DBMS > Alibaba Cloud MaxCompute vs. Apache Druid vs. Microsoft SQL Server vs. RDF4J vs. Spark SQL

System Properties Comparison Alibaba Cloud MaxCompute vs. Apache Druid vs. Microsoft SQL Server vs. RDF4J vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

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

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Microsofts flagship relational DBMS

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

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

Relational DBMS
Time Series DBMS

Relational DBMS

RDF store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.78
Rank	#221	Overall
	#102	Relational DBMS

Score	3.29
Rank	#95	Overall
	#49	Relational DBMS
	#7	Time Series DBMS

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

www.alibabacloud.com/product/maxcompute

druid.apache.org

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

rdf4j.org

spark.apache.org/sql

Technical documentation

www.alibabacloud.com/help/en/maxcompute

druid.apache.org/docs/latest/design

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

rdf4j.org/documentation

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Alibaba

Apache Software Foundation and contributors

Microsoft

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

Apache Software Foundation

Initial release

2016

2012

1989

2004

2014

Current release

29.0.1, April 2024

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache license v2

commercial

restricted free version is available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

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++

Java

Scala

Server operating systems

hosted

Linux
OS X
Unix

Linux
Windows

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

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 for querying

yes

SQL-like DML and DDL statements

APIs and other access methods

Fluentd
Flume
MaxCompute Console

JDBC
RESTful HTTP/JSON API

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

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

Supported programming languages

Java

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Java

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

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

manual/auto, time-based

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

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes, via HDFS, S3 or other storage engines

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

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

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

fine grained access rights according to SQL-standard

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