DBMS > Ehcache vs. Microsoft Azure Cosmos DB vs. MonetDB vs. Oracle Berkeley DB

System Properties Comparison Ehcache vs. Microsoft Azure Cosmos DB vs. MonetDB vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

Globally distributed, horizontally scalable, multi-model database service

A relational database management system that stores data in columns

Widely used in-process key-value store

Primary database model

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	4.79
Rank	#66	Overall
	#8	Key-value stores

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.72
Rank	#135	Overall
	#62	Relational DBMS

Score	1.88
Rank	#130	Overall
	#23	Key-value stores
	#3	Native XML DBMS

Website

www.ehcache.org

azure.microsoft.com/services/cosmos-db

www.monetdb.org

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

Technical documentation

www.ehcache.org/documentation

learn.microsoft.com/azure/cosmos-db

www.monetdb.org/Documentation

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Terracotta Inc, owned by Software AG

Microsoft

MonetDB BV

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2009

2014

2004

1994

Current release

3.10.0, March 2022

Dec2023 (11.49), December 2023

18.1.40, May 2020

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Mozilla Public License 2.0

Open Source

commercial license 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, Java, C++ (depending on the Berkeley DB edition)

Server operating systems

All OS with a Java VM

hosted

FreeBSD
Linux
OS X
Solaris
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

only with the Berkeley DB XML edition

Secondary indexes

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

yes

SQL 2003 with some extensions

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JCache

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

Java

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

JavaScript

yes, in SQL, C, R

Triggers

yes

Cache Event Listeners

JavaScript

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

Implicit feature of the cloud service

Sharding via remote tables

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

yes

Implicit feature of the cloud service

none

Source-replica replication available in experimental status

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

supports JTA and can work as an XA resource

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

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 can be defined down to the item level

fine grained access rights according to SQL-standard

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