DBMS > AlaSQL vs. GridGain vs. Kinetica vs. Tkrzw

System Properties Comparison AlaSQL vs. GridGain vs. Kinetica vs. Tkrzw

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Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

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

Fully vectorized database across both GPUs and CPUs

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Document store
Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.46
Rank	#260	Overall
	#40	Document stores
	#121	Relational DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

Technical documentation

github.com/AlaSQL/alasql

www.gridgain.com/docs/index.html

docs.kinetica.com

Developer

Andrey Gershun & Mathias R. Wulff

GridGain Systems, Inc.

Kinetica

Mikio Hirabayashi

Initial release

2014

2007

2012

2020

Current release

GridGain 8.5.1

7.1, August 2021

0.9.3, August 2020

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

Apache Version 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

JavaScript

Java, C++, .Net

C, C++

C++

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X
Solaris
Windows

Linux

Linux
macOS

Data scheme

schema-free

yes

schema-free

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

Close to SQL99, but no user access control, stored procedures and host language bindings.

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

SQL-like DML and DDL statements

APIs and other access methods

JavaScript API

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

JDBC
ODBC
RESTful HTTP API

Supported programming languages

JavaScript

C#
C++
Java
PHP
Python
Ruby
Scala

C++
Java
JavaScript (Node.js)
Python

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

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

user defined functions

Triggers

yes

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

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes (replicated cache)

Source-replica replication

none

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

none

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

only for local storage and DOM-storage

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, SQL.JS or proprietary FileStorage

yes

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

using specific database classes

User concepts

Access control

Security Hooks for custom implementations

Access rights for users and roles on table level

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