Accessing a global lookup Apache Spark - apache-spark

Question

I have a list of csv files each with a bunch of category names as header columns. Each row is a list of users with a boolean value (0, 1) whether they are part of that category or not. Each of the csv files does not have the same set of header categories.
I want to create a composite csv across all the files which has the following output:
Header is a union of all the headers
Each row is a unique user with a boolean value corresponding to the category column
The way I wanted to tackle this is to create a tuple of a user_id and a unique category_id for each cell with a '1'. Then reduce all these columns for each user to get the final output.
How do I create the tuple to begin with? Can I have a global lookup for all the categories?
Example Data:
File 1
user_id,cat1,cat2,cat3
21321,,,1,
21322,1,1,1,
21323,1,,,
File 2
user_id,cat4,cat5
21321,1,,,
21323,,1,,
Output
user_id,cat1,cat2,cat3,cat4,cat5
21321,,1,1,,,
21322,1,1,1,,,
21323,1,1,,,,

Answer 1

Probably the title of the question is misleading in the sense that conveys a certain implementation choice as there's no need for a global lookup in order to solve the problem at hand.
In big data, there's a basic principle guiding most solutions: divide and conquer. In this case, the input CSV files could be divided in tuples of (user,category).
Any number of CSV files containing an arbitrary number of categories can be transformed to this simple format. The resulting CSV results of the union of the previous step, extraction of the total nr of categories present and some data transformation to get it in the desired format.
In code this algorithm would look like this:
import org.apache.spark.SparkContext._
val file1 = """user_id,cat1,cat2,cat3|21321,,,1|21322,1,1,1|21323,1,,""".split("\\|")
val file2 = """user_id,cat4,cat5|21321,1,|21323,,1""".split("\\|")
val csv1 = sparkContext.parallelize(file1)
val csv2 = sparkContext.parallelize(file2)
import org.apache.spark.rdd.RDD
def toTuples(csv:RDD[String]):RDD[(String, String)] = {
val headerLine = csv.first
val header = headerLine.split(",")
val data = csv.filter(_ != headerLine).map(line => line.split(","))
data.flatMap{elem =>
val merged = elem.zip(header)
val id = elem.head
merged.tail.collect{case (v,cat) if v == "1" => (id, cat)}
}
}
val data1 = toTuples(csv1)
val data2 = toTuples(csv2)
val union = data1.union(data2)
val categories = union.map{case (id, cat) => cat}.distinct.collect.sorted //sorted category names
val categoriesByUser = union.groupByKey.mapValues(v=>v.toSet)
val numericCategoriesByUser = categoriesByUser.mapValues{catSet => categories.map(cat=> if (catSet(cat)) "1" else "")}
val asCsv = numericCategoriesByUser.collect.map{case (id, cats)=> id + "," + cats.mkString(",")}
Results in:
21321,,,1,1,
21322,1,1,1,,
21323,1,,,,1
(Generating the header is simple and left as an exercise for the reader)

Answer 2

You dont need to do this as a 2 step process if all you need is the resulting values.
A possible design:
1/ Parse your csv. You dont mention whether your data is on a distributed FS, so i'll assume it is not.
2/ Enter your (K,V) pairs into a mutable parallelized (to take advantage of Spark) map.
pseudo-code:
val directory = ..
mutable.ParHashMap map = new mutable.ParHashMap()
while (files[i] != null)
{
val file = directory.spark.textFile("/myfile...")
val cols = file.map(_.split(","))
map.put(col[0], col[i++])
}
and then you can access your (K/V) tuples by way of an iterator on the map.