BadgerDB is an embeddable, persistent, simple and fast key-value (KV) store, written purely in Go. It's meant to be a performant alternative to non Go based key-value stores like RocksDB. It is written out of frustration with existing KV stores which are either written in pure Go and slow, or fast but require usage of Cgo. Badger aims to provide an equal or better speed compared to industry leading KV stores (like RocksDB), while maintaining the entire code base in pure Go.

BadgerDB - Fast key-value store in Go.

Dgraph is a horizontally scalable and distributed graph database, providing ACID transactions, consistent replication and linearizable reads. It's built from ground up to perform for a rich set of queries. Being a native graph database, it tightly controls how the data is arranged on disk to optimize for query performance and throughput, reducing disk seeks and network calls in a cluster.
 
Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC.

Dgraph is a horizontally scalable and distributed graph database, providing ACID transactions, consistent replication and linearizable reads. It's built from ground up to perform for a rich set of queries. Being a native graph database, it tightly controls how the data is arranged on disk to optimize for query performance and throughput, reducing disk seeks and network calls in a cluster.

Dgraph - Fast, Transactional, Distributed Graph Database

InfluxDB is an open source time series platform. This includes APIs for storing and querying data, processing it in the background for ETL or monitoring and alerting purposes, user dashboards, and visualizing and exploring the data and more. The master branch on this repo now represents the latest InfluxDB, which now includes functionality for Kapacitor (background processing) and Chronograf (the UI) all in a single binary.  It is designed to handle high write and query loads and provides a SQL-like query language called InfluxQL for interacting with data.

InfluxDB - Scalable datastore for metrics, events, and real-time analytics

FerretDB is an open-source proxy that translates MongoDB wire protocol queries to SQL, with PostgreSQL as the database engine.
MongoDB was initially built as open-source software and it is a game-changer for many developers, enabling them to build fast and robust applications. Its ease of use and extensive documentation made it a top choice for many developers looking for an open-source database. However, all this changed when they switched to an SSPL license, moving away from their open-source roots.
FerretDB was founded to become the true open-source alternative to MongoDB, making it the go-to choice for most MongoDB users looking for an open-source alternative to MongoDB. With FerretDB, users can run the same MongoDB protocol queries without needing to learn a new language or command.
FerretDB is a document database that uses similar commands, drivers, and tools to those of MongoDB for storing data. Unlike relational databases, which use tables, rows, and columns to define the schema of the database, document databases use key-value pairs to build the structure of a document. In FerretDB, data is stored as <a href="https://bsonspec.org/spec.html" target="_blank" rel="noopener">BSON</a> - a binary representation of JSON - so you can store more types of data than in regular JSON.
Refer to <a href="https://docs.ferretdb.io/diff/" target="_blank" rel="noopener">known differences with MongoDB</a>
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FerretDB is an open-source proxy that translates MongoDB wire protocol queries to SQL, with PostgreSQL as the database engine.

FerretDB - A truly Open Source MongoDB alternative, built on Postgres

BuntDB is a low-level, in-memory, key/value store in pure Go. It persists to disk, is ACID compliant, and uses locking for multiple readers and a single writer. It supports custom indexes and geospatial data. It's ideal for projects that need a dependable database and favor speed over data size.The desire to create BuntDB stems from the need for a new embeddable database for Tile38 and SummitDB.

BuntDB - Embeddable, in-memory key/value database for Go with custom indexing and geospatial support

Tile38 is a in-memory geolocation data store, spatial index, and realtime geofence. It supports a variety of object types including lat/lon points, bounding boxes, XYZ tiles, Geohashes, and GeoJSON. It supports spatial index with search methods such as Nearby, Within, and Intersects, Realtime geofencing through persistent sockets or webhooks and lot more.
 
Tile38 uses the Redis RESP protocol natively. Therefore most clients that support basic Redis commands will in turn support Tile38.

Tile38 is a  in-memory geolocation data store, spatial index, and realtime geofence. It supports a variety of object types including lat/lon points, bounding boxes, XYZ tiles, Geohashes, and GeoJSON. It supports spatial index with search methods such as Nearby, Within, and Intersects, Realtime geofencing through persistent sockets or webhooks and lot more. 

Tile38 - Geospatial database, spatial index, and realtime geofence

SummitDB is an in-memory, NoSQL key/value database. It persists to disk, uses the Raft consensus algorithm, is ACID compliant, and built on a transactional and strongly-consistent model. It supports custom indexes, geospatial data, JSON documents, and user-defined JS scripting.Under the hood it utilizes Finn, Redcon, BuntDB, GJSON, and Otto.

