Dolt vs TerminusDB vs CouchDB: Self-Hosted Version-Controlled Databases Guide 2026

Databases that track changes to your data over time are essential for auditing, collaboration, and rollback capabilities. While traditional databases require external tools or custom application logic to track history, version-controlled databases build this directly into their core engine.

In this guide, we compare three open-source, self-hosted databases with native version control: Dolt (SQL with Git semantics), TerminusDB (document/graph with schema-driven versioning), and CouchDB (document database with multi-primary sync and revision tracking).

Here is a quick look at each project’s current status:

Why Self-Host a Version-Controlled Database?

Version-controlled databases solve several real-world problems:

• Data auditing: Track who changed what and when without adding application-level audit tables.
• Rollback and recovery: Revert to any previous state of your data, similar to git revert.
• Branching and merging: Experiment with data changes in isolation, then merge or discard.
• Multi-site synchronization: Replicate data across locations with conflict resolution.
• Collaborative workflows: Multiple teams can work on separate branches of the same dataset.

For organizations handling sensitive data — financial records, healthcare information, or regulatory compliance datasets — having a database that natively tracks every change is often a requirement, not a luxury. If you’re also looking at data versioning for ML pipelines and large datasets, check out our DVC vs lakeFS vs Pachyderm guide for complementary tools.

Dolt: Git for SQL Data

Dolt is a SQL database that implements Git-style version control at the data layer. Every table supports commit, branch, merge, diff, clone, and push operations — the same workflow you use for code, applied to rows and columns.

Key Features

• Full SQL compatibility: Supports MySQL-compatible SQL syntax, so existing applications can connect without changes.
• Git-style operations: dolt commit, dolt branch, dolt merge, dolt diff work exactly like their Git counterparts.
• Cell-level diffs: See exactly which cells changed between commits, not just which rows.
• DoltHub/DoltLab: Optional remote hosting for sharing databases between teams.
• Triggers and procedures: Full support for stored procedures, triggers, and views.

Installation

Install Dolt via the official installer script:

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sudo bash -c 'curl -L https://github.com/dolthub/dolt/releases/latest/download/install.sh | bash'

Or via Homebrew on macOS:

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brew install dolthub/dolt/dolt

Docker Deployment

Dolt provides an official Docker image. Run a Dolt server with:

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docker run --name dolt-server \
  -p 3306:3306 \
  -v /opt/dolt/data:/var/lib/dolt \
  -e DOLT_DEFAULT_BRANCH=main \
  dolthub/dolt:latest \
  dolt sql-server --host=0.0.0.0

Connect using any MySQL-compatible client:

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mysql --host 127.0.0.1 --port 3306 -u root

Docker Compose Setup

For a production-ready deployment with persistent storage:

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version: "3.8"

services:
  dolt:
    image: dolthub/dolt:latest
    container_name: dolt-server
    ports:
      - "3306:3306"
    volumes:
      - dolt-data:/var/lib/dolt
      - ./dolt-config:/etc/dolt
    environment:
      - DOLT_DEFAULT_BRANCH=main
      - DOLT_USER_NAME=admin
      - DOLT_USER_EMAIL=admin@example.com
    command: dolt sql-server --host=0.0.0.0 --password=secure_password
    restart: unless-stopped

volumes:
  dolt-data:

Example: Creating and Versioning Data

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-- Create a database (this initializes a new Dolt repo)
CREATE DATABASE myproject;
USE myproject;

-- Create a table
CREATE TABLE users (
  id INT PRIMARY KEY,
  name VARCHAR(100),
  email VARCHAR(255),
  created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);

-- Insert data and commit
INSERT INTO users (id, name, email) VALUES
  (1, 'Alice', 'alice@example.com'),
  (2, 'Bob', 'bob@example.com');

CALL DOLT_ADD('-A');
CALL DOLT_COMMIT('-m', 'Initial user data');

-- Make changes on a branch
CALL DOLT_BRANCH('experiment');
CALL DOLT_CHECKOUT('experiment');

UPDATE users SET email = 'alice@newdomain.com' WHERE id = 1;
CALL DOLT_ADD('-A');
CALL DOLT_COMMIT('-m', 'Update Alice email');

-- Diff to see changes
SELECT * FROM DOLT_DIFF_users;

-- Merge back to main
CALL DOLT_CHECKOUT('main');
CALL DOLT_MERGE('experiment');

TerminusDB: Version-Controlled Document & Graph Database

TerminusDB is a distributed database designed for collaborative, versioned data management. It combines document and graph database models with built-in Git-like versioning and a schema-first approach.

