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DBMS > Google Cloud Datastore vs. Hawkular Metrics vs. Kinetica vs. Microsoft Azure AI Search vs. searchxml

System Properties Comparison Google Cloud Datastore vs. Hawkular Metrics vs. Kinetica vs. Microsoft Azure AI Search vs. searchxml

Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonKinetica  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonsearchxml  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Fully vectorized database across both GPUs and CPUsSearch-as-a-service for web and mobile app developmentDBMS for structured and unstructured content wrapped with an application server
Primary database modelDocument storeTime Series DBMSRelational DBMSSearch engineNative XML DBMS
Search engine
Secondary database modelsSpatial DBMS
Time Series DBMS
Vector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score5.59
Rank#63  Overall
#7  Search engines
Score0.00
Rank#383  Overall
#7  Native XML DBMS
#25  Search engines
Websitecloud.google.com/­datastorewww.hawkular.orgwww.kinetica.comazure.microsoft.com/­en-us/­services/­searchwww.searchxml.net/­category/­products
Technical documentationcloud.google.com/­datastore/­docswww.hawkular.org/­hawkular-metrics/­docs/­user-guidedocs.kinetica.comlearn.microsoft.com/­en-us/­azure/­searchwww.searchxml.net/­support/­handouts
DeveloperGoogleCommunity supported by Red HatKineticaMicrosoftinformationpartners gmbh
Initial release20082014201220152015
Current release7.1, August 2021V11.0
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialcommercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesnonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC, C++C++
Server operating systemshostedLinux
OS X
Windows
LinuxhostedWindows
Data schemeschema-freeschema-freeyesyesschema-free
Typing infopredefined data types such as float or dateyes, details hereyesyesyesyes
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.nonononoyes
Secondary indexesyesnoyesyesyes
SQL infoSupport of SQLSQL-like query language (GQL)noSQL-like DML and DDL statementsnono
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
HTTP RESTJDBC
ODBC
RESTful HTTP API
RESTful HTTP APIRESTful HTTP API
WebDAV
XQuery
XSLT
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Go
Java
Python
Ruby
C++
Java
JavaScript (Node.js)
Python
C#
Java
JavaScript
Python
C++ infomost other programming languages supported via APIs
Server-side scripts infoStored proceduresusing Google App Enginenouser defined functionsnoyes infoon the application server
TriggersCallbacks using the Google Apps Engineyes infovia Hawkular Alertingyes infotriggers when inserted values for one or more columns fall within a specified rangenono
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infobased on CassandraShardingSharding infoImplicit feature of the cloud servicenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosselectable replication factor infobased on CassandraSource-replica replicationyes infoImplicit feature of the cloud serviceyes infosychronisation to multiple collections
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.Eventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnononomultiple 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.nonoyes infoGPU vRAM or System RAMnono
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noAccess rights for users and roles on table levelyes infousing Azure authenticationDomain, group and role-based access control at the document level and for application services

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More resources
Google Cloud DatastoreHawkular MetricsKineticaMicrosoft Azure AI Searchsearchxml
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