Home > Databricks, Spark SQL > Spark SQL – Beware of Implicit datatype conversions (TypeCoercion)

Spark SQL – Beware of Implicit datatype conversions (TypeCoercion)


 
While working on some data analysis I saw one Spark SQL query was not getting me expected results. The table had some good amount of data, I was filtering on a value but some records were missing. So, I checked online and found that Spark SQL works differently compared to SQL Server, in this case while comparing 2 different datatypes columns or variables.

–> I’m populating some test data to reproduce the scenario, for that I’m inserting 9 rows and storing decimal values as String, query below:

CREATE OR REPLACE TEMPORARY VIEW vwTestDataType as 
select * from values 
("row1", "2.0"), 
("row2", "1.5"), 
("row3", "1.0"), 
("row4", "0.8"), 
("row5", "0.6"), 
("row6", "0.4"), 
("row7", "0.2"), 
("row8", "0.0"),
("row9", null);

describe vwTestDataType;

col_name | data_type | comment
col1           | string         | null
col2           | string         | null

 

–> Now, I’ll create a similar query where I was observing the issue. The below query should return me 7 rows, but instead it returns just 3 rows.

select * from vwTestDataType where col2 > 0

Running above query in “SQL Server” throws below error for the same dataset:

Conversion failed when converting the varchar value ‘2.0’ to data type int.

 

–> Let’s check why Spark SQL query didn’t failed and why its behaving like this.

I will use EXPLAIN EXTENDED operator to know what’s happening with the query while creating the Logical Plan.

explain extended select * from vwTestDataType where col2 > 0

Here is the plan you can see that under Analyzed Logical Plan the column “col2” is getting implicitly typecasted to INT, as the comparison value is an INT type. Thus it is converting all 0.x values to 0 and filtering them out.

Plan

== Parsed Logical Plan ==
‘Project [*]
+- ‘Filter (‘col2 > 0)
+- ‘UnresolvedRelation `vwTestDataType`

== Analyzed Logical Plan ==
col1: string, col2: string
Project [col1#13284, col2#13285]
+- Filter (cast(col2#13285 as int) > 0)
+- SubqueryAlias `vwtestdatatype`
+- Project [col1#13284, col2#13285]
+- LocalRelation [col1#13284, col2#13285]

== Optimized Logical Plan ==
LocalRelation [col1#13284, col2#13285]

== Physical Plan ==
LocalTableScan [col1#13284, col2#13285]

 

–> Now to avoid this issue you must explicitly type cast the column and value to the exact datatype to get expected result. Like here we should convert the String column & value to Double, this way the query returns all 7 rows as expected:

select * from vwTestDataType where double(col2) > double(0)
--OR--select * from vwTestDataType where col2 > 0.0

Let’s again check the Logical Plan of the modified query by using EXPLAIN EXTENDED operator how it looks like:

explain extended select * from vwTestDataType where double(col2) > double(0)
--OR--explain extended select * from vwTestDataType where col2 > 0.0

plan
== Parsed Logical Plan ==
‘Project [*]
+- ‘Filter (‘double(‘col2) > ‘double(0))
+- ‘UnresolvedRelation `vwTestDataType`

== Analyzed Logical Plan ==
col1: string, col2: string
Project [col1#13213, col2#13214]
+- Filter (cast(col2#13214 as double) > cast(0 as double))
+- SubqueryAlias `vwtestdatatype`
+- Project [col1#13213, col2#13214]
+- LocalRelation [col1#13213, col2#13214]

== Optimized Logical Plan ==
LocalRelation [col1#13213, col2#13214]

== Physical Plan ==
LocalTableScan [col1#13213, col2#13214]

 

So while working with Spark SQL we should make sure there should not be such datatype conflicts, and moreover these type of issues should be handled in way beginning while modelling the tables with correct datatype.


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  1. March 6, 2020 at 6:43 pm

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