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DBMS > Google Cloud Datastore vs. GridDB vs. HBase vs. JSqlDb

System Properties Comparison Google Cloud Datastore vs. GridDB vs. HBase vs. JSqlDb

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonGridDB  Xexclude from comparisonHBase  Xexclude from comparisonJSqlDb  Xexclude from comparison
JSqlDB 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 PlatformScalable in-memory time series database optimized for IoT and Big DataWide-column store based on Apache Hadoop and on concepts of BigTableJavaScript Query Language Database, stores JavaScript objects and primitives
Primary database modelDocument storeTime Series DBMSWide column storeDocument store
Object oriented DBMS
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score30.50
Rank#26  Overall
#2  Wide column stores
Websitecloud.google.com/­datastoregriddb.nethbase.apache.orgjsqldb.org (offline)
Technical documentationcloud.google.com/­datastore/­docsdocs.griddb.nethbase.apache.org/­book.html
DeveloperGoogleToshiba CorporationApache Software Foundation infoApache top-level project, originally developed by PowersetKonrad von Backstrom
Initial release2008201320082018
Current release5.1, August 20222.3.4, January 20210.8, December 2018
License infoCommercial or Open SourcecommercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache version 2Open Source
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++Java
Server operating systemshostedLinuxLinux
Unix
Windows infousing Cygwin
Linux
macOS
Windows
Data schemeschema-freeyesschema-free, schema definition possibleschema-free
Typing infopredefined data types such as float or dateyes, details hereyes infonumerical, string, blob, geometry, boolean, timestampoptions to bring your own types, AVROno
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.nononono
Secondary indexesyesyesnono
SQL infoSupport of SQLSQL-like query language (GQL)SQL92, SQL-like TQL (Toshiba Query Language)nono
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Java API
RESTful HTTP API
Thrift
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++
Groovy
Java
PHP
Python
Scala
JavaScript
Server-side scripts infoStored proceduresusing Google App Enginenoyes infoCoprocessors in Javafunctions in JavaScript
TriggersCallbacks using the Google Apps Engineyesyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosSource-replica replicationMulti-source replication
Source-replica replication
none
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 jobsyesno
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 or Eventual Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID at container levelSingle row ACID (across millions of columns)
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyesyes infousing RocksDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes
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 databaseAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC
More information provided by the system vendor
Google Cloud DatastoreGridDBHBaseJSqlDb
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
Google Cloud DatastoreGridDBHBaseJSqlDb
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