DBMS > Elasticsearch vs. Infobright vs. PouchDB

System Properties Comparison Elasticsearch vs. Infobright vs. PouchDB

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Editorial information provided by DB-Engines

Name

Description

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Search engine

Relational DBMS

Document store

Secondary database models

Document store
Spatial DBMS
Vector DBMS

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

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	0.96
Rank	#194	Overall
	#91	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Website

www.elastic.co/elasticsearch

ignitetech.com/softwarelibrary/infobrightdb

pouchdb.com

Technical documentation

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

pouchdb.com/guides

Developer

Elastic

Ignite Technologies Inc.; formerly InfoBright Inc.

Apache Software Foundation

Initial release

2010

2005

2012

Current release

8.6, January 2023

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Elastic License

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

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

Server operating systems

All OS with a Java VM

Linux
Windows

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

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

yes

schema-free

Typing

predefined data types such as float or date

yes

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 search fields are automatically indexed

Knowledge Grid Technology used instead

yes

via views

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

HTTP REST

only for PouchDB Server
JavaScript API

Supported programming languages

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript

Server-side scripts

Stored procedures

yes

View functions in JavaScript

Triggers

yes

by using the 'percolation' feature

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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.

Memcached and Redis integration

yes

User concepts

Access control

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

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More resources

DB-Engines blog posts

PostgreSQL is the DBMS of the Year 2017
2 January 2018, Paul Andlinger, Matthias Gelbmann

Elasticsearch moved into the top 10 most popular database management systems
3 July 2017, Matthias Gelbmann

MySQL, PostgreSQL and Redis are the winners of the March ranking
2 March 2016, Paul Andlinger

show all

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

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Recent citations in the news

Understanding Elasticsearch Reindexing: When to Reindex, Best Practices and Alternatives
8 May 2024, hackernoon.com

Netflix Uses Elasticsearch Percolate Queries to Implement Reverse Searches Efficiently
29 April 2024, InfoQ.com

The Total Economic Impact™️ of Elasticsearch
8 May 2024, BankInfoSecurity.com

Red Hat and Elastic Fuel Retrieval Augmented Generation for GenAI Use Cases
7 May 2024, Business Wire

Splunk vs Elasticsearch | A Comparison and How to Choose
12 January 2024, SentinelOne

provided by Google News

Building an Offline First App with PouchDB — SitePoint
10 March 2014, SitePoint

Getting Started with PouchDB Client-Side JavaScript Database — SitePoint
7 September 2016, SitePoint

3 Reasons To Think Offline First
22 March 2017, ibm.com

Offline-first web and mobile apps: Top frameworks and components
22 January 2019, TechBeacon

Create Offline Web Apps Using Service Workers & PouchDB — SitePoint
7 March 2017, SitePoint

provided by Google News

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