DBMS > Apache Drill vs. Microsoft Azure Cosmos DB vs. Oracle NoSQL vs. PouchDB

System Properties Comparison Apache Drill vs. Microsoft Azure Cosmos DB vs. Oracle NoSQL vs. PouchDB

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Globally distributed, horizontally scalable, multi-model database service

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Document store
Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Document store
Key-value store
Relational DBMS

Document store

Secondary database models

Spatial DBMS

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

Score	2.02
Rank	#124	Overall
	#22	Document stores
	#59	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Score	2.34
Rank	#112	Overall
	#21	Document stores

Website

drill.apache.org

azure.microsoft.com/services/cosmos-db

www.oracle.com/database/nosql/technologies/nosql

pouchdb.com

Technical documentation

drill.apache.org/docs

learn.microsoft.com/azure/cosmos-db

docs.oracle.com/en/database/other-databases/nosql-database/index.html

pouchdb.com/guides

Developer

Apache Software Foundation

Microsoft

Oracle

Apache Software Foundation

Initial release

2012

2014

2011

2012

Current release

1.20.3, January 2023

24.1, May 2024

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

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

Server operating systems

Linux
OS X
Windows

hosted

Linux
Solaris SPARC/x86

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

Data scheme

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

schema-free

Typing

predefined data types such as float or date

yes

JSON types

optional

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

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

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

RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

C++

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

C
C#
Go
Java
JavaScript (Node.js)
Python

JavaScript

Server-side scripts

Stored procedures

user defined functions

JavaScript

View functions in JavaScript

Triggers

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

by using IndexedDB, WebSQL or LevelDB as backend

In-memory capabilities

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

Depending on the underlying data source

yes

off heap cache

yes

User concepts

Access control

Depending on the underlying data source

Access rights can be defined down to the item level

Access rights for users and roles

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