Open Source Rust Database Software - Page 2
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Rust Database Software
View 472 business solutionsBrowse free open source Rust Database Software and projects below. Use the toggles on the left to filter open source Rust Database Software by OS, license, language, programming language, and project status.
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tabularis
An open-source desktop client for modern databases.
Tabularis is a free, open-source desktop database client for PostgreSQL, MySQL/MariaDB, and SQLite. Built with Rust and Tauri v2, it is fast, lightweight, and cross-platform (Windows, macOS, Linux). Features: SQL Notebooks with inline charts and cross-cell variables. AI-powered SQL assistant (OpenAI, Claude, Ollama, and more). Built-in MCP server for AI agent integration. Visual Query Builder with drag-and-drop JOINs. Plugin system to add any database driver. Monaco-based editor with smart autocomplete. High-performance data grid with inline editing. ER Diagram visualization. SSH tunneling. Secure credential storage via OS keychain. Split view, saved queries, SQL dump/import, 10+ themes, customizable shortcuts. Hackable, extensible, and built for everyday SQL work. https://tabularis.dev https://github.com/debba/tabularis -
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IndraDB
A graph database written in rust
A graph database written in rust. IndraDB consists of a server and an underlying library. Most users would use the server, which is available via releases as pre-compiled binaries. But if you're a rust developer that wants to embed a graph database directly in your application, you can use the library. IndraDB's original design is heavily inspired by TAO, facebook's graph datastore. In particular, IndraDB emphasizes simplicity of implementation and query semantics, and is similarly designed with the assumption that it may be representing a graph large enough that full graph processing is not possible. IndraDB departs from TAO (and most graph databases) in its support for properties. IndraDB offers a variety ways to work with it: as a server with cross-language support, as a rust library, and via CLI. What follows are a few examples of each use case. -
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SurrealDB
A scalable, distributed, collaborative, document-graph database
With an SQL-style query language, real-time queries with highly-efficient related data retrieval, advanced security permissions for multi-tenant access, and support for performant analytical workloads, SurrealDB is the next generation serverless database. SurrealDB is the ultimate cloud database for tomorrow's applications. SurrealDB is an innovative NewSQL cloud database, suitable for serverless applications, jamstack applications, single-page applications, and traditional applications. It is unmatched in its versatility and financial value, with the ability for deployment on cloud, on-premise, embedded, and edge computing environments. For a hassle-free setup, get started with SurrealDB Cloud in one-click. There is no need for your team to learn new complicated database languages. Getting started with SurrealDB is as simple as one command - and advanced functionality is simple to understand, whilst still being fast and performant. -
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WebApp.rs
A web application completely written in Rust
A web application completely written in Rust. Target of this project is to write a complete web application including the backend and front within Rust. The app consists of a frontend and a backend. For getting started with hacking, the backend can be tested via make run-backend, whereas the frontend can be tested with make run-frontend. You can adapt the application configuration within Config.toml if needed. make deploy uses podman to start a PostgreSQL container and the Rust backend container. If you wish to use docker instead of podman, set CONTAINER_RUNTIME=docker in the top of Makefile. Edit Config.toml if needed to set the backend url and PostgreSQL credentials. The login screen will accept any username and password that are equal, such as me (username) and me (password). There is currently no further user authentication yet, but nonmatching combination will result in an authentication failure. -
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rust-rocksdb
Rust wrapper for rocksdb
Binding is statically linked with a specific version of RocksDB. If you want to build it yourself, make sure you've also cloned the RocksDB and compression submodules. By default, support for the Snappy, LZ4, Zstd, Zlib, and Bzip2 compression is enabled through crate features. If support for all of these compression algorithms is not needed, default features can be disabled and specific compression algorithms can be enabled. The underlying RocksDB does allow column families to be created and dropped from multiple threads concurrently. But this crate doesn't allow it by default for compatibility. If you need to modify column families concurrently, enable crate feature called multi-threaded-cf, which makes this binding's data structures to use RwLock by default. -
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sentinel
Sentinel is a filesystem-backed document DBMS written in Rust.
Sentinel is a filesystem-backed document DBMS built in Rust that prioritizes compliance, transparency, and auditability over raw performance. Unlike traditional databases, every document is a plain JSON file, making your data immediately forensic-friendly and Git-versionable. Perfect for regulated industries requiring GDPR, SOC 2, HIPAA, or PCI-DSS compliance. Sentinel provides async operations with automatic BLAKE3 hashing and optional Ed25519 signatures for cryptographic integrity. Data encryption supports AES-256-GCM, XChaCha20-Poly1305, and Ascon-128. Each document includes full audit metadata—creation timestamps, version history, hashes, and signatures—viewable with standard UNIX tools. Designed for edge deployments, compliance systems, audit logs, certificate management, and regulatory reporting. No server required, works entirely on filesystem with Git-based replication. Zero vendor lock-in—migrate anytime using standard tools like rsync, tar, or git. -
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DB3 Network
DB3 is an open-source decentralized firebase firestore alternative.
