I have a DataFrame
which I have to apply a series of filter queries against. For example, I load my DataFrame
as follows.
val df = spark.read.parquet("hdfs://box/some-parquet")
I then have a bunch of "arbitrary" filters as follows.
- C0='true' and C1='false'
- C0='false' and C3='true'
- and so on...
I typically get these filters dynamically using a util method.
val filters: List[String] = getFilters()
All I do is apply these filters to the DataFrame
to get the counts. For example.
val counts = filters.map(filter => {
df.where(filter).count
})
I noticed that is NOT a parallel/distributed operation when mapping over the filters. If I stick the filters into an RDD/DataFrame, this approach won't work either, because I'd then be performing nested data frame operations (which, as I've read on SO, is not allowed in Spark). Something like the following gives a NullPointerException (NPE).
val df = spark.read.parquet("hdfs://box/some-parquet")
val filterRDD = spark.sparkContext.parallelize(List("C0='false'", "C1='true'"))
val counts = filterRDD.map(df.filter(_).count).collect
Caused by: java.lang.NullPointerException at org.apache.spark.sql.Dataset.filter(Dataset.scala:1127) at $anonfun$1.apply(:27) at $anonfun$1.apply(:27) at scala.collection.Iterator$$anon$11.next(Iterator.scala:409) at scala.collection.Iterator$class.foreach(Iterator.scala:893) at scala.collection.AbstractIterator.foreach(Iterator.scala:1336) at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48) at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310) at scala.collection.AbstractIterator.to(Iterator.scala:1336) at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302) at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336) at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289) at scala.collection.AbstractIterator.toArray(Iterator.scala:1336) at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:912) at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:912) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1899) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1899) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70) at org.apache.spark.scheduler.Task.run(Task.scala:86) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745)
Is there any way to parallelize/distribute the count filters on a DataFrame
in Spark? By the way, I am on Spark v2.0.2.
By doing that, the only expectable gain (which can be very substantial) would be to pass only once on the input data.
I would do it like so (programmatic solution, but equivalent SQL is possible) :
A sample spark session looks like :