DBMS > Firebase Realtime Database vs. GigaSpaces vs. Microsoft SQL Server vs. mSQL vs. TimescaleDB

System Properties Comparison Firebase Realtime Database vs. GigaSpaces vs. Microsoft SQL Server vs. mSQL vs. TimescaleDB

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.

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Microsofts flagship relational DBMS

mSQL (Mini SQL) is a simple and lightweight RDBMS

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Document store

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Time Series DBMS

Secondary database models

Graph DBMS
Search engine

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	0.97
Rank	#192	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

firebase.google.com/products/realtime-database

www.gigaspaces.com

www.microsoft.com/en-us/sql-server

hughestech.com.au/products/msql

www.timescale.com

Technical documentation

firebase.google.com/docs/database

docs.gigaspaces.com/latest/landing.html

learn.microsoft.com/en-US/sql/sql-server

docs.timescale.com

Developer

Google

acquired by Google 2014

Gigaspaces Technologies

Microsoft

Hughes Technologies

Timescale

Initial release

2012

2000

1989

1994

2017

Current release

15.5, September 2020

SQL Server 2022, November 2022

4.4, October 2021

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

commercial

restricted free version is available

commercial

free licenses can be provided

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

C++

Server operating systems

hosted

Linux
macOS
Solaris
Windows

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data 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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

full PostgreSQL SQL syntax

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
ODBC

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

Supported programming languages

Java
JavaScript
Objective-C

.Net
C++
Java
Python
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

Callbacks are triggered when data changes

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes, but depending on the SQL-Server Edition

none

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

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

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

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.

yes

User concepts

Access control

yes, based on authentication and database rules

Role-based access control

fine grained access rights according to SQL-standard

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