DBMS > HBase vs. Microsoft Azure Cosmos DB vs. PouchDB vs. Sequoiadb

System Properties Comparison HBase vs. Microsoft Azure Cosmos DB vs. PouchDB vs. Sequoiadb

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

Globally distributed, horizontally scalable, multi-model database service

JavaScript DBMS with an API inspired by CouchDB

NewSQL database with distributed OLTP and SQL

Primary database model

Wide column store

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Document store
Relational DBMS

Secondary database models

Spatial DBMS

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

Score	31.25
Rank	#26	Overall
	#2	Wide column stores

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	2.35
Rank	#116	Overall
	#22	Document stores

Score	0.48
Rank	#260	Overall
	#41	Document stores
	#121	Relational DBMS

Website

hbase.apache.org

azure.microsoft.com/services/cosmos-db

pouchdb.com

www.sequoiadb.com

Technical documentation

hbase.apache.org/book.html

learn.microsoft.com/azure/cosmos-db

pouchdb.com/guides

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

Apache Software Foundation

Sequoiadb Ltd.

Initial release

2008

2014

2012

2013

Current release

2.3.4, January 2021

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Apache version 2

commercial

Open Source

Server: AGPL; Client: Apache V2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

JavaScript

C++

Server operating systems

Linux
Unix
Windows

using Cygwin

hosted

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

Linux

Data scheme

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

JSON types

yes

oid, date, timestamp, binary, regex

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

yes

via views

yes

SQL

Support of SQL

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP API
Thrift

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

HTTP REST

only for PouchDB Server
JavaScript API

proprietary protocol using JSON

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

JavaScript

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

yes

Coprocessors in Java

JavaScript

View functions in JavaScript

JavaScript

Triggers

yes

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Single row ACID (across millions of columns)

Multi-item ACID transactions with snapshot isolation within a partition

Document is locked during a transaction

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

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 Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Access rights can be defined down to the item level

simple password-based access control

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