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README.md edit md Sep 21, 2017
myiris.csv exercises Aug 28, 2017
myiris_wheader.csv exercises Aug 28, 2017
outex1.txt md Aug 30, 2017
usqlscriptEx1.usql exercises Aug 26, 2017

README.md

Excercise 1

In this exercise, we will read a csv file with headers using usql and pass the file to the R script inside usql. Within the R script we will add a new column containing the number of columns in the input form USQL and notice the value. We will pass the results back to usql and save it with headers as csv file.

Please upload the csv files myiris_wheader.csv and myiris.csv to the folder TutorialMaterial that we created earlier in Data lake store. These csv files will be used in all the exercises.

Some concepts and keywords found in the script:

  • REFERENCE ASSEMBLY statement enables R extensions for the U-SQL Script.
  • USQL uses schema on read paradigm, hence the headers will need to be skipped. We will do this by using 'skipFirstNRows' in the EXTRACT statement. EXTRACT gives you the ability to define a schema on read. The schema is specified by a column name and C# type name pair per column. The query expression produces a rowset which we are assigning to a rowset variable @InputIrisData.
  • The scalar variables @myInputFile and @myOutputFile help make your script maintenance easier.
  • In this example we will embed the R code in the U-SQL script. (In Exercise 2 we will keep the same R code in a separate file and reference it in the U-SQL script). We can inline the R code on our U-SQL script by using the command parameter of the Extension.R.Reducer. We declare the R script as a string variable and pass it as a parameter to the Reducer.
  • Notice the use of dedicated named data frames called inputFromUSQL and outputToUSQL respectively to pass data between U-SQL and R. We use inputFromUSQL for retrieving the input data in our R script and outputToUSQL as returned result to U-SQL. These input and output DataFrame identifier names are fixed (that is, users cannot change these predefined names of input and output DataFrame identifiers).
  • More on REDUCE in Exercise 5. However please note the pseudo partition. Since we did not need partitioning, we specified the pseudo partition (same partition for all rows).

Finally we will save the output to a csv file with and without headers.