DBMS > MarkLogic vs. MongoDB vs. OpenTenBase

System Properties Comparison MarkLogic vs. MongoDB vs. OpenTenBase

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Editorial information provided by DB-Engines

Name

Description

Operational and transactional Enterprise NoSQL database

One of the most popular document stores available both as a fully managed cloud service and for deployment on self-managed infrastructure

An enterprise-level distributed HTAP open source database based on PostgreSQL

Primary database model

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Document store

Relational DBMS

Secondary database models

Spatial DBMS
Search engine

integrated Lucene index, currently in MongoDB Atlas only.
Time Series DBMS

Time Series Collections introduced in Release 5.0
Vector DBMS

currently available in the MongoDB Atlas cloud service only

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

Score	4.24
Rank	#64	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#8	Search engines

Score	402.51
Rank	#5	Overall
	#1	Document stores

Score	0.00
Rank	#385	Overall
	#157	Relational DBMS

Website

www.progress.com/marklogic

www.mongodb.com

github.com/OpenTenBase/OpenTenBase
www.opentenbase.org

Technical documentation

www.progress.com/marklogic/documentation

www.mongodb.com/docs/manual

docs.opentenbase.org
docs.opentenbase.org/en

Developer

MarkLogic Corp.

MongoDB, Inc

OpenAtom Foundation, previously Tencent

Initial release

2001

2009

Current release

11.0, December 2022

7.0.5, January 2024

2.5, January 2024

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

MongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.

Open Source

BSD-3

Cloud-based only

Only available as a cloud service

MongoDB available as DBaaS (MongoDB Atlas)

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Implementation language

C++

Server operating systems

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

Schema can be enforced

schema-free

Although schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.

yes

Typing

predefined data types such as float or date

yes

string, integer, double, decimal, boolean, date, object_id, geospatial

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

SQL

Support of SQL

yes

SQL92

Read-only SQL queries via the MongoDB Atlas SQL Interface

yes

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

GraphQL
HTTP REST
Prisma
proprietary protocol using JSON

Supported programming languages

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

Actionscript

unofficial driver
C
C#
C++
Clojure

unofficial driver
ColdFusion

unofficial driver
D

unofficial driver
Dart

unofficial driver
Delphi

unofficial driver
Erlang
Go
Groovy

unofficial driver
Haskell
Java
JavaScript
Kotlin
Lisp

unofficial driver
Lua

unofficial driver
MatLab

unofficial driver
Perl
PHP
PowerShell

unofficial driver
Prolog

unofficial driver
Python
R

unofficial driver
Ruby
Rust
Scala
Smalltalk

unofficial driver
Swift

C
Go
Java
PHP
Python

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

JavaScript

Triggers

yes

in MongoDB Atlas only

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioned by hashed, ranged, or zoned sharding keys. Live resharding allows users to change their shard keys as an online operation with zero downtime.

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-Source deployments with MongoDB Atlas Global Clusters
Source-replica replication

yes

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

can be individually decided for each read operation
Immediate Consistency

default behaviour

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

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

Multi-document ACID Transactions with snapshot isolation

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional, enabled by default

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

In-memory storage engine introduced with MongoDB version 3.2

User concepts

Access control

Role-based access control at the document and subdocument levels

Access rights for users and roles

Access rights for users, groups and roles according to SQL-standard

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