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DBMS > Google Cloud Datastore vs. HugeGraph vs. Microsoft Azure SQL Database vs. PostGIS vs. RDF4J

System Properties Comparison Google Cloud Datastore vs. HugeGraph vs. Microsoft Azure SQL Database vs. PostGIS vs. RDF4J

Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonHugeGraph  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  Xexclude from comparisonPostGIS  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA fast-speed and highly-scalable Graph DBMSDatabase as a Service offering with high compatibility to Microsoft SQL ServerSpatial extension of PostgreSQLRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.
Primary database modelDocument storeGraph DBMSRelational DBMSSpatial DBMSRDF store
Secondary database modelsDocument store
Graph DBMS
Spatial DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score0.13
Rank#336  Overall
#32  Graph DBMS
Score77.99
Rank#16  Overall
#11  Relational DBMS
Score22.69
Rank#29  Overall
#1  Spatial DBMS
Score0.69
Rank#230  Overall
#9  RDF stores
Websitecloud.google.com/­datastoregithub.com/­hugegraph
hugegraph.apache.org
azure.microsoft.com/­en-us/­products/­azure-sql/­databasepostgis.netrdf4j.org
Technical documentationcloud.google.com/­datastore/­docshugegraph.apache.org/­docsdocs.microsoft.com/­en-us/­azure/­azure-sqlpostgis.net/­documentationrdf4j.org/­documentation
DeveloperGoogleBaiduMicrosoftSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release20082018201020052004
Current release0.9V123.4.2, February 2024
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0commercialOpen Source infoGPL v2.0Open Source infoEclipse Distribution License (EDL), v1.0.
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++CJava
Server operating systemshostedLinux
macOS
Unix
hostedLinux
OS X
Unix
Windows
Data schemeschema-freeyesyesyesyes infoRDF Schemas
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.nonoyesyes
Secondary indexesyesyes infoalso supports composite index and range indexyesyesyes
SQL infoSupport of SQLSQL-like query language (GQL)noyesyesno
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RESTful HTTP API
TinkerPop Gremlin
ADO.NET
JDBC
ODBC
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Groovy
Java
Python
.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
PHP
Python
Server-side scripts infoStored proceduresusing Google App Engineasynchronous Gremlin script jobsTransact SQLuser defined functionsyes
TriggersCallbacks using the Google Apps Enginenoyesyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingyes infodepending on used storage backend, e.g. Cassandra and HBaseyes infobased on PostgreSQLnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosyes infodepending on used storage backend, e.g. Cassandra and HBaseyes, with always 3 replicas availableyes infobased on PostgreSQLnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowvia hugegraph-sparknonono
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 ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsyes infoedges in graphyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACIDACIDACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infoin-memory storage is supported as well
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Users, roles and permissionsfine grained access rights according to SQL-standardyes infobased on PostgreSQLno

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More resources
Google Cloud DatastoreHugeGraphMicrosoft Azure SQL Database infoformerly SQL AzurePostGISRDF4J infoformerly known as Sesame
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