DBMS > Firebase Realtime Database vs. GigaSpaces vs. HugeGraph vs. Hypertable vs. Oracle

System Properties Comparison Firebase Realtime Database vs. GigaSpaces vs. HugeGraph vs. Hypertable vs. Oracle

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

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

A fast-speed and highly-scalable Graph DBMS

An open source BigTable implementation based on distributed file systems such as Hadoop

Widely used RDBMS

Primary database model

Document store

Document store
Object oriented DBMS

Values are user defined objects

Graph DBMS

Wide column store

Relational DBMS

Secondary database models

Graph DBMS
Search engine

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Website

firebase.google.com/products/realtime-database

www.gigaspaces.com

github.com/hugegraph
hugegraph.apache.org

www.oracle.com/database

Technical documentation

firebase.google.com/docs/database

docs.gigaspaces.com/latest/landing.html

hugegraph.apache.org/docs

docs.oracle.com/en/database

Developer

Google

acquired by Google 2014

Gigaspaces Technologies

Baidu

Hypertable Inc.

Oracle

Initial release

2012

2000

2018

2009

1980

Current release

15.5, September 2020

0.9

0.9.8.11, March 2016

23c, September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial licenses available

Open Source

Apache Version 2.0

Open Source

GNU version 3. Commercial license available

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, C++, .Net

Java

C++

C and C++

Server operating systems

hosted

Linux
macOS
Solaris
Windows

Linux
macOS
Unix

Linux
OS X
Windows

an inofficial Windows port is available

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Data scheme

schema-free

yes

schema-free

yes

Schemaless in JSON and XML columns

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.

XML can be used for describing objects metadata

yes

Secondary indexes

yes

also supports composite index and range index

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

with proprietary extensions

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

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

Java API
RESTful HTTP API
TinkerPop Gremlin

C++ API
Thrift

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Java
JavaScript
Objective-C

.Net
C++
Java
Python
Scala

Groovy
Java
Python

C++
Java
Perl
PHP
Python
Ruby

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes

asynchronous Gremlin script jobs

PL/SQL

also stored procedures in Java possible

Triggers

Callbacks are triggered when data changes

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

yes

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

selectable replication factor on file system level

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

via hugegraph-spark

yes

can be realized in PL/SQL

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

yes

ACID

isolation level can be parameterized

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

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

yes, based on authentication and database rules

Role-based access control

Users, roles and permissions

fine grained access rights according to SQL-standard

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