DBMS > Amazon SimpleDB vs. Apache IoTDB vs. Manticore Search vs. Teradata

System Properties Comparison Amazon SimpleDB vs. Apache IoTDB vs. Manticore Search vs. Teradata

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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

Multi-storage database for search, including full-text search.

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Graph DBMS
Spatial DBMS
Time Series 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	0.22
Rank	#312	Overall
	#21	Search engines

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

aws.amazon.com/simpledb

iotdb.apache.org

manticoresearch.com

www.teradata.com

Technical documentation

docs.aws.amazon.com/simpledb

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

manual.manticoresearch.com

docs.teradata.com

Developer

Amazon

Apache Software Foundation

Manticore Software

Teradata

Initial release

2007

2018

2017

1984

Current release

1.1.0, April 2023

6.0, February 2023

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPL version 2

commercial

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)

FreeBSD
Linux
macOS
Windows

hosted
Linux

Data scheme

schema-free

yes

Fixed schema

yes

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

Secondary indexes

yes

All columns are indexed automatically

yes

full-text index on all search fields

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL-like query language

yes

SQL 2016 + extensions

APIs and other access methods

RESTful HTTP API

JDBC
Native API

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

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

C
C#
C++
Go
Java
Python
Scala

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

yes

user defined functions

yes

UDFs, stored procedures, table functions in parallel

Triggers

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

Partitioning is done manually, search queries against distributed index is supported

Sharding

Hashing

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

Synchronous replication based on Galera library

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

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

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

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

fine grained access rights according to SQL-standard

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