Using apply functions in SparkR - sparkr

Question

I am currently trying to implement some functions using sparkR version 1.5.1. I have seen older (version 1.3) examples, where people used the apply function on DataFrames, but it looks like this is no longer directly available. Example:
x = c(1,2)
xDF_R = data.frame(x)
colnames(xDF_R) = c("number")
xDF_S = createDataFrame(sqlContext,xDF_R)
Now, I can use the function sapply on the data.frame object
xDF_R$result = sapply(xDF_R$number, ppois, q=10)
When I use a similar logic on the DataFrame
xDF_S$result = sapply(xDF_S$number, ppois, q=10)
I get the error message "Error in as.list.default(X) :
no method for coercing this S4 class to a vector"
Can I somehow do this?

Answer 1

This is possible with user defined functions in Spark 2.0.
wrapper = function(df){
+ out = df
+ out$result = sapply(df$number, ppois, q=10)
+ return(out)
+ }
> xDF_S2 = dapplyCollect(xDF_S, wrapper)
> identical(xDF_S2, xDF_R)
[1] TRUE
Note you need a wrapper function like this because you can't pass the extra arguments in directly, but that may change in the future.

Answer 2

The native R functions do not support Spark DataFrames. We can use user defined functions in SparkR to execute native R modules. These are executed on the executors and thus the libraries must be available on all the executors.
For example, suppose we have a custom function holt_forecast which takes in a data.table as an argument.
Sample R code
sales_R_df %>%
group_by(product_id) %>%
do(holt_forecast(data.table(.))) %>%
data.table(.) -> dt_holt
For using UDFs, we need to specify the schema of the output data.frame returned by the execution of the native R method. This schema is used by Spark to generate back the Spark DataFrame.
Equivalent SparkR code
Define the schema
structField("product_id", "integer"),
structField("audit_date", "date"),
structField("holt_unit_forecast", "double"),
structField("holt_unit_forecast_std", "double")
)
Execute the method
library(data.table)
library(lubridate)
library(dplyr)
library(forecast)
sales <- data.table(x)
y <- data.frame(key,holt_forecast(sales))
}, dt_holt_schema)
Reference: https://shbhmrzd.medium.com/stl-and-holt-from-r-to-sparkr-1815bacfe1cc