DBMS > Cubrid vs. Google BigQuery vs. Graph Engine vs. Kdb vs. openGemini

System Properties Comparison Cubrid vs. Google BigQuery vs. Graph Engine vs. Kdb vs. openGemini

Editorial information provided by DB-Engines

Name

Description

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Large scale data warehouse service with append-only tables

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

High performance Time Series DBMS

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Time Series DBMS
Vector DBMS

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

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

Score	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	0.01
Rank	#375	Overall
	#39	Time Series DBMS

Website

cubrid.com (korean)
cubrid.org (english)

cloud.google.com/bigquery

www.graphengine.io

kx.com

www.opengemini.org
github.com/openGemini

Technical documentation

cubrid.org/manuals

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

code.kx.com

docs.opengemini.org/guide

Developer

CUBRID Corporation, CUBRID Foundation

Google

Microsoft

Kx Systems, a division of First Derivatives plc

Huawei and openGemini community

Initial release

2008

2010

2000

kdb was released 2000, kdb+ in 2003

2022

Current release

11.0, January 2021

3.6, May 2018

1.1, July 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

MIT License

commercial

free 32-bit version

Open Source

Apache Version 2.0

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, C++, Java

.NET and C

Server operating systems

Linux
Windows

hosted

.NET

Linux
OS X
Solaris
Windows

Linux
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Integer, Float, Boolean, String

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

table attribute 'grouped'

yes

SQL

Support of SQL

yes

SQL-like query language (q)

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP/JSON API

RESTful HTTP API

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

HTTP REST

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

C
C++
Go
Java
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

yes

user defined functions

Triggers

yes

with views

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

rights management via user accounts

Administrators and common users accounts

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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