Key Features

• Schema-driven design: Define data models with JSON schemas; the database enforces constraints.
• Graph queries: Navigate relationships using WOQL (Web Object Query Language).
• Branch and merge: Full branching, merging, and rebase operations on data.
• Diff and patch: Generate diffs between commits and apply patches programmatically.
• TerminusCMS: Optional web UI for visual data management and collaboration.
• RESTful API: All operations available via HTTP endpoints.

Installation

Install TerminusDB via pip:

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pip install terminusdb-client

Docker Compose Deployment

TerminusDB provides an official Docker Compose configuration:

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version: "3.8"

services:
  terminusdb-server:
    image: terminusdb/terminusdb-server:latest
    container_name: terminusdb-server
    pull_policy: always
    hostname: terminusdb-server
    tty: true
    ports:
      - "6364:6363"
    env_file: .env
    environment:
      - TERMINUSDB_ADMIN_USER=admin
      - TERMINUSDB_ADMIN_PASS=your_admin_password
      - TERMINUSDB_JWT_SECRET=your_jwt_secret_key
    volumes:
      - terminus-data:/terminusdb/data
      - terminus-logs:/terminusdb/logs
    restart: unless-stopped

volumes:
  terminus-data:
  terminus-logs:

Create a .env file alongside the compose file:

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TERMINUSDB_ADMIN_PASS=your_secure_password
TERMINUSDB_JWT_SECRET=your_jwt_secret_key
TERMINUSDB_STARDOG_LICENSE_KEY=

Then start the server:

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docker compose up -d
docker compose logs -f

Access the web interface at http://localhost:6364.

Example: Creating a Versioned Database

Using the Python client:

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from terminusdb_client import Client, WOQL

# Connect to the server
client = Client("http://localhost:6363")
client.connect(account="admin", key="your_admin_password")

# Create a new database
client.create_database("employees", account="admin")

# Define a schema
schema = {
    "@type": "Class",
    "@id": "Employee",
    "name": "xsd:string",
    "department": "xsd:string",
    "salary": "xsd:decimal",
    "start_date": "xsd:date"
}

client.insert_document(schema, graph_type="schema")

# Insert data
employee_data = {
    "@type": "Employee",
    "name": "Alice Smith",
    "department": "Engineering",
    "salary": 95000,
    "start_date": "2024-01-15"
}

client.insert_document(employee_data)

# Create a commit with a message
client.commit(commit_msg="Add initial employee records")

# Create a branch for experiments
client.create_branch("salary-review", commit_msg="Start salary review branch")
client.checkout("salary-review")

# Update data on the branch
client.update_document({
    "@type": "Employee",
    "@id": "Employee/0",
    "salary": 105000
})

client.commit(commit_msg="Propose salary increase for Alice")

WOQL Query Example

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# Query employees by department
query = WOQL().select("v:name", "v:salary").triple(
    "v:employee", "rdf:type", "@schema:Employee"
).triple(
    "v:employee", "schema:name", "v:name"
).triple(
    "v:employee", "schema:salary", "v:salary"
).woql_or(
    WOQL().triple("v:employee", "schema:department", WOQL().string("Engineering"))
)

result = client.query(query)
for doc in result.get("bindings", []):
    print(f"{doc['name']}: ${doc['salary']}")

CouchDB: Multi-Primary Sync with Revision Tracking

CouchDB is an Apache project — a document-oriented database that uses HTTP/JSON as its native interface. Every document carries a revision ID (_rev), enabling conflict detection and multi-master replication across distributed nodes.

Key Features

• Multi-master replication: Any node can accept writes; changes sync bidirectionally.
• Revision history: Every document change creates a new revision with a unique ID.
• Conflict detection: Automatic conflict detection with manual resolution options.
• Futon/Fauxton: Built-in web administration interface.
• MapReduce views: Query and aggregate data using JavaScript functions.
• Offline-first design: Ideal for edge computing and disconnected environments.

