Open Source Database Software - Page 21
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Vitess
Vitess is a database clustering system for horizontal scaling of MySQL
Vitess is a database clustering system for horizontal scaling of MySQL through generalized sharding. By encapsulating shard-routing logic, Vitess allows application code and database queries to remain agnostic to the distribution of data onto multiple shards. With Vitess, you can even split and merge shards as your needs grow, with an atomic cutover step that takes only a few seconds. Vitess has been a core component of YouTube's database infrastructure since 2011, and has grown to encompass tens of thousands of MySQL nodes. -
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Voldemort
A distributed key-value storage system
Voldemort is a distributed database that’s an open source clone of Amazon’s Dynamo. It automatically replicates data over multiple servers, and automatically partitions them as well so each server only contains a subset of the total data. It offers many other features such as pluggable serialization support, data item versioning and an SSD Optimized Read Write storage engine. Voldemort is not a relational database or an object database. It is essentially a big, distributed, persistent, fault-tolerant hash table. This doesn’t make it ideal for all applications but does offer unique advantages, such as the ability to read and write scale horizontally, and the ability to combine in memory caching with the storage system so that a separate caching tier is not required. -
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Walrus
Lightweight Python utilities for working with Redis
The purpose of walrus is to make working with Redis in Python a little easier. Rather than ask you to learn a new library, walrus subclasses and extends the popular redis-py client, allowing it to be used as a drop-in replacement. In addition to all the features in redis-py, walrus adds support for some newer commands, including full support for streams and consumer groups. Persistent structures implemented on top of Hashes. Supports secondary indexes to allow filtering on equality, inequality, ranges, less/greater-than, and a basic full-text search index. The full-text search features a boolean search query parser, porter stemmer, stop-word filtering, and optional double-metaphone implementation. -
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YugabyteDB
The high-performance distributed SQL database for global apps
Microservices need a cloud-native relational database that is resilient, scalable, and geo-distributed. YugabyteDB powers your modern applications. Get instantly productive with a Postgres compatible RDBMS. YugabyteDB reuses PostgreSQL’s query layer and supports all advanced features. Scale-out and in with zero impact. Proven in production to scale beyond 300K TPS, over 100TB of data, and thousands of concurrent connections. Achieve continuous availability during infrastructure failures and on maintenance tasks such as software upgrades and distributed backups. Use powerful replication and data geo-partitioning capabilities to achieve the latency, resilience, and compliance your applications need. Deploy YugabyteDB in public, private, and hybrid cloud environments, on VMs, containers, or bare metal. Lower TCO for dev, staging, and production deployments. Powering a wide range of cloud-native workloads. Risk-free migration from on-prem to the cloud. -
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ZeusDB Vector Database
Blazing-fast vector DB with similarity search and metadata filtering
ZeusDB is a vector database built for fast, scalable similarity search with strong production ergonomics. It combines high-performance approximate nearest neighbor indexes with clean APIs and metadata filtering so applications can retrieve semantically relevant items at low latency. The storage layer is designed for durability and growth, supporting sharding, replication, and background compaction while keeping query tails predictable. Developers get multiple ingestion paths—batch, streaming, and upsert—making it easy to keep embeddings synchronized as content changes. Hybrid search is a core design goal, allowing you to mix vector, keyword, and filter queries in a single request for practical relevance. Observability and safety round out the system, with metrics, tracing, and guardrails to manage recalls, deletions, and privacy-sensitive data at scale. -
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atk4/data
Data Access PHP Framework for SQL & high-latency databases
ATK Data is a data persistence and modeling framework for PHP, developed as part of the Agile Toolkit. It provides a high-level abstraction for working with databases, making it easier to define and manipulate data models with minimal boilerplate code. It supports various SQL and NoSQL databases and integrates seamlessly with Agile UI and other PHP frameworks. -
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data-diff
Efficiently diff rows across two different databases
We're excited to announce the launch of a new open-source product, data-diff that makes comparing datasets across databases fast at any scale. data-diff automates data quality checks for data replication and migration. In modern data platforms, data is constantly moving between systems, and at the modern data volume and complexity, systems go out of sync all the time. Until now, there has not been any tooling to ensure that when the data is correctly copied. Replicating data at scale, across hundreds of tables, with low latency and at a reasonable infrastructure cost is a hard problem, and most data teams we’ve talked to, have faced data quality issues in their replication processes. The hard truth is that the quality of the replication is the quality of the data. Since copying entire datasets in batch is often infeasible at the modern data scale, businesses rely on the Change Data Capture (CDC) approach of replicating data using a continuous stream of updates. -
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doobie
Functional JDBC layer for Scala
