DBMS > Amazon DocumentDB vs. Hazelcast vs. SiteWhere vs. STSdb

System Properties Comparison Amazon DocumentDB vs. Hazelcast vs. SiteWhere vs. STSdb

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Editorial information provided by DB-Engines

Name

Description

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

A widely adopted in-memory data grid

M2M integration platform for persisting/querying time series data

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	1.91
Rank	#124	Overall
	#22	Document stores

Score	5.72
Rank	#59	Overall
	#6	Key-value stores

Score	0.07
Rank	#347	Overall
	#33	Time Series DBMS

Score	0.03
Rank	#365	Overall
	#54	Key-value stores

Website

aws.amazon.com/documentdb

hazelcast.com

github.com/sitewhere/sitewhere

github.com/STSSoft/STSdb4

Technical documentation

aws.amazon.com/documentdb/resources

hazelcast.org/imdg/docs

sitewhere1.sitewhere.io/index.html

Developer

Hazelcast

SiteWhere

STS Soft SC

Initial release

2019

2008

2010

2011

Current release

5.3.6, November 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

Common Public Attribution License Version 1.0

Open Source

GPLv2, commercial license available

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

Java

Server operating systems

hosted

All OS with a Java VM

Linux
OS X
Windows

Windows

Data scheme

schema-free

predefined scheme

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

APIs and other access methods

proprietary protocol using JSON (MongoDB compatible)

JCache
JPA
Memcached protocol
RESTful HTTP API

HTTP REST

.NET Client API

Supported programming languages

Go
Java
JavaScript (Node.js)
PHP
Python

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C#
Java

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on HBase

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

Replicated Map

selectable replication factor

based on HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

Transaction concepts

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

Atomic single-document operations

one or two-phase-commit; repeatable reads; read commited

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

Role-based access control

Users with fine-grained authorization concept

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More resources

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