I have thousands of small files in HDFS. Need to process a slightly smaller subset of files (which is again in thousands), fileList contains list of filepaths which need to be processed.
// fileList == list of filepaths in HDFS
var masterRDD: org.apache.spark.rdd.RDD[(String, String)] = sparkContext.emptyRDD
for (i <- 0 to fileList.size() - 1) {
val filePath = fileStatus.get(i)
val fileRDD = sparkContext.textFile(filePath)
val sampleRDD = fileRDD.filter(line => line.startsWith("#####")).map(line => (filePath, line))
masterRDD = masterRDD.union(sampleRDD)
}
masterRDD.first()
//Once out of loop, performing any action results in stackoverflow error due to long lineage of RDD
Exception in thread "main" java.lang.StackOverflowError
at scala.runtime.AbstractFunction1.<init>(AbstractFunction1.scala:12)
at org.apache.spark.rdd.UnionRDD$$anonfun$1.<init>(UnionRDD.scala:66)
at org.apache.spark.rdd.UnionRDD.getPartitions(UnionRDD.scala:66)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:34)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at org.apache.spark.rdd.UnionRDD.getPartitions(UnionRDD.scala:66)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:34)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.AbstractTraversable.map(Traversable.scala:105)
at org.apache.spark.rdd.UnionRDD.getPartitions(UnionRDD.scala:66)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
at scala.Option.getOrElse(Option.scala:120)
at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
at org.apache.spark.rdd.UnionRDD$$anonfun$1.apply(UnionRDD.scala:66)
=====================================================================
=====================================================================
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
In general you can use checkpoints to break long lineages. Some more or less similar to this should work:
import org.apache.spark.rdd.RDD
import scala.reflect.ClassTag
val checkpointInterval: Int = ???
def loadAndFilter(path: String) = sc.textFile(path)
.filter(_.startsWith("#####"))
.map((path, _))
def mergeWithLocalCheckpoint[T: ClassTag](interval: Int)
(acc: RDD[T], xi: (RDD[T], Int)) = {
if(xi._2 % interval == 0 & xi._2 > 0) xi._1.union(acc).localCheckpoint
else xi._1.union(acc)
}
val zero: RDD[(String, String)] = sc.emptyRDD[(String, String)]
fileList.map(loadAndFilter).zipWithIndex
.foldLeft(zero)(mergeWithLocalCheckpoint(checkpointInterval))
In this particular situation a much simpler solution should be to use SparkContext.union
method:
val masterRDD = sc.union(
fileList.map(path => sc.textFile(path)
.filter(_.startsWith("#####"))
.map((path, _)))
)
A difference between these methods should be obvious when you take a look at the DAG generated by loop / reduce
:
and a single union
:
Of course if files are small you can combine wholeTextFiles
with flatMap
and read all files at once:
sc.wholeTextFiles(fileList.mkString(","))
.flatMap{case (path, text) =>
text.split("\n").filter(_.startsWith("#####")).map((path, _))}
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With