DBMS > Apache Druid vs. MarkLogic vs. Microsoft SQL Server vs. PouchDB

System Properties Comparison Apache Druid vs. MarkLogic vs. Microsoft SQL Server vs. PouchDB

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Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Operational and transactional Enterprise NoSQL database

Microsofts flagship relational DBMS

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Relational DBMS
Time Series DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Document store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

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

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Website

druid.apache.org

www.marklogic.com

www.microsoft.com/en-us/sql-server

pouchdb.com

Technical documentation

druid.apache.org/docs/latest/design

docs.marklogic.com

learn.microsoft.com/en-US/sql/sql-server

pouchdb.com/guides

Developer

Apache Software Foundation and contributors

MarkLogic Corp.

Microsoft

Apache Software Foundation

Initial release

2012

2001

1989

2012

Current release

29.0.1, April 2024

11.0, December 2022

SQL Server 2022, November 2022

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Apache license v2

commercial

restricted free version is available

commercial

restricted free version is available

Open Source

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

JavaScript

Server operating systems

Linux
OS X
Unix

Linux
OS X
Windows

Linux
Windows

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

yes

schema-less columns are supported

schema-free

Schema can be enforced

yes

schema-free

Typing

predefined data types such as float or date

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

Secondary indexes

yes

via views

SQL

Support of SQL

SQL for querying

yes

SQL92

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

JavaScript

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

yes

yes, but depending on the SQL-Server Edition

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

In-memory capabilities

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

yes, with Range Indexes

yes

User concepts

Access control

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Role-based access control at the document and subdocument levels

fine grained access rights according to SQL-standard

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