Zawa-ll opened a new pull request, #47246:
URL: https://github.com/apache/spark/pull/47246

   **What changes were proposed in this pull request?**
   This PR addresses a type casting issue in Spark SQL where comparing a string 
column to an integer value results in an empty result set. The changes ensure 
that Spark SQL handles such comparisons in a manner similar to Redshift by 
casting the integer to a string before comparison.
   
   **Why are the changes needed?**
   The current behavior in Spark SQL leads to unexpected empty result sets when 
comparing a string column to an integer value. This is inconsistent with the 
behavior of other SQL systems like Redshift, which cast the integer to a string 
for comparison. The changes are needed to make Spark SQL's behavior more 
intuitive and consistent with other systems.
   
   
   **Does this PR introduce any user-facing change?**
   Yes. Users will now get expected results when comparing string columns to 
integer values in Spark SQL. Previously, such comparisons would yield empty 
result sets due to type casting issues.
   
   **How was this patch tested?**
   The patch was tested by adding new unit tests in the BinaryComparisonSuite 
to cover various comparison scenarios between string columns and integer 
values. These tests ensure that the comparisons behave as expected and return 
correct results.
   
   **Was this patch authored or co-authored using generative AI tooling?**
   No.
   
   **Example**
   Here is a concise example to reproduce the issue and verify the fix:
   
   ``` import org.apache.spark.sql.SparkSession
   
   case class Person(id: String, name: String)
   
   val spark = SparkSession.builder()
     .appName("Spark SQL Type Casting Issue")
     .master("local[*]")
     .getOrCreate()
   
   import spark.implicits._
   
   val personDF = Seq(
     Person("a", "amit"),
     Person("b", "abhishek")
   ).toDF()
   
   personDF.createOrReplaceTempView("person_ddf")
   
   val sqlQuery = "SELECT * FROM person_ddf WHERE id <> -1"
   val resultDF = spark.sql(sqlQuery)
   resultDF.show()
    ```
   Before applying the fix, resultDF.show() would display an empty result set 
due to the type casting issue. After applying the fix, the resultDF will 
correctly display the records in the person_ddf table.
   
   
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   ### What changes were proposed in this pull request?
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   ### Why are the changes needed?
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   ### Does this PR introduce _any_ user-facing change?
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   ### How was this patch tested?
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