Docker Deployment

CouchDB’s official Docker image:

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docker run --name couchdb \
  -p 5984:5984 \
  -e COUCHDB_USER=admin \
  -e COUCHDB_PASSWORD=secure_password \
  -v /opt/couchdb/data:/opt/couchdb/data \
  apache/couchdb:3

Docker Compose Setup

For a single-node production setup:

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version: "3.8"

services:
  couchdb:
    image: apache/couchdb:3
    container_name: couchdb
    ports:
      - "5984:5984"
    environment:
      - COUCHDB_USER=admin
      - COUCHDB_PASSWORD=secure_password
      - COUCHDB_SECRET=your_secret_key_for_couchdb
    volumes:
      - couchdb-data:/opt/couchdb/data
      - ./local.ini:/opt/couchdb/etc/local.d/docker.ini
    restart: unless-stopped

volumes:
  couchdb-data:

Create a local.ini configuration file:

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[couchdb]
single_node=true
max_document_size = 50000000

[chttpd]
require_valid_user = true
max_http_request_size = 4294967296

[chttpd_auth]
require_valid_user = true
authentication_redirect = /_utils/session.html

Start the server:

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docker compose up -d

Access Fauxton at http://localhost:5984/_utils/.

Multi-Node Cluster Setup

For a 3-node cluster:

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version: "3.8"

services:
  couchdb1:
    image: apache/couchdb:3
    container_name: couchdb-node1
    ports:
      - "5984:5984"
    environment:
      - COUCHDB_USER=admin
      - COUCHDB_PASSWORD=secure_password
      - COUCHDB_SECRET=cluster_secret_key
    volumes:
      - couchdb1-data:/opt/couchdb/data

  couchdb2:
    image: apache/couchdb:3
    container_name: couchdb-node2
    ports:
      - "5985:5984"
    environment:
      - COUCHDB_USER=admin
      - COUCHDB_PASSWORD=secure_password
      - COUCHDB_SECRET=cluster_secret_key
    volumes:
      - couchdb2-data:/opt/couchdb/data

  couchdb3:
    image: apache/couchdb:3
    container_name: couchdb-node3
    ports:
      - "5986:5984"
    environment:
      - COUCHDB_USER=admin
      - COUCHDB_PASSWORD=secure_password
      - COUCHDB_SECRET=cluster_secret_key
    volumes:
      - couchdb3-data:/opt/couchdb/data

volumes:
  couchdb1-data:
  couchdb2-data:
  couchdb3-data:

After starting all nodes, configure replication via the Fauxton UI or API:

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# Create a database
curl -X PUT http://admin:secure_password@localhost:5984/mydata

# Add a document
curl -X POST http://admin:secure_password@localhost:5984/mydata \
  -H "Content-Type: application/json" \
  -d '{"name": "Test Document", "status": "active"}'

# Set up replication between nodes
curl -X POST http://admin:secure_password@localhost:5984/_replicator \
  -H "Content-Type: application/json" \
  -d '{
    "source": "mydata",
    "target": "http://admin:secure_password@couchdb-node2:5984/mydata",
    "continuous": true
  }'

Working with Revisions

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# Create a document
curl -X POST http://admin:secure_password@localhost:5984/mydata \
  -H "Content-Type: application/json" \
  -d '{"name": "Alice", "role": "developer"}'

# Response includes _rev: {"ok":true,"id":"...","rev":"1-abc123"}

# Update the document (must include _rev)
curl -X PUT http://admin:secure_password@localhost:5984/mydata/doc_id \
  -H "Content-Type: application/json" \
  -d '{"_rev": "1-abc123", "name": "Alice", "role": "senior developer"}'

# View revision history
curl http://admin:secure_password@localhost:5984/mydata/doc_id?revs_info=true

Comparison: Dolt vs TerminusDB vs CouchDB

When to Choose Each Database

Choose Dolt if:

• Your team already uses SQL and MySQL-compatible tooling. For a broader comparison of relational databases, see our PostgreSQL vs MySQL vs MariaDB guide.
• You need Git-like workflows: branching, merging, and cell-level diffs.
• You want to track changes to relational data without application-level audit tables.
• Use cases: financial data tracking, regulatory compliance, data science experiments.

Choose TerminusDB if:

• Your data has complex relationships that benefit from a graph model. For more on graph databases, see our complete guide to Neo4j, ArangoDB, and NebulaGraph.
• You need schema enforcement to maintain data quality across teams.
• You want a web UI (TerminusCMS) for non-technical team members.
• Use cases: knowledge graphs, supply chain tracking, regulatory data modeling.

Choose CouchDB if:

• You need offline-first capabilities with automatic sync when connectivity returns.
• Multi-master replication across geographically distributed sites is required.
• Your application works with JSON documents and doesn’t need SQL.
• Use cases: mobile apps with offline mode, edge computing, distributed field operations.

