DBMS > EJDB vs. Firebase Realtime Database vs. IBM Db2 vs. LevelDB vs. Microsoft Azure Cosmos DB

System Properties Comparison EJDB vs. Firebase Realtime Database vs. IBM Db2 vs. LevelDB vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

Description

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

Common in IBM host environments, 2 different versions for host and Windows/Linux

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Document store

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Spatial DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

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

Website

github.com/Softmotions/ejdb

firebase.google.com/products/realtime-database

www.ibm.com/products/db2

github.com/google/leveldb

azure.microsoft.com/services/cosmos-db

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

firebase.google.com/docs/database

www.ibm.com/docs/en/db2

github.com/google/leveldb/blob/main/doc/index.md

learn.microsoft.com/azure/cosmos-db

Developer

Softmotions

Google

acquired by Google 2014

IBM

Google

Microsoft

Initial release

2012

1983

host version

2011

2014

Current release

12.1, October 2016

1.23, February 2021

License

Commercial or Open Source

Open Source

GPLv2

commercial

free version is available

Open Source

BSD

commercial

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

C and C++

C++

Server operating systems

server-less

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Illumos
Linux
OS X
Windows

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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.

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

in-process shared library

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

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

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

Java
JavaScript
Objective-C

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes

JavaScript

Triggers

Callbacks are triggered when data changes

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

only with Windows/Unix/Linux Version

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

with separate tools (MQ, InfoSphere)

none

yes

Implicit feature of the cloud service

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

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

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

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

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

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