Dolt

Dolt is a relational database engine that integrates version control directly into the database management layer. It functions as a version-controlled SQL database that tracks every row and schema change using a commit-based history, allowing users to branch, merge, and audit data modifications. By implementing a wire-protocol-compatible server, the system enables standard SQL clients and tools to interact with versioned data as if they were connecting to a traditional relational database.

The platform distinguishes itself by applying repository-style workflows to data management, including support for forking, pull requests, and issue tracking. It utilizes a Merkle-tree-based storage engine to calculate structural and row-level differences between database states, surfacing merge conflicts as queryable relational tables. This architecture allows teams to isolate experimental changes in branches and maintain a tamper-evident history of all modifications that can be queried via SQL.

Beyond its core versioning capabilities, the system provides comprehensive infrastructure for data engineering, including remote synchronization, replication, and automated workflow triggers. It supports standard SQL query execution and data import from common file formats, while offering granular access control and role-based permissions to secure database states. The software is designed to operate as a drop-in replacement for existing database environments, maintaining compatibility with standard drivers and management tools.

Features

Data Versioning - Tracks every row and schema change using commits, branches, and merges to enable auditing, rollbacks, and collaborative workflows.

Distributed SQL Databases - Acts as a version-controlled SQL database engine that tracks row-level changes using Git-style branching and commit history.

Database Drivers - Implements standard database wire protocols to allow seamless connectivity with existing SQL clients and management tools.

SQL Query Execution - Provides a standard SQL interface for managing tables, indexes, and stored procedures using common database drivers.

Database Protocol Compatibility - Implements standard wire protocols to ensure compatibility with existing SQL clients and management tools.

Database Versioning - Tracks row and schema changes using commits, branches, and merges to enable historical analysis and data rollbacks.

Collaborative Data Platforms - Enables team collaboration on datasets using repository-style workflows like forks, pull requests, and merge operations.

Historical Data Querying Interfaces - Retrieves past table states and full change logs directly through SQL queries to audit data history.

Relational Database Engines - Functions as a relational database engine that provides queryable historical logs and row-level diffs for auditing.

Database Versioning Engines - Maps database operations to a directed acyclic graph of commits, branches, and merges to track historical data evolution.

Merkle-Tree Storage Engines - Organizes data into a content-addressed tree structure to enable efficient snapshots, deduplication, and rapid comparison of database states.

Change Data Capture - Captures and records all database modifications in a tamper-evident history queryable via SQL.

Database Synchronization - Pushes and pulls entire database states between instances to facilitate distributed collaboration and off-site backups.

Database Replication - Configures read replicas and synchronization topologies to distribute data and maintain historical records.

Distributed Databases - Facilitates distributed collaboration and data replication by pushing and pulling database states between remote instances.

Row-Level Change Logs - Records every modification as a delta within the versioning engine to provide granular auditing and historical data recovery.

Branch-Based Isolation - Isolates experimental changes in separate branches to allow for review, testing, and safe merging into the main production dataset.

Audit and Compliance - Maintains a tamper-evident, queryable history of all database modifications for auditing and compliance purposes.

Team Collaboration Tools - Facilitates team workflows through forks, pull requests, and issue tracking to review, share, and integrate data changes.

Data Management - Version-controlled SQL database with git-like operations.

Database Engines - Version-controlled database with Git-like functionality.

Database Systems - Listed in the “Database Systems” section of the Awesome Go awesome list.

Database Tools - Git-like database management.

Databases and Analytics - Version-controlled database with Git-like functionality.

Experiment and Data Management - SQL database with Git-like versioning capabilities.

Conflict Resolution - Surfaces merge conflicts as queryable relational tables to facilitate manual or programmatic data reconciliation.

Schema Comparison Tools - Identifies structural, row-level, and schema differences between database states, branches, or points in time.

Version Control Automation - Executes versioning tasks like committing, merging, and branching through stored procedures and functions callable from any standard SQL client.

Self-Hosted Infrastructure - Supports self-hosted deployment on private infrastructure with full control over data and configuration.

Access Control - Manages user authentication, role-based privileges, and branch-level write restrictions to secure database access.

Schema-Agnostic Diffing Engines - Calculates structural and row-level differences by traversing the underlying data tree regardless of the specific table schema.

Database Workflow Automation - Triggers webhooks and continuous integration pipelines on data changes to validate, test, or transform information automatically.

Database Performance Monitors - Provides performance and operational metrics via standard monitoring interfaces to track server health.

Integration APIs - Exposes programmatic interfaces for querying and managing database state and versioning history.

Data Import - Loads structured information from formats like CSV, JSON, or SQL dumps into the database to populate tables.

SQL Dialect Adapters - Maintains compatibility with common SQL dialects while supporting core version control capabilities.

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Features

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