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DBMS > GridGain vs. Spark SQL vs. Trafodion vs. YTsaurus

System Properties Comparison GridGain vs. Spark SQL vs. Trafodion vs. YTsaurus

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Editorial information provided by DB-Engines
NameGridGain  Xexclude from comparisonSpark SQL  Xexclude from comparisonTrafodion  Xexclude from comparisonYTsaurus  Xexclude from comparison
Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.
DescriptionGridGain is an in-memory computing platform, built on Apache IgniteSpark SQL is a component on top of 'Spark Core' for structured data processingTransactional SQL-on-Hadoop DBMSYTsaurus is an open source platform for distributed storage and processing.
Primary database modelKey-value store
Relational DBMS
Relational DBMSRelational DBMSDocument store
Key-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.11
Rank#340  Overall
#45  Document stores
#50  Key-value stores
Websitewww.gridgain.comspark.apache.org/­sqltrafodion.apache.orgytsaurus.tech
Technical documentationwww.gridgain.com/­docs/­index.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.htmltrafodion.apache.org/­documentation.htmlytsaurus.tech/­docs/­en
DeveloperGridGain Systems, Inc.Apache Software FoundationApache Software Foundation, originally developed by HPYandex
Initial release2007201420142023
Current releaseGridGain 8.5.13.5.0 ( 2.13), September 20232.3.0, February 2019
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoApache 2.0Open Source infoApache License, Version 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, C++, .NetScalaC++, JavaC++
Server operating systemsLinux
OS X
Solaris
Windows
Linux
OS X
Windows
LinuxUbuntu
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.yesnonono
Secondary indexesyesnoyes
SQL infoSupport of SQLANSI-99 for query and DML statements, subset of DDLSQL-like DML and DDL statementsyesYQL, an SQL-based language, is supported
APIs and other access methodsHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JDBC
ODBC
ADO.NET
JDBC
ODBC
Supported programming languagesC#
C++
Java
PHP
Python
Ruby
Scala
Java
Python
R
Scala
All languages supporting JDBC/ODBC/ADO.NetC++
Go
Java
JavaScript
Python
Server-side scripts infoStored proceduresyes (compute grid and cache interceptors can be used instead)noJava Stored Procedures
Triggersyes (cache interceptors and events)nono
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark CoreShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes (replicated cache)noneyes, via HBaseyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)yes infovia user defined functions and HBaseyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDACID
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.yesnono
User concepts infoAccess controlSecurity Hooks for custom implementationsnofine grained access rights according to SQL-standardAccess Control Lists

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More resources
GridGainSpark SQLTrafodionYTsaurus
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