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DBMS > Coveo vs. NSDb vs. Spark SQL vs. Tkrzw

System Properties Comparison Coveo vs. NSDb vs. Spark SQL vs. Tkrzw

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Editorial information provided by DB-Engines
NameCoveo  Xexclude from comparisonNSDb  Xexclude from comparisonSpark SQL  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionAI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code developmentScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesSpark SQL is a component on top of 'Spark Core' for structured data processingA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelSearch engineTime Series DBMSRelational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.11
Rank#118  Overall
#11  Search engines
Score0.08
Rank#369  Overall
#40  Time Series DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.07
Rank#372  Overall
#57  Key-value stores
Websitewww.coveo.comnsdb.iospark.apache.org/­sqldbmx.net/­tkrzw
Technical documentationdocs.coveo.comnsdb.io/­Architecturespark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperCoveoApache Software FoundationMikio Hirabayashi
Initial release2012201720142020
Current release3.5.0 ( 2.13), September 20230.9.3, August 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoApache 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ScalaScalaC++
Server operating systemshostedLinux
macOS
Linux
OS X
Windows
Linux
macOS
Data schemehybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configurationyesschema-free
Typing infopredefined data types such as float or dateyesyes: int, bigint, decimal, stringyesno
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.nononono
Secondary indexesyesall fields are automatically indexedno
SQL infoSupport of SQLnoSQL-like query languageSQL-like DML and DDL statementsno
APIs and other access methodsRESTful HTTP APIgRPC
HTTP REST
WebSocket
JDBC
ODBC
Supported programming languagesC#
Java
JavaScript
Python
Java
Scala
Java
Python
R
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresnononono
Triggersyesnono
Partitioning methods infoMethods for storing different data on different nodesyesShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesyesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesUsing Apache Luceneyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infousing specific database classes
User concepts infoAccess controlgranular access controls, API key management, content filtersnono

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More resources
CoveoNSDbSpark SQLTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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