DBMS > Apache Kylin vs. Faircom DB vs. HEAVY.AI vs. Informix

System Properties Comparison Apache Kylin vs. Faircom DB vs. HEAVY.AI vs. Informix

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Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A secure embeddable database from IBM, positioned besides IBM Db2 as a relatively low-cost product optimized for OLTP and Internet of Things data

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Since Version 12.10 support for JSON/BSON datatypes compatible with MongoDB

Secondary database models

Spatial DBMS

Document store
Spatial DBMS
Time Series DBMS

with Informix TimeSeries Extension

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

Score	1.18
Rank	#172	Overall
	#79	Relational DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	17.87
Rank	#35	Overall
	#22	Relational DBMS

Website

kylin.apache.org

www.faircom.com/products/faircom-db

github.com/heavyai/heavydb
www.heavy.ai

www.ibm.com/products/informix

Technical documentation

kylin.apache.org/docs

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

docs.heavy.ai

informix.hcldoc.com
www.ibm.com/support/knowledgecenter/SSGU8G/welcomeIfxServers.html

Developer

Apache Software Foundation, originally contributed from eBay Inc

FairCom Corporation

HEAVY.AI, Inc.

IBM, HCL Technologies

Effective May 1st, 2017, HCL took on development, technical support, and product management teams, and works jointly with IBM on product strategy, marketing, and sales.

Initial release

2015

1979

2016

1984

Current release

3.1.0, July 2020

V12, November 2020

5.10, January 2022

14.10.FC5, November 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Restricted, free version available

Open Source

Apache Version 2; enterprise edition available

commercial

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

Java

ANSI C, C++

C++ and CUDA

C, C++ and Java

Server operating systems

Linux

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

easily portable to other OSs

Linux

AIX
HP-UX
Linux
macOS
Solaris
Windows

Data scheme

yes

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

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

yes

Since Version 12.10 support for JSON/BSON datatypes

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

ANSI SQL for queries (using Apache Calcite)

yes, ANSI SQL with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

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

JDBC
ODBC
Thrift
Vega

JDBC
JSON API

MongoDB compatible
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

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

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Round robin

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).

Multi-source replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

ACID

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

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

fine grained access rights according to SQL-standard

Users with fine-grained authentication, authorization, and auditing controls

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