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DBMS > InfluxDB vs. Kinetica vs. Splice Machine vs. Vitess

System Properties Comparison InfluxDB vs. Kinetica vs. Splice Machine vs. Vitess

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Editorial information provided by DB-Engines
NameInfluxDB  Xexclude from comparisonKinetica  Xexclude from comparisonSplice Machine  Xexclude from comparisonVitess  Xexclude from comparison
DescriptionDBMS for storing time series, events and metricsFully vectorized database across both GPUs and CPUsOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkScalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelTime Series DBMSRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS infowith GEO packageSpatial DBMS
Time Series DBMS
Document store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score25.83
Rank#28  Overall
#1  Time Series DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Score0.82
Rank#209  Overall
#97  Relational DBMS
Websitewww.influxdata.com/­products/­influxdb-overviewwww.kinetica.comsplicemachine.comvitess.io
Technical documentationdocs.influxdata.com/­influxdbdocs.kinetica.comsplicemachine.com/­how-it-worksvitess.io/­docs
DeveloperKineticaSplice MachineThe Linux Foundation, PlanetScale
Initial release2013201220142013
Current release2.7.6, April 20247.1, August 20213.1, March 202115.0.2, December 2022
License infoCommercial or Open SourceOpen Source infoMIT-License; commercial enterprise version availablecommercialOpen Source infoAGPL 3.0, commercial license availableOpen 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 languageGoC, C++JavaGo
Server operating systemsLinux
OS X infothrough Homebrew
LinuxLinux
OS X
Solaris
Windows
Docker
Linux
macOS
Data schemeschema-freeyesyesyes
Typing infopredefined data types such as float or dateNumeric data and Stringsyesyesyes
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 indexesnoyesyesyes
SQL infoSupport of SQLSQL-like query languageSQL-like DML and DDL statementsyesyes infowith proprietary extensions
APIs and other access methodsHTTP API
JSON over UDP
JDBC
ODBC
RESTful HTTP API
JDBC
Native Spark Datasource
ODBC
ADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languages.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala
C++
Java
JavaScript (Node.js)
Python
C#
C++
Java
JavaScript (Node.js)
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 proceduresnouser defined functionsyes infoJavayes infoproprietary syntax
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangeyesyes
Partitioning methods infoMethods for storing different data on different nodesSharding infoin enterprise version onlyShardingShared Nothhing Auto-Sharding, Columnar PartitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infoin enterprise version onlySource-replica replicationMulti-source replication
Source-replica replication
Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoYes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integritynoyesyesyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)yes 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 engineyes infoGPU vRAM or System RAMyesyes
User concepts infoAccess controlsimple rights management via user accountsAccess rights for users and roles on table levelAccess rights for users, groups and roles according to SQL-standardUsers with fine-grained authorization concept infono user groups or roles
More information provided by the system vendor
InfluxDBKineticaSplice MachineVitess
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
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
InfluxDBKineticaSplice MachineVitess
DB-Engines blog posts

Why Build a Time Series Data Platform?
20 July 2017, Paul Dix (guest author)

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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Recent citations in the news

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20 May 2024, AWS Blog

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14 March 2024, The New Stack

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15 March 2024, Datanami

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5 April 2024, VentureBeat

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18 March 2024, SiliconANGLE News

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Machine learning data pipeline outfit Splice Machine files for insolvency
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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

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

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