DBMS > Alibaba Cloud MaxCompute vs. IBM Db2 Event Store vs. Microsoft Azure AI Search vs. RDF4J vs. VoltDB

System Properties Comparison Alibaba Cloud MaxCompute vs. IBM Db2 Event Store vs. Microsoft Azure AI Search vs. RDF4J vs. VoltDB

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

Distributed Event Store optimized for Internet of Things use cases

Search-as-a-service for web and mobile app development

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

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

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

Event Store
Time Series DBMS

Search engine

RDF store

Relational DBMS

Secondary database models

Vector DBMS

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

www.alibabacloud.com/product/maxcompute

www.ibm.com/products/db2-event-store

azure.microsoft.com/en-us/services/search

rdf4j.org

www.voltdb.com

Technical documentation

www.alibabacloud.com/help/en/maxcompute

www.ibm.com/docs/en/db2-event-store

learn.microsoft.com/en-us/azure/search

rdf4j.org/documentation

docs.voltdb.com

Developer

Alibaba

IBM

Microsoft

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

VoltDB Inc.

Initial release

2016

2017

2015

2004

2010

Current release

2.0

11.3, April 2022

License

Commercial or Open Source

commercial

free developer edition available

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

C and C++

Java

Java, C++

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

hosted

Linux
OS X
Unix
Windows

Linux
OS X

for development

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

through the embedded Spark runtime

yes

only a subset of SQL 99

APIs and other access methods

Fluentd
Flume
MaxCompute Console

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Java

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

C#
Java
JavaScript
Python

Java
PHP
Python

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Java

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Active-active shard replication

yes

Implicit feature of the cloud service

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

in-memory storage is supported as well

yes

Snapshots and command logging

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

fine grained access rights according to SQL-standard

yes

using Azure authentication

Users and roles with access to stored procedures

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