doobie is a pure functional JDBC layer for Scala and Cats. It is not an ORM, nor is it a relational algebra; it simply provides a functional way to construct programs (and higher-level libraries) that use JDBC. For common use cases doobie provides a minimal but expressive high-level API. doobie is a Typelevel project. This means we embrace pure, typeful, functional programming, and provide a safe and friendly environment for teaching, learning, and contributing as described in the Scala Code of Conduct. Note that doobie is pre-1.0 software and is still undergoing active development. New versions are not binary compatible with prior versions, although in most cases user code will be source compatible. Starting with the 0.5.x we’re trying to be a bit more careful about versioning. If you want to build and run the tests for yourself, you’ll need a local postgresql database. -
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elasticsearch-head
A web front end for an elastic search cluster
elasticsearch-head is a web front end for browsing and interacting with an Elastic Search cluster. elasticsearch-head is hosted and can be downloaded or forked at github. There are two ways of running and installing elasticsearch-head. Running as a plugin of ElasticSearch (this is the preferred method). And running as a standalone webapp. By default es-head will immediately attempt to connect to a cluster node at http://localhost:9200/. Enter a different node address in the connect box and click 'Connect' if required. A ClusterOverview, which shows the topology of your cluster and allows you to perform index and node level operations. A couple of search interfaces that allow you to query the cluster a retrieve results in raw json or tabular format. Several quick access tabs that show the status of the cluster. An input section that allows arbitrary call to the RESTful API to be made. This interface includes several options that can be combined to produce interesting results. -
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gen
Converts a database into gorm structs and RESTful api
The gen tool produces a CRUD (Create, read, update and delete) REST API project template from a given database. The gen tool will connect to the db connection string analyze the database and generate the code based on the flags provided. By reading details from the database about the column structure, gen generates a go-compatible struct type with the required column names, data types, and annotations. It supports gorm tags and implements some usable methods. Generated data types include support for nullable columns sql.NullX types or guregu null.X types and the expected basic built-in go types. gen is based / inspired by the work of Seth Shelnutt's db2struct, and Db2Struct is based/inspired by the work of ChimeraCoder's gojson package gojson. Code generation for a complete CRUD rest project is possible with DAO crud functions, http handlers, makefile, sample server are available. -
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go-clean-arch
Golang clean architecture based on Reading Uncle Bob's Clean Arch
Independent of Frameworks. The architecture does not depend on the existence of some library of feature-laden software. This allows you to use such frameworks as tools, rather than having to cram your system into their limited constraints. Testable. The business rules can be tested without the UI, Database, Web Server, or any other external element. Independent of UI. The UI can change easily, without changing the rest of the system. A Web UI could be replaced with a console UI, for example, without changing the business rules. Independent of Database. You can swap out Oracle or SQL Server, for Mongo, BigTable, CouchDB, or something else. Your business rules are not bound to the database. Independent of any external agency. In fact your business rules simply don’t know anything at all about the outside world. -
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goleveldb
LevelDB key/value database in Go
This is an implementation of the LevelDB key/value database in the Go programming language. Package leveldb provides an implementation of LevelDB key/value database. OpenFile opens or creates a DB for the given path. The DB will be created if not exist, unless ErrorIfMissing is true. Also, if ErrorIfExist is true and the DB exist OpenFile will returns os.ErrExist error. OpenFile uses standard file-system backed storage implementation as described in the leveldb/storage package. OpenFile will return an error with type of ErrCorrupted if corruption is detected in the DB. Use errors.IsCorrupted to test whether an error is due to corruption. Corrupted DB can be recovered with Recover function. CompactRange compacts the underlying DB for the given key range. In particular, deleted and overwritten versions are discarded, and the data is rearranged to reduce the cost of operations needed to access the data. -
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goqu
SQL builder and query library for golang
SQL builder and query library for golang. goqu is an expressive SQL builder and executor. Query builder, parameter interpolation (e.g SELECT * FROM "items" WHERE "id" = ? -> SELECT * FROM "items" WHERE "id" = 1). Built from the ground up with multiple dialects in mind. Insert, multi insert, update, and delete support. Scanning of rows to struct[s] or primitive value[s]. While goqu may support the scanning of rows into structs it is not intended to be used as an ORM if you are looking for common ORM features like associations, or hooks I would recommend looking at some of the great ORM libraries. The test suite requires a postgres, mysql and sqlserver databases. -
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gormt
database to golang struct
mysql database to golang struct conversion tools base on gorm(v1/v2). You can automatically generate golang sturct from mysql database. big Camel-Case Name Rule, JSON tag. Database tables, column field annotation support. JSON tag and JSON tag output. gorm.Model Support export gorm.model. PRIMARY_KEY Specifies column as primary key. UNIQUE Specifies column as unique. NOT NULL Specifies column as NOT NULL. INDEX Create index with or without name, same name creates composite indexes. UNIQUE_INDEX Like INDEX, create unique index. Support foreign key related properties Support export gorm.model. Support function export (foreign key, association, index , unique and more)Support export function. model.Condition{} sql link. The exported function is only the auxiliary class function of Gorm, and calls Gorm completely. -
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immudb
Immutable database based on zero trust, SQL and Key-Value, tamperproof
