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DBMS > GBase vs. OrigoDB vs. Spark SQL

System Properties Comparison GBase vs. OrigoDB vs. Spark SQL

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Editorial information provided by DB-Engines
NameGBase  Xexclude from comparisonOrigoDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionAn analytical database for business intelligence with large customers in China.A fully ACID in-memory object graph databaseSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSDocument store
Object oriented DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.04
Rank#190  Overall
#88  Relational DBMS
Score0.03
Rank#378  Overall
#51  Document stores
#18  Object oriented DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Websitewww.gbase.cnorigodb.comspark.apache.org/­sql
Technical documentationorigodb.com/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperRobert Friberg et alApache Software Foundation
Initial release2009 infounder the name LiveDB2014
Current releaseGBase 8a3.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen SourceOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC#Scala
Server operating systemsLinux
Windows
Linux
OS X
Windows
Data schemeyesyes
Typing infopredefined data types such as float or dateUser defined using .NET types and collectionsyes
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.no infocan be achieved using .NETno
Secondary indexesyesno
SQL infoSupport of SQLnoSQL-like DML and DDL statements
APIs and other access methods.NET Client API
HTTP API
LINQ
JDBC
ODBC
Supported programming languages.NetJava
Python
R
Scala
Server-side scripts infoStored proceduresyesno
Triggersyes infoDomain Eventsno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning infoclient side managed; servers are not synchronizedyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Foreign keys infoReferential integritydepending on modelno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyes infoWrite ahead logyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesno
User concepts infoAccess controlRole based authorizationno

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More resources
GBaseOrigoDBSpark SQL
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