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DBMS > Google Cloud Datastore vs. MarkLogic vs. RDF4J vs. Solr

System Properties Comparison Google Cloud Datastore vs. MarkLogic vs. RDF4J vs. Solr

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonMarkLogic  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonSolr  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformOperational and transactional Enterprise NoSQL databaseRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.A widely used distributed, scalable search engine based on Apache Lucene
Primary database modelDocument storeDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
RDF storeSearch engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score5.18
Rank#63  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#7  Search engines
Score0.74
Rank#222  Overall
#9  RDF stores
Score41.02
Rank#24  Overall
#3  Search engines
Websitecloud.google.com/­datastorewww.progress.com/­marklogicrdf4j.orgsolr.apache.org
Technical documentationcloud.google.com/­datastore/­docswww.progress.com/­marklogic/­documentationrdf4j.org/­documentationsolr.apache.org/­resources.html
DeveloperGoogleMarkLogic Corp.Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.Apache Software Foundation
Initial release2008200120042006
Current release11.0, December 20229.6.1, May 2024
License infoCommercial or Open Sourcecommercialcommercial inforestricted free version is availableOpen Source infoEclipse Distribution License (EDL), v1.0.Open Source infoApache Version 2
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaJava
Server operating systemshostedLinux
OS X
Windows
Linux
OS X
Unix
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeschema-freeschema-free infoSchema can be enforcedyes infoRDF Schemasyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateyes, details hereyesyesyes infosupports customizable data types and automatic typing
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.noyesyes
Secondary indexesyesyesyesyes infoAll search fields are automatically indexed
SQL infoSupport of SQLSQL-like query language (GQL)yes infoSQL92noSolr Parallel SQL Interface
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Java API
RESTful HTTP/JSON API
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Java
PHP
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresusing Google App Engineyes infovia XQuery or JavaScriptyesJava plugins
TriggersCallbacks using the Google Apps Engineyesyesyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosyesnoneyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsnospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
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 ConsistencyEventual Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID infocan act as a resource manager in an XA/JTA transactionACID infoIsolation support depends on the API usedoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoin-memory storage is supported as wellyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes, with Range Indexesyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Role-based access control at the document and subdocument levelsnoyes

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More resources
Google Cloud DatastoreMarkLogicRDF4J infoformerly known as SesameSolr
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