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DBMS > Datomic vs. Google Cloud Datastore vs. Stardog vs. TimescaleDB

System Properties Comparison Datomic vs. Google Cloud Datastore vs. Stardog vs. TimescaleDB

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonStardog  Xexclude from comparisonTimescaleDB  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 PlatformEnterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualizationA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSDocument storeGraph DBMS
RDF store
Time Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score2.02
Rank#123  Overall
#11  Graph DBMS
#6  RDF stores
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitewww.datomic.comcloud.google.com/­datastorewww.stardog.comwww.timescale.com
Technical documentationdocs.datomic.comcloud.google.com/­datastore/­docsdocs.stardog.comdocs.timescale.com
DeveloperCognitectGoogleStardog-UnionTimescale
Initial release2012200820102017
Current release1.0.6735, June 20237.3.0, May 20202.15.0, May 2024
License infoCommercial or Open Sourcecommercial infolimited edition freecommercialcommercial info60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/studentsOpen Source infoApache 2.0
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
macOS
Windows
Linux
OS X
Windows
Data schemeyesschema-freeschema-free and OWL/RDFS-schema supportyes
Typing infopredefined data types such as float or dateyesyes, details hereyesnumerics, 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.nonono infoImport/export of XML data possibleyes
Secondary indexesyesyesyes infosupports real-time indexing in full-text and geospatialyes
SQL infoSupport of SQLnoSQL-like query language (GQL)Yes, compatible with all major SQL variants through dedicated BI/SQL Serveryes infofull PostgreSQL SQL syntax
APIs and other access methodsRESTful HTTP APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesClojure
Java
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyes infoTransaction Functionsusing Google App Engineuser defined functions and aggregates, HTTP Server extensions in Javauser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
TriggersBy using transaction functionsCallbacks using the Google Apps Engineyes infovia event handlersyes
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingnoneyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersMulti-source replication using PaxosMulti-source replication in HA-ClusterSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownono
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.Immediate Consistency in HA-ClusterImmediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsyes inforelationships in graphsyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACID
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)yesyesyes
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 developmentnoyesno
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users and rolesfine grained access rights according to SQL-standard

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More resources
DatomicGoogle Cloud DatastoreStardogTimescaleDB
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