DBMS > DolphinDB vs. Graph Engine vs. Kinetica vs. PostGIS

System Properties Comparison DolphinDB vs. Graph Engine vs. Kinetica vs. PostGIS

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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.

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Fully vectorized database across both GPUs and CPUs

Spatial extension of PostgreSQL

Primary database model

Time Series DBMS

Graph DBMS
Key-value store

Relational DBMS

Spatial DBMS

Secondary database models

Relational DBMS

Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Website

Technical documentation

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

www.graphengine.io/docs/manual

docs.kinetica.com

postgis.net/documentation

Developer

DolphinDB, Inc

Microsoft

Kinetica

Initial release

2018

2010

2012

2005

Current release

v2.00.4, January 2022

7.1, August 2021

3.4.2, February 2024

License

Commercial or Open Source

commercial

free community version available

Open Source

MIT License

commercial

Open Source

GPL v2.0

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++

.NET and C

C, C++

Server operating systems

Linux
Windows

.NET

Linux

Data scheme

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-like query language

SQL-like DML and DDL statements

yes

APIs and other access methods

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

RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

Supported programming languages

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

C#
C++
F#
Visual Basic

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

yes

based on PostgreSQL

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

based on PostgreSQL

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

User concepts

Access control

Administrators, Users, Groups

Access rights for users and roles on table level

yes

based on PostgreSQL

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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