DBMS > Alibaba Cloud AnalyticDB for MySQL vs. BaseX vs. Microsoft Azure Cosmos DB vs. Stardog

System Properties Comparison Alibaba Cloud AnalyticDB for MySQL vs. BaseX vs. Microsoft Azure Cosmos DB vs. Stardog

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

A real-time data warehousing service that can process petabytes of data with high concurrency and low latency. It is fully compatible with the MySQL protocol.

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

Globally distributed, horizontally scalable, multi-model database service

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Relational DBMS

Native XML DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS
RDF store

Secondary database models

Document store

Spatial DBMS

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

Score	0.76
Rank	#223	Overall
	#103	Relational DBMS

Score	1.90
Rank	#136	Overall
	#4	Native XML DBMS

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	2.05
Rank	#129	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

www.alibabacloud.com/product/analyticdb-for-mysql

basex.org

azure.microsoft.com/services/cosmos-db

www.stardog.com

Technical documentation

www.alibabacloud.com/help/doc-detail/93776.htm

docs.basex.org

learn.microsoft.com/azure/cosmos-db

docs.stardog.com

Developer

Alibaba

BaseX GmbH

Microsoft

Stardog-Union

Initial release

2007

2014

2010

Current release

10.7, August 2023

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

BSD license

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

Java

Server operating systems

hosted

Linux
OS X
Windows

hosted

Linux
macOS
Windows

Data scheme

yes

schema-free

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

XQuery supports types

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.

yes

Import/export of XML data possible

Secondary indexes

yes

All properties auto-indexed by default

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

SQL-like query language

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JDBC
ODBC

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

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

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

C#
Java
PHP
Python

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

JavaScript

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via events

JavaScript

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

yes

Implicit feature of the cloud service

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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 in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

ACID

multiple readers, single writer

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept on 4 levels

Access rights can be defined down to the item level

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

A real-time data warehousing service that can process PB data with high concurrency...
» more

Competitive advantages

TPC Benchmark: The world leading result in TPC-DS benchmark . TPC-H benchmark for...
» more

Licensing and pricing models

Available regions: America US Virginia US Silicon Valley Asia China Hong Kong India...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.