DBMS > BaseX vs. CrateDB vs. Ingres vs. Microsoft Azure Cosmos DB

System Properties Comparison BaseX vs. CrateDB vs. Ingres vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

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

Distributed Database based on Lucene

Well established RDBMS

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Native XML DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Relational DBMS

Spatial DBMS

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

Score	1.56
Rank	#141	Overall
	#4	Native XML DBMS

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	3.63
Rank	#79	Overall
	#43	Relational DBMS

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

basex.org

cratedb.com

www.actian.com/databases/ingres

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.basex.org

cratedb.com/docs

docs.actian.com/ingres

learn.microsoft.com/azure/cosmos-db

Developer

BaseX GmbH

Crate

Actian Corporation

Microsoft

Initial release

2007

2013

1974

originally developed at University Berkely in early 1970s

2014

Current release

11.2, August 2024

5.8.1, August 2024

12.0, July 2024

License

Commercial or Open Source

Open Source

BSD license

Open Source

commercial

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

Linux
OS X
Windows

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

hosted

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

Typing

predefined data types such as float or date

XQuery supports types

yes

JSON types

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

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

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

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

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

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

Server-side scripts

Stored procedures

yes

user defined functions (Javascript)

yes

JavaScript

Triggers

yes

via events

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

none

Configurable replication on table/partition-level

Ingres Replicator

yes

Implicit feature of the cloud service

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

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

multiple readers, single writer

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

Users with fine-grained authorization concept on 4 levels

rights management via user accounts

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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