DBMS > AlaSQL vs. Amazon Redshift vs. Apache IoTDB vs. Kyligence Enterprise

System Properties Comparison AlaSQL vs. Amazon Redshift vs. Apache IoTDB vs. Kyligence Enterprise

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Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Large scale data warehouse service for use with business intelligence tools

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

A distributed analytics engine for big data, built on top of Apache Kylin

Primary database model

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	0.49
Rank	#259	Overall
	#40	Document stores
	#120	Relational DBMS

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	1.19
Rank	#176	Overall
	#15	Time Series DBMS

Score	0.39
Rank	#276	Overall
	#125	Relational DBMS

Website

alasql.org

aws.amazon.com/redshift

iotdb.apache.org

kyligence.io/kyligence-enterprise

Technical documentation

github.com/AlaSQL/alasql

docs.aws.amazon.com/redshift

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

Developer

Andrey Gershun & Mathias R. Wulff

Amazon (based on PostgreSQL)

Apache Software Foundation

Kyligence, Inc.

Initial release

2014

2012

2018

2016

Current release

1.1.0, April 2023

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

Apache Version 2.0

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

Java

Server operating systems

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

hosted

All OS with a Java VM (>= 1.8)

Linux

Data scheme

schema-free

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

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

yes

does not fully support an SQL-standard

SQL-like query language

ANSI SQL for queries (using Apache Calcite)

APIs and other access methods

JavaScript API

JDBC
ODBC

JDBC
Native API

JDBC
ODBC
RESTful HTTP API

Supported programming languages

JavaScript

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
Python
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

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

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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