DBMS > Couchbase vs. Datomic vs. Dgraph vs. ReductStore vs. SAP SQL Anywhere

System Properties Comparison Couchbase vs. Datomic vs. Dgraph vs. ReductStore vs. SAP SQL Anywhere

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

Distributed and scalable native Graph DBMS

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

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

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

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Website

www.couchbase.com

www.datomic.com

dgraph.io

github.com/reductstore
www.reduct.store

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

docs.couchbase.com

docs.datomic.com

dgraph.io/docs

www.reduct.store/docs

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Couchbase, Inc.

Cognitect

Dgraph Labs, Inc.

ReductStore LLC

SAP

formerly Sybase

Initial release

2011

2012

2016

2023

1992

Current release

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

1.0.7075, December 2023

1.9, March 2024

17, July 2015

License

Commercial or Open Source

Open Source

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

commercial

limited edition free

Open Source

Apache 2.0

Open Source

Business Source License 1.1

commercial

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++, Rust

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

Linux
OS X
Windows

Docker
Linux
macOS
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

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.

yes

Secondary indexes

yes

SQL

Support of SQL

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

yes

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

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

HTTP API

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Clojure
Java

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

C++
JavaScript (Node.js)
Python
Rust

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

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

yes

Transaction Functions

yes, in C/C++, Java, .Net or Perl

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

yes

none

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

Synchronous replication via Raft

Source-replica replication

Database mirroring

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

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

yes

User concepts

Access control

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

Planned for future releases

fine grained access rights according to SQL-standard

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