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DBMS > Apache Pinot vs. Atos Standard Common Repository vs. Spark SQL vs. Tkrzw

System Properties Comparison Apache Pinot vs. Atos Standard Common Repository vs. Spark SQL vs. Tkrzw

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Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonSpark SQL  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksSpark 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 DBMSDocument store
Key-value store
Relational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.38
Rank#275  Overall
#126  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.07
Rank#372  Overall
#57  Key-value stores
Websitepinot.apache.orgatos.net/en/convergence-creators/portfolio/standard-common-repositoryspark.apache.org/­sqldbmx.net/­tkrzw
Technical documentationdocs.pinot.apache.orgspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation and contributorsAtos Convergence CreatorsApache Software FoundationMikio Hirabayashi
Initial release2015201620142020
Current release1.0.0, September 202317033.5.0 ( 2.13), September 20230.9.3, August 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen 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 languageJavaJavaScalaC++
Server operating systemsAll OS with a Java JDK11 or higherLinuxLinux
OS X
Windows
Linux
macOS
Data schemeyesSchema and schema-less with LDAP viewsyesschema-free
Typing infopredefined data types such as float or dateyesoptionalyesno
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.yesnono
Secondary indexesyesno
SQL infoSupport of SQLSQL-like query languagenoSQL-like DML and DDL statementsno
APIs and other access methodsJDBCLDAPJDBC
ODBC
Supported programming languagesGo
Java
Python
All languages with LDAP bindingsJava
Python
R
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresnonono
Triggersyesnono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningSharding infocell divisionyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesyesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes infousing specific database classes
User concepts infoAccess controlLDAP bind authenticationnono

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More resources
Apache PinotAtos Standard Common RepositorySpark SQLTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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