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DBMS > Datomic vs. Google Cloud Datastore vs. RDF4J vs. TDengine

System Properties Comparison Datomic vs. Google Cloud Datastore vs. RDF4J vs. TDengine

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonTDengine  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.Time Series DBMS and big data platform
Primary database modelRelational DBMSDocument storeRDF storeTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score0.74
Rank#222  Overall
#9  RDF stores
Score2.68
Rank#106  Overall
#9  Time Series DBMS
Websitewww.datomic.comcloud.google.com/­datastorerdf4j.orggithub.com/­taosdata/­TDengine
tdengine.com
Technical documentationdocs.datomic.comcloud.google.com/­datastore/­docsrdf4j.org/­documentationdocs.tdengine.com
DeveloperCognitectGoogleSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.TDEngine, previously Taos Data
Initial release2012200820042019
Current release1.0.6735, June 20233.0, August 2022
License infoCommercial or Open Sourcecommercial infolimited edition freecommercialOpen Source infoEclipse Distribution License (EDL), v1.0.Open Source infoAGPL V3, also commercial editions available
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ClojureJavaC
Server operating systemsAll OS with a Java VMhostedLinux
OS X
Unix
Windows
Linux
Windows
Data schemeyesschema-freeyes infoRDF Schemasyes
Typing infopredefined data types such as float or dateyesyes, details hereyesyes
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 indexesyesyesyesno
SQL infoSupport of SQLnoSQL-like query language (GQL)noStandard SQL with extensions for time-series applications
APIs and other access methodsRESTful HTTP APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
JDBC
RESTful HTTP API
Supported programming languagesClojure
Java
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
PHP
Python
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust
Server-side scripts infoStored proceduresyes infoTransaction Functionsusing Google App Engineyesno
TriggersBy using transaction functionsCallbacks using the Google Apps Engineyesyes, via alarm monitoring
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersMulti-source replication using Paxosnoneyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate 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.
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes 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.yes inforecommended only for testing and developmentno
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)noyes
More information provided by the system vendor
DatomicGoogle Cloud DatastoreRDF4J infoformerly known as SesameTDengine
Specific characteristicsTDengineā„¢ is a next generation data historian purpose-built for Industry 4.0 and...
» more
Competitive advantagesHigh Performance at any Scale: TDengine is purpose-built for handling massive industrial...
» more
Typical application scenariosTDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more
Market metricsTDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more
Licensing and pricing modelsTDengine OSS is an open source, cloud native time series database. It includes built-in...
» more
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DatomicGoogle Cloud DatastoreRDF4J infoformerly known as SesameTDengine
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