Re: SparkR DataFrame Column Casts esp. from CSV Files
I created https://issues.apache.org/jira/browse/SPARK-8085 for this. On Wed, Jun 3, 2015 at 12:12 PM, Shivaram Venkataraman shiva...@eecs.berkeley.edu wrote: Hmm - the schema=myschema doesn't seem to work in SparkR from my simple local test. I'm filing a JIRA for this now On Wed, Jun 3, 2015 at 11:04 AM, Eskilson,Aleksander alek.eskil...@cerner.com wrote: Neat, thanks for the info Hossein. My use case was just to reset the schema for a CSV dataset, but if either a. I can specify it at load, or b. it will be inferred in the future, I’ll likely not need to cast columns, much less reset the whole schema. I’ll still file a JIRA for the capability, but with lower priority. —Alek From: Hossein Falaki hoss...@databricks.com Date: Wednesday, June 3, 2015 at 12:55 PM To: shiva...@eecs.berkeley.edu shiva...@eecs.berkeley.edu Cc: Aleksander Eskilson alek.eskil...@cerner.com, dev@spark.apache.org dev@spark.apache.org Subject: Re: SparkR DataFrame Column Casts esp. from CSV Files 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 shiva...@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 [1] http://spark.apache.org/docs/latest/sql-programming-guide.html#json-datasets https://urldefense.proofpoint.com/v2/url?u=http-3A__spark.apache.org_docs_latest_sql-2Dprogramming-2Dguide.html-23json-2Ddatasetsd=AwMFaQc=NRtzTzKNaCCmhN_9N2YJR-XrNU1huIgYP99yDsEzaJor=0vZw1rBdgaYvDJYLyKglbrax9kvQfRPdzxLUyWSyxPMm=jttL5G8owvc7e3__uVdYKnu0D5nxr2rZnq2twPUTtyQs=HrpRObaR19Nr992p61rCA9h_44qxPkg3u3G9QPEGKcEe= On Wed, Jun 3, 2015 at 7:51 AM, Eskilson,Aleksander alek.eskil...@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
Re: SparkR DataFrame Column Casts esp. from CSV Files
Hmm - the schema=myschema doesn't seem to work in SparkR from my simple local test. I'm filing a JIRA for this now On Wed, Jun 3, 2015 at 11:04 AM, Eskilson,Aleksander alek.eskil...@cerner.com wrote: Neat, thanks for the info Hossein. My use case was just to reset the schema for a CSV dataset, but if either a. I can specify it at load, or b. it will be inferred in the future, I’ll likely not need to cast columns, much less reset the whole schema. I’ll still file a JIRA for the capability, but with lower priority. —Alek From: Hossein Falaki hoss...@databricks.com Date: Wednesday, June 3, 2015 at 12:55 PM To: shiva...@eecs.berkeley.edu shiva...@eecs.berkeley.edu Cc: Aleksander Eskilson alek.eskil...@cerner.com, dev@spark.apache.org dev@spark.apache.org Subject: Re: SparkR DataFrame Column Casts esp. from CSV Files 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 shiva...@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 [1] http://spark.apache.org/docs/latest/sql-programming-guide.html#json-datasets https://urldefense.proofpoint.com/v2/url?u=http-3A__spark.apache.org_docs_latest_sql-2Dprogramming-2Dguide.html-23json-2Ddatasetsd=AwMFaQc=NRtzTzKNaCCmhN_9N2YJR-XrNU1huIgYP99yDsEzaJor=0vZw1rBdgaYvDJYLyKglbrax9kvQfRPdzxLUyWSyxPMm=jttL5G8owvc7e3__uVdYKnu0D5nxr2rZnq2twPUTtyQs=HrpRObaR19Nr992p61rCA9h_44qxPkg3u3G9QPEGKcEe= On Wed, Jun 3, 2015 at 7:51 AM, Eskilson,Aleksander alek.eskil...@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 [1] - https://github.com/apache/spark/blob/master/R
Re: SparkR DataFrame Column Casts esp. from CSV Files
