DBMS > MarkLogic vs. Memcached vs. Microsoft Azure Cosmos DB vs. SwayDB

System Properties Comparison MarkLogic vs. Memcached vs. Microsoft Azure Cosmos DB vs. SwayDB

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Editorial information provided by DB-Engines

Name

Description

Operational and transactional Enterprise NoSQL database

In-memory key-value store, originally intended for caching

Globally distributed, horizontally scalable, multi-model database service

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Secondary database models

Spatial DBMS

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

Score	4.15
Rank	#70	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#8	Search engines

Score	16.84
Rank	#32	Overall
	#4	Key-value stores

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

Score	0.00
Rank	#384	Overall
	#60	Key-value stores

Website

www.progress.com/marklogic

www.memcached.org

azure.microsoft.com/services/cosmos-db

swaydb.simer.au

Technical documentation

www.progress.com/marklogic/documentation

github.com/memcached/memcached/wiki

learn.microsoft.com/azure/cosmos-db

Developer

MarkLogic Corp.

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Microsoft

Simer Plaha

Initial release

2001

2003

2014

2018

Current release

11.0, December 2022

1.6.29, June 2024

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

BSD license

commercial

Open Source

GNU Affero GPL V3.0

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++

Scala

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
OS X
Unix
Windows

hosted

Data scheme

schema-free

Schema can be enforced

schema-free

Typing

predefined data types such as float or date

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

yes

SQL92

SQL-like query language

APIs and other access methods

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

Proprietary protocol

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

Supported programming languages

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

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

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

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Repcached, a Memcached patch, provides this functionallity

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

Multi-item ACID transactions with snapshot isolation within a partition

Atomic execution of operations

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

yes

User concepts

Access control

Role-based access control at the document and subdocument levels

yes

using SASL (Simple Authentication and Security Layer) protocol

Access rights can be defined down to the item level

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