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Posted to issues@spark.apache.org by "Ruslan Dautkhanov (JIRA)" <ji...@apache.org> on 2017/11/13 19:48:00 UTC
[jira] [Comment Edited] (SPARK-22505) toDF() / createDataFrame()
type inference doesn't work as expected
[ https://issues.apache.org/jira/browse/SPARK-22505?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16250054#comment-16250054 ]
Ruslan Dautkhanov edited comment on SPARK-22505 at 11/13/17 7:47 PM:
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Looks like we already discussed very similar topic 1.5 years ago on github )
https://github.com/databricks/spark-csv/issues/264#issuecomment-184943114
Any chance this can be added as a core Spark functionality?
Not sure if we can even call that CsvParser().csvRdd from pySpark..
This is what I am asking exactly
support for transforming dataframe to RDD[String] #188
https://github.com/databricks/spark-csv/commit/2eb90153a2d6a77b9cde4aee3f6e382df3da1746
I don't see CsvRdd from spark-csv module anywhere in Spark codebase
https://github.com/databricks/spark-csv/commit/2eb90153a2d6a77b9cde4aee3f6e382df3da1746#diff-c6f09c5a3e6aedc2e6bfb1c16358e970R123
Did it make its way into Spark?
What I see is only private class CSVInferSchema
https://github.com/apache/spark/blob/v2.2.0/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala#L29
Any other way to achieve this? Thanks a lot for any leads.
was (Author: tagar):
Looks like we already discussed very similar topic 1.5 years ago on github )
https://github.com/databricks/spark-csv/issues/264#issuecomment-184943114
Any chance this can be added as a core Spark functionality?
Not sure if we can even call that CsvParser().csvRdd from pySpark..
> 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
> Labels: csvparser, inference, pyspark, schema, spark-sql
>
> {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 files 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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