Writing RDD based application using pyspark will bring in additional
overheads, Spark is running on the JVM whereas your python code is running
on python runtime, so data should be communicated between JVM world and
python world, this requires additional serialization-deserialization, IPC.
Also other parts will bring in overheads. So the performance difference is
expected, but you could tune the application to reduce the gap.
Also because python RDD wraps a lot, so the DAG you saw is different from
Scala, that is also expected.
Thanks
Saisai
On Fri, May 6, 2016 at 12:47 PM, pratik gawande <pratik.gawa...@hotmail.com>
wrote:
> Hello,
>
> I am new to spark. For one of job I am finding significant performance
> difference when run in pyspark vs scala. Could you please let me know if
> this is known and scala is preferred over python for writing spark jobs?
> Also DAG visualization shows completely different DAGs for scala and
> pyspark. I have pasted DAG for both using toDebugString() method. Let me
> know if you need any additional information.
>
> *Time for Job in scala* : 52 secs
>
> *Time for job in pyspark *: 4.2 min
>
>
> *Scala code in Zepplin:*
>
> val lines = sc.textFile("s3://[test-bucket]/output2/")
> val words = lines.flatMap(line => line.split(" "))
> val filteredWords = words.filter(word => word.equals("Gutenberg") ||
> word.equals("flower") || word.equals("a"))
> val wordMap = filteredWords.map(word => (word, 1)).reduceByKey(_ + _)
> wordMap.collect()
>
> *pyspark code in Zepplin:*
>
> lines = sc.textFile("s3://[test-bucket]/output2/")
> words = lines.flatMap(lambda x: x.split())
> filteredWords = words.filter(lambda x: (x == "Gutenberg" or x == "flower"
> or x == "a"))
> result = filteredWords.map(lambda x: (x, 1)).reduceByKey(lambda a,b:
> a+b).collect()
> print result
>
> *Scala final RDD:*
>
>
> *print wordMap.toDebugString() *
>
> lines: org.apache.spark.rdd.RDD[String] = s3://[test-bucket]/output2/
> MapPartitionsRDD[108] at textFile at <console>:30 words:
> org.apache.spark.rdd.RDD[String] = MapPartitionsRDD[109] at flatMap at
> <console>:31 filteredWords: org.apache.spark.rdd.RDD[String] =
> MapPartitionsRDD[110] at filter at <console>:33 wordMap:
> org.apache.spark.rdd.RDD[(String, Int)] = ShuffledRDD[112] at reduceByKey
> at <console>:35 (10) ShuffledRDD[112] at reduceByKey at <console>:35 []
> +-(10) MapPartitionsRDD[111] at map at <console>:35 [] |
> MapPartitionsRDD[110] at filter at <console>:33 [] | MapPartitionsRDD[109]
> at flatMap at <console>:31 [] | s3://[test-bucket]/output2/
> MapPartitionsRDD[108] at textFile at <console>:30 [] | s3://[test-bucket]/
> output2/ HadoopRDD[107] at textFile at <console>:30 []
>
>
> *PySpark final RDD:*
>
>
> *println(wordMap.toDebugString) *
>
> (10) PythonRDD[119] at RDD at PythonRDD.scala:43 [] | s3://[test-bucket]/
> output2/ MapPartitionsRDD[114] at textFile at null:-1 [] |
> s3://[test-bucket]/output2/HadoopRDD[113] at textFile at null:-1 []
> PythonRDD[120] at RDD at PythonRDD.scala:43
>
>
> Thanks,
>
> Pratik
>
