DBMS > Alibaba Cloud MaxCompute vs. DataFS vs. Heroic vs. Linter vs. Vitess

System Properties Comparison Alibaba Cloud MaxCompute vs. DataFS vs. Heroic vs. Linter vs. Vitess

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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

RDBMS for high security requirements

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Relational DBMS

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

Object oriented DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Graph DBMS

Spatial DBMS

Document store
Spatial DBMS

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

www.alibabacloud.com/product/maxcompute

newdatabase.com

github.com/spotify/heroic

linter.ru

vitess.io

Technical documentation

www.alibabacloud.com/help/en/maxcompute

dev.mobiland.com/Overview.xsp

spotify.github.io/heroic

vitess.io/docs

Developer

Alibaba

Mobiland AG

Spotify

relex.ru

The Linux Foundation, PlanetScale

Initial release

2016

2018

2014

1990

2013

Current release

1.1.263, October 2022

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0, commercial licenses available

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

Windows

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Docker
Linux
macOS

Data scheme

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

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

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

Fluentd
Flume
MaxCompute Console

.NET Client API
Proprietary client DLL
WinRT client

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

Java

.Net
C
C#
C++
VB.Net

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

Server-side scripts

Stored procedures

user defined functions

in Java

yes

proprietary syntax with the possibility to convert from PL/SQL

yes

proprietary syntax

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Proprietary Sharding system

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

Source-replica replication

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

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

User concepts

Access control

Access rights for users and roles

Windows-Profile

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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