DBMS > Alibaba Cloud Table Store vs. Ehcache vs. Faircom EDGE vs. FatDB vs. Splice Machine

System Properties Comparison Alibaba Cloud Table Store vs. Ehcache vs. Faircom EDGE vs. FatDB vs. Splice Machine

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

A widely adopted Java cache with tiered storage options

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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

Primary database model

Wide column store

Key-value store

Key-value store
Relational DBMS

Document store
Key-value store

Relational DBMS

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

Score	0.24
Rank	#310	Overall
	#12	Wide column stores

Score	5.23
Rank	#68	Overall
	#8	Key-value stores

Score	0.03
Rank	#380	Overall
	#59	Key-value stores
	#157	Relational DBMS

Score	0.54
Rank	#255	Overall
	#116	Relational DBMS

Website

www.alibabacloud.com/product/table-store

www.ehcache.org

www.faircom.com/products/faircom-edge

splicemachine.com

Technical documentation

www.alibabacloud.com/help/en/tablestore

www.ehcache.org/documentation

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

splicemachine.com/how-it-works

Developer

Alibaba

Terracotta Inc, owned by Software AG

FairCom Corporation

FatCloud

Splice Machine

Initial release

2016

2009

1979

2012

2014

Current release

3.10.0, March 2022

V3, October 2020

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Restricted, free version available

commercial

Open Source

AGPL 3.0, 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

ANSI C, C++

Java

Server operating systems

hosted

All OS with a Java VM

Android
Linux

ARM, x86
Raspbian
Windows

Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL 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

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL queries

Via inetgration in SQL Server

yes

APIs and other access methods

HTTP API

JCache

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Java
Python

Java

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

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

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

via applications

yes

Java

Triggers

yes

Cache Event Listeners

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

by using Terracotta Server

File partitioning

Customizable business rules for partitioning

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

by using Terracotta Server

yes

Synchronous and asynchronous realtime replication based on transaction logs

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency
Tunable Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

Atomic single-row operations

yes

supports JTA and can work as an XA resource

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

yes, multi-version concurrency control (MVCC)

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 subaccounts and tokens

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Can implement custom security layer via applications

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

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