[GitHub] HyukjinKwon opened a new pull request #23787: [SPARK-26830][SQL][R] Vectorized R dapply() implementation

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HyukjinKwon opened a new pull request #23787: [SPARK-26830][SQL][R] Vectorized R dapply() implementation
URL: https://github.com/apache/spark/pull/23787
 
 
   ## What changes were proposed in this pull request?
   
   This PR targets to add vectorized `dapply()` in R, Arrow optimization.
   
   This can be tested as below:
   
   ```bash
   $ ./bin/sparkR --conf spark.sql.execution.arrow.enabled=true
   ```
   
   ```r
   df <- createDataFrame(mtcars)
   collect(dapply(df, function(rdf) { data.frame(rdf$gear + 1) }, structType("gear double")))
   ```
   
   ### Requirements
     - R 3.5.x 
     - Arrow package 0.12+
       ```bash
       Rscript -e 'remotes::install_github("apache/arrow@apache-arrow-0.12.0", subdir = "r")'
       ```
   
   **Note:** currently, Arrow R package is not in CRAN. Please take a look at ARROW-3204.
   **Note:** currently, Arrow R package seems not supporting Windows. Please take a look at ARROW-3204.
   
   
   ### Benchmarks
   
   **Shall**
   
   ```bash
   sync && sudo purge
   ./bin/sparkR --conf spark.sql.execution.arrow.enabled=false --driver-memory 4g
   ```
   
   ```bash
   sync && sudo purge
   ./bin/sparkR --conf spark.sql.execution.arrow.enabled=true --driver-memory 4g
   ```
   
   **R code**
   
   ```r
   rdf <- read.csv("500000.csv")
   rdf <- rdf[, c("First.Name", "Month.of.Joining")]  # We're only interested in the key and values to calculate.
   df <- cache(createDataFrame(rdf))
   count(df)
   
   test <- function() {
     options(digits.secs = 6) # milliseconds
     start.time <- Sys.time()
     count(dapply(df,
                  function(rdf) {
                    rdf$Month_of_Joining <- rdf$Month_of_Joining + 1
                    rdf
                  },
                  structType("First_Name string, Month_of_Joining double")))
     end.time <- Sys.time()
     time.taken <- end.time - start.time
     print(time.taken)
   }
   
   test()
   ```
   
   **Data (350 MB):**
   
   ```r
   object.size(read.csv("500000.csv"))
   350379504 bytes
   ```
   
   "500000 Records"  http://eforexcel.com/wp/downloads-16-sample-csv-files-data-sets-for-testing/
   
   **Results**
   
   ```
   Time difference of 92.78868 secs
   ```
   
   ```
   Time difference of 1.997686 secs
   ```
   
   The performance improvement was around **4644%**.
   
   
   ### Limitations
   
   - For now, Arrow optimization with R does not support when the data is `raw`, and when user explicitly gives float type in the schema. They produce corrupt values.
   
   - Due to ARROW-4512, it cannot send and receive batch by batch. It has to send all batches in Arrow stream format at once. It needs improvement later.
   
   ## How was this patch tested?
   
   Unit tests were added, and manually tested.
   

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