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DBMS > Google Cloud Datastore vs. InterSystems IRIS vs. Kinetica vs. searchxml vs. Tkrzw

System Properties Comparison Google Cloud Datastore vs. InterSystems IRIS vs. Kinetica vs. searchxml vs. Tkrzw

Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonInterSystems IRIS  Xexclude from comparisonKinetica  Xexclude from comparisonsearchxml  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA containerised multi-model DBMS, interoperability and analytics data platform with wide capabilities for vertical and horizontal scalabilityFully vectorized database across both GPUs and CPUsDBMS for structured and unstructured content wrapped with an application serverA 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 modelDocument storeDocument store
Key-value store
Object oriented DBMS
Relational DBMS
Relational DBMSNative XML DBMS
Search engine
Key-value store
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score4.05
Rank#83  Overall
#14  Document stores
#10  Key-value stores
#1  Object oriented DBMS
#45  Relational DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.00
Rank#383  Overall
#7  Native XML DBMS
#25  Search engines
Score0.00
Rank#383  Overall
#60  Key-value stores
Websitecloud.google.com/­datastorewww.intersystems.com/­products/­intersystems-iriswww.kinetica.comwww.searchxml.net/­category/­productsdbmx.net/­tkrzw
Technical documentationcloud.google.com/­datastore/­docsdocs.intersystems.com/­irislatest/­csp/­docbook/­DocBook.UI.Page.clsdocs.kinetica.comwww.searchxml.net/­support/­handouts
DeveloperGoogleInterSystemsKineticainformationpartners gmbhMikio Hirabayashi
Initial release20082018201220152020
Current release2023.3, June 20237.1, August 20211.00.9.3, August 2020
License infoCommercial or Open SourcecommercialcommercialcommercialcommercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++C++C++
Server operating systemshostedAIX
Linux
macOS
Ubuntu
Windows
LinuxWindowsLinux
macOS
Data schemeschema-freedepending on used data modelyesschema-freeschema-free
Typing infopredefined data types such as float or dateyes, details hereyesyesyesno
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.noyesnoyesno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like query language (GQL)yesSQL-like DML and DDL statementsnono
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
RESTful HTTP API
WebDAV
XQuery
XSLT
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C++
Java
JavaScript (Node.js)
Python
C++
Java
JavaScript (Node.js)
Python
C++ infomost other programming languages supported via APIsC++
Java
Python
Ruby
Server-side scripts infoStored proceduresusing Google App Engineyesuser defined functionsyes infoon the application serverno
TriggersCallbacks using the Google Apps Engineyesyes infotriggers when inserted values for one or more columns fall within a specified rangenono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingnonenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosSource-replica replicationSource-replica replicationyes infosychronisation to multiple collectionsnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDnomultiple readers, single writer
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes infoGPU vRAM or System RAMnoyes infousing specific database classes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)yesAccess rights for users and roles on table levelDomain, group and role-based access control at the document level and for application servicesno
More information provided by the system vendor
Google Cloud DatastoreInterSystems IRISKineticasearchxmlTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
Specific characteristicsInterSystems IRIS is a complete cloud-first data platform which includes a multi-model...
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Google Cloud DatastoreInterSystems IRISKineticasearchxmlTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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