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DBMS > Google Cloud Datastore vs. JanusGraph vs. RDF4J vs. TimescaleDB

System Properties Comparison Google Cloud Datastore vs. JanusGraph vs. RDF4J vs. TimescaleDB

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonJanusGraph infosuccessor of Titan  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA Graph DBMS optimized for distributed clusters infoIt was forked from the latest code base of Titan in January 2017RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelDocument storeGraph DBMSRDF storeTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score1.94
Rank#129  Overall
#12  Graph DBMS
Score0.69
Rank#230  Overall
#9  RDF stores
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitecloud.google.com/­datastorejanusgraph.orgrdf4j.orgwww.timescale.com
Technical documentationcloud.google.com/­datastore/­docsdocs.janusgraph.orgrdf4j.org/­documentationdocs.timescale.com
DeveloperGoogleLinux Foundation; originally developed as Titan by AureliusSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.Timescale
Initial release2008201720042017
Current release0.6.3, February 20232.13.0, November 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoEclipse Distribution License (EDL), v1.0.Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC
Server operating systemshostedLinux
OS X
Unix
Windows
Linux
OS X
Unix
Windows
Linux
OS X
Windows
Data schemeschema-freeyesyes infoRDF Schemasyes
Typing infopredefined data types such as float or dateyes, details hereyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.nonoyes
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like query language (GQL)nonoyes infofull PostgreSQL SQL syntax
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Clojure
Java
Python
Java
PHP
Python
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresusing Google App Engineyesyesuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
TriggersCallbacks using the Google Apps Engineyesyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingyes infodepending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)noneyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosyesnoneSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowyes infovia Faunus, a graph analytics enginenono
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
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsyes infoRelationships in graphsyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACID infoIsolation support depends on the API usedACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcastyes 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.nono
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)User authentification and security via Rexster Graph Servernofine grained access rights according to SQL-standard

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More resources
Google Cloud DatastoreJanusGraph infosuccessor of TitanRDF4J infoformerly known as SesameTimescaleDB
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