[jira] [Created] (SPARK-22505) toDF() / createDataFrame() type inference doesn't work as expected

["Ruslan Dautkhanov (JIRA)" <ji...@apache.org>]

Ruslan Dautkhanov created SPARK-22505:
-----------------------------------------

             Summary: toDF() / createDataFrame() type inference doesn't work as expected
                 Key: SPARK-22505
                 URL: https://issues.apache.org/jira/browse/SPARK-22505
             Project: Spark
          Issue Type: Bug
          Components: PySpark, Spark Core
    Affects Versions: 2.2.0
            Reporter: Ruslan Dautkhanov


{code}
df = sc.parallelize([('1','a'),('2','b'),('3','c')]).toDF(['should_be_int','should_be_str'])
df.printSchema()
{code}

produces
{noformat}
root
 |-- should_be_int: string (nullable = true)
 |-- should_be_str: string (nullable = true)
{noformat}

Notice `should_be_int` has `string` datatype, according to documentation:
https://spark.apache.org/docs/latest/sql-programming-guide.html#inferring-the-schema-using-reflection

{quote}
Spark SQL can convert an RDD of Row objects to a DataFrame, inferring the datatypes. Rows are constructed by passing a list of key/value pairs as kwargs to the Row class. The keys of this list define the column names of the table, *and the types are inferred by sampling the whole dataset*, similar to the inference that is performed on JSON files.
{quote}

Schema inference works as expected when reading delimited filed like
{code}
spark.read.format('csv').option('inferSchema', True)...
{code}
but not when using toDF() / createDataFrame() API calls.

Spark 2.2.



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