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DBMS > Kinetica vs. Microsoft Azure Table Storage vs. Solr vs. TimescaleDB

System Properties Comparison Kinetica vs. Microsoft Azure Table Storage vs. Solr vs. TimescaleDB

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonSolr  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsA Wide Column Store for rapid development using massive semi-structured datasetsA widely used distributed, scalable search engine based on Apache LuceneA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSWide column storeSearch engineTime Series DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Spatial DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score4.04
Rank#77  Overall
#6  Wide column stores
Score41.02
Rank#24  Overall
#3  Search engines
Score4.46
Rank#71  Overall
#5  Time Series DBMS
Websitewww.kinetica.comazure.microsoft.com/­en-us/­services/­storage/­tablessolr.apache.orgwww.timescale.com
Technical documentationdocs.kinetica.comsolr.apache.org/­resources.htmldocs.timescale.com
DeveloperKineticaMicrosoftApache Software FoundationTimescale
Initial release2012201220062017
Current release7.1, August 20219.6.1, May 20242.15.0, May 2024
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache Version 2Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++JavaC
Server operating systemsLinuxhostedAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Linux
OS X
Windows
Data schemeyesschema-freeyes infoDynamic Fields enables on-the-fly addition of new fieldsyes
Typing infopredefined data types such as float or dateyesyesyes infosupports customizable data types and automatic typingnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.nonoyesyes
Secondary indexesyesnoyes infoAll search fields are automatically indexedyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnoSolr Parallel SQL Interfaceyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
RESTful HTTP APIJava API
RESTful HTTP/JSON API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresuser defined functionsnoJava pluginsuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenoyes infoUser configurable commands triggered on index changesyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud serviceShardingyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yesSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanooptimistic lockingoptimistic lockingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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 infoGPU vRAM or System RAMnoyesno
User concepts infoAccess controlAccess rights for users and roles on table levelAccess rights based on private key authentication or shared access signaturesyesfine grained access rights according to SQL-standard

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More resources
KineticaMicrosoft Azure Table StorageSolrTimescaleDB
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