DBMS > Ehcache vs. Microsoft Azure Table Storage vs. Splice Machine vs. STSdb

System Properties Comparison Ehcache vs. Microsoft Azure Table Storage vs. Splice Machine vs. STSdb

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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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

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	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

www.ehcache.org

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

splicemachine.com

github.com/STSSoft/STSdb4

Technical documentation

www.ehcache.org/documentation

splicemachine.com/how-it-works

Developer

Terracotta Inc, owned by Software AG

Microsoft

Splice Machine

STS Soft SC

Initial release

2009

2012

2014

2011

Current release

3.10.0, March 2022

3.1, March 2021

4.0.8, September 2015

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

GPLv2, 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

Server operating systems

All OS with a Java VM

hosted

Linux
OS X
Solaris
Windows

Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

yes

APIs and other access methods

JCache

RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

.NET Client API

Supported programming languages

Java

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

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

Sharding

Implicit feature of the cloud service

Shared Nothhing Auto-Sharding, Columnar Partitioning

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.

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate 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

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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 based on private key authentication or shared access signatures

Access rights for users, groups and roles according to SQL-standard

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