DBMS > Apache Impala vs. BaseX vs. MarkLogic vs. Microsoft Azure Cosmos DB

System Properties Comparison Apache Impala vs. BaseX vs. MarkLogic vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Operational and transactional Enterprise NoSQL database

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

Native XML DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store

Spatial DBMS

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.84
Rank	#135	Overall
	#4	Native XML DBMS

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

impala.apache.org

basex.org

www.marklogic.com

azure.microsoft.com/services/cosmos-db

Technical documentation

impala.apache.org/impala-docs.html

docs.basex.org

docs.marklogic.com

learn.microsoft.com/azure/cosmos-db

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

BaseX GmbH

MarkLogic Corp.

Microsoft

Initial release

2013

2007

2001

2014

Current release

4.1.0, June 2022

10.7, August 2023

11.0, December 2022

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

BSD license

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

C++

Java

C++

Server operating systems

Linux

Linux
OS X
Windows

hosted

Data scheme

yes

schema-free

Schema can be enforced

schema-free

Typing

predefined data types such as float or date

yes

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.

yes

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL92

SQL-like query language

APIs and other access methods

JDBC
ODBC

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

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

Supported programming languages

All languages supporting JDBC/ODBC

Actionscript
C
C#
Haskell
Java
JavaScript

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

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

.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 and integration of map-reduce

yes

via XQuery or JavaScript

JavaScript

Triggers

yes

via events

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

Transaction concepts

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

multiple readers, single writer

ACID

can act as a resource manager in an XA/JTA transaction

Multi-item ACID transactions with snapshot isolation within a partition

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, with Range Indexes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Users with fine-grained authorization concept on 4 levels

Role-based access control at the document and subdocument levels

Access rights can be defined down to the item level

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