DBMS > AgensGraph vs. Firebase Realtime Database vs. GeoMesa vs. GridGain vs. Microsoft Azure Cosmos DB

System Properties Comparison AgensGraph vs. Firebase Realtime Database vs. GeoMesa vs. GridGain vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

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

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Graph DBMS
Relational DBMS

Document store

Spatial DBMS

Key-value store
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

bitnine.net/agensgraph

firebase.google.com/products/realtime-database

www.geomesa.org

www.gridgain.com

azure.microsoft.com/services/cosmos-db

Technical documentation

bitnine.net/documentation

firebase.google.com/docs/database

www.geomesa.org/documentation/stable/user/index.html

www.gridgain.com/docs/index.html

learn.microsoft.com/azure/cosmos-db

Developer

Bitnine Global Inc.

Google

acquired by Google 2014

CCRi and others

GridGain Systems, Inc.

Microsoft

Initial release

2016

2012

2014

2007

2014

Current release

2.1, December 2018

5.0.0, May 2024

GridGain 8.5.1

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

Apache License 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

Java, C++, .Net

Server operating systems

Linux
OS X
Windows

hosted

Linux
OS X
Solaris
Windows

hosted

Data scheme

depending on used data model

schema-free

yes

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

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

SQL-like query language

APIs and other access methods

Cypher Query Language
JDBC

Android
iOS
JavaScript API
RESTful HTTP API

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

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

Supported programming languages

C
Java
JavaScript
Python

Java
JavaScript
Objective-C

C#
C++
Java
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

yes

limited functionality with using 'rules'

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

JavaScript

Triggers

Callbacks are triggered when data changes

yes (cache interceptors and events)

JavaScript

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

depending on storage layer

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

depending on storage layer

yes (replicated cache)

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

depending on storage layer

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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.

depending on storage layer

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes, based on authentication and database rules

yes

depending on the DBMS used for storage

Security Hooks for custom implementations

Access rights can be defined down to the item level

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