immudb is a database with built-in cryptographic proof and verification. It tracks changes in sensitive data and the integrity of the history will be protected by the clients, without the need to trust the database. It can operate both as a key-value store, and/or as a relational database (SQL). Traditional database transactions and logs are mutable, and therefore there is no way to know for sure if your data has been compromised. immudb is immutable. You can add new versions of existing records, but never change or delete records. This lets you store critical data without fear of it being tampered with. Data stored in immudb is cryptographically coherent and verifiable. Unlike blockchains, immudb can handle millions of transactions per second, and can be used both as a lightweight service or embedded in your application as a library. immudb runs everywhere, on an IoT device, your notebook, a server, on-premise or in the cloud. -
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libmdbx
One of the fastest embeddable key-value ACID database
libmdbx (Lightning Memory-Mapped Database Extended) is a high-performance, key-value database library derived from LMDB (Lightning Memory-Mapped Database). It is designed for fast, scalable, and ACID-compliant data storage with minimal resource consumption. -
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mongo-express
Web-based MongoDB admin interface, written with Node.js
A web-based MongoDB admin interface written with Node.js, Express, and Bootstrap 5. -
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pgCenter
Command-line admin tool for observing and troubleshooting Postgres
pgCenter is a command-line monitoring and troubleshooting tool for PostgreSQL, inspired by tools like top. It provides real-time insight into query activity, locks, I/O, and system resources directly from the terminal. With its interactive UI, pgCenter helps DBAs diagnose performance issues and manage server health without switching tools. -
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pg_timetable
pg_timetable: Advanced scheduling for PostgreSQL
pg_timetable is a flexible job scheduler for PostgreSQL that allows scheduling of SQL or shell tasks directly within the database. It supports complex workflows, error handling, and dependency management, making it a powerful alternative to cron or external schedulers for database-related jobs. -
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pgjdbc-ng
A new JDBC driver for PostgreSQL
pgjdbc-ng is an alternative PostgreSQL JDBC driver for Java that focuses on advanced protocol features and better performance. It supports full PostgreSQL type mapping, asynchronous communication, and efficient prepared statement handling. pgjdbc-ng is built for developers needing more control and lower latency from their Java/PostgreSQL stack. -
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pgsync
Postgres to Elasticsearch/OpenSearch sync
pgsync is a lightweight tool for syncing Postgres databases across environments, such as from production to staging. It allows selective table syncing, data masking, and parallel copying for fast and safe data migration. pgsync is ideal for developers who need realistic test data without exposing sensitive information. -
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react-imgpro
Image Processing Component for React
react-imgpro is an image-processing component for React. This component process an image with filters supplied as props and returns a base64 image. I was working on a project last month which involved a lot of image processing and I'd to rely on third party libraries. But before using them directly, I'd to learn different concepts in gl (shaders) and then try to implement them in React. The difficult part was not learning but it was the verbosity, boilerplate code and redundancy introduced by the libraries in the codebase. It was getting difficult to organize all the things. And React's component-based model was perfect for hiding all the implementation details in a component. -
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restful.js
A pure JS client for interacting with server-side RESTful resources
A pure JS client for interacting with server-side RESTful resources. Think Restangular without Angular. All examples written in this README use the ES6 specification. The dist folder contains two built versions which you can use to include either restful.js or a standalone version. Standalone version already embeds fetch. Restful.js needs an HTTP backend in order to perform queries. A custom endpoint acts like a member, and therefore you can use one and all to chain another endpoint with it. Once you have collections and members endpoints, fetch them to get entities. Restful.js exposes get() and getAll() methods for fetching endpoints. Since these methods are asynchronous, they return a native Promise for response. A response is made from the HTTP response fetched from the endpoint. It exposes statusCode(), headers(), and body() methods. For a GET request, the body method will return one or an array of entities. Therefore you can disable this hydration by calling body(false). -
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rosedb
High performance NoSQL database based on bitcask
A high-performance NoSQL database based on bitcask, supports string, list, hash, set, and sorted set. rosedb is a fast, stable, and embedded NoSQL database based on bitcask, that supports a variety of data structures such as string, list, hash, set, and sorted set. Easy to embed into your own Go application. High performance, suitable for both read and write-intensive workloads. Values are not limited by RAM. It is similar to Redis but store values on disk. RoseDB is based on bitcask. -
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sqlit
A user friendly TUI for SQL databases
sqlit is a keyboard-first terminal UI that lets you connect to, browse, and query SQL databases quickly without relying on heavyweight GUI clients. It positions itself as a “lazygit-style” experience for databases, aiming for fast startup, intuitive navigation, and developer-friendly workflows directly inside your terminal. The tool supports a wide range of database providers, so you can use one interface across local databases, remote servers, and cloud-hosted instances rather than juggling multiple clients. It includes a connection manager so you can save connections and switch between them without constantly retyping CLI arguments or connection strings. For querying, it emphasizes productivity features like syntax highlighting, searchable query history, and vim-style keybindings so power users can move fast. For exploring data at scale, it can load and inspect very large result sets and provides filtering and fuzzy search to find rows and values efficiently.
