DBMS > Faircom DB vs. Hazelcast vs. Heroic vs. Ingres vs. Percona Server for MongoDB

System Properties Comparison Faircom DB vs. Hazelcast vs. Heroic vs. Ingres vs. Percona Server for MongoDB

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Well established RDBMS

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Primary database model

Key-value store
Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	4.11
Rank	#81	Overall
	#44	Relational DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Website

www.faircom.com/products/faircom-db

hazelcast.com

github.com/spotify/heroic

www.actian.com/databases/ingres

www.percona.com/mongodb/software/percona-server-for-mongodb

Technical documentation

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

hazelcast.org/imdg/docs

spotify.github.io/heroic

docs.actian.com/ingres

docs.percona.com/percona-distribution-for-mongodb

Developer

FairCom Corporation

Hazelcast

Spotify

Actian Corporation

Percona

Initial release

1979

2008

2014

1974

originally developed at University Berkely in early 1970s

2015

Current release

V12, November 2020

5.3.6, November 2023

11.2, May 2022

3.4.10-2.10, November 2017

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache 2.0

commercial

Open Source

GPL Version 2

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

ANSI C, C++

Java

C++

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

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux

Data scheme

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

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

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

the object must implement a serialization strategy

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

SQL-like query language

yes

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

HQL (Heroic Query Language, a JSON-based language)
HTTP API

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

proprietary protocol using JSON

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

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Event Listeners, Executor Services

yes

JavaScript

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

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

Ingres Replicator

Source-replica replication

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

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

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

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

via In-Memory Engine

User concepts

Access control

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

Role-based access control

fine grained access rights according to SQL-standard

Access rights for users and roles

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