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

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

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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

Globally distributed, horizontally scalable, multi-model database service

Spatial extension of PostgreSQL

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Spatial DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Website

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

azure.microsoft.com/services/cosmos-db

postgis.net

Technical documentation

docs.heavy.ai

learn.microsoft.com/azure/cosmos-db

postgis.net/documentation

Developer

HEAVY.AI, Inc.

Microsoft

Initial release

2016

2014

2005

Current release

5.10, January 2022

3.4.2, February 2024

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

GPL v2.0

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

yes

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

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

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

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

Server-side scripts

Stored procedures

JavaScript

user defined functions

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Implicit feature of the cloud service

yes

based on PostgreSQL

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Implicit feature of the cloud service

yes

based on PostgreSQL

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

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

Access rights can be defined down to the item level

yes

based on PostgreSQL

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