Spark-Optimization Techniques - scala

Question

Hi I have 90 GB data In CSV file I'm loading this data into one temp table and then from temp table to orc table using select insert command but for converting and loading data into orc format its taking 4 hrs in spark sql.Is there any kind of optimization technique which i can use to reduce this time.As of now I'm not using any kind of optimization technique I'm just using spark sql and loading data from csv file to table(textformat) and then from this temp table to orc table(using select insert)
using spark submit as:
spark-submit \
--class class-name\
--jar file
or can I add any extra Parameter in spark submit for improving the optimization.
scala code(sample):
All Imports
object sample_1 {
def main(args: Array[String]) {
//sparksession with enabled hivesuppport
var a1=sparksession.sql("load data inpath 'filepath' overwrite into table table_name")
var b1=sparksession.sql("insert into tablename (all_column) select 'ALL_COLUMNS' from source_table")
}
}

Answer 1

First of all, you don't need to store the data in the temp table to write into hive table later. You can straightaway read the file and write the output using the DataFrameWriter API. This will reduce one step from your code.
You can write as follows:
val spark = SparkSession.builder.enableHiveSupport().getOrCreate()
val df = spark.read.csv(filePath) //Add header or delimiter options if needed
inputDF.write.mode("append").format(outputFormat).saveAsTable(outputDB + "." + outputTableName)
Here, the outputFormat will be orc, the outputDB will be your hive database and outputTableName will be your Hive table name.
I think using the above technique, your write time will reduce significantly. Also, please mention the resources your job is using and I may be able to optimize it further.
Another optimization you can use is to partition your dataframe while writing. This will make the write operation faster. However, you need to decide the columns on which to partition carefully so that you don't end up creating a lot of partitions.