DBMS > Amazon DocumentDB vs. GridGain vs. IBM Cloudant vs. Microsoft Azure Cosmos DB vs. Realm

System Properties Comparison Amazon DocumentDB vs. GridGain vs. IBM Cloudant vs. Microsoft Azure Cosmos DB vs. Realm

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

GridGain is an in-memory computing platform, built on Apache Ignite

Database as a Service offering based on Apache CouchDB

Globally distributed, horizontally scalable, multi-model database service

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Primary database model

Document store

Key-value store
Relational DBMS

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Secondary database models

Spatial DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	7.60
Rank	#52	Overall
	#9	Document stores

Website

aws.amazon.com/documentdb

www.gridgain.com

www.ibm.com/products/cloudant

azure.microsoft.com/services/cosmos-db

realm.io

Technical documentation

aws.amazon.com/documentdb/resources

www.gridgain.com/docs/index.html

cloud.ibm.com/docs/Cloudant

learn.microsoft.com/azure/cosmos-db

realm.io/docs

Developer

GridGain Systems, Inc.

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Microsoft

Realm, acquired by MongoDB in May 2019

Initial release

2019

2007

2010

2014

Current release

GridGain 8.5.1

License

Commercial or Open Source

commercial

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

Erlang

Server operating systems

hosted

Linux
OS X
Solaris
Windows

hosted

Android
Backend: server-less
iOS
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

RESTful HTTP/JSON API

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

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

C#
C++
Java
PHP
Python
Ruby
Scala

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

View functions (Map-Reduce) in JavaScript

JavaScript

runs within the applications so server-side scripts are unnecessary

Triggers

yes (cache interceptors and events)

yes

JavaScript

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

yes (replicated cache)

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes (compute grid and hadoop accelerator)

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

Atomic single-document operations

ACID

atomic operations within a document possible

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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

In-Memory realm

User concepts

Access control

Access rights for users and roles

Security Hooks for custom implementations

Access rights for users can be defined per database

Access rights can be defined down to the item level

yes

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