DBMS > Elasticsearch vs. Firebase Realtime Database vs. Google BigQuery vs. Solr

System Properties Comparison Elasticsearch vs. Firebase Realtime Database vs. Google BigQuery vs. Solr

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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

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

Large scale data warehouse service with append-only tables

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Spatial DBMS

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

Score	134.78
Rank	#7	Overall
	#1	Search engines

Score	15.00
Rank	#39	Overall
	#6	Document stores

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	44.28
Rank	#24	Overall
	#3	Search engines

Website

www.elastic.co/elasticsearch

firebase.google.com/products/realtime-database

cloud.google.com/bigquery

solr.apache.org

Technical documentation

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

firebase.google.com/docs/database

cloud.google.com/bigquery/docs

solr.apache.org/resources.html

Developer

Elastic

Google

acquired by Google 2014

Google

Apache Software Foundation

Initial release

2010

2012

2010

2006

Current release

8.6, January 2023

9.5.0, February 2024

License

Commercial or Open Source

Open Source

Elastic License

commercial

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

Server operating systems

All OS with a Java VM

hosted

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

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

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

Solr Parallel SQL Interface

APIs and other access methods

Java API
RESTful HTTP/JSON API

Android
iOS
JavaScript API
RESTful HTTP API

RESTful HTTP/JSON API

Java API
RESTful HTTP/JSON API

Supported programming languages

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

Java
JavaScript
Objective-C

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

limited functionality with using 'rules'

user defined functions

in JavaScript

Java plugins

Triggers

yes

by using the 'percolation' feature

Callbacks are triggered when data changes

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

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

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

Since BigQuery is designed for querying data

optimistic locking

Concurrency

Support for concurrent manipulation of data

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes

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