Spark Multiple Output Paths result in Multiple Input Reads - scala

Question

First, apologies for the title, I wasn't sure how to eloquently describe this succinctly.
I have a spark job that parses logs into JSON, and then using spark-sql converts specific columns into ORC and writes to various paths. For example:
val logs = sc.textFile("s3://raw/logs")
val jsonRows = logs.mapPartitions(partition => {
partition.map(log => {
logToJson.parse(log)
}
}
jsonRows.foreach(r => {
val contentPath = "s3://content/events/"
val userPath = "s3://users/events/"
val contentDf = sqlSession.read.schema(contentSchema).json(r)
val userDf = sqlSession.read.schema(userSchema).json(r)
val userDfFiltered = userDf.select("*").where(userDf("type").isin("users")
// Save Data
val contentWriter = contentDf.write.mode("append").format("orc")
eventWriter.save(contentPath)
val userWriter = userDf.write.mode("append").format("orc")
userWriter.save(userPath)
When I wrote this I expected that the parsing would occur one time, and then it would write to the respective locations afterward. However, it seems that it is executing all of the code in the file twice - once for content and once for users. Is this expected? I would prefer that I don't end up transferring the data from S3 and parsing twice, as that is the largest bottleneck. I am attaching an image from the Spark UI to show the duplication of tasks for a single Streaming Window. Thanks for any help you can provide!

Answer 1

Okay, this kind of nested DFs is a no go. DataFrames are meant to be a data structure for big datasets that won't fit into normal data structures (like Seq or List) and that needs to be processed in a distributed way. It is not just another kind of array. What you are attempting to do here is to create a DataFrame per log line, which makes little sense.
As far as I can tell from the (incomplete) code you have posted here, you want to create two new DataFrames from your original input (the logs) which you then want to store in two different locations. Something like this:
val logs = sc.textFile("s3://raw/logs")
val contentPath = "s3://content/events/"
val userPath = "s3://users/events/"
val jsonRows = logs
.mapPartitions(partition => {
partition.map(log => logToJson.parse(log))
}
.toDF()
.cache() // Or use persist() if dataset is larger than will fit in memory
jsonRows
.write
.format("orc")
.save(contentPath)
jsonRows
.filter(col("type").isin("users"))
.write
.format("orc")
.save(userPath)
Hope this helps.