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DBMS > Kinetica vs. NSDb vs. Qdrant vs. Spark SQL

System Properties Comparison Kinetica vs. NSDb vs. Qdrant vs. Spark SQL

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonNSDb  Xexclude from comparisonQdrant  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesA high-performance vector database with neural network or semantic-based matchingSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSTime Series DBMSVector DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score0.08
Rank#369  Overall
#40  Time Series DBMS
Score1.28
Rank#167  Overall
#8  Vector DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitewww.kinetica.comnsdb.iogithub.com/­qdrant/­qdrant
qdrant.tech
spark.apache.org/­sql
Technical documentationdocs.kinetica.comnsdb.io/­Architectureqdrant.tech/­documentationspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperKineticaQdrantApache Software Foundation
Initial release2012201720212014
Current release7.1, August 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoApache Version 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++Java, ScalaRustScala
Server operating systemsLinuxLinux
macOS
Docker
Linux
macOS
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyes
Typing infopredefined data types such as float or dateyesyes: int, bigint, decimal, stringNumbers, Strings, Geo, Booleanyes
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 indexedyes infoKeywords, numberic ranges, geo, full-textno
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languagenoSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
gRPC
HTTP REST
WebSocket
gRPC
OpenAPI 3.0
RESTful HTTP/JSON API infoOpenAPI 3.0
JDBC
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
Java
Scala
.Net
Go
Java
JavaScript (Node.js)
Python
Rust
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsnono
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationCollection-level replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationEventual ConsistencyEventual Consistency, tunable consistency
Foreign keys infoReferential integrityyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonono
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.yes infoGPU vRAM or System RAMyesno
User concepts infoAccess controlAccess rights for users and roles on table levelKey-based authenticationno

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More resources
KineticaNSDbQdrantSpark SQL
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