DBMS > Alibaba Cloud AnalyticDB for PostgreSQL vs. Amazon Redshift vs. Apache IoTDB vs. NuoDB vs. SQLite

System Properties Comparison Alibaba Cloud AnalyticDB for PostgreSQL vs. Amazon Redshift vs. Apache IoTDB vs. NuoDB vs. SQLite

Editorial information provided by DB-Engines

Name

Description

An online MPP (Massively Parallel Processing) data warehousing service based on Greenplum

Large scale data warehouse service for use with business intelligence tools

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

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	0.68
Rank	#231	Overall
	#105	Relational DBMS

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

www.alibabacloud.com/product/hybriddb-postgresql

aws.amazon.com/redshift

iotdb.apache.org

www.3ds.com/nuodb-distributed-sql-database

www.sqlite.org

Technical documentation

www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview

docs.aws.amazon.com/redshift

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

doc.nuodb.com

www.sqlite.org/docs.html

Developer

Alibaba / Pivotal Software Inc. / PostgreSQL Global Development Group

Amazon (based on PostgreSQL)

Apache Software Foundation

Dassault Systèmes

originally NuoDB, Inc.

Dwayne Richard Hipp

Initial release

2016

2012

2018

2013

2000

Current release

1.1.0, April 2023

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

limited edition free

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

server-less

Data scheme

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC

JDBC
Native API

ADO.NET
JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
Java
Perl
Python
R

All languages supporting JDBC/ODBC

C
C#
C++
Go
Java
Python
Scala

.Net
C
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

user defined functions

in Python

yes

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

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

data is dynamically stored/cached on the nodes where it is read/written

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

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

yes

Managed transparently by NuoDB

none

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

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

tunable commit protocol

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

via file-system locks

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

Temporary table

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

Standard SQL roles/ privileges, Administrative Users

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