DBMS > Apache Pinot vs. Elasticsearch vs. Firebase Realtime Database vs. MySQL vs. WakandaDB

System Properties Comparison Apache Pinot vs. Elasticsearch vs. Firebase Realtime Database vs. MySQL vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

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

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Widely used open source RDBMS

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Key/Value like access via memcached API

Object oriented DBMS

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Document store
Spatial DBMS

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

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	132.83
Rank	#7	Overall
	#1	Search engines

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.10
Rank	#356	Overall
	#16	Object oriented DBMS

Website

pinot.apache.org

www.elastic.co/elasticsearch

firebase.google.com/products/realtime-database

www.mysql.com

wakanda.github.io

Technical documentation

docs.pinot.apache.org

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

firebase.google.com/docs/database

dev.mysql.com/doc

wakanda.github.io/doc

Developer

Apache Software Foundation and contributors

Elastic

Google

acquired by Google 2014

Oracle

since 2010, originally MySQL AB, then Sun

Wakanda SAS

Initial release

2015

2010

2012

1995

2012

Current release

1.0.0, September 2023

8.6, January 2023

8.4.0, April 2024

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Elastic License

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPLv3, extended commercial license available

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C and C++

C++, JavaScript

Server operating systems

All OS with a Java JDK11 or higher

All OS with a Java VM

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

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

schema-free

yes

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JDBC

Java API
RESTful HTTP/JSON API

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

RESTful HTTP API

Supported programming languages

Go
Java
Python

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

Java
JavaScript
Objective-C

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

limited functionality with using 'rules'

yes

proprietary syntax

yes

Triggers

yes

by using the 'percolation' feature

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

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

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

yes

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

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

yes, based on authentication and database rules

Users with fine-grained authorization concept

no user groups or roles

yes

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