DBMS > Faircom DB vs. Hazelcast vs. Oracle Coherence vs. PlanetScale vs. SQLite

System Properties Comparison Faircom DB vs. Hazelcast vs. Oracle Coherence vs. PlanetScale vs. SQLite

Editorial information provided by DB-Engines

Name

Description

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

A widely adopted in-memory data grid

Oracles in-memory data grid solution

Scalable, distributed, serverless MySQL database platform built on top of Vitess

Widely used embeddable, in-process RDBMS

Primary database model

Key-value store
Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial DBMS

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

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	1.59
Rank	#151	Overall
	#70	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

www.faircom.com/products/faircom-db

hazelcast.com

www.oracle.com/java/coherence

planetscale.com

www.sqlite.org

Technical documentation

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

hazelcast.org/imdg/docs

docs.oracle.com/en/middleware/standalone/coherence

planetscale.com/docs

www.sqlite.org/docs.html

Developer

FairCom Corporation

Hazelcast

Oracle

PlanetScale

Dwayne Richard Hipp

Initial release

1979

2008

2007

2020

2000

Current release

V12, November 2020

5.3.6, November 2023

14.1, August 2023

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

Public Domain

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

ANSI C, C++

Java

Server operating systems

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

easily portable to other OSs

All OS with a Java VM

Docker
Linux
macOS

server-less

Data scheme

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

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

yes

not rigid because of 'dynamic typing' concept.

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

SQL-like query language

yes

with proprietary extensions

yes

SQL-92 is not fully supported

APIs and other access methods

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

JCache
JPA
Memcached protocol
RESTful HTTP API

JCache
JPA
RESTful HTTP API

ADO.NET
JDBC
MySQL protocol
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

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

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

.Net
C++
Java

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Event Listeners, Executor Services

yes

proprietary syntax

Triggers

yes

Events

yes

Live Events

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

Replicated Map

yes, with selectable consistency level

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency
Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

one or two-phase-commit; repeatable reads; read commited

configurable

ACID at shard level

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

via file-system locks

Durability

Support for making data persistent

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

yes

optionally

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

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

Role-based access control

authentification to access the cache via certificates or http basic authentication

Users with fine-grained authorization concept

no user groups or roles

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