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DBMS > Apache Doris vs. NSDb vs. Spark SQL vs. Tkrzw

System Properties Comparison Apache Doris vs. NSDb vs. Spark SQL vs. Tkrzw

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Editorial information provided by DB-Engines
NameApache Doris  Xexclude from comparisonNSDb  Xexclude from comparisonSpark SQL  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionAn MPP-based analytics DBMS embracing the MySQL protocolScalable, 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 modelRelational DBMSTime Series DBMSRelational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.57
Rank#244  Overall
#113  Relational DBMS
Score0.00
Rank#383  Overall
#41  Time Series DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.00
Rank#383  Overall
#60  Key-value stores
Websitedoris.apache.org
github.com/­apache/­doris
nsdb.iospark.apache.org/­sqldbmx.net/­tkrzw
Technical documentationgithub.com/­apache/­doris/­wikinsdb.io/­Architecturespark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation, originally contributed from BaiduApache Software FoundationMikio Hirabayashi
Initial release2017201720142020
Current release1.2.2, February 20233.5.0 ( 2.13), September 20230.9.3, August 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0Open Source infoApache 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJava, ScalaScalaC++
Server operating systemsLinuxLinux
macOS
Linux
OS X
Windows
Linux
macOS
Data schemeyesyesschema-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 SQLyesSQL-like query languageSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
MySQL client
gRPC
HTTP REST
WebSocket
JDBC
ODBC
Supported programming languagesJavaJava
Scala
Java
Python
R
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresuser defined functionsnonono
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesnonenonenone
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 integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono
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.nonoyes infousing specific database classes
User concepts infoAccess controlfine grained access rights according to SQL-standardnono

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More resources
Apache DorisNSDbSpark SQLTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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