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DBMS > Google Cloud Datastore vs. Sphinx vs. Stardog vs. Tkrzw

System Properties Comparison Google Cloud Datastore vs. Sphinx vs. Stardog vs. Tkrzw

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonSphinx  Xexclude from comparisonStardog  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 PlatformOpen source search engine for searching in data from different sources, e.g. relational databasesEnterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualizationA 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 storeSearch engineGraph DBMS
RDF store
Key-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score5.95
Rank#55  Overall
#5  Search engines
Score2.07
Rank#122  Overall
#11  Graph DBMS
#6  RDF stores
Score0.07
Rank#372  Overall
#57  Key-value stores
Websitecloud.google.com/­datastoresphinxsearch.comwww.stardog.comdbmx.net/­tkrzw
Technical documentationcloud.google.com/­datastore/­docssphinxsearch.com/­docsdocs.stardog.com
DeveloperGoogleSphinx Technologies Inc.Stardog-UnionMikio Hirabayashi
Initial release2008200120102020
Current release3.5.1, February 20237.3.0, May 20200.9.3, August 2020
License infoCommercial or Open SourcecommercialOpen Source infoGPL version 2, commercial licence availablecommercial info60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/studentsOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++JavaC++
Server operating systemshostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
macOS
Windows
Linux
macOS
Data schemeschema-freeyesschema-free and OWL/RDFS-schema supportschema-free
Typing infopredefined data types such as float or dateyes, details herenoyesno
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.nono infoImport/export of XML data possibleno
Secondary indexesyesyes infofull-text index on all search fieldsyes infosupports real-time indexing in full-text and geospatial
SQL infoSupport of SQLSQL-like query language (GQL)SQL-like query language (SphinxQL)Yes, compatible with all major SQL variants through dedicated BI/SQL Serverno
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Proprietary protocolGraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresusing Google App Enginenouser defined functions and aggregates, HTTP Server extensions in Javano
TriggersCallbacks using the Google Apps Enginenoyes infovia event handlersno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoPartitioning is done manually, search queries against distributed index is supportednonenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosnoneMulti-source replication in HA-Clusternone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownonono
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 Consistency in HA-ClusterImmediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnoyes inforelationships in graphsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infoThe original contents of fields are not stored in the Sphinx index.yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes infousing specific database classes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noAccess rights for users and rolesno

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More resources
Google Cloud DatastoreSphinxStardogTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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