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A simple, fast, embeddable, persistent key/value store written in pure Go. It supports fully serializable transactions and many data structures such as list, set, sorted set.
The main features of xujiajun/nutsdb are: Database Engines, Databases, Key-Value Stores.
Open-source alternatives to xujiajun/nutsdb include: roseduan/rosedb — RoseDB is an embedded key-value database and log-structured storage engine. It functions as a library-based database… tidwall/buntdb — BuntDB is an embedded key-value store for Go applications, providing in-memory storage with optional disk persistence.… akrylysov/pogreb — Embedded key-value store for read-heavy workloads written in Go. dgraph-io/badger — Badger is an embeddable key-value store written in Go that provides persistent data storage for byte keys and values.… peterbourgon/diskv — A disk-backed key-value store. dicedb/dice — Dice is a low-latency key-value store based on Valkey that functions as a hierarchical storage engine. It combines a…
Badger is an embeddable key-value store written in Go that provides persistent data storage for byte keys and values. It is a persistent database that utilizes a tiered LSM tree storage model to optimize disk storage and retrieval efficiency. The system features an ACID transaction engine that ensures data integrity through serializable snapshot isolation and multi-version concurrency control. It also provides an encrypted key-value store with data-at-rest encryption and a managed encrypted key registry to secure stored information. The engine covers a broad set of capabilities including hig
Embedded key-value store for read-heavy workloads written in Go
RoseDB is an embedded key-value database and log-structured storage engine. It functions as a library-based database that runs within an application process to provide local data persistence without the need for a separate database server. The project implements a Bitcask-style store, utilizing an append-only log format to maximize write throughput and minimize seek time. It ensures data consistency by grouping multiple read and write operations into single atomic units. The storage architecture relies on an in-memory key index for constant-time lookups and uses segment-based file management
