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DBMS > FatDB vs. InfluxDB vs. Spark SQL vs. Vitess

System Properties Comparison FatDB vs. InfluxDB vs. Spark SQL vs. Vitess

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Editorial information provided by DB-Engines
NameFatDB  Xexclude from comparisonInfluxDB  Xexclude from comparisonSpark SQL  Xexclude from comparisonVitess  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.
DescriptionA .NET NoSQL DBMS that can integrate with and extend SQL Server.DBMS for storing time series, events and metricsSpark SQL is a component on top of 'Spark Core' for structured data processingScalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelDocument store
Key-value store
Time Series DBMSRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS infowith GEO packageDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score26.56
Rank#28  Overall
#1  Time Series DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Score1.04
Rank#191  Overall
#89  Relational DBMS
Websitewww.influxdata.com/­products/­influxdb-overviewspark.apache.org/­sqlvitess.io
Technical documentationdocs.influxdata.com/­influxdbspark.apache.org/­docs/­latest/­sql-programming-guide.htmlvitess.io/­docs
DeveloperFatCloudApache Software FoundationThe Linux Foundation, PlanetScale
Initial release2012201320142013
Current release2.7.6, April 20243.5.0 ( 2.13), September 202315.0.2, December 2022
License infoCommercial or Open SourcecommercialOpen Source infoMIT-License; commercial enterprise version availableOpen Source infoApache 2.0Open Source infoApache Version 2.0, commercial licenses available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC#GoScalaGo
Server operating systemsWindowsLinux
OS X infothrough Homebrew
Linux
OS X
Windows
Docker
Linux
macOS
Data schemeschema-freeschema-freeyesyes
Typing infopredefined data types such as float or dateyesNumeric data and Stringsyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nono
Secondary indexesyesnonoyes
SQL infoSupport of SQLno infoVia inetgration in SQL ServerSQL-like query languageSQL-like DML and DDL statementsyes infowith proprietary extensions
APIs and other access methods.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
HTTP API
JSON over UDP
JDBC
ODBC
ADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languagesC#.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala
Java
Python
R
Scala
Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyes infovia applicationsnonoyes infoproprietary syntax
Triggersyes infovia applicationsnonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoin enterprise version onlyyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorselectable replication factor infoin enterprise version onlynoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Eventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integritynononoyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infotable locks or row locks depending on storage engine
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoDepending on used storage enginenoyes
User concepts infoAccess controlno infoCan implement custom security layer via applicationssimple rights management via user accountsnoUsers with fine-grained authorization concept infono user groups or roles
More information provided by the system vendor
FatDBInfluxDBSpark SQLVitess
Specific characteristicsInfluxData is the creator of InfluxDB , the open source time series database. It...
» more
Competitive advantagesTime to Value InfluxDB is available in all the popular languages and frameworks,...
» more
Typical application scenariosIoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
» more
Key customersInfluxData has more than 1,900 paying customers, including customers include MuleSoft,...
» more
Market metricsFastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
» more
Licensing and pricing modelsOpen source core with closed source clustering available either on-premise or on...
» more
News

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8 April 2024

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Python ARIMA Tutorial
29 March 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
FatDBInfluxDBSpark SQLVitess
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Recent citations in the news

Run and manage open source InfluxDB databases with Amazon Timestream | Amazon Web Services
14 March 2024, AWS Blog

Amazon Timestream: Managed InfluxDB for Time Series Data
14 March 2024, The New Stack

InfluxData Collaborating with AWS to Bring InfluxDB and Time Series Analytics to Developers Around the World
14 March 2024, Business Wire

How the FDAP Stack Gives InfluxDB 3.0 Real-Time Speed, Efficiency
15 March 2024, Datanami

AWS and InfluxData partner to offer managed time series database Timestream for InfluxDB
5 April 2024, VentureBeat

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3 April 2024, InfoWorld

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1 April 2024, Simplilearn

1.5 Years of Spark Knowledge in 8 Tips | by Michael Berk
23 December 2023, Towards Data Science

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services
18 October 2023, AWS Blog

provided by Google News

Vitess, the database clustering system powering YouTube, graduates CNCF incubation
5 November 2019, SiliconANGLE News

PlanetScale Unveils Distributed MySQL Database Service Based on Vitess
18 May 2021, Datanami

PlanetScale grabs YouTube-developed open-source tech, promises Vitess DBaaS with on-the-fly schema changes
18 May 2021, The Register

They scaled YouTube — now they’ll shard everyone with PlanetScale
13 December 2018, TechCrunch

Massively Scaling MySQL Using Vitess
19 February 2019, InfoQ.com

provided by Google News



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