Hi Shivaram, As far as databricks’ spark-csv API shows, it seems there’s currently only support for explicit definition of column types. In JSON we have nice typed fields, but in CSVs, all bets are off. In the SQL version of the API, it appears you specify the column types when you create the table you’re populating with CSV data. Thanks for the clarification on individual column casting, I was missing the more obvious syntax. I’ll file a JIRA for resetting the schema after loading a DF. Thanks, Alek From: Shivaram Venkataraman shiva...@eecs.berkeley.edumailto:shiva...@eecs.berkeley.edu Reply-To: shiva...@eecs.berkeley.edumailto:shiva...@eecs.berkeley.edu shiva...@eecs.berkeley.edumailto:shiva...@eecs.berkeley.edu Date: Wednesday, June 3, 2015 at 12:29 PM To: Aleksander Eskilson alek.eskil...@cerner.commailto:alek.eskil...@cerner.com Cc: dev@spark.apache.orgmailto:dev@spark.apache.org dev@spark.apache.orgmailto:dev@spark.apache.org, hoss...@databricks.commailto:hoss...@databricks.com hoss...@databricks.commailto:hoss...@databricks.com Subject: Re: SparkR DataFrame Column Casts esp. from CSV Files 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 [1] http://spark.apache.org/docs/latest/sql-programming-guide.html#json-datasetshttps://urldefense.proofpoint.com/v2/url?u=http-3A__spark.apache.org_docs_latest_sql-2Dprogramming-2Dguide.html-23json-2Ddatasetsd=AwMFaQc=NRtzTzKNaCCmhN_9N2YJR-XrNU1huIgYP99yDsEzaJor=0vZw1rBdgaYvDJYLyKglbrax9kvQfRPdzxLUyWSyxPMm=aCZhOxAn5Iu762hWogwQK__JsZigsbLZFMaz44UcKQws=BX3MuobG748zhfm7hc_SnZA4MnFbwgFreNVEjkzkENce= On Wed, Jun 3, 2015 at 7:51 AM, Eskilson,Aleksander alek.eskil...@cerner.commailto:alek.eskil...@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 [1] - https://github.com/apache/spark/blob/master/R/pkg/R/column.R#L190https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_spark_blob_master_R_pkg_R_column.R-23L190d=AwMFaQc=NRtzTzKNaCCmhN_9N2YJR-XrNU1huIgYP99yDsEzaJor=0vZw1rBdgaYvDJYLyKglbrax9kvQfRPdzxLUyWSyxPMm=aCZhOxAn5Iu762hWogwQK__JsZigsbLZFMaz44UcKQws
Re: SparkR DataFrame Column Casts esp. from CSV Files
Neat, thanks for the info Hossein. My use case was just to reset the schema for a CSV dataset, but if either a. I can specify it at load, or b. it will be inferred in the future, I’ll likely not need to cast columns, much less reset the whole schema. I’ll still file a JIRA for the capability, but with lower priority. —Alek From: Hossein Falaki hoss...@databricks.commailto:hoss...@databricks.com Date: Wednesday, June 3, 2015 at 12:55 PM To: shiva...@eecs.berkeley.edumailto:shiva...@eecs.berkeley.edu shiva...@eecs.berkeley.edumailto:shiva...@eecs.berkeley.edu Cc: Aleksander Eskilson alek.eskil...@cerner.commailto:alek.eskil...@cerner.com, dev@spark.apache.orgmailto:dev@spark.apache.org dev@spark.apache.orgmailto:dev@spark.apache.org Subject: Re: SparkR DataFrame Column Casts esp. from CSV Files 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 shiva...@eecs.berkeley.edumailto:shiva...@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 [1] http://spark.apache.org/docs/latest/sql-programming-guide.html#json-datasetshttps://urldefense.proofpoint.com/v2/url?u=http-3A__spark.apache.org_docs_latest_sql-2Dprogramming-2Dguide.html-23json-2Ddatasetsd=AwMFaQc=NRtzTzKNaCCmhN_9N2YJR-XrNU1huIgYP99yDsEzaJor=0vZw1rBdgaYvDJYLyKglbrax9kvQfRPdzxLUyWSyxPMm=jttL5G8owvc7e3__uVdYKnu0D5nxr2rZnq2twPUTtyQs=HrpRObaR19Nr992p61rCA9h_44qxPkg3u3G9QPEGKcEe= On Wed, Jun 3, 2015 at 7:51 AM, Eskilson,Aleksander alek.eskil...@cerner.commailto:alek.eskil...@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 [1] - https://github.com/apache/spark/blob/master/R/pkg/R/column.R#L190https
