How to get started with LibraDB in a Go project?

Install it via 'go get', open a database with LibraDB.Open, and use transactions to create collections and perform operations. The README provides basic code examples to follow for setup and usage.

LibraDB vs BoltDB: which one should I use for learning?

LibraDB is better for learning database internals due to its minimal, under-1000-line codebase, while BoltDB is a more robust, production-ready key/value store. Use LibraDB to understand concepts, BoltDB for actual applications.

Does LibraDB handle concurrent reads and writes safely?

Yes, it supports goroutine-safe transactions with multiple concurrent reads or a single write at a time, ensuring data integrity but limiting write concurrency, as described in the transactions section.

Can I use LibraDB for a production web app?

Not recommended; it's designed for education, not performance. For production, use established databases like BadgerDB or PostgreSQL that offer better scalability, features, and support.

How to implement transactions with collections in LibraDB?

Create a write or read transaction using db.WriteTx() or db.ReadTx(), then use tx.CreateCollection() or tx.GetCollection() to manage collections, and commit with tx.Commit(). Examples in the README show this step-by-step.

What are the main limitations of LibraDB?

It's slow, has basic concurrency control with single-write transactions, lacks advanced features like joins or replication, and is not optimized for large datasets. It's best for learning, not real-world use.

Open-Awesome

libradb

GPL-3.0Go

A simple, persistent key/value store written in pure Go for learning database internals.

GitHub

202 stars26 forks0 contributors

What is libradb?

LibraDB is a minimal, persistent key/value database implemented in pure Go under 1000 lines of code. It provides a practical example of database fundamentals like storage, ACID transactions, and B-tree indexing, making it an accessible starting point for understanding how databases work internally.

Target Audience

Go developers and students interested in learning database internals, such as how transactions, indexing, and persistent storage are implemented in a simplified, educational codebase.

Value Proposition

Developers choose LibraDB for its clarity and minimalism as a learning tool, offering a fully functional key/value store with ACID transactions and serializable isolation in a codebase small enough to be easily studied and understood.

Overview

LibraDB is a simple, persistent key/value store written in pure Go in less than 1000 lines for learning purposes.

Use Cases

Best For

Learning how to implement a basic key/value database from scratch in Go
Studying ACID transaction mechanics with serializable isolation in a minimal codebase
Understanding B-tree indexing and collection-based data organization in databases
Exploring concurrent database design with goroutine-safe transactions
Educational projects or workshops on database internals and storage engines
Prototyping or experimenting with persistent storage in Go without the complexity of production databases

Not Ideal For

Production applications requiring high throughput and scalability for large datasets
Projects needing advanced query capabilities like SQL or complex data relationships
Systems with high-concurrency write requirements where multiple simultaneous writes are essential
Environments where database durability and fault tolerance beyond basic persistence are critical

Pros & Cons

Pros

Minimal Codebase for Learning

With under 1000 lines of Go code, it provides a clear, digestible example of database implementation, perfect for studying core concepts like transactions and indexing.

ACID with Serializable Isolation

Implements full ACID transactions with serializable isolation, as shown in the README, offering a practical look at transaction handling in a simplified setting.

Persistent Key/Value Storage

Stores data durably on disk with simple get/put/remove operations, demonstrating how databases achieve persistence without complexity.

Collection-Based B-Tree Indexing

Organizes data into collections with B-tree structures for efficient access, illustrating indexing fundamentals in an approachable way.

Cons

Limited Performance and Scalability

Designed for clarity over speed, it lacks optimizations for high-performance scenarios, making it unsuitable for real-world applications with large data volumes.

Single Write Concurrency

Allows only one write transaction at a time, as mentioned in the README, which severely limits throughput and can bottleneck write-intensive workloads.

Basic Feature Set

Missing essential production features like backup tools, replication, or advanced querying, as it's purely educational and minimal by design.

Frequently Asked Questions

Related Projects

Prometheus.io

The Prometheus monitoring system and time series database.

Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search

Stars45,359

Forks4,137

Last commit19 hours ago

TiDB

TiDB is built for agentic workloads that grow unpredictably, with ACID guarantees and native support for transactions, analytics, and vector search. No data silos. No noisy neighbors. No infrastructure ceiling.

Stars40,332

Forks6,221