DBMS > HEAVY.AI vs. Kinetica vs. MongoDB

System Properties Comparison HEAVY.AI vs. Kinetica vs. MongoDB

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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

Fully vectorized database across both GPUs and CPUs

One of the most popular document stores available both as a fully managed cloud service and for deployment on self-managed infrastructure

Primary database model

Relational DBMS

Document store

Secondary database models

Spatial DBMS

Spatial DBMS
Search engine

integrated Lucene index, currently in MongoDB Atlas only.
Time Series DBMS

Time Series Collections introduced in Release 5.0

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

Score	1.48
Rank	#146	Overall
	#70	Relational DBMS

Score	0.55
Rank	#230	Overall
	#110	Relational DBMS

Score	477.66
Rank	#5	Overall
	#1	Document stores

Website

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

www.kinetica.com

www.mongodb.com

Technical documentation

docs.heavy.ai

www.kinetica.com/docs

docs.mongodb.com/manual

Developer

HEAVY.AI, Inc.

Kinetica

MongoDB, Inc

Initial release

2016

2012

2009

Current release

5.10, January 2022

7.1, August 2021

6.0.0, July 2022

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

MongoDB Inc.'s Server Side Public License v1. Prior versions were published under GNU AGPL v3.0. Commercial licenses are also available.

Cloud-based only

Only available as a cloud service

MongoDB available as DBaaS (MongoDB Atlas)

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

MongoDB Atlas: Global multi-cloud database with unmatched data distribution and mobility across AWS, Azure, and Google Cloud, built-in automation for resource and workload optimization, and so much more.
ScaleGrid for MongoDB: Fully managed hosting for MongoDB database on AWS, Azure and DigitalOcean with high availability and SSH access on the #1 multi-cloud DBaaS.

Implementation language

C++ and CUDA

C, C++

C++

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

Although schema-free, documents of the same collection often follow the same structure. Optionally impose all or part of a schema by defining a JSON schema.

Typing

predefined data types such as float or date

yes

string, integer, double, decimal, boolean, date, object_id, geospatial

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

SQL

Support of SQL

yes

SQL-like DML and DDL statements

Read-only SQL queries via the MongoDB Connector for BI

APIs and other access methods

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

proprietary protocol using JSON

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
JavaScript (Node.js)
Python

Actionscript

unofficial driver
C
C#
C++
Clojure

unofficial driver
ColdFusion

unofficial driver
D

unofficial driver
Dart

unofficial driver
Delphi

unofficial driver
Erlang
Go
Groovy

unofficial driver
Haskell
Java
JavaScript
Lisp

unofficial driver
Lua

unofficial driver
MatLab

unofficial driver
Perl
PHP
PowerShell

unofficial driver
Prolog

unofficial driver
Python
R

unofficial driver
Ruby
Rust
Scala
Smalltalk

unofficial driver
Swift

Server-side scripts

Stored procedures

user defined functions

JavaScript

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

in MongoDB Atlas only

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Partitioned by hashed, ranged, or zoned sharding keys. Live resharding allows users to change their shard keys as an online operation with zero downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

Multi-Source deployments with MongoDB Atlas Global Clusters
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Foreign keys

Referential integrity

yes

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

Multi-document ACID Transactions with snapshot isolation

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional, enabled by default

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

In-memory storage engine introduced with MongoDB version 3.2

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

Optimizes memory and compute layers to deliver unprecedented performance. Natively...
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Native and fully vectorized database across both GPUs and CPUs, with memory first...
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MongoDB’s application data platform provides developers a unified interface to power...
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Competitive advantages

Only HEAVY.AI allows users to visually and interactively discover high-value patterns,...
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Best in class geospatial and temporal analytics. Lockless architecture provides real-time...
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The MongoDB platform can be used by developers building transactional, operational,...
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Typical application scenarios

Advanced analytics, geospatial and time-series analytics, accelerated data science,...
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Any application requiring (1) real time insights, (2) time series analysis (3) geospatial...
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Internet of Things and Time Series (Bosch, Jaguar Land Rover) Mobile (The Weather...
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Key customers

Citibank, US Air Force, Softbank, OVO, Telkomsel, USPS, 2 of the top 3 US Telcos,...
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ADP, Adobe, Amadeus, AstraZeneca, Auto Trader, Barclays, BBVA, Bosch, Cisco, CERN,...
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Market metrics

The largest IoT deployment in the world, NORAD, runs on Kinetica.
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200+ million downloads, over 1.5M Atlas database clusters under management handling...
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Licensing and pricing models

HEAVY.AI Free (Free Forever), Foundations ($2k/month), and Enterprise Licensure (Inquire...
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Always Free Tier in the Cloud, Pay-As-You-Go Consumption Based Pricing, and Portable...
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MongoDB database server: Server-Side Public License (SSPL) . Commercial licenses...
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