DBMS > Elasticsearch vs. Hazelcast vs. HBase vs. MySQL vs. PouchDB

System Properties Comparison Elasticsearch vs. Hazelcast vs. HBase vs. MySQL vs. PouchDB

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

A widely adopted in-memory data grid

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

Widely used open source RDBMS

JavaScript DBMS with an API inspired by CouchDB

Primary database model

Key/Value like access via memcached API

Document store

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Document store

JSON support with IMDG 3.12

Document store
Spatial DBMS

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

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Website

www.elastic.co/elasticsearch

Technical documentation

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

hazelcast.org/imdg/docs

hbase.apache.org/book.html

dev.mysql.com/doc

pouchdb.com/guides

Developer

Elastic

Hazelcast

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

Initial release

2010

2008

1995

2012

Current release

8.6, January 2023

5.3.6, November 2023

2.3.4, January 2021

8.4.0, April 2024

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Elastic License

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C and C++

JavaScript

Server operating systems

All OS with a Java VM

Linux
Unix
Windows

using Cygwin

FreeBSD
Linux
OS X
Solaris
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

schema-free

schema-free, schema definition possible

yes

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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.

yes

the object must implement a serialization strategy

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

via views

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

Java API
RESTful HTTP/JSON API

JCache
JPA
Memcached protocol
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

ADO.NET
JDBC
ODBC
Proprietary native API

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++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

Coprocessors in Java

yes

proprietary syntax

View functions in JavaScript

Triggers

yes

by using the 'percolation' feature

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

with a proxy-based framework, named couchdb-lounge

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Multi-source replication
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 or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

Single row ACID (across millions of columns)

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Role-based access control

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

Users with fine-grained authorization concept

no user groups or roles

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