Installation Quick Reference

Dolt

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# Install
curl -L https://github.com/dolthub/dolt/releases/latest/download/install.sh | sudo bash

# Run server
dolt sql-server --host=0.0.0.0 --password=my_password

# Connect
mysql --host 127.0.0.1 --port 3306 -u root

TerminusDB

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# Start with Docker Compose
docker compose up -d

# Connect via Python
pip install terminusdb-client

CouchDB

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# Start with Docker
docker run -p 5984:5984 -e COUCHDB_USER=admin -e COUCHDB_PASSWORD=pass apache/couchdb:3

# Access Fauxton UI
open http://localhost:5984/_utils/

Data Migration Considerations

Moving from a traditional database to a version-controlled one requires planning:

• Dolt: Use dolt table import to load CSV files, or connect via MySQL protocol and run INSERT INTO ... SELECT from a linked server.
• TerminusDB: Use the Python client to map existing data into the schema, or import CSV/JSON via the REST API.
• CouchDB: Use couchimport CLI tool for bulk CSV/JSON loading, or replicate from an existing CouchDB instance.

Monitoring and Maintenance

All three databases provide monitoring hooks:

• Dolt: Query DOLT_LOG, DOLT_DIFF, and DOLT_STATUS system tables for version control state. Use standard MySQL SHOW PROCESSLIST for connection monitoring.
• TerminusDB: Check /api/status endpoint for server health. TerminusCMS provides a dashboard for database activity.
• CouchDB: Monitor /_active_tasks, /_stats, and /_membership endpoints. Use Fauxton’s built-in stats dashboard for real-time metrics.

FAQ

What is a version-controlled database?

A version-controlled database tracks every change to data over time, allowing you to query historical states, revert to previous versions, branch data for experimentation, and merge changes — similar to how Git manages code. Instead of requiring separate audit tables or application-level logic, the database engine handles versioning natively.

Is Dolt a drop-in replacement for MySQL?

Dolt is MySQL-compatible at the protocol level, meaning existing MySQL clients, ORMs, and connectors can connect without modification. However, Dolt adds version control SQL procedures (DOLT_COMMIT, DOLT_BRANCH, etc.) that are not available in standard MySQL. Your existing SELECT, INSERT, UPDATE, and DELETE queries work identically.

Can CouchDB replace MongoDB?

CouchDB and MongoDB are both document databases using JSON, but they have different design philosophies. CouchDB emphasizes multi-master replication and offline-first operation, while MongoDB focuses on horizontal scaling and rich query capabilities. If your priority is distributed sync and revision tracking, CouchDB is the better choice. For complex aggregation pipelines, MongoDB may be preferable.

Does TerminusDB support GraphQL?

TerminusDB provides a RESTful API and its own query language (WOQL). While it doesn’t have built-in GraphQL support, you can build a GraphQL layer on top of the REST API using tools like Apollo Server or Hasura to expose your TerminusDB data as a GraphQL endpoint.

How does multi-master replication work in CouchDB?

CouchDB uses a peer-to-peer replication model where any node can accept reads and writes. Each document has a revision ID (_rev). When two nodes are connected, they exchange changes bidirectionally. If the same document is modified on both nodes independently, CouchDB detects the conflict and stores both versions for manual resolution. Replication can be continuous (streaming) or one-time.

Which version-controlled database is best for compliance auditing?

For compliance scenarios requiring detailed audit trails, Dolt is the strongest choice because it provides cell-level diffs, human-readable commit messages, and a complete history of every change. You can query DOLT_DIFF to see exactly which cell changed, when, and by whom. TerminusDB also provides full commit history with schema validation, which adds an extra layer of data integrity.

Can I run these databases on a Raspberry Pi?

CouchDB runs well on a Raspberry Pi 4 — it’s the most lightweight of the three. TerminusDB can run on a Pi 4 with 4GB+ RAM, though performance may be limited for large datasets. Dolt is built in Go and can run on ARM64, but the SQL server requires at least 2GB RAM for comfortable operation. For production workloads, a dedicated server or VPS is recommended.

How do I back up a version-controlled database?

With version-controlled databases, backups work differently:

• Dolt: Use dolt backup to push to a remote, or export with dolt table export. Since the entire database is a directory of files, standard filesystem backups also work.
• TerminusDB: Use the dump API endpoint to export database state, or back up the data volume.
• CouchDB: Use curl to replicate to a backup database, or use couchbackup CLI tool for full database dumps.