DBMS > Alibaba Cloud MaxCompute vs. Elasticsearch vs. MariaDB vs. OushuDB

System Properties Comparison Alibaba Cloud MaxCompute vs. Elasticsearch vs. MariaDB vs. OushuDB

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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

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

MySQL application compatible open source RDBMS, enhanced with high availability, security, interoperability and performance capabilities. MariaDB ColumnStore provides a column-oriented storage engine and MariaDB Xpand supports distributed SQL.

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

Primary database model

Relational DBMS

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

Search engine

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Document store
Graph DBMS

with OQGraph storage engine
Spatial DBMS

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	93.21
Rank	#13	Overall
	#9	Relational DBMS

Score	0.04
Rank	#363	Overall
	#154	Relational DBMS

Website

www.alibabacloud.com/product/maxcompute

www.elastic.co/elasticsearch

mariadb.com

Site of MariaDB Corporation
mariadb.org

Site of MariaDB Foundation

www.oushu.com/product/oushuDB

Technical documentation

www.alibabacloud.com/help/en/maxcompute

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

mariadb.com/kb/en/library

www.oushu.com/documentation

Developer

Alibaba

Elastic

MariaDB Corporation Ab (MariaDB Enterprise),
MariaDB Foundation (community MariaDB Server)

The lead developer Monty Widenius is the original author of MySQL

Oushu

Initial release

2016

2010

2009

Fork of MySQL, which was first released in 1995

Current release

8.6, January 2023

11.3.2, February 2024

4.0.1, August 2020

License

Commercial or Open Source

commercial

Open Source

Elastic License

Open Source

GPL version 2, commercial enterprise subscription available

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 and C++

Server operating systems

hosted

All OS with a Java VM

FreeBSD
Linux
Solaris
Windows

ColumnStore storage engine not available on Windows

Linux

Data scheme

yes

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

Dynamic columns are supported

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

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

Full-featured ANSI SQL support

APIs and other access methods

Fluentd
Flume
MaxCompute Console

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

Supported programming languages

Java

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

Ada
C
C#
C++
D
Eiffel
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C++

Server-side scripts

Stored procedures

user defined functions

in Java

yes

PL/SQL compatibility added with version 10.3

yes

Triggers

yes

by using the 'percolation' feature

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

several options for horizontal partitioning and Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

ES-Hadoop Connector

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

not for in-memory storage engine

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Memcached and Redis integration

yes

with MEMORY storage engine

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

Kerberos, SSL and role based access

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Web, SaaS and Cloud operational applications that require high availability, scalability...
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Key customers

Deutsche Bank, DBS Bank, Nasdaq, Red Hat, ServiceNow, Verizon and Walgreens Featured...
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Market metrics

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Licensing and pricing models

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