DBMS > Ehcache vs. Microsoft Azure Table Storage vs. RDFox vs. XTDB

System Properties Comparison Ehcache vs. Microsoft Azure Table Storage vs. RDFox vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

A Wide Column Store for rapid development using massive semi-structured datasets

High performance knowledge graph and semantic reasoning engine

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Secondary database models

Relational DBMS

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

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

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	0.23
Rank	#308	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.ehcache.org

azure.microsoft.com/en-us/services/storage/tables

www.oxfordsemantic.tech

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

www.ehcache.org/documentation

docs.oxfordsemantic.tech

www.xtdb.com/docs

Developer

Terracotta Inc, owned by Software AG

Microsoft

Oxford Semantic Technologies

Juxt Ltd.

Initial release

2009

2012

2017

2019

Current release

3.10.0, March 2022

6.0, Septermber 2022

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

MIT License

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++

Clojure

Server operating systems

All OS with a Java VM

hosted

Linux
macOS
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

RDF schemas

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

limited SQL, making use of Apache Calcite

APIs and other access methods

JCache

RESTful HTTP API

RESTful HTTP API
SPARQL 1.1

HTTP REST
JDBC

Supported programming languages

Java

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

C
Java

Clojure
Java

Server-side scripts

Stored procedures

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

replication via a shared file system

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Foreign keys

Referential integrity

Transaction concepts

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

yes

supports JTA and can work as an XA resource

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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 based on private key authentication or shared access signatures

Roles, resources, and access types

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