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DBMS > Google Cloud Datastore vs. GridDB vs. MarkLogic vs. YTsaurus

System Properties Comparison Google Cloud Datastore vs. GridDB vs. MarkLogic vs. YTsaurus

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonGridDB  Xexclude from comparisonMarkLogic  Xexclude from comparisonYTsaurus  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformScalable in-memory time series database optimized for IoT and Big DataOperational and transactional Enterprise NoSQL databaseYTsaurus is an open source platform for distributed storage and processing.
Primary database modelDocument storeTime Series DBMSDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Document store
Key-value store
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.13
Rank#71  Overall
#12  Document stores
Score1.91
Rank#123  Overall
#10  Time Series DBMS
Score4.15
Rank#70  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#8  Search engines
Score0.15
Rank#323  Overall
#44  Document stores
#47  Key-value stores
Websitecloud.google.com/­datastoregriddb.netwww.progress.com/­marklogicytsaurus.tech
Technical documentationcloud.google.com/­datastore/­docsdocs.griddb.netwww.progress.com/­marklogic/­documentationytsaurus.tech/­docs/­en
DeveloperGoogleToshiba CorporationMarkLogic Corp.Yandex
Initial release2008201320012023
Current release5.1, August 202211.0, December 2022
License infoCommercial or Open SourcecommercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercial inforestricted free version is availableOpen Source infoApache License, Version 2.0
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++C++
Server operating systemshostedLinuxLinux
OS X
Windows
Ubuntu
Data schemeschema-freeyesschema-free infoSchema can be enforced
Typing infopredefined data types such as float or dateyes, details hereyes infonumerical, string, blob, geometry, boolean, timestampyesyes
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.nonoyesno
Secondary indexesyesyesyes
SQL infoSupport of SQLSQL-like query language (GQL)SQL92, SQL-like TQL (Toshiba Query Language)yes infoSQL92YQL, an SQL-based language, is supported
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C++
Go
Java
JavaScript
Python
Server-side scripts infoStored proceduresusing Google App Enginenoyes infovia XQuery or JavaScript
TriggersCallbacks using the Google Apps Engineyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosSource-replica replicationyesyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud DataflowConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsyes
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 within container, eventual consistency across containersImmediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID at container levelACID infocan act as a resource manager in an XA/JTA transactionACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes, with Range Indexes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users can be defined per databaseRole-based access control at the document and subdocument levelsAccess Control Lists

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More resources
Google Cloud DatastoreGridDBMarkLogicYTsaurus
Conferences, events and webinars

Progress MarkLogic World
Washington DC, 23-25 September 2024 (finished)

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