DBMS > HEAVY.AI vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB

System Properties Comparison HEAVY.AI vs. Microsoft Azure AI Search vs. Microsoft Azure Cosmos DB

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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

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

Vector DBMS

Spatial DBMS

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

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	5.71
Rank	#64	Overall
	#8	Search engines

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

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

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

azure.microsoft.com/services/cosmos-db

Technical documentation

docs.heavy.ai

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

learn.microsoft.com/azure/cosmos-db

Developer

HEAVY.AI, Inc.

Microsoft

Initial release

2016

2015

2014

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

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

JDBC
ODBC
Thrift
Vega

RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C#
Java
JavaScript
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Implicit feature of the cloud service

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

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

Access rights can be defined down to the item level

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