DBMS > Firebase Realtime Database vs. HugeGraph vs. MySQL vs. RisingWave vs. SAP SQL Anywhere

System Properties Comparison Firebase Realtime Database vs. HugeGraph vs. MySQL vs. RisingWave vs. SAP SQL Anywhere

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.

A fast-speed and highly-scalable Graph DBMS

Widely used open source RDBMS

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

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

Primary database model

Document store

Graph DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.58
Rank	#242	Overall
	#111	Relational DBMS

Score	4.25
Rank	#79	Overall
	#43	Relational DBMS

Website

firebase.google.com/products/realtime-database

github.com/hugegraph
hugegraph.apache.org

www.mysql.com

www.risingwave.com/database

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

Technical documentation

firebase.google.com/docs/database

hugegraph.apache.org/docs

dev.mysql.com/doc

docs.risingwave.com/docs/current/intro

help.sap.com/docs/SAP_SQL_Anywhere

Developer

Google

acquired by Google 2014

Baidu

Oracle

since 2010, originally MySQL AB, then Sun

RisingWave Labs

SAP

formerly Sybase

Initial release

2012

2018

1995

2022

1992

Current release

0.9

8.4.0, April 2024

1.2, September 2023

17, July 2015

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C and C++

Rust

Server operating systems

hosted

Linux
macOS
Unix

FreeBSD
Linux
OS X
Solaris
Windows

Docker
Linux
macOS

AIX
HP-UX
Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

Standard SQL-types and JSON

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

also supports composite index and range index

yes

SQL

Support of SQL

yes

with proprietary extensions

yes

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
PostgreSQL wire protocol

ADO.NET
HTTP API
JDBC
ODBC

Supported programming languages

Java
JavaScript
Objective-C

Groovy
Java
Python

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

Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

limited functionality with using 'rules'

asynchronous Gremlin script jobs

yes

proprietary syntax

UDFs in Python or Java

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

Triggers

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

Multi-source replication
Source-replica replication

Source-replica replication

Database mirroring

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

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

yes, based on authentication and database rules

Users, roles and permissions

Users with fine-grained authorization concept

no user groups or roles

Users and Roles

fine grained access rights according to SQL-standard

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