DBMS > BigObject vs. Ehcache vs. etcd vs. FatDB

System Properties Comparison BigObject vs. Ehcache vs. etcd vs. FatDB

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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

Analytic DBMS for real-time computations and queries

A widely adopted Java cache with tiered storage options

A distributed reliable key-value store

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Document store
Key-value store

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

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

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Website

bigobject.io

www.ehcache.org

etcd.io
github.com/etcd-io/etcd

Technical documentation

docs.bigobject.io

www.ehcache.org/documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

Developer

BigObject, Inc.

Terracotta Inc, owned by Software AG

FatCloud

Initial release

2015

2009

2012

Current release

3.10.0, March 2022

3.4, August 2019

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

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

FreeBSD
Linux
Windows

experimental

Windows

Data scheme

yes

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

Via inetgration in SQL Server

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

JCache

gRPC
JSON over HTTP

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

Supported programming languages

Java

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

Server-side scripts

Stored procedures

Lua

yes

via applications

Triggers

yes

Cache Event Listeners

yes, watching key changes

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

by using Terracotta Server

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

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

yes

supports JTA and can work as an XA resource

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

Can implement custom security layer via applications

More information provided by the system vendor

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