DBMS > CouchDB vs. DolphinDB vs. Kinetica vs. Stardog

System Properties Comparison CouchDB vs. DolphinDB vs. Kinetica vs. Stardog

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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.

Fully vectorized database across both GPUs and CPUs

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

Relational DBMS

Spatial DBMS
Time Series DBMS

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

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	4.03
Rank	#78	Overall
	#6	Time Series DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

Technical documentation

docs.couchdb.org/en/stable

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

docs.kinetica.com

docs.stardog.com

Developer

Apache Software Foundation

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

DolphinDB, Inc

Kinetica

Stardog-Union

Initial release

2005

2018

2012

2010

Current release

3.3.3, December 2023

v2.00.4, January 2022

7.1, August 2021

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free community version available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

Erlang

C++

C, C++

Java

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

Linux
Windows

Linux

Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free and OWL/RDFS-schema support

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.

Import/export of XML data possible

Secondary indexes

yes

via views

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

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

JDBC
ODBC
RESTful HTTP API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

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

C++
Java
JavaScript (Node.js)
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

View functions in JavaScript

yes

user defined functions

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Source-replica replication

Multi-source replication in HA-Cluster

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

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency in HA-Cluster

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

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

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users can be defined per database

Administrators, Users, Groups

Access rights for users and roles on table level

Access rights for users and roles

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More resources

DB-Engines blog posts

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