DBMS > Cubrid vs. Google BigQuery vs. Graph Engine vs. HugeGraph

System Properties Comparison Cubrid vs. Google BigQuery vs. Graph Engine vs. HugeGraph

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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

A fast-speed and highly-scalable Graph DBMS

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Graph DBMS

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

Score	1.31
Rank	#169	Overall
	#78	Relational DBMS

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.62
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	0.15
Rank	#339	Overall
	#33	Graph DBMS

Website

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

cloud.google.com/bigquery

www.graphengine.io

github.com/hugegraph
hugegraph.apache.org

Technical documentation

cubrid.org/manuals

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

hugegraph.apache.org/docs

Developer

CUBRID Corporation, CUBRID Foundation

Google

Microsoft

Baidu

Initial release

2008

2010

2018

Current release

11.0, January 2021

0.9

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

MIT License

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

Java

Server operating systems

Linux
Windows

hosted

.NET

Linux
macOS
Unix

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.

Secondary indexes

yes

also supports composite index and range index

SQL

Support of SQL

yes

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP/JSON API

RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

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

Groovy
Java
Python

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

yes

asynchronous Gremlin script jobs

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

yes

depending on used storage backend, e.g. Cassandra and HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

depending on used storage backend, e.g. Cassandra and HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

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

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)

Users, roles and permissions

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