DBMS > HEAVY.AI vs. MarkLogic vs. Microsoft Azure AI Search vs. MySQL vs. Sadas Engine

System Properties Comparison HEAVY.AI vs. MarkLogic vs. Microsoft Azure AI Search vs. MySQL vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Operational and transactional Enterprise NoSQL database

Search-as-a-service for web and mobile app development

Widely used open source RDBMS

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

Primary database model

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Search engine

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Spatial DBMS

Vector DBMS

Document store
Spatial DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

github.com/heavyai/heavydb
www.heavy.ai

www.marklogic.com

azure.microsoft.com/en-us/services/search

www.mysql.com

www.sadasengine.com

Technical documentation

docs.heavy.ai

docs.marklogic.com

learn.microsoft.com/en-us/azure/search

dev.mysql.com/doc

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

Developer

HEAVY.AI, Inc.

MarkLogic Corp.

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

SADAS s.r.l.

Initial release

2016

2001

2015

1995

2006

Current release

5.10, January 2022

11.0, December 2022

8.4.0, April 2024

8.0

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

restricted free version is available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

free trial version 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

C++ and CUDA

C++

C and C++

C++

Server operating systems

Linux

Linux
OS X
Windows

hosted

FreeBSD
Linux
OS X
Solaris
Windows

AIX
Linux
Windows

Data scheme

yes

schema-free

Schema can be enforced

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

SQL92

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC
Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

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

C#
Java
JavaScript
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Implicit feature of the cloud service

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

can act as a resource manager in an XA/JTA transaction

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

yes, with Range Indexes

yes

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control at the document and subdocument levels

yes

using Azure authentication

Users with fine-grained authorization concept

no user groups or roles

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

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