3 Repos
Mechanisms that track multiple versions of data to provide snapshot isolation for concurrent operations.
Distinct from Concurrency Data Isolation: None of the candidates cover MVCC specifically; they focus on thread safety or namespace isolation.
Explore 3 awesome GitHub repositories matching data & databases · Multi-Version Concurrency Control. Refine with filters or upvote what's useful.
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
Implements MVCC to provide serializable snapshot isolation for concurrent read and write transactions.
Bolt is a single-file embedded key-value store for Go applications. It is an ACID transactional database that organizes data in B+trees on disk to provide efficient sorted key retrieval and range scans. The system uses a memory-mapped model to map the database file directly into the process address space for fast random-access reads. The project distinguishes itself through a multi-version concurrency control architecture that allows multiple simultaneous readers to access a consistent snapshot of data without blocking a writer. It employs a single-writer multi-reader locking model and uses a
Provides a consistent snapshot of data to readers without blocking writers using multi-version concurrency control.
ToyDB is a distributed SQL database that provides a system for storing and querying data across multiple nodes. It focuses on maintaining strong consistency and fault tolerance through the implementation of a distributed consensus algorithm. The project distinguishes itself by supporting historical data versioning, enabling time-travel queries to retrieve the state of the database from a specific point in the past. It utilizes multi-version concurrency control to manage ACID transactions and ensure data integrity during concurrent operations. The system covers relational data modeling with t
Tracks data versions via logical timestamps to provide snapshot isolation and enable time-travel queries.