DBMS > Google Cloud Firestore vs. GridGain vs. MySQL vs. PostGIS vs. SAP SQL Anywhere

System Properties Comparison Google Cloud Firestore vs. GridGain vs. MySQL vs. PostGIS vs. SAP SQL Anywhere

Editorial information provided by DB-Engines

Name

Description

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.

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

Widely used open source RDBMS

Spatial extension of PostgreSQL

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

Primary database model

Document store

Key-value store
Relational DBMS

Relational DBMS

Key/Value like access via memcached API

Spatial DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Relational DBMS

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

Score	7.36
Rank	#53	Overall
	#9	Document stores

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

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	21.72
Rank	#29	Overall
	#1	Spatial DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Website

firebase.google.com/products/firestore

www.gridgain.com

www.mysql.com

postgis.net

www.sap.com/products/technology-platform/sql-anywhere.html

Technical documentation

firebase.google.com/docs/firestore

www.gridgain.com/docs/index.html

dev.mysql.com/doc

postgis.net/documentation

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Google

GridGain Systems, Inc.

Oracle

since 2010, originally MySQL AB, then Sun

SAP

formerly Sybase

Initial release

2017

2007

1995

2005

1992

Current release

GridGain 8.5.1

8.4.0, April 2024

3.4.2, February 2024

17, July 2015

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPL v2.0

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java, C++, .Net

C and C++

Server operating systems

hosted

Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

SQL

Support of SQL

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

yes

with proprietary extensions

yes

APIs and other access methods

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

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

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

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

C#
C++
Java
PHP
Python
Ruby
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes, Firebase Rules & Cloud Functions

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

yes

proprietary syntax

user defined functions

yes, in C/C++, Java, .Net or Perl

Triggers

yes, with Cloud Functions

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

based on PostgreSQL

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes (replicated cache)

Multi-source replication
Source-replica replication

yes

based on PostgreSQL

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

yes

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

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.

Security Hooks for custom implementations

Users with fine-grained authorization concept

no user groups or roles

yes

based on PostgreSQL

fine grained access rights according to SQL-standard

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