DBMS > AgensGraph vs. Microsoft Azure Cosmos DB vs. openGauss vs. Riak KV

System Properties Comparison AgensGraph vs. Microsoft Azure Cosmos DB vs. openGauss vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Globally distributed, horizontally scalable, multi-model database service

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

Distributed, fault tolerant key-value store

Primary database model

Graph DBMS
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.08
Rank	#183	Overall
	#84	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

bitnine.net/agensgraph

azure.microsoft.com/services/cosmos-db

gitee.com/opengauss
opengauss.org

Technical documentation

bitnine.net/documentation

learn.microsoft.com/azure/cosmos-db

docs.opengauss.org/en
gitee.com/opengauss/docs

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Bitnine Global Inc.

Microsoft

Huawei and openGauss community

OpenSource, formerly Basho Technologies

Initial release

2016

2014

2019

2009

Current release

2.1, December 2018

3.0, March 2022

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

Apache version 2, commercial enterprise edition

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

Erlang

Server operating systems

Linux
OS X
Windows

hosted

Linux

Linux
OS X

Data scheme

depending on used data model

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

restricted

SQL

Support of SQL

yes

SQL-like query language

ANSI SQL 2011

APIs and other access methods

Cypher Query Language
JDBC

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC

HTTP API
Native Erlang Interface

Supported programming languages

C
Java
JavaScript
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C++
Java

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

JavaScript

yes

Erlang

Triggers

JavaScript

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

Implicit feature of the cloud service

horizontal partitioning (by range, list and hash)

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Implicit feature of the cloud service

Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

Access rights for users, groups and roles according to SQL-standard

yes, using Riak Security

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