DB3 Network is an open-source and decentralized firebase firestore alternative for building fully decentralized dApps quickly with minimal engineering effort. -
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GreptimeDB
An open-source, cloud-native, unified time series database for metrics
GreptimeDB treats all time series as contextual events with timestamps, and thus unifies the processing of metrics, logs, and events. It supports analyzing metrics, logs, and events with SQL, PromQL, and streaming with continuous aggregation. GreptimeDB is a time-series database optimized for storing and querying large amounts of time-series data, commonly used in monitoring and IoT applications. -
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Lantern Database
PostgreSQL vector database extension for building AI applications
Lantern is a real-time data transformation engine that enables data engineers to build, run, and monitor streaming data pipelines with SQL. It’s designed to process events in motion, offering low-latency stream transformations, aggregations, and enrichment in a declarative way. Lantern is especially suited for modern data infrastructure and analytics platforms. -
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LocustDB
Massively parallel, high performance analytics database
An experimental analytics database aiming to set a new standard for query performance and storage efficiency on commodity hardware. See How to Analyze Billions of Records per Second on a Single Desktop PC and How to Read 100s of Millions of Records per Second from a Single Disk for an overview of current capabilities. Download the latest binary release, which can be run from the command line on most x64 Linux systems, including Windows Subsystem for Linux. When loading .csv or .csv.gz files with --load, the first line of each file is assumed to be a header containing the names for all columns. The type of each column will be derived automatically, but this might break for columns that contain a mixture of numbers/strings/empty entries. To persist data to disk in LocustDB's internal storage format (which allows fast queries from disk after the initial load), specify the storage location with --db-path. -
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Neon Database
Neon: Serverless Postgres. We separated storage and compute
The database you love, on a serverless platform designed to help you build reliable and scalable applications faster. Focus on building applications with time and money-saving features like instant provisioning, autoscaling according to load, and scale to zero. Instantly branch your data and schema to access isolated DB copies for development, CI/CD, and schema migrations with copy-on-write storage. Integrate it into your language or framework within minutes and unlock a simpler developer workflow. -
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Replibyte
Seed your development database with real data
Replibyte is a blazingly fast tool to seed your databases with your production data while keeping sensitive data safe. Support data backup and restore for PostgreSQL, MySQL and MongoDB. Replace sensitive data with fake data. Works on large database (> 10GB). Database Subsetting: Scale down a production database to a more reasonable size. Start a local database with the prod data in a single command. On-the-fly data (de)compression (Zlib). On-the-fly data de/encryption (AES-256). Fully stateless (no server, no daemon) and lightweight binary. Use custom transformers. Auto-detect and version database schema change. Auto-detect sensitive fields. Auto-clean backed up data. At Qovery (the company behind Replibyte), developers can clone their applications and databases just with one click. However, the cloning process can be tedious and time-consuming, and we end up copying the information multiple times. -
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Reshape
An easy-to-use, zero-downtime schema migration tool for Postgres
Reshape is a declarative SQL schema migration tool for PostgreSQL that allows developers to define the desired schema in a single file. It compares the current database schema with the desired state and generates safe, dependency-aware migrations automatically. Reshape is built for simplicity, safety, and version control integration. -
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TensorBase
TensorBase is a new big data warehousing with modern efforts
TensorBase hopes the open source not become a copy game. TensorBase has a clear-cut opposition to fork communities, repeat wheels, or hack traffic for so-called reputations (like Github stars). After thoughts, we decided to temporarily leave the general data warehousing field. For people who want to learn how a database system can be built up, or how to apply modern Rust to the high-performance field, or embed a lightweight data analysis system into your own big one. You can still try, ask or contribute to TensorBase. The committers are still around the community. We will help you in all kinds of interesting things pursued in the project by us and maybe you. We still maintain the project to look forward to meeting more database geniuses in this world, although no new feature will be added in the near future. -
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falkordb
A super fast Graph Database
A super fast Graph Database uses GraphBLAS under the hood for its sparse adjacency matrix graph representation. Our goal is to provide the best Knowledge Graph for LLM (GraphRAG). -
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seladb/pickledb-rs
PickleDB-rs is a lightweight and simple key-value store
PickleDB-RS is a lightweight, fast, and key-value store written in Rust. Inspired by the Python PickleDB, it provides a simple, NoSQL database for small applications that require persistent key-value storage without the overhead of full-fledged databases. It is designed for speed and ease of use, with a JSON-based storage format. -
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sled
The champagne of beta embedded databases
Serializable (ACID) transactions for atomically reading and writing to multiple keys in multiple keyspaces. Fully atomic single-key operations, including compare and swap. Forward and reverse iterators over ranges of items. A crash-safe monotonic ID generator capable of generating 75-125 million unique ID's per second. Zstd compression (use the compression build feature, disabled by default). Cpu-scalable lock-free implementation. Flash-optimized log-structured storage. Uses modern b-tree techniques such as prefix encoding and suffix truncation for reducing the storage costs of long keys with shared prefixes. If keys are the same length and sequential then the system can avoid storing 99%+ of the key data in most cases, essentially acting like a learned index. sled performs prefix encoding on long keys with similar prefixes that are grouped together in a range, as well as suffix truncation to further reduce the indexing costs of long keys.
