DBMS > Oracle NoSQL vs. PouchDB vs. SiteWhere vs. XTDB

System Properties Comparison Oracle NoSQL vs. PouchDB vs. SiteWhere vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

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

M2M integration platform for persisting/querying time series data

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store
Key-value store
Relational DBMS

Document store

Time Series DBMS

Document store

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

Score	3.07
Rank	#86	Overall
	#15	Document stores
	#11	Key-value stores
	#47	Relational DBMS

Score	2.18
Rank	#114	Overall
	#21	Document stores

Score	0.07
Rank	#347	Overall
	#33	Time Series DBMS

Score	0.13
Rank	#330	Overall
	#45	Document stores

Website

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

pouchdb.com

github.com/sitewhere/sitewhere

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

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

pouchdb.com/guides

sitewhere1.sitewhere.io/index.html

www.xtdb.com/docs

Developer

Oracle

Apache Software Foundation

SiteWhere

Juxt Ltd.

Initial release

2011

2012

2010

2019

Current release

24.1, May 2024

7.1.1, June 2019

1.19, September 2021

License

Commercial or Open Source

Open Source

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

Open Source

Common Public Attribution License Version 1.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

JavaScript

Java

Clojure

Server operating systems

Linux
Solaris SPARC/x86

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

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

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

schema-free

predefined scheme

schema-free

Typing

predefined data types such as float or date

optional

yes

yes, extensible-data-notation format

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

SQL

Support of SQL

SQL-like DML and DDL statements

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

HTTP REST

HTTP REST
JDBC

Supported programming languages

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

JavaScript

Clojure
Java

Server-side scripts

Stored procedures

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

based on HBase

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

selectable replication factor

based on HBase

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

depending on configuration

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

configurable

ACID within a storage node (=shard)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

off heap cache

yes

User concepts

Access control

Access rights for users and roles

Users with fine-grained authorization concept

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More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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