SummitDB - In-memory NoSQL database with ACID transactions, Raft consensus, and Redis API

Noms is a decentralized database based on ideas from Git. It has different features from database. It is Decentralized, You can work with your own copy of data and merge them to main database. Duplicate data is automatically ignored. When you commit data to Noms, you aren't overwriting anything. Instead you're adding to a historical record. By default, data is never removed from Noms. 

 Noms - The versioned, forkable, syncable database 

Cayley is an open-source graph inspired by the graph database behind Freebase and Google's Knowledge Graph. Its goal is to be a part of the developer's toolbox where Linked Data and graph-shaped data (semantic webs, social networks, etc) in general are concerned. 
 
It supports built-in query editor and visualizer. It works well with multiple backend stores.

Cayley is an open-source graph inspired by the graph database behind Freebase and Google's Knowledge Graph. Its goal is to be a part of the developer's toolbox where Linked Data and graph-shaped data (semantic webs, social networks, etc) in general are concerned. 

Cayley - An open-source Graph Database 

rqlite is a lightweight, distributed relational database, which uses SQLite as its storage engine. Forming a cluster is very straightforward, it gracefully handles leader elections, and tolerates failures of machines, including the leader. rqlite gives you the functionality of a rock solid, fault-tolerant, replicated relational database, but with very easy installation, deployment, and operation. With it you've got a lightweight and reliable distributed relational data store. Think etcd or Consul, but with relational data modelling also available.
 
if you're interested in understanding how distributed systems actually work, rqlite is a good example to study. Much thought has gone into its design and implementation, with clear separation between the various components, including storage, distributed consensus, and API. rqlite uses Raft to achieve consensus across all the instances of the SQLite databases, ensuring that every change made to the system is made to a quorum of SQLite databases, or none at all.

rqlite is a lightweight, distributed relational database, which uses SQLite as its storage engine. Forming a cluster is very straightforward, it gracefully handles leader elections, and tolerates failures of machines, including the leader. rqlite gives you the functionality of a rock solid, fault-tolerant, replicated relational database, but with very easy installation, deployment, and operation. With it you've got a lightweight and reliable distributed relational data store. Think etcd or Consul, but with relational data modelling also available.


rqlite - The lightweight, distributed relational database built on SQLite

Ledisdb is a high-performance NoSQL database library and server written in Go. It's similar to Redis but store data in disk. It supports many data structures including kv, list, hash, zset, set. LedisDB supports multiple different databases as backends. 

LedisDB -  A high performance NoSQL Database Server powered by Go

SpiceDB is a database system for managing security-critical application permissions. Developers create a schema that models their permissions requirements and use a client library to apply the schema to the database, insert data into the database, and query the data to efficiently check permissions in their applications.
 
The data used to calculate permissions have the most critical correctness requirements in the entirety a software system. Despite that, developers continue to build their own ad-hoc solutions coupled to the internal code of each new project. By developing a SpiceDB schema, you can iterate far more quickly and exhaustively test designs before altering any application code. This becomes especially important as you introduce backwards-compatible changes to the schema and want to ensure that the system remains secure.

SpiceDB is a database system for managing security-critical application permissions. Developers create a schema that models their permissions requirements and use a client library to apply the schema to the database, insert data into the database, and query the data to efficiently check permissions in their applications.

SpiceDB - Inspired by Google's Zanzibar paper, SpiceDB is a database system for managing security-critical application permissions

Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility.
As the Internet grew and evolved, unstructured data became more and more common, including emails, papers, IoT sensor data, Facebook photos, protein structures, and much more. In order for computers to understand and process unstructured data, these are converted into vectors using embedding techniques. Milvus stores and indexes these vectors. Milvus is able to analyze the correlation between two vectors by calculating their similarity distance. If the two embedding vectors are very similar, it means that the original data sources are similar as well. &nbsp; Millvus is capable of indexing vectors on a trillion scale. Unlike existing relational databases which mainly deal with structured data following a pre-defined pattern, Milvus is designed from the bottom-up to handle embedding vectors converted from unstructured data.
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Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility.



Milvus - An open-source vector database for embedding similarity search and AI applications

Prometheus is a systems and service monitoring system. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true.

Prometheus - Service Monitoring System and Time Series Database

Fast, concurrent, evicting in-memory cache written to keep big number of entries without impact on performance. BigCache keeps entries on heap but omits GC for them. To achieve that operations on bytes arrays take place, therefore entries (de)serialization in front of the cache will be needed in most use cases. 

bigcache - Efficient cache for gigabytes of data written in Go.

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