[GitHub] [spark] shivsood opened a new pull request #25238: [WIP][SPARK-28152][SQL] Mapped ShortType to SMALLINT and FloatType to REAL for MsSqlServerDialect

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shivsood opened a new pull request #25238: [WIP][SPARK-28152][SQL] Mapped ShortType to SMALLINT and FloatType to REAL for MsSqlServerDialect
URL: https://github.com/apache/spark/pull/25238
 
 
   ## What changes were proposed in this pull request?
   This is a backport of SPARK-28152 to Spark 2.4. Because the fix in 3.0 was based on different base files, following relevant fixes have also been cherry-picked as part of this fix. 
   - [SPARK-27159][SQL] update mssql server dialect to support binary type 
   - SPARK-27168 [SQL][TEST] Add docker integration test for MsSql server
   
   SPARK-28152  corrects mappings in `MsSqlServerDialect`. Post fix `ShortType` is mapped to `SMALLINT` and `FloatType` is mapped to `REAL` per [JBDC mapping]( https://docs.microsoft.com/en-us/sql/connect/jdbc/using-basic-data-types?view=sql-server-2017) respectively.
   
   ShortType and FloatTypes are not correctly mapped to right JDBC types when using JDBC connector. This results in tables and spark data frame being created with unintended types. The issue was observed when validating against SQLServer.
   
   Refer [JBDC mapping]( https://docs.microsoft.com/en-us/sql/connect/jdbc/using-basic-data-types?view=sql-server-2017  ) for guidance on mappings between SQLServer, JDBC and Java. Note that java "Short" type should be mapped to JDBC "SMALLINT" and java Float should be mapped to JDBC "REAL".
   
   Some example issue that can happen because of wrong mappings
       - Write from df with column type results in a SQL table of with column type as INTEGER as opposed to SMALLINT.Thus a larger table that expected.
       - Read results in a dataframe with type INTEGER as opposed to ShortType
   
   - ShortType has a problem in both the the write and read path
   - FloatTypes only have an issue with read path. In the write path Spark data type 'FloatType' is correctly mapped to JDBC equivalent data type 'Real'. But in the read path when JDBC data types need to be converted to Catalyst data types ( getCatalystType) 'Real' gets incorrectly gets mapped to 'DoubleType' rather than 'FloatType'.
   
   ## How was this patch tested?
   Integration test using : ./build/mvn test -Pdocker-integration-tests -pl :spark-docker-integration-tests_2.11 -Dtest=none -DwildcardSuites=org.apache.spark.sql.jdbc.MsSqlServerIntegrationSuite
   
   (Please explain how this patch was tested. E.g. unit tests, integration tests, manual tests)
   (If this patch involves UI changes, please attach a screenshot; otherwise, remove this)
   
   Please review https://spark.apache.org/contributing.html before opening a pull request.
   

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