DBMS > CouchDB vs. DolphinDB vs. Microsoft Azure Cosmos DB vs. MonetDB vs. TigerGraph

System Properties Comparison CouchDB vs. DolphinDB vs. Microsoft Azure Cosmos DB vs. MonetDB vs. TigerGraph

Editorial information provided by DB-Engines

Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Globally distributed, horizontally scalable, multi-model database service

A relational database management system that stores data in columns

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

Primary database model

Document store

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Graph DBMS

Secondary database models

Spatial DBMS

using the Geocouch extension

Relational DBMS

Spatial DBMS

Document store
Spatial DBMS

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

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

couchdb.apache.org

www.dolphindb.com

azure.microsoft.com/services/cosmos-db

www.monetdb.org

www.tigergraph.com

Technical documentation

docs.couchdb.org/en/stable

docs.dolphindb.cn/en/help200/index.html

learn.microsoft.com/azure/cosmos-db

www.monetdb.org/Documentation

docs.tigergraph.com

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

DolphinDB, Inc

Microsoft

MonetDB BV

Initial release

2005

2018

2014

2004

2017

Current release

3.3.3, December 2023

v2.00.4, January 2022

Dec2023 (11.49), December 2023

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free community version available

commercial

Open Source

Mozilla Public License 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Erlang

C++

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

Linux
Windows

hosted

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

JSON types

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

via views

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

SQL 2003 with some extensions

SQL-like query language (GSQL)

APIs and other access methods

RESTful HTTP/JSON API

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

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

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

C++
Java

Server-side scripts

Stored procedures

View functions in JavaScript

yes

JavaScript

yes, in SQL, C, R

yes

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

horizontal partitioning

Sharding

Implicit feature of the cloud service

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

atomic operations within a single document possible

yes

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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

User concepts

Access control

Access rights for users can be defined per database

Administrators, Users, Groups

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

Role-based access control

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