DBMS > AlaSQL vs. Google BigQuery vs. Manticore Search vs. Microsoft Azure SQL Database vs. Sadas Engine

System Properties Comparison AlaSQL vs. Google BigQuery vs. Manticore Search vs. Microsoft Azure SQL Database vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

Large scale data warehouse service with append-only tables

Multi-storage database for search, including full-text search.

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

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Document store
Relational DBMS

Relational DBMS

Search engine

Relational DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Graph DBMS
Spatial DBMS

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

Score	0.51
Rank	#256	Overall
	#40	Document stores
	#118	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	76.78
Rank	#16	Overall
	#11	Relational DBMS

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Website

alasql.org

cloud.google.com/bigquery

manticoresearch.com

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

www.sadasengine.com

Technical documentation

github.com/AlaSQL/alasql

cloud.google.com/bigquery/docs

manual.manticoresearch.com

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

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Andrey Gershun & Mathias R. Wulff

Google

Manticore Software

Microsoft

SADAS s.r.l.

Initial release

2014

2010

2017

2010

2006

Current release

6.0, February 2023

V12

8.0

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

GPL version 2

commercial

free trial version available

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++

Server operating systems

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

hosted

FreeBSD
Linux
macOS
Windows

hosted

AIX
Linux
Windows

Data scheme

schema-free

yes

Fixed schema

yes

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

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

yes

SQL-like query language

yes

APIs and other access methods

JavaScript API

RESTful HTTP/JSON API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

ADO.NET
JDBC
ODBC

JDBC
ODBC
Proprietary protocol

Supported programming languages

JavaScript

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Synchronous replication based on Galera library

yes, with always 3 replicas available

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

Transaction concepts

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

yes

only for local storage and DOM-storage

Since BigQuery is designed for querying data

yes

isolated transactions for atomic changes and binary logging for safe writes

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

The original contents of fields are not stored in the Manticore index.

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

managed by 'Learn by Usage'

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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