DBMS > Firebase Realtime Database vs. InfluxDB vs. Solr vs. SQLite vs. TimescaleDB

System Properties Comparison Firebase Realtime Database vs. InfluxDB vs. Solr vs. SQLite vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

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

DBMS for storing time series, events and metrics

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

Widely used embeddable, in-process RDBMS

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Spatial DBMS

with GEO package

Spatial DBMS

Relational DBMS

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

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	24.39
Rank	#28	Overall
	#1	Time Series DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

firebase.google.com/products/realtime-database

www.influxdata.com/products/influxdb-overview

solr.apache.org

www.sqlite.org

www.timescale.com

Technical documentation

firebase.google.com/docs/database

docs.influxdata.com/influxdb

solr.apache.org/resources.html

www.sqlite.org/docs.html

docs.timescale.com

Developer

Google

acquired by Google 2014

Apache Software Foundation

Dwayne Richard Hipp

Timescale

Initial release

2012

2013

2006

2000

2017

Current release

2.7.6, April 2024

9.6.1, May 2024

3.46.0 (23 May 2024), May 2024

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

MIT-License; commercial enterprise version available

Open Source

Apache Version 2

Open Source

Public Domain

Open Source

Apache 2.0

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

hosted

Linux
OS X

through Homebrew

All OS with a Java VM

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

server-less

Linux
OS X
Windows

Data scheme

schema-free

yes

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

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

Numeric data and Strings

yes

supports customizable data types and automatic typing

yes

not rigid because of 'dynamic typing' concept.

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

Solr Parallel SQL Interface

yes

SQL-92 is not fully supported

yes

full PostgreSQL SQL syntax

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

HTTP API
JSON over UDP

Java API
RESTful HTTP/JSON API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

Java
JavaScript
Objective-C

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

limited functionality with using 'rules'

Java plugins

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

Callbacks are triggered when data changes

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

in enterprise version only

Sharding

none

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

in enterprise version only

yes

none

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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.

yes

Depending on used storage engine

yes

User concepts

Access control

yes, based on authentication and database rules

simple rights management via user accounts

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

Fastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
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Licensing and pricing models

Open source core with closed source clustering available either on-premise or on...
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