DBMS > Couchbase vs. MonetDB vs. TerarkDB vs. TigerGraph

System Properties Comparison Couchbase vs. MonetDB vs. TerarkDB vs. TigerGraph

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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

A relational database management system that stores data in columns

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

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

Document store
Spatial DBMS

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

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

www.couchbase.com

www.monetdb.org

github.com/bytedance/terarkdb

www.tigergraph.com

Technical documentation

docs.couchbase.com

www.monetdb.org/Documentation

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

docs.tigergraph.com

Developer

Couchbase, Inc.

MonetDB BV

ByteDance, originally Terark

Initial release

2011

2004

2016

2017

Current release

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

Dec2023 (11.49), December 2023

License

Commercial or Open Source

Open Source

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

Open Source

Mozilla Public License 2.0

commercial

restricted open source version available

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

C++

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux

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.

Secondary indexes

yes

SQL

Support of SQL

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

yes

SQL 2003 with some extensions

SQL-like query language (GSQL)

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

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

C++ API
Java API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

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

C++
Java

Server-side scripts

Stored procedures

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

yes, in SQL, C, R

yes

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding via remote tables

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

none

Source-replica replication available in experimental status

none

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

Foreign keys

Referential integrity

yes

Relationships in graphs

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

In-memory capabilities

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

yes

Ephemeral buckets

yes

User concepts

Access control

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

fine grained access rights according to SQL-standard

Role-based access control

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