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

# # Temporal impact: machine time waste

import json
import pandas
from IPython import display
import findspark
findspark.init()
import pyspark
import pyspark.sql
import sys

from pyspark.sql.functions import col, lag, when, concat_ws, last, first
from pyspark.sql import Window
from pyspark.sql.types import LongType

if len(sys.argv) != 2 or len(sys.argv[1]) != 1:
  print("usage: " + sys.argv[0] + " {cluster}", file=sys.stderr)
  sys.exit(1)

cluster=sys.argv[1]

spark = pyspark.sql.SparkSession.builder \
  .appName("machine_time_waste") \
  .config("spark.local.dir", "/tmp/ramdisk/spark") \
  .config("spark.driver.memory", "124g") \
  .getOrCreate()

df = spark.read.json("/home/claudio/google_2019/instance_events/" + cluster + "/" + cluster + "_instance_events*.json.gz")
# df = spark.read.json("/home/claudio/google_2019/instance_events/" + cluster + "/" + cluster + "_test.json")

df.printSchema()

df.show()

#  .filter(df.collection_type == 0) \
df2 = df \
  .withColumn("time", col("time").cast(LongType())) \
  .withColumn("type", col("type").cast(LongType())) \
  .withColumn("type", when(col("type").isNull(), 0).otherwise(col("type"))) \
  .withColumn("id", concat_ws("-", "collection_id", "instance_index")) \
  .where(col("time").isNotNull()) \
  .where(col("type").isNotNull()) \
  .where((col("instance_index").isNotNull()) & (col("collection_id").isNotNull())) \
  .select("time", "type", "id")

df2.show()

total = df.count()
filtered = df2.count()

print("Total: " + str(total))
print("Filtered: " + str(filtered))

r = df2.rdd

def for_each_task(ts):
  ts = sorted(ts, key=lambda x: x.time)
  last_term = None
  prev = None
  tr = {}

  for i,t in enumerate(ts):
    if prev is not None and t.type == prev.type: # remove useless transitions
      if (i == len(ts)-1): # if last
        tr[str(prev.type) + "-" + str(t.type)] = t.time - prev.time # keep "loops" if last
      else:      
        continue
    if t.type >= 4 and t.type <= 8:
      last_term = t.type
    if prev is not None:
      tr[str(prev.type) + "-" + str(t.type)] = t.time - prev.time
    prev = t
  return {"last_term": last_term, 'tr': tr}

def sum_values(ds):
  dsum = {}
  for dt in ds:
    d = dt["tr"]
    for key in d:
      if key not in dsum:
        dsum[key] = d[key]
      else:
        dsum[key] += d[key]
  return dsum

r2 = r \
  .groupBy(lambda x: x.id) \
  .mapValues(for_each_task) \
  .map(lambda x: x[1]) \
  .groupBy(lambda x: x["last_term"]) \
  .mapValues(sum_values) \
  .collect()

with open(cluster + "_state_changes.json", "w") as out:
  json.dump({"filtered": filtered, "total": total, "data": r2}, out)

# .withColumn("prev_time", lag(df2.time).over(my_window)) \
# .withColumn("prev_type", lag(df2.type).over(my_window)) \

# vim: set ts=2 sw=2 et tw=120: