DBMS > Alibaba Cloud Table Store vs. CouchDB vs. Hive vs. Microsoft Azure Cosmos DB

System Properties Comparison Alibaba Cloud Table Store vs. CouchDB vs. Hive vs. Microsoft Azure Cosmos DB

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Editorial information provided by DB-Engines

Name

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Wide column store

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

using the Geocouch extension

Spatial DBMS

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

Score	0.26
Rank	#301	Overall
	#12	Wide column stores

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	61.17
Rank	#18	Overall
	#12	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/table-store

couchdb.apache.org

hive.apache.org

azure.microsoft.com/services/cosmos-db

Technical documentation

www.alibabacloud.com/help/en/tablestore

docs.couchdb.org/en/stable

cwiki.apache.org/confluence/display/Hive/Home

learn.microsoft.com/azure/cosmos-db

Developer

Alibaba

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Apache Software Foundation

initially developed by Facebook

Microsoft

Initial release

2016

2005

2012

2014

Current release

3.3.3, December 2023

3.1.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Erlang

Java

Server operating systems

hosted

Android
BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

hosted

Data scheme

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

via views

yes

All properties auto-indexed by default

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

HTTP API

RESTful HTTP/JSON API

JDBC
ODBC
Thrift

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

Supported programming languages

Java
Python

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

C++
Java
PHP
Python

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

Server-side scripts

Stored procedures

View functions in JavaScript

yes

user defined functions and integration of map-reduce

JavaScript

Triggers

yes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

improved architecture with release 2.0

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-source replication
Source-replica replication

selectable replication factor

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic single-row operations

atomic operations within a single document possible

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

yes

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.

User concepts

Access control

Access rights based on subaccounts and tokens

Access rights for users can be defined per database

Access rights for users, groups and roles

Access rights can be defined down to the item level

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