DBMS > EJDB vs. Google Cloud Firestore vs. H2 vs. PostgreSQL vs. Sadas Engine

System Properties Comparison EJDB vs. Google Cloud Firestore vs. H2 vs. PostgreSQL vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Description

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

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

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

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Document store

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

github.com/Softmotions/ejdb

firebase.google.com/products/firestore

www.h2database.com

www.postgresql.org

www.sadasengine.com

Technical documentation

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

firebase.google.com/docs/firestore

www.h2database.com/html/main.html

www.postgresql.org/docs

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Softmotions

Google

Thomas Mueller

PostgreSQL Global Development Group

www.postgresql.org/developer

SADAS s.r.l.

Initial release

2012

2017

2005

1989

1989: Postgres, 1996: PostgreSQL

2006

Current release

2.2.220, July 2023

16.3, May 2024

8.0

License

Commercial or Open Source

Open Source

GPLv2

commercial

Open Source

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

Open Source

BSD

commercial

free trial version available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

Java

C++

Server operating systems

server-less

hosted

All OS with a Java VM

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

AIX
Linux
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

yes

standard with numerous extensions

yes

APIs and other access methods

in-process shared library

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC
Proprietary protocol

Supported programming languages

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

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

Java

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

Java Stored Procedures and User-Defined Functions

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

partitioning by range, list and (since PostgreSQL 11) by hash

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

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

Source-replica replication

other methods possible by using 3rd party extensions

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate 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

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

yes, multi-version concurrency control (MVCC)

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

managed by 'Learn by Usage'

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

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