DBMS > Apache Ignite vs. Kinetica vs. TimesTen

System Properties Comparison Apache Ignite vs. Kinetica vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Fully vectorized database across both GPUs and CPUs

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	3.10
Rank	#82	Overall
	#12	Key-value stores
	#43	Relational DBMS

Score	0.47
Rank	#248	Overall
	#116	Relational DBMS

Score	1.13
Rank	#171	Overall
	#76	Relational DBMS

Website

ignite.apache.org

www.kinetica.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

apacheignite.readme.io/docs

docs.kinetica.com

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Apache Software Foundation

Kinetica

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2015

2012

1998

Current release

2.16.0, December 2023

7.1, August 2021

Release 22.1

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java, .Net

C, C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

yes

Typing

predefined data types such as float or date

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

Secondary indexes

yes

SQL

Support of SQL

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

SQL-like DML and DDL statements

yes

APIs and other access methods

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

JDBC
ODBC
RESTful HTTP API

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C++
Java
JavaScript (Node.js)
Python

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

user defined functions

PL/SQL

Triggers

yes (cache interceptors and events)

yes

triggers when inserted values for one or more columns fall within a specified range

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)

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Security Hooks for custom implementations

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

The Apache Software Foundation Announces Apache Ignite® 3.0
24 February 2025, GlobeNewswire

Critical Flaw in Apache Ignite (CVE-2024-52577) Allows Attackers to Execute Code Remotely
19 February 2025, GBHackers News

GridGain Helps Advance Real-Time Distributed Databases for High-Performance Applications Through Contributions to Apache Ignite 3.0
24 February 2025, PR Newswire

Critical Apache Ignite Vulnerability Let Attackers Execute Remote Code
19 February 2025, CybersecurityNews

Announcing the Ignite Summit 2025 Conference Schedule
20 February 2025, PR Newswire

provided by Google News

Kinetica: AI is a ‘killer app’ for data analytics
2 May 2023, Blocks and Files

How GPUs Are Helping Paris’ Public Hospital System Combat the Spread of COVID-19
15 October 2020, NVIDIA Blog

Kinetica rolls machine learning libraries into its GPU-powered database
24 January 2017, SiliconANGLE

GPU Database Market Size, Share & Trends Report, 2030
29 July 2024, Grand View Research

Kinetica Adds $6 Million in Acceleration Funding and Expands Management Team
26 September 2016, Business Wire

provided by Google News

Oracle starts peddling Exalytics in-memory appliance
12 March 2012, theregister.com

provided by Google News

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