DBMS > Dragonfly vs. Firebase Realtime Database vs. gStore vs. Microsoft SQL Server vs. TimescaleDB

System Properties Comparison Dragonfly vs. Firebase Realtime Database vs. gStore vs. Microsoft SQL Server vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

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

A native Graph DBMS to store and maintain very large RDF datasets.

Microsofts flagship relational DBMS

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

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	0.04
Rank	#359	Overall
	#37	Graph DBMS
	#18	RDF stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

github.com/dragonflydb/dragonfly
www.dragonflydb.io

firebase.google.com/products/realtime-database

en.gstore.cn

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

www.timescale.com

Technical documentation

www.dragonflydb.io/docs

firebase.google.com/docs/database

en.gstore.cn/#/enDocs

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

docs.timescale.com

Developer

DragonflyDB team and community contributors

Google

acquired by Google 2014

Microsoft

Timescale

Initial release

2023

2012

2016

1989

2017

Current release

1.0, March 2023

1.2, November 2023

SQL Server 2022, November 2022

2.15.0, May 2024

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

BSD

commercial

restricted free version is available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Server operating systems

Linux

hosted

Linux

Linux
Windows

Linux
OS X
Windows

Data scheme

scheme-free

schema-free

schema-free and OWL/RDFS-schema support

yes

Typing

predefined data types such as float or date

strings, hashes, lists, sets, sorted sets, bit arrays

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

full PostgreSQL SQL syntax

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

Android
iOS
JavaScript API
RESTful HTTP API

HTTP API
SPARQL 1.1

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

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

Supported programming languages

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

Java
JavaScript
Objective-C

C++
Java
JavaScript (Node.js)
PHP
Python

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

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Lua

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

publish/subscribe channels provide some trigger functionality

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

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

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes, but depending on the SQL-Server Edition

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

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

Password-based authentication

yes, based on authentication and database rules

Users, roles and permissions, Role-Based Access Control (RBAC) supported

fine grained access rights according to SQL-standard

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