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From "Eskilson,Aleksander" <Alek.Eskil...@Cerner.com>
Subject SparkR DataFrame Column Casts esp. from CSV Files
Date Wed, 03 Jun 2015 14:51:50 GMT
It appears that casting columns remains a bit of a trick in Spark’s DataFrames. This is an
issue because tools like spark-csv will set column types to String by default and will not
attempt to infer types. Although spark-csv supports specifying  types for columns in its options,
it’s not clear how that might be integrated into SparkR (when loading the spark-csv package
into the R session).

Looking at the column.R spec we can cast a column to a different data type with the cast function
[1], but it’s notable that this is not a mutator, and it returns a column object as opposed
to a DataFrame. It appears the column cast can only be ‘applied’ by using the withColumn()
or mutate() (an alias for withColumn).

The other way to cast with Spark DataFrames is to write UDFs that operate on a column value
and return a coerced value. It looks like SparkR doesn’t have UDFs just yet [2], but it
seems like they’d be necessary to do a natural one-off column cast in R, something like

df.col1toInt <- withColumn(df, “intCol1”, udf(df$col1, function(x) as.numeric(x)))

(where col1 was originally ‘character’ type)

Currently it seems one has to
df.col1cast <- cast(df$col1, “int”)
df.col1toInt <- withColumn(df, df.col1cast)

If we wanted just our casted columns and not the original column from the data frame, we’d
still have to do a select. There was a conversation about CSV files just yesterday. Types
are already problematic, but they’re a very common data source in R, even at scale.

But only being able to coerce one column at a time is really unwieldy. Can the current spark-csv
SQL API for specifying types [3] be extended SparkR? And are there any thoughts on implementing
some kind of type inferencing perhaps based on a sampling of some number of rows (an implementation
I’ve seen before)? R’s read.csv() and read.delim() get types by inferring from the whole
file. Getting something that can achieve that functionality via explicit definition of types
or sampling will probably be necessary to work with CSV files that have enough columns to
merit R at Spark’s scale.

Regards,
Alek Eskilson

[1] - https://github.com/apache/spark/blob/master/R/pkg/R/column.R#L190
[2] - https://issues.apache.org/jira/browse/SPARK-6817
[3] - https://github.com/databricks/spark-csv#sql-api

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