Spark-submit job performance - hadoop

Question

I am currently running spark-submit on the following environment:
Single node (RAM: 40GB, VCores: 8, Spark Version: 2.0.2, Python: 3.5)
My pyspark program basically will read one 450MB unstructured file from HDFS. Then it will loop through each lines and grab the necessary data and place it list. Finally it will use createDataFrame and save the data frame into Hive table.
My pyspark program code snippet:
sparkSession = (SparkSession
.builder
.master("yarn")
.appName("FileProcessing")
.enableHiveSupport()
.config("hive.exec.dynamic.partition", "true")
.config("hive.exec.dynamic.partition.mode", "nonstrict")
.getOrCreate())
lines = sparkSession.read.text('/user/test/testfiles').collect()
for line in lines:
// perform some data extrating and place it into rowList and colList using normal python operation
df = sparkSession.createDataFrame(rowList, colList)
df.registerTempTable("tempTable")
sparkSession.sql("create table test as select * from tempTable");
My spark-submit command is as the following:
spark-submit --master yarn --deploy-mode cluster --num-executors 2 --driver-memory 4g --executor-memory 8g --executor-cores 3 --files /usr/lib/spark-2.0.2-bin-hadoop2.7/conf/hive-site.xml FileProcessing.py
It took around 5 minutes to complete the processing. Is the performance consider good? How can I tune it in terms of setting the executor memory and executor cores so that the process can complete within 1-2 minutes, is it possible?
Appreciate your response. Thanks.

Answer 1

For tuning you application you need to know few things
1) You Need to Monitor your application whether your cluster is under utilized or not how much resources are used by your application which you have created
Monitoring can be done using various tools eg. Ganglia From Ganglia you can find CPU, Memory and Network Usage.
2) Based on Observation about CPU and Memory Usage you can get a better idea what kind of tuning is needed for your application
Form Spark point of you
In spark-defaults.conf
you can specify what kind of serialization is needed how much Driver Memory and Executor Memory needed by your application even you can change Garbage collection algorithm.
Below are few Example you can tune this parameter based on your requirements
spark.serializer org.apache.spark.serializer.KryoSerializer
spark.driver.memory 5g
spark.executor.memory 3g
spark.executor.extraJavaOptions -XX:MaxPermSize=2G -XX:+UseG1GC
spark.driver.extraJavaOptions -XX:MaxPermSize=6G -XX:+UseG1GC
For More details refer http://spark.apache.org/docs/latest/tuning.html
Hope this Helps!!