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DBMS > Kinetica vs. Microsoft Azure Cosmos DB vs. ObjectBox vs. Solr

System Properties Comparison Kinetica vs. Microsoft Azure Cosmos DB vs. ObjectBox vs. Solr

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonMicrosoft Azure Cosmos DB infoformer name was Azure DocumentDB  Xexclude from comparisonObjectBox  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsGlobally distributed, horizontally scalable, multi-model database serviceExtremely fast embedded database for small devices, IoT and MobileA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelRelational DBMSDocument store
Graph DBMS
Key-value store
Wide column store
Object oriented DBMSSearch engine
Secondary database modelsSpatial DBMS
Time Series DBMS
Spatial DBMSTime Series DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score27.71
Rank#27  Overall
#4  Document stores
#2  Graph DBMS
#3  Key-value stores
#3  Wide column stores
Score1.29
Rank#166  Overall
#5  Object oriented DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websitewww.kinetica.comazure.microsoft.com/­services/­cosmos-dbobjectbox.iosolr.apache.org
Technical documentationdocs.kinetica.comlearn.microsoft.com/­azure/­cosmos-dbdocs.objectbox.iosolr.apache.org/­resources.html
DeveloperKineticaMicrosoftObjectBox LimitedApache Software Foundation
Initial release2012201420172006
Current release7.1, August 20219.6.1, May 2024
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache License 2.0Open Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++C and C++Java
Server operating systemsLinuxhostedAndroid
iOS
Linux
macOS
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeyesschema-freeyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyesyes infoJSON typesyesyes infosupports customizable data types and automatic typing
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.nonoyes
Secondary indexesyesyes infoAll properties auto-indexed by defaultyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languagenoSolr Parallel SQL Interface
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API
Proprietary native APIJava API
RESTful HTTP/JSON API
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python
C
C++
Dart
Go
Java
JavaScript infoplanned (as of Jan 2019)
Kotlin
Python infoplanned (as of Jan 2019)
Swift
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresuser defined functionsJavaScriptnoJava plugins
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangeJavaScriptnoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud servicenoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes infoImplicit feature of the cloud serviceonline/offline synchronization between client and serveryes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnowith Hadoop integration infoIntegration with Hadoop/HDInsight on Azure*nospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationBounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency infoConsistency level configurable on request level
Session Consistency
Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyesnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoMulti-item ACID transactions with snapshot isolation within a partitionACIDoptimistic locking
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 RAMnoyes
User concepts infoAccess controlAccess rights for users and roles on table levelAccess rights can be defined down to the item levelyesyes
More information provided by the system vendor
KineticaMicrosoft Azure Cosmos DB infoformer name was Azure DocumentDBObjectBoxSolr
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21 May 2024

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More resources
KineticaMicrosoft Azure Cosmos DB infoformer name was Azure DocumentDBObjectBoxSolr
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