DBMS > DolphinDB vs. GridDB vs. IBM Cloudant vs. IBM Db2 warehouse vs. SQLite

System Properties Comparison DolphinDB vs. GridDB vs. IBM Cloudant vs. IBM Db2 warehouse vs. SQLite

Editorial information provided by DB-Engines

Name

Description

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.

Scalable in-memory time series database optimized for IoT and Big Data

Database as a Service offering based on Apache CouchDB

Cloud-based data warehousing service

Widely used embeddable, in-process RDBMS

Primary database model

Time Series DBMS
Vector DBMS

Time Series DBMS

Document store

Relational DBMS

Secondary database models

Relational DBMS

Key-value store
Relational DBMS

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

Score	2.72
Rank	#100	Overall
	#8	Time Series DBMS
	#5	Vector DBMS

Score	1.91
Rank	#123	Overall
	#10	Time Series DBMS

Score	2.85
Rank	#95	Overall
	#18	Document stores

Score	1.26
Rank	#162	Overall
	#73	Relational DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Website

www.dolphindb.com

griddb.net

www.ibm.com/products/cloudant

www.ibm.com/products/db2/warehouse

www.sqlite.org

Technical documentation

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

docs.griddb.net

cloud.ibm.com/docs/Cloudant

www.sqlite.org/docs.html

Developer

DolphinDB, Inc

Toshiba Corporation

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

IBM

Dwayne Richard Hipp

Initial release

2018

2013

2010

2014

2000

Current release

v2.00.4, January 2022

5.1, August 2022

3.46.1 (13 August 2024), August 2024

License

Commercial or Open Source

commercial

free community version available

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

commercial

Open Source

Public Domain

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

C++

Erlang

Server operating systems

Linux
Windows

Linux

hosted

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

yes

not rigid because of 'dynamic typing' concept.

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

SQL

Support of SQL

SQL-like query language

SQL92, SQL-like TQL (Toshiba Query Language)

yes

SQL-92 is not fully supported

APIs and other access methods

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

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
OLE DB

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

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

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

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

View functions (Map-Reduce) in JavaScript

PL/SQL, SQL PL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency within container, eventual consistency across containers

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID at container level

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

via file-system locks

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

Administrators, Users, Groups

Access rights for users can be defined per database

fine grained access rights according to SQL-standard

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