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

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

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Editorial information provided by DB-Engines

Name

Description

Native multi-model DBMS for graph, document, key/value and search. All in one engine and accessible with one query language.

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

Primary database model

Document store
Graph DBMS
Key-value store
Search engine

Relational DBMS

Graph DBMS
Key-value store

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

Score	3.77
Rank	#87	Overall
	#15	Document stores
	#5	Graph DBMS
	#12	Key-value stores
	#10	Search engines

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

Website

arangodb.com

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

cloud.google.com/bigquery

www.graphengine.io

Technical documentation

docs.arangodb.com

cubrid.org/manuals

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

Social network pages

Developer

ArangoDB Inc.

CUBRID Corporation, CUBRID Foundation

Google

Microsoft

Initial release

2012

2008

2010

Current release

3.11.5, November 2023

11.0, January 2021

License

Commercial or Open Source

Open Source

Apache Version 2; Commercial license (Enterprise) available

Open Source

Apache Version 2.0

commercial

Open Source

MIT License

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.

ArangoDB Cloud –The Managed Cloud Service of ArangoDB. Provides fully managed, and monitored cluster deployments of any size, with enterprise-grade security. Get started for free and continue for as little as $0,21/hour.

Implementation language

C++

C, C++, Java

.NET and C

Server operating systems

Linux
OS X
Windows

Linux
Windows

hosted

.NET

Data scheme

schema-free

automatically recognizes schema within a collection

yes

Typing

predefined data types such as float or date

yes

string, double, boolean, list, hash

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

SQL

Support of SQL

yes

APIs and other access methods

AQL
Foxx Framework
Graph API (Gremlin)
GraphQL query language
HTTP API
Java & SpringData
JSON style queries
VelocyPack/VelocyStream

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

C#
C++
Clojure
Elixir
Go
Java
JavaScript (Node.js)
PHP
Python
R
Rust

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

Server-side scripts

Stored procedures

JavaScript

Java Stored Procedures

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

since version 2.0

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with configurable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

can be done with stored procedures in JavaScript

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

configurable per collection or per write
Immediate Consistency
OneShard (highly available, fault-tolerant deployment mode with ACID semantics)

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

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

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

yes

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)

More information provided by the system vendor

Specific characteristics

Graph and Beyond. With more than 11,000 stargazers on GitHub, ArangoDB is the leading...
» more

Competitive advantages

Consolidation: As a native multi-model database, can be used as a full blown document...
» more

Typical application scenarios

Native multi-model in ArangoDB is being used for a broad range of projects across...
» more

Key customers

Cisco, Barclays, Refinitive, Siemens Mentor, Kabbage, Liaison, Douglas, MakeMyTrip,...
» more

Market metrics

ArangoDB is the leading native multi-model database with over 11,000 stargazers on...
» more

Licensing and pricing models

Very permissive Apache 2 License for Community Edition & commercial licenses are...
» more

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