DBMS > Alibaba Cloud MaxCompute vs. Amazon DocumentDB vs. Kinetica vs. Lovefield vs. Microsoft Azure Cosmos DB

System Properties Comparison Alibaba Cloud MaxCompute vs. Amazon DocumentDB vs. Kinetica vs. Lovefield vs. Microsoft Azure Cosmos DB

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

Fast, scalable, highly available, and fully managed MongoDB-compatible database service

Fully vectorized database across both GPUs and CPUs

Embeddable relational database for web apps written in pure JavaScript

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Relational DBMS

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

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	0.77
Rank	#214	Overall
	#98	Relational DBMS

Score	1.91
Rank	#132	Overall
	#24	Document stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

www.alibabacloud.com/product/maxcompute

aws.amazon.com/documentdb

www.kinetica.com

google.github.io/lovefield

azure.microsoft.com/services/cosmos-db

Technical documentation

www.alibabacloud.com/help/en/maxcompute

aws.amazon.com/documentdb/resources

docs.kinetica.com

github.com/google/lovefield/blob/master/docs/spec_index.md

learn.microsoft.com/azure/cosmos-db

Developer

Alibaba

Kinetica

Google

Microsoft

Initial release

2016

2019

2012

2014

Current release

7.1, August 2021

2.1.12, February 2017

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

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

C, C++

JavaScript

Server operating systems

hosted

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

hosted

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

SQL-like query language

via JavaScript builder pattern

SQL-like query language

APIs and other access methods

Fluentd
Flume
MaxCompute Console

proprietary protocol using JSON (MongoDB compatible)

JDBC
ODBC
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

Java

Go
Java
JavaScript (Node.js)
PHP
Python

C++
Java
JavaScript (Node.js)
Python

JavaScript

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Server-side scripts

Stored procedures

user defined functions

in Java

user defined functions

JavaScript

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Using read-only observers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-availability zones for high availability, asynchronous replication for up to 15 read replicas

Source-replica replication

none

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

typically not used, however similar functionality with DBRef possible

yes

Transaction concepts

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

Atomic single-document operations

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

using MemoryDB

User concepts

Access control

Access rights for users and roles

Access rights for users and roles on table level

Access rights can be defined down to the item level

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