How to decide whether to use Spark RDD filter or not - apache-spark

Question

I am using spark to read and analyse a data file, file contains data like following:
1,unit1,category1_1,100
2,unit1,category1_2,150
3,unit2,category2_1,200
4,unit3,category3_1,200
5,unit3,category3_2,300
The file contains around 20 million records. If user input unit or category, spark need filter the data by inputUnit or inputCategory.
Solution 1:
sc.textFile(file).map(line => {
val Array(id,unit,category,amount) = line.split(",")
if ( (StringUtils.isNotBlank(inputUnit) && unit != inputUnit ) ||
(StringUtils.isNotBlank(inputCategory) && category != inputCategory)){
null
} else {
val obj = new MyObj(id,unit,category,amount)
(id,obj)
}
}).filter(_!=null).collectAsMap()
Solution 2:
var rdd = sc.textFile(file).map(line => {
val (id,unit,category,amount) = line.split(",")
(id,unit,category,amount)
})
if (StringUtils.isNotBlank(inputUnit)) {
rdd = rdd.filter(_._2 == inputUnit)
}
if (StringUtils.isNotBlank(inputCategory)) {
rdd = rdd.filter(_._3 == inputCategory)
}
rdd.map(e => {
val obj = new MyObject(e._1, e._2, e._3, e._4)
(e._1, obj)
}).collectAsMap
I want to understand, which solution is better, or both of them are poor? If both are poor, how to make a good one? Personally, I think second one is better, but I am not quite sure whether it is nice to declare a rdd as var... (I am new to Spark, and I am using Spark 1.5.0 and Scala 2.10.4 to write the code, this is my first time asking a question in StackOverFlow, feel free to edit if it is not well formatted) Thanks.