DBMS > Couchbase vs. Datomic vs. Dolt vs. JaguarDB

System Properties Comparison Couchbase vs. Datomic vs. Dolt vs. JaguarDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

A MySQL compatible DBMS with Git-like versioning of data and schema

Performant, highly scalable DBMS for AI and IoT applications

Primary database model

Document store

Relational DBMS

Key-value store
Vector DBMS

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Document store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.96
Rank	#193	Overall
	#90	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Website

www.couchbase.com

www.datomic.com

github.com/dolthub/dolt
www.dolthub.com

www.jaguardb.com

Technical documentation

docs.couchbase.com

docs.datomic.com

docs.dolthub.com

www.jaguardb.com/support.html

Developer

Couchbase, Inc.

Cognitect

DoltHub Inc

DataJaguar, Inc.

Initial release

2011

2012

2018

2015

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

1.0.6735, June 2023

3.3 July 2023

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

commercial

limited edition free

Open Source

Apache Version 2.0

Open Source

GPL V3.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++, Go and Erlang

Java, Clojure

C++

the server part. Clients available in other languages

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

Linux
macOS
Windows

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

RESTful HTTP API

CLI Client
HTTP REST

JDBC
ODBC

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Clojure
Java

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

yes

Transaction Functions

yes

currently in alpha release

Triggers

yes

via the TAP protocol

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

none

But extensive use of caching in the application peers

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

none

But extensive use of caching in the application peers

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Ephemeral buckets

yes

recommended only for testing and development

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Only one user is configurable, and must be specified in the config file at startup

rights management via user accounts

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.