DBMS > EXASOL vs. Faircom DB vs. IRONdb vs. Spark SQL

System Properties Comparison EXASOL vs. Faircom DB vs. IRONdb vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	3.27
Rank	#97	Overall
	#50	Relational DBMS

Score	0.34
Rank	#266	Overall
	#40	Key-value stores
	#122	Relational DBMS

Score	0.00
Rank	#354	Overall
	#33	Time Series DBMS

Score	22.86
Rank	#35	Overall
	#21	Relational DBMS

Website

www.exasol.com

www.faircom.com/products/faircom-db

www.circonus.com/solutions/time-series-database

spark.apache.org/sql

Technical documentation

www.exasol.com/resources

learn.faircomcorp.com/developers/documentation_directory

docs.circonus.com/irondb

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Exasol

FairCom Corporation

Circonus LLC.

Apache Software Foundation

Initial release

2000

1979

2017

2014

Current release

V12, November 2020

V0.10.20, January 2018

3.2.0, October 2021

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Apache 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

ANSI C, C++

C and C++

Scala

Server operating systems

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

easily portable to other OSs

Linux

Linux
OS X
Windows

Data scheme

yes

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

yes

text, numeric, histograms

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

yes

yes, ANSI SQL with proprietary extensions

SQL-like query language (Circonus Analytics Query Language: CAQL)

SQL-like DML and DDL statements

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

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

HTTP API

JDBC
ODBC

Supported programming languages

Java
Lua
Python
R

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

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

yes

.Net, JavaScript, C/C++

yes, in Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Automatic, metric affinity per node

yes, utilizing Spark Core

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).

configurable replication factor, datacenter aware

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

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

More information provided by the system vendor

Specific characteristics

Achieve Hundreds of Thousands of Transactions Per Second on A Single Database Server...
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Competitive advantages

Top Features SQL Key-value store and navigational NoSQL API Indexed binary objects...
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Typical application scenarios

Real-time transaction processing solutions leverage FairCom DB because its NoSQL...
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Key customers

Microsoft, UPS, Verizon, Motorola, Commvault, Thomson Reuters, ACI, BNY Mellon, Rockwell...
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Market metrics

From banking and financial transactions, transportation, warehouse automation, package...
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Licensing and pricing models

Just as the technical aspects of FairCom DB are flexible to meet operation requirements,...
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