bachelorThesis/figure_9/figure9b.py

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2021-04-20 16:46:51 +00:00
#!/usr/bin/env python3
# coding: utf-8
import json
import pandas
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 ByteType
if len(sys.argv) is not 4:
print(sys.argv[0] + " {cluster} {tmpdir} {maxram}")
sys.exit()
cluster=sys.argv[1]
spark = pyspark.sql.SparkSession.builder \
.appName("figure_9b") \
.config("spark.driver.maxResultSize", "128g") \
.config("spark.local.dir", sys.argv[2]) \
.config("spark.driver.memory", sys.argv[3]) \
.getOrCreate()
sc = spark.sparkContext
dfepath="/home/claudio/google_2019/collection_events/" + cluster + "/" + cluster + "_collection_events*.json.gz"
#dfepath="/home/claudio/google_2019/collection_events/" + 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()))
MICROS = 1000000
def sumrow(l, p, t, c):
t = t // (MICROS * 60)
if t < 1:
t = "<1"
elif t < 2:
t = "1-2"
elif t < 4:
t = "2-4"
elif t < 10:
t = "4-10"
elif t < 60:
t = "10-60"
elif t < 60 * 24:
t = "60-1d"
else:
t = ">=1d"
return (l, p, t, c)
def sumid(sr):
return (sr[0], sr[1], sr[2])
def for_each_task(ts):
global non
ts = sorted(ts, key=lambda x: x["time"])
in_exec = False
exec_start = None
exec_tot = 0
priority = -1
l = len(ts)
last_term = -1
for i,t in enumerate(ts):
if t["priority"] is not -1 and priority is -1:
priority = t["priority"]
if t["type"] >= 4 and t["type"] <= 8:
last_term = t["type"]
if in_exec and (t["type"] == 1 or (t["type"] >= 4 and t["type"] <= 8)):
exec_tot += t["time"] - exec_start
in_exec = False
if (not in_exec) and (t["type"] == 3):
exec_start = t["time"]
in_exec = True
return sumrow(last_term, priority, exec_tot, l)
def cleanup(x):
return {
"time": int(x.time),
"type": 0 if x.type is None else int(x.type),
"id": x.collection_id + "-" + x.instance_index,
"priority": -1 if x.priority is None else int(x.priority)
}
def sum_rows(xs):
csum = 0
for x in xs:
csum += x[3]
return csum
df2 = df.rdd \
.filter(lambda x: x.collection_type is None or x.collection_type == 0) \
.filter(lambda x: x.time is not None and x.instance_index is not None and x.collection_id is not None) \
.map(cleanup) \
.groupBy(lambda x: x["id"]) \
.mapValues(for_each_task) \
.map(lambda x: x[1]) \
.groupBy(lambda x: sumid(x)) \
.mapValues(sum_rows) \
.map(lambda x: str(x[0][0]) + "," + str(x[0][1]) + "," + str(x[0][2]) + "," + str(x[1])) \
.coalesce(1) \
.saveAsTextFile(cluster + "_priority_exectime")
# vim: set ts=4 sw=4 et tw=80: