Yes, spark-csv does not infer types yet, but it is planned to be implemented soon.

To work around the current limitations (of spark-csv and SparkR), you can specify the schema in read.df() to get your desired types from spark-csv. For example:

myschema <- structType(structField(“id", "integer"), structField(“name", "string”), structField(“location”, “string”))
df <- read.df(sqlContext, "path/to/file.csv", source = “com.databricks.spark.csv”, schema = myschema)

—Hossein

On Jun 3, 2015, at 10:29 AM, Shivaram Venkataraman <shivaram@eecs.berkeley.edu> wrote:

cc Hossein who knows more about the spark-csv options

You are right that the default CSV reader options end up creating all columns as string. I know that the JSON reader infers the schema [1] but I don't know if the CSV reader has any options to do that.  Regarding the SparkR syntax to cast columns, I think there is a simpler way to do it by just assigning to the same column name. For example I have a flights DataFrame with the `year` column typed as string. To cast it to int I just use

flights$year <- cast(flights$year, "int")

Now the dataframe has the same number of columns as before and you don't need a selection.

However this still doesn't address the part about casting multiple columns -- Could you file a new JIRA to track the need for casting multiple columns or rather being able to set the schema after loading a DF ?

Thanks
Shivaram


On Wed, Jun 3, 2015 at 7:51 AM, Eskilson,Aleksander <Alek.Eskilson@cerner.com> wrote:
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


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