DBMS > AlaSQL vs. Kinetica vs. OushuDB vs. Snowflake

System Properties Comparison AlaSQL vs. Kinetica vs. OushuDB vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Fully vectorized database across both GPUs and CPUs

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Document 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	0.42
Rank	#264	Overall
	#42	Document stores
	#122	Relational DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	0.05
Rank	#357	Overall
	#151	Relational DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

alasql.org

www.kinetica.com

www.oushu.com/product/oushuDB

www.snowflake.com

Technical documentation

github.com/AlaSQL/alasql

docs.kinetica.com

www.oushu.com/documentation

docs.snowflake.net/manuals/index.html

Developer

Andrey Gershun & Mathias R. Wulff

Kinetica

Oushu

Snowflake Computing Inc.

Initial release

2014

2012

2014

Current release

7.1, August 2021

4.0.1, August 2020

License

Commercial or Open Source

Open Source

MIT-License

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

JavaScript

C, C++

Server operating systems

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

Linux

hosted

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

SQL-like DML and DDL statements

Full-featured ANSI SQL support

yes

APIs and other access methods

JavaScript API

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

CLI Client
JDBC
ODBC

Supported programming languages

JavaScript

C++
Java
JavaScript (Node.js)
Python

C
C++

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions

Triggers

yes

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

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

User concepts

Access control

Access rights for users and roles on table level

Kerberos, SSL and role based access

Users with fine-grained authorization concept, user roles and pluggable authentication

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