DBMS > AlaSQL vs. Microsoft Azure SQL Database vs. Postgres-XL vs. Snowflake vs. Solr

System Properties Comparison AlaSQL vs. Microsoft Azure SQL Database vs. Postgres-XL vs. Snowflake vs. Solr

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Database as a Service offering with high compatibility to Microsoft SQL Server

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Document store
Relational DBMS

Relational DBMS

Search engine

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

Spatial 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	78.40
Rank	#15	Overall
	#10	Relational DBMS

Score	0.52
Rank	#256	Overall
	#117	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Score	44.28
Rank	#24	Overall
	#3	Search engines

Website

alasql.org

azure.microsoft.com/en-us/products/azure-sql/database

www.postgres-xl.org

www.snowflake.com

solr.apache.org

Technical documentation

github.com/AlaSQL/alasql

docs.microsoft.com/en-us/azure/azure-sql

www.postgres-xl.org/documentation

docs.snowflake.net/manuals/index.html

solr.apache.org/resources.html

Developer

Andrey Gershun & Mathias R. Wulff

Microsoft

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2014

2010

2014

since 2012, originally named StormDB

2014

2006

Current release

V12

10 R1, October 2018

9.5.0, February 2024

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

Mozilla public license

commercial

Open Source

Apache Version 2

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++

Java

Server operating systems

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

hosted

Linux
macOS

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

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

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

XML type, but no XML query functionality

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

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

yes

distributed, parallel query execution

yes

Solr Parallel SQL Interface

APIs and other access methods

JavaScript API

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

CLI Client
JDBC
ODBC

Java API
RESTful HTTP/JSON API

Supported programming languages

JavaScript

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

JavaScript (Node.js)
Python

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Transact SQL

user defined functions

Java plugins

Triggers

yes

similar concept for controling cloud resources

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, with always 3 replicas available

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual 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

MVCC

ACID

optimistic locking

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

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

yes

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