DBMS > Atos Standard Common Repository vs. Hazelcast vs. MonetDB vs. MongoDB vs. Quasardb

System Properties Comparison Atos Standard Common Repository vs. Hazelcast vs. MonetDB vs. MongoDB vs. Quasardb

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

A widely adopted in-memory data grid

A relational database management system that stores data in columns

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

Distributed, high-performance timeseries database

Primary database model

Document store
Key-value store

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial DBMS

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	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	421.65
Rank	#5	Overall
	#1	Document stores

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

Technical documentation

hazelcast.org/imdg/docs

www.monetdb.org/Documentation

www.mongodb.com/docs/manual

doc.quasar.ai/master

Developer

Atos Convergence Creators

Hazelcast

MonetDB BV

MongoDB, Inc

quasardb

Initial release

2016

2008

2004

2009

Current release

1703

5.3.6, November 2023

Dec2023 (11.49), December 2023

6.0.7, June 2023

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

Mozilla Public License 2.0

Open Source

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

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Cloud-based only

Only available as a cloud service

MongoDB available as DBaaS (MongoDB Atlas)

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

MongoDB Atlas: Global multi-cloud database with unmatched data distribution and mobility across AWS, Azure, and Google Cloud, built-in automation for resource and workload optimization, and so much more.

Implementation language

Java

C++

Server operating systems

Linux

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

BSD
Linux
OS X
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

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.

schema-free

Typing

predefined data types such as float or date

optional

yes

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

yes

integer and binary

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

the object must implement a serialization strategy

Secondary indexes

yes

with tags

SQL

Support of SQL

SQL-like query language

yes

SQL 2003 with some extensions

Read-only SQL queries via the MongoDB Atlas SQL Interface

SQL-like query language

APIs and other access methods

LDAP

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

GraphQL
HTTP REST
Prisma
proprietary protocol using JSON

HTTP API

Supported programming languages

All languages with LDAP bindings

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes, in SQL, C, R

JavaScript

Triggers

yes

Events

yes

in MongoDB Atlas only

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

Sharding via remote tables

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

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

none

Source-replica replication available in experimental status

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

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

can be individually decided for each read operation
Immediate Consistency

default behaviour

Immediate Consistency

Foreign keys

Referential integrity

yes

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

Atomic execution of specific operations

one or two-phase-commit; repeatable reads; read commited

ACID

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

by using LevelDB

In-memory capabilities

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

yes

In-memory storage engine introduced with MongoDB version 3.2

yes

Transient mode

User concepts

Access control

LDAP bind authentication

Role-based access control

fine grained access rights according to SQL-standard

Access rights for users and roles

Cryptographically strong user authentication and audit trail

More information provided by the system vendor

Specific characteristics

MongoDB provides an integrated suite of cloud database and data services to accelerate...
» more

Competitive advantages

Built around the flexible document data model and unified API, MongoDB is a developer...
» more

Typical application scenarios

AI-enriched intelligent apps (Continental, Telefonica, Iron Mountain) Internet of...
» more

Key customers

ADP, Adobe, Amadeus, AstraZeneca, Auto Trader, Barclays, BBVA, Bosch, Cisco, CERN,...
» more

Market metrics

Hundreds of millions downloads, over 150,000+ Atlas clusters provisioned every month...
» more

Licensing and pricing models

MongoDB database server: Server-Side Public License (SSPL) . Commercial licenses...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.