#!/usr/bin/env python3
# coding: utf-8

import json
import pandas
import findspark
findspark.init()
import pyspark
import pyspark.sql
import sys
import gzip
import os

import pyspark.sql.functions as F
from pyspark import AccumulatorParam
from pyspark.sql.functions import lit
from pyspark.sql import Window
from pyspark.sql.types import StructType, LongType, StringType, ByteType

def main():
    global DIR
    global NAME
    global launched

    if len(sys.argv) != 4:
        print(sys.argv[0] + " {cluster} {tmpdir} {maxram}")
        sys.exit()

    DIR = os.path.dirname(__file__)
    NAME = "table-iv-tasks-all"

    if os.path.exists(DIR + "/" + NAME + "-working") or os.path.exists(DIR + "/" + NAME + ".parquet"):
        print("already launched")
        launched = True
        sys.exit()

    os.system("touch " + DIR + "/" + NAME + "-working")

    spark = pyspark.sql.SparkSession.builder \
      .appName(NAME) \
      .config("spark.driver.maxResultSize", "32g") \
      .config("spark.local.dir", sys.argv[2]) \
      .config("spark.driver.memory", sys.argv[3]) \
      .getOrCreate()
    sc = spark.sparkContext

    usage_schema = StructType() \
        .add("jobid", StringType(), False) \
        .add("job_term", LongType(), False) \
        .add("task_count", LongType(), False)    
    df = spark.read.format("csv") \
        .option("header", False) \
        .schema(usage_schema) \
        .load("/home/claudio/google_2019/thesis_queries/figure_9/?_task_count")
 
    df = df.groupBy("job_term").agg( \
        F.expr("avg(task_count)").alias('mean_tc'),
        F.expr("percentile(task_count, array(0.95))")[0].alias('%95_tc'))

    df.repartition(1).write.csv(DIR + "/" + NAME + ".csv")

if __name__ == "__main__":
    launched = False
    try:
        main()
    finally:    
        if not launched:
            os.system("rm -v " + DIR + "/" + NAME + "-working")

# vim: set ts=4 sw=4 et tw=120: