windows x86_64

# Download and extract
Invoke-WebRequest -Uri "https://github.com/muhammad-fiaz/opendb/releases/latest/download/opendb-windows-x86_64.zip" -OutFile "opendb-windows-x86_64.zip"
Expand-Archive -Path opendb-windows-x86_64.zip -DestinationPath .

# Copy to system PATH or your project directory
Copy-Item opendb.dll C:\Windows\System32\
# Or add to your project directory

Build Dependencies

If building from source or using the library, ensure you have:

Clang and LLVM (required for RocksDB bindings)
- Linux: sudo apt-get install clang llvm
- macOS: brew install llvm
- Windows: choco install llvm

For Alpine Linux (musl libc), use the bindgen-static feature:

[dependencies.opendb]

default-features = false

features = ["bindgen-static"]

For Windows /MT runtime, use the mt_static feature:

[dependencies.opendb]

features = ["mt_static"]

Use Cases

AI Agent Memory: Persistent memory for AI agents with semantic search
Knowledge Graphs: Store entities and relationships with vector embeddings
Semantic Search: Fast similarity search over document embeddings
Graph Analytics: Relationship analysis and traversal
Embedded Database: Lightweight, embedded storage for Rust applications

Changelog

See the GitHub Releases page for version history and changelog.

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

🐛 Bug Reports: Open an issue
✨ Feature Requests: Suggest a feature
🔀 Pull Requests: Submit a PR

License

Licensed under the Apache License, Version 2.0 (LICENSE or http://www.apache.org/licenses/LICENSE-2.0).

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you shall be licensed under the Apache License, Version 2.0, without any additional terms or conditions.

Contact

Author: Muhammad Fiaz
Email: contact@muhammadfiaz.com
GitHub: @muhammad-fiaz
Repository: muhammad-fiaz/opendb

OpenDB - High-performance hybrid embedded database for Rust 🦀

opendb 0.0.0

🚀 OpenDB

High-Performance Hybrid Embedded Database for Rust

Features

Quick Start

Installation

Basic Usage

Key-Value Operations

Transactions

Configuration & Custom Storage

Multimodal AI Applications

Architecture

Database Structure

OpenDB Database Folder

Installation

Prerequisites

From crates.io

From source

Examples

Documentation

Performance

Benchmarks

Performance Characteristics

Manual Installation

Linux x86_64

Linux ARM64 (aarch64)

macOS x86_64 (Intel)

macOS ARM64 (Apple Silicon)

Windows x86_64

Build Dependencies

Use Cases

Changelog

Contributing

License

Contribution

Contact