DBMS > HEAVY.AI vs. Microsoft Azure AI Search vs. Snowflake vs. TDSQL for MySQL

System Properties Comparison HEAVY.AI vs. Microsoft Azure AI Search vs. Snowflake vs. TDSQL for MySQL

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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

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

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

A high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.

Primary database model

Relational DBMS

Search engine

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	5.59
Rank	#63	Overall
	#7	Search engines

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.85
Rank	#205	Overall
	#95	Relational DBMS

Website

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

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

www.snowflake.com

www.tencentcloud.com/products/dcdb

Technical documentation

docs.heavy.ai

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

docs.snowflake.net/manuals/index.html

www.tencentcloud.com/document/product/1042

Developer

HEAVY.AI, Inc.

Microsoft

Snowflake Computing Inc.

Tencent

Initial release

2016

2015

2014

2013

Current release

5.10, January 2022

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

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

C++ and CUDA

Server operating systems

Linux

hosted

Data scheme

yes

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

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

APIs and other access methods

JDBC
ODBC
Thrift
Vega

RESTful HTTP API

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C#
Java
JavaScript
Python

JavaScript (Node.js)
Python

C
C#
C++
Java
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Implicit feature of the cloud service

yes

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Implicit feature of the cloud service

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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

User concepts

Access control

fine grained access rights according to SQL-standard

yes

using Azure authentication

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

Users with fine-grained authorization concept

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