DBMS > Alibaba Cloud MaxCompute vs. Apache IoTDB vs. IRONdb vs. Microsoft Azure SQL Database vs. RDF4J

System Properties Comparison Alibaba Cloud MaxCompute vs. Apache IoTDB vs. IRONdb vs. Microsoft Azure SQL Database vs. RDF4J

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

MaxCompute (previously known as ODPS) is a general purpose, fully managed, multi-tenancy data processing platform for large-scale data warehousing

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 distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Database as a Service offering with high compatibility to Microsoft SQL Server

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Relational DBMS

a graph-processing framework is available with the graphs being stored in tables

Time Series DBMS

Relational DBMS

RDF store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.84
Rank	#209	Overall
	#98	Relational DBMS

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	76.78
Rank	#16	Overall
	#11	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

www.alibabacloud.com/product/maxcompute

iotdb.apache.org

www.circonus.com/solutions/time-series-database/

azure.microsoft.com/en-us/products/azure-sql/database

rdf4j.org

Technical documentation

www.alibabacloud.com/help/en/maxcompute

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

docs.circonus.com/irondb/category/getting-started

docs.microsoft.com/en-us/azure/azure-sql

rdf4j.org/documentation

Developer

Alibaba

Apache Software Foundation

Circonus LLC.

Microsoft

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2016

2018

2017

2010

2004

Current release

1.1.0, April 2023

V0.10.20, January 2018

V12

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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

Java

C and C++

C++

Java

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

Linux

hosted

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

APIs and other access methods

Fluentd
Flume
MaxCompute Console

JDBC
Native API

HTTP API

ADO.NET
JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

Java

C
C#
C++
Go
Java
Python
Scala

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

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

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Java

yes

yes, in Lua

Transact SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

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

Automatic, metric affinity per node

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

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

configurable replication factor, datacenter aware

yes, with always 3 replicas available

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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 per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

yes

fine grained access rights according to SQL-standard

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