Scala/Spark - Find total number of value in row based on a key - scala

Question

I have a large text file which contains the page views of some Wikimedia projects. (You can find it here if you're really interested) Each line, delimited by a space, contains the statistics for one Wikimedia page. The schema looks as follows:
<project code> <page title> <num hits> <page size>
In Scala, using Spark RDDs or Dataframes, I wish to compute the total number of hits for each project, based on the project code.
So for example for projects with the code "zw", I would like to find all the rows that begin with project code "zw", and add up their hits. Obviously this should be done for all project codes simultaneously.
I have looked at functions like aggregateByKey etc, but the examples I found don't go into enough detail, especially for a file with 4 fields. I imagine it's some kind of MapReduce job, but how exactly to implement it is beyond me.
Any help would be greatly appreciated.

Answer 1

First, you have to read the file in as a Dataset[String]. Then, parse each string into a tuple, so that it can be easily converted to a Dataframe. Once you have a Dataframe, a simple .GroupBy().agg() is enough to finish the computation.
import org.apache.spark.sql.functions.sum
val df = spark.read.textFile("/tmp/pagecounts.gz").map(l => {
val a = l.split(" ")
(a(0), a(2).toLong)
}).toDF("project_code", "num_hits")
val agg_df = df.groupBy("project_code")
.agg(sum("num_hits").as("total_hits"))
.orderBy($"total_hits".desc)
agg_df.show(10)
The above snippet shows the top 10 project codes by total hits.
+------------+----------+
|project_code|total_hits|
+------------+----------+
| en.mw| 5466346|
| en| 5310694|
| es.mw| 695531|
| ja.mw| 611443|
| de.mw| 572119|
| fr.mw| 536978|
| ru.mw| 466742|
| ru| 463437|
| es| 400632|
| it.mw| 400297|
+------------+----------+
It is certainly also possible to do this with the older API as an RDD map/reduce, but you lose many of the optimizations that Dataset/Dataframe api brings.