DBMS > Faircom DB vs. Hazelcast vs. NuoDB vs. Splunk

System Properties Comparison Faircom DB vs. Hazelcast vs. NuoDB vs. Splunk

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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

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Analytics Platform for Big Data

Primary database model

Key-value store
Relational DBMS

Key-value store

Relational DBMS

Search engine

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	0.18
Rank	#314	Overall
	#45	Key-value stores
	#141	Relational DBMS

Score	5.72
Rank	#59	Overall
	#6	Key-value stores

Score	0.87
Rank	#201	Overall
	#94	Relational DBMS

Score	93.02
Rank	#13	Overall
	#2	Search engines

Website

www.faircom.com/products/faircom-db

hazelcast.com

www.3ds.com/nuodb-distributed-sql-database

www.splunk.com

Technical documentation

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

hazelcast.org/imdg/docs

doc.nuodb.com

docs.splunk.com/Documentation/Splunk

Developer

FairCom Corporation

Hazelcast

Dassault Systèmes

originally NuoDB, Inc.

Splunk Inc.

Initial release

1979

2008

2013

2003

Current release

V13, July 2024

5.3.6, November 2023

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache Version 2; commercial licenses available

commercial

limited edition free

commercial

Limited free edition and free developer edition available

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

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Data scheme

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

schema-free

yes

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

yes

Secondary indexes

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

SQL-like query language

yes

Splunk Search Processing Language for search commands

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

ADO.NET
JDBC
ODBC

HTTP REST

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
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Event Listeners, Executor Services

Java, SQL

yes

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

data is dynamically stored/cached on the nodes where it is read/written

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

Managed transparently by NuoDB

Multi-source 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

Immediate Consistency

Eventual 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

tunable commit protocol

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

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

Temporary table

User concepts

Access control

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

Role-based access control

Standard SQL roles/ privileges, Administrative Users

Access rights for users and roles

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More resources

DB-Engines blog posts

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