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DBMS > Google Cloud Datastore vs. RDFox vs. Tarantool vs. Valentina Server

System Properties Comparison Google Cloud Datastore vs. RDFox vs. Tarantool vs. Valentina Server

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonRDFox  Xexclude from comparisonTarantool  Xexclude from comparisonValentina Server  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformHigh performance knowledge graph and semantic reasoning engineIn-memory computing platform with a flexible data schema for efficiently building high-performance applicationsObject-relational database and reports server
Primary database modelDocument storeGraph DBMS
RDF store
Document store
Key-value store
Relational DBMS
Relational DBMS
Secondary database modelsRelational DBMSSpatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score0.23
Rank#308  Overall
#25  Graph DBMS
#14  RDF stores
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Score0.17
Rank#327  Overall
#145  Relational DBMS
Websitecloud.google.com/­datastorewww.oxfordsemantic.techwww.tarantool.iowww.valentina-db.net
Technical documentationcloud.google.com/­datastore/­docsdocs.oxfordsemantic.techwww.tarantool.io/­en/­docvalentina-db.com/­docs/­dokuwiki/­v5/­doku.php
DeveloperGoogleOxford Semantic TechnologiesVKParadigma Software
Initial release2008201720081999
Current release6.0, Septermber 20222.10.0, May 20225.7.5
License infoCommercial or Open SourcecommercialcommercialOpen Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprisecommercial
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C and C++
Server operating systemshostedLinux
macOS
Windows
BSD
Linux
macOS
Linux
OS X
Windows
Data schemeschema-freeyes infoRDF schemasFlexible data schema: relational definition for tables with ability to store json-like documents in columnsyes
Typing infopredefined data types such as float or dateyes, details hereyesstring, double, decimal, uuid, integer, blob, boolean, datetimeyes
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.nono
Secondary indexesyesyesyes
SQL infoSupport of SQLSQL-like query language (GQL)noFull-featured ANSI SQL supportyes
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
RESTful HTTP API
SPARQL 1.1
Open binary protocolODBC
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
Java
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET
Server-side scripts infoStored proceduresusing Google App EngineLua, C and SQL stored proceduresyes
TriggersCallbacks using the Google Apps Engineyes, before/after data modification events, on replication events, client session eventsyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosreplication via a shared file systemAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowno
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 in stand-alone mode, Eventual Consistency in replicated setupsCasual consistency across sharding partitions
Eventual consistency within replicaset partition infowhen using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition infowhen using Raft
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyes, cooperative multitasking
Durability infoSupport for making data persistentyesyesyes, write ahead loggingyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes, full featured in-memory storage engine with persistenceyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Roles, resources, and access typesAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles
fine grained access rights according to SQL-standard

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More resources
Google Cloud DatastoreRDFoxTarantoolValentina Server
DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
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