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

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

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonIRONdb  Xexclude from comparisonJanusGraph infosuccessor of Titan  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicityA 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.
Primary database modelDocument storeTime Series DBMSGraph DBMSRDF store
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
Websitecloud.google.com/­datastorewww.circonus.com/solutions/time-series-database/janusgraph.orgrdf4j.org
Technical documentationcloud.google.com/­datastore/­docsdocs.circonus.com/irondb/category/getting-starteddocs.janusgraph.orgrdf4j.org/­documentation
DeveloperGoogleCirconus LLC.Linux Foundation; originally developed as Titan by AureliusSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release2008201720172004
Current releaseV0.10.20, January 20180.6.3, February 2023
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0Open Source infoEclipse Distribution License (EDL), v1.0.
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC and C++JavaJava
Server operating systemshostedLinuxLinux
OS X
Unix
Windows
Linux
OS X
Unix
Windows
Data schemeschema-freeschema-freeyesyes infoRDF Schemas
Typing infopredefined data types such as float or dateyes, details hereyes infotext, numeric, histogramsyesyes
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.nonono
Secondary indexesyesnoyesyes
SQL infoSupport of SQLSQL-like query language (GQL)SQL-like query language (Circonus Analytics Query Language: CAQL)nono
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
HTTP APIJava 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
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
Clojure
Java
Python
Java
PHP
Python
Server-side scripts infoStored proceduresusing Google App Engineyes, in Luayesyes
TriggersCallbacks using the Google Apps Enginenoyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic, metric affinity per nodeyes infodepending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)none
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosconfigurable replication factor, datacenter awareyesnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownoyes infovia Faunus, a graph analytics engineno
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 per node, eventual consistency across nodesEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnoyes infoRelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACIDACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcastyes 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.nono
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noUser authentification and security via Rexster Graph Serverno

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More resources
Google Cloud DatastoreIRONdbJanusGraph infosuccessor of TitanRDF4J infoformerly known as Sesame
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