DBMS > Amazon SimpleDB vs. Apache IoTDB vs. Hazelcast vs. Microsoft SQL Server vs. STSdb

System Properties Comparison Amazon SimpleDB vs. Apache IoTDB vs. Hazelcast vs. Microsoft SQL Server vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

A widely adopted in-memory data grid

Microsofts flagship relational DBMS

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

Document store
Graph DBMS
Spatial DBMS

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

Score	1.85
Rank	#138	Overall
	#24	Key-value stores

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

aws.amazon.com/simpledb

iotdb.apache.org

hazelcast.com

www.microsoft.com/en-us/sql-server

github.com/STSSoft/STSdb4

Technical documentation

docs.aws.amazon.com/simpledb

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

hazelcast.org/imdg/docs

learn.microsoft.com/en-US/sql/sql-server

Developer

Amazon

Apache Software Foundation

Hazelcast

Microsoft

STS Soft SC

Initial release

2007

2018

2008

1989

2011

Current release

1.1.0, April 2023

5.3.6, November 2023

SQL Server 2022, November 2022

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available

commercial

restricted free version is available

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

C++

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

All OS with a Java VM

Linux
Windows

Windows

Data scheme

schema-free

yes

schema-free

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

yes

Secondary indexes

yes

All columns are indexed automatically

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

RESTful HTTP API

JDBC
Native API

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

.NET Client API

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

C
C#
C++
Go
Java
Python
Scala

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C#
Java

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

Replicated Map

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Concurrent data updates can be detected by the application

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

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

yes

Role-based access control

fine grained access rights according to SQL-standard

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