DBMS > Firebase Realtime Database vs. Graph Engine vs. H2 vs. Microsoft Azure Cosmos DB vs. WakandaDB

System Properties Comparison Firebase Realtime Database vs. Graph Engine vs. H2 vs. Microsoft Azure Cosmos DB vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Globally distributed, horizontally scalable, multi-model database service

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Document store

Graph DBMS
Key-value store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Object oriented DBMS

Secondary database models

Spatial DBMS

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

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

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

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

firebase.google.com/products/realtime-database

www.graphengine.io

www.h2database.com

azure.microsoft.com/services/cosmos-db

wakanda.github.io

Technical documentation

firebase.google.com/docs/database

www.graphengine.io/docs/manual

www.h2database.com/html/main.html

learn.microsoft.com/azure/cosmos-db

wakanda.github.io/doc

Developer

Google

acquired by Google 2014

Microsoft

Thomas Mueller

Microsoft

Wakanda SAS

Initial release

2012

2010

2005

2014

2012

Current release

2.2.220, July 2023

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

dual-licence (Mozilla public license, Eclipse public license)

commercial

Open Source

AGPLv3, extended commercial license available

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

.NET and C

Java

C++, JavaScript

Server operating systems

hosted

.NET

All OS with a Java VM

hosted

Linux
OS X
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.

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

RESTful HTTP API

JDBC
ODBC

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

RESTful HTTP API

Supported programming languages

Java
JavaScript
Objective-C

C#
C++
F#
Visual Basic

Java

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

JavaScript

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes

Java Stored Procedures and User-Defined Functions

JavaScript

yes

Triggers

Callbacks are triggered when data changes

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

With clustering: 2 database servers on different computers operate on identical copies of a database

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

In-memory capabilities

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

yes

User concepts

Access control

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

yes

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