DBMS > Apache Kylin vs. GridGain vs. Informix vs. SQLite

System Properties Comparison Apache Kylin vs. GridGain vs. Informix vs. SQLite

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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

GridGain is an in-memory computing platform, built on Apache Ignite

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

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

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

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS

with Informix TimeSeries Extension

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

Score	1.25
Rank	#170	Overall
	#77	Relational DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	17.12
Rank	#34	Overall
	#21	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

kylin.apache.org

www.gridgain.com

www.ibm.com/products/informix

www.sqlite.org

Technical documentation

kylin.apache.org/docs

www.gridgain.com/docs/index.html

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

www.sqlite.org/docs.html

Developer

Apache Software Foundation, originally contributed from eBay Inc

GridGain Systems, 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.

Dwayne Richard Hipp

Initial release

2015

2007

1984

2000

Current release

3.1.0, July 2020

GridGain 8.5.1

14.10.FC5, November 2020

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free developer edition available

Open Source

Public Domain

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

Java, C++, .Net

C, C++ and Java

Server operating systems

Linux

Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
macOS
Solaris
Windows

server-less

Data scheme

yes

dynamic column types

Typing

predefined data types such as float or date

yes

Since Version 12.10 support for JSON/BSON datatypes

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

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

ANSI-99 for query and DML statements, subset of DDL

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

JDBC
JSON API

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

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

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

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 (compute grid and cache interceptors can be used instead)

yes

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

Durability

Support for making data persistent

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

Security Hooks for custom implementations

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

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