DBMS > 4D vs. Ehcache vs. Faircom DB vs. KeyDB vs. ObjectBox

System Properties Comparison 4D vs. Ehcache vs. Faircom DB vs. KeyDB vs. ObjectBox

Editorial information provided by DB-Engines

Name

Description

Application development environment with integrated database management system

A widely adopted Java cache with tiered storage options

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Extremely fast embedded database for small devices, IoT and Mobile

Primary database model

Relational DBMS

Key-value store

Key-value store
Relational DBMS

Key-value store

Object oriented DBMS

Secondary database models

Time Series DBMS

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	4.64
Rank	#68	Overall
	#8	Key-value stores

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	0.70
Rank	#229	Overall
	#32	Key-value stores

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Website

www.4d.com

www.ehcache.org

www.faircom.com/products/faircom-db

github.com/Snapchat/KeyDB
keydb.dev

objectbox.io

Technical documentation

developer.4d.com

www.ehcache.org/documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

docs.keydb.dev

docs.objectbox.io

Developer

4D, Inc

Terracotta Inc, owned by Software AG

FairCom Corporation

EQ Alpha Technology Ltd.

ObjectBox Limited

Initial release

1984

2009

1979

2019

2017

Current release

v20, April 2023

3.10.0, March 2022

V12, November 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Restricted, free version available

Open Source

BSD-3

Open Source

Apache License 2.0

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

ANSI C, C++

C++

C and C++

Server operating systems

OS X
Windows

All OS with a Java VM

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux

Android
iOS
Linux
macOS
Windows

Data scheme

yes

schema-free

schema free, schema optional, schema required, partial schema,

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

yes

SQL

Support of SQL

yes

close to SQL 92

yes, ANSI SQL with proprietary extensions

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

JCache

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Proprietary protocol

RESP - REdis Serialization Protoco

Proprietary native API

Supported programming languages

4D proprietary IDE
PHP

Java

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

Lua

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

by using Terracotta Server

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Multi-source replication
Source-replica replication

online/offline synchronization between client and server

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

supports JTA and can work as an XA resource

tunable from ACID to Eventually Consistent

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

Yes, tunable from durable to delayed durability to in-memory

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

Users and groups

Fine grained access rights according to SQL-standard with additional protections for files

simple password-based access control and ACL

yes

More information provided by the system vendor

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More resources

DB-Engines blog posts

MySQL, PostgreSQL and Redis are the winners of the March ranking
2 March 2016, Paul Andlinger

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