Thanks Shao for quick reply. I will look into how pyspark jobs are executed. Any suggestions or reference to docs on how to tune pyspark jobs?
On Thu, May 5, 2016 at 10:12 PM -0700, "Saisai Shao" <sai.sai.s...@gmail.com<mailto:sai.sai.s...@gmail.com>> wrote: 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<mailto: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
