DBMS > Amazon DocumentDB vs. HEAVY.AI vs. Postgres-XL vs. SiteWhere

System Properties Comparison Amazon DocumentDB vs. HEAVY.AI vs. Postgres-XL vs. SiteWhere

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

M2M integration platform for persisting/querying time series data

Primary database model

Document store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

aws.amazon.com/documentdb

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

www.postgres-xl.org

github.com/sitewhere/sitewhere

Technical documentation

aws.amazon.com/documentdb/resources

docs.heavy.ai

www.postgres-xl.org/documentation

sitewhere1.sitewhere.io/index.html

Developer

HEAVY.AI, Inc.

SiteWhere

Initial release

2019

2016

2014

since 2012, originally named StormDB

2010

Current release

5.10, January 2022

10 R1, October 2018

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Mozilla public license

Open Source

Common Public Attribution License Version 1.0

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

Java

Server operating systems

hosted

Linux

Linux
macOS

Linux
OS X
Windows

Data scheme

schema-free

yes

predefined scheme

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

distributed, parallel query execution

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP REST

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

horizontal partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Multi-source replication

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

ACID

MVCC

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

Access rights for users and roles

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Vector search for Amazon DocumentDB (with MongoDB compatibility) is now generally available | Amazon Web Services
29 November 2023, AWS Blog

AWS announces Amazon DocumentDB I/O-Optimized
21 November 2023, AWS Blog

Use headless clusters in Amazon DocumentDB for cost-effective multi-Region resiliency | Amazon Web Services
8 March 2024, AWS Blog

Game Developer's Guide to Amazon DocumentDB (with MongoDB compatibility) Part Three: Operation Best Practices ...
25 January 2024, AWS Blog

Reduce cost and improve performance by migrating to Amazon DocumentDB 5.0 | Amazon Web Services
15 April 2024, AWS Blog

provided by Google News

HEAVY.AI Introduces HeavyIQ, Delivering Powerful Conversational Analytics Focused on Location and Time Data
19 March 2024, Datanami

Big Data Analytics: A Game Changer for Infrastructure
13 July 2023, Spiceworks News and Insights

HEAVY.AI Launches HEAVY 7.0, Introducing Real-Time Machine Learning Capabilities
19 April 2023, Business Wire

Making the most of geospatial intelligence
14 April 2023, InfoWorld

The insideBIGDATA IMPACT 50 List for Q4 2023
11 October 2023, insideBIGDATA

provided by Google News

SiteWhere: An open platform for connected devices
11 July 2017, Open Source For You

11 Best Open source IoT Platforms To Develop Smart Projects
9 March 2023, H2S Media

provided by Google News

Share this page