DBMS > BigObject vs. Ehcache vs. Firebase Realtime Database

System Properties Comparison BigObject vs. Ehcache vs. Firebase Realtime Database

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

A widely adopted Java cache with tiered storage options

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Document store

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	14.29
Rank	#39	Overall
	#6	Document stores

Website

bigobject.io

www.ehcache.org

firebase.google.com/products/realtime-database

Technical documentation

docs.bigobject.io

www.ehcache.org/documentation

firebase.google.com/docs/database

Developer

BigObject, Inc.

Terracotta Inc, owned by Software AG

Google

acquired by Google 2014

Initial release

2015

2009

2012

Current release

3.10.0, March 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2; commercial licenses available

commercial

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

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

All OS with a Java VM

hosted

Data scheme

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JCache

Android
iOS
JavaScript API
RESTful HTTP API

Supported programming languages

Java

Java
JavaScript
Objective-C

Server-side scripts

Stored procedures

Lua

limited functionality with using 'rules'

Triggers

yes

Cache Event Listeners

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

by using Terracotta Server

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Tunable Consistency (Strong, Eventual, Weak)

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

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

yes

supports JTA and can work as an XA resource

yes

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

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

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More resources

DB-Engines blog posts

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