DBMS > Amazon DocumentDB vs. BigObject vs. Ehcache vs. Oracle Rdb

System Properties Comparison Amazon DocumentDB vs. BigObject vs. Ehcache vs. Oracle Rdb

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Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Analytic DBMS for real-time computations and queries

A widely adopted Java cache with tiered storage options

Primary database model

Document store

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Relational DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

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

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Website

aws.amazon.com/documentdb

bigobject.io

www.ehcache.org

www.oracle.com/database/technologies/related/rdb.html

Technical documentation

aws.amazon.com/documentdb/resources

docs.bigobject.io

www.ehcache.org/documentation

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

BigObject, Inc.

Terracotta Inc, owned by Software AG

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2019

2015

2009

1984

Current release

3.10.0, March 2022

7.4.1.1, 2021

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

hosted

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

HP Open VMS

Data scheme

schema-free

yes

schema-free

Flexible Schema (defined schema, partial schema, 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

yes

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

fluentd
ODBC
RESTful HTTP API

JCache

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

Java

Server-side scripts

Stored procedures

Lua

Triggers

yes

Cache Event Listeners

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

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

none

yes

by using Terracotta Server

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

Atomic single-document operations

yes

supports JTA and can work as an XA resource

yes, on a single node

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

Access rights for users and roles

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More resources

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