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

import os
import json
import pandas as pd
import findspark
findspark.init()
import pyspark
import pyspark.sql
import sys
import gzip
from pyspark import AccumulatorParam
from pyspark.sql.functions import lit
from pyspark.sql import Window
from pyspark.sql.types import *
from decimal import *
import os

if len(sys.argv) is not 5:
    print(sys.argv[0] + " {cluster} {tmpdir} {maxram} {basedir}")
    sys.exit()

cluster=sys.argv[1]

DIR = os.path.dirname(__file__)
NAME = "figure-9c-machine-count-" + cluster

if os.path.exists(DIR + "/" + NAME + "-working"):
    print("already done")
    sys.exit()

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

spark = pyspark.sql.SparkSession.builder \
  .appName(NAME) \
  .config("spark.driver.maxResultSize", "128g") \
  .config("spark.local.dir", sys.argv[2]) \
  .config("spark.driver.memory", sys.argv[3]) \
  .getOrCreate()
sc = spark.sparkContext

# READING INSTANCE EVENTS DATA
dfepath = sys.argv[4] + "/" + cluster + "/" + cluster + "_instance_events*.json.gz"
#dfepath = sys.argv[4] + "/" + cluster + "/" + cluster + "_test.json"
df = spark.read.json(dfepath)

try:
    df["collection_type"] = df["collection_type"].cast(ByteType())
except:
    df = df.withColumn("collection_type", lit(None).cast(ByteType()))

df = df.rdd.filter(lambda x: x.collection_id is not None and x.machine_id is not None and
        int(x.machine_id) is not -1 and int(x.machine_id) is not 0 and
        (x.collection_type == 0 or x.collection_type is None)) \
        .map(lambda x: (x.collection_id, int(x.machine_id))) \
        .groupBy(lambda x: x[0]) \
        .map(lambda x: [x[0], len(set(x[1]))]) \
        .toDF(["jobid", "machine_count"])

df.write.parquet(DIR + "/" + NAME + ".parquet")

os.system("rm " + DIR + "/" + NAME + "-working")
# vim: set ts=4 sw=4 et tw=120: