DBMS > AlaSQL vs. Amazon Redshift vs. Realm vs. Spark SQL vs. STSdb

System Properties Comparison AlaSQL vs. Amazon Redshift vs. Realm vs. Spark SQL vs. STSdb

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Large scale data warehouse service for use with business intelligence tools

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Spark SQL is a component on top of 'Spark Core' for structured data processing

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Document store
Relational 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	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

alasql.org

aws.amazon.com/redshift

realm.io

spark.apache.org/sql

github.com/STSSoft/STSdb4

Technical documentation

github.com/AlaSQL/alasql

docs.aws.amazon.com/redshift

realm.io/docs

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Andrey Gershun & Mathias R. Wulff

Amazon (based on PostgreSQL)

Realm, acquired by MongoDB in May 2019

Apache Software Foundation

STS Soft SC

Initial release

2014

2012

2014

2011

Current release

3.5.0 ( 2.13), September 2023

4.0.8, September 2015

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

Apache 2.0

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

JavaScript

Scala

Server operating systems

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

hosted

Android
Backend: server-less
iOS
Windows

Linux
OS X
Windows

Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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 DML and DDL statements

APIs and other access methods

JavaScript API

JDBC
ODBC

.NET Client API

Supported programming languages

JavaScript

All languages supporting JDBC/ODBC

.Net
Java

with Android only
Objective-C
React Native
Swift

Java
Python
R
Scala

C#
Java

Server-side scripts

Stored procedures

user defined functions

in Python

runs within the applications so server-side scripts are unnecessary

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

In-Memory realm

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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