Re: SparkR DataFrame Column Casts esp. from CSV Files
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 [1] http://spark.apache.org/docs/latest/sql-programming-guide.html#json-datasets On Wed, Jun 3, 2015 at 7:51 AM, Eskilson,Aleksander alek.eskil...@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 [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 CONFIDENTIALITY NOTICE This message and any included attachments are from Cerner Corporation and are intended only for the addressee. The information contained in this message is confidential and may constitute inside or non-public information under international, federal, or state securities laws. Unauthorized forwarding, printing, copying, distribution, or use of such information is strictly prohibited and may be unlawful. If you are not the addressee, please promptly delete this message and notify the sender of the delivery error by e-mail or you may call Cerner's corporate offices in Kansas City, Missouri, U.S.A at (+1) (816)221-1024.
Re: SparkR DataFrame Column Casts esp. from CSV Files
I think Hossein does want to implement schema inference for CSV -- then it'd be easy. Another way you can do this is to use R dataframe/table to read the CSV files in, and then convert it into a Spark DataFrames. Not going to be scalable, but could work. On Wed, Jun 3, 2015 at 10:49 AM, Eskilson,Aleksander alek.eskil...@cerner.com wrote: Hi Shivaram, As far as databricks’ spark-csv API shows, it seems there’s currently only support for explicit definition of column types. In JSON we have nice typed fields, but in CSVs, all bets are off. In the SQL version of the API, it appears you specify the column types when you create the table you’re populating with CSV data. Thanks for the clarification on individual column casting, I was missing the more obvious syntax. I’ll file a JIRA for resetting the schema after loading a DF. Thanks, Alek From: Shivaram Venkataraman shiva...@eecs.berkeley.edu Reply-To: shiva...@eecs.berkeley.edu shiva...@eecs.berkeley.edu Date: Wednesday, June 3, 2015 at 12:29 PM To: Aleksander Eskilson alek.eskil...@cerner.com Cc: dev@spark.apache.org dev@spark.apache.org, hoss...@databricks.com hoss...@databricks.com Subject: Re: SparkR DataFrame Column Casts esp. from CSV Files 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 [1] http://spark.apache.org/docs/latest/sql-programming-guide.html#json-datasets https://urldefense.proofpoint.com/v2/url?u=http-3A__spark.apache.org_docs_latest_sql-2Dprogramming-2Dguide.html-23json-2Ddatasetsd=AwMFaQc=NRtzTzKNaCCmhN_9N2YJR-XrNU1huIgYP99yDsEzaJor=0vZw1rBdgaYvDJYLyKglbrax9kvQfRPdzxLUyWSyxPMm=aCZhOxAn5Iu762hWogwQK__JsZigsbLZFMaz44UcKQws=BX3MuobG748zhfm7hc_SnZA4MnFbwgFreNVEjkzkENce= On Wed, Jun 3, 2015 at 7:51 AM, Eskilson,Aleksander alek.eskil...@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 [1] - https://github.com/apache/spark/blob/master/R/pkg/R/column.R#L190 https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_apache_spark_blob_master_R_pkg_R_column.R-23L190d=AwMFaQc=NRtzTzKNaCCmhN_9N2YJR
