99 lines
2.8 KiB
Python
Executable file
99 lines
2.8 KiB
Python
Executable file
#!/usr/bin/env python3
|
|
# coding: utf-8
|
|
|
|
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
|
|
|
|
cluster=sys.argv[1]
|
|
|
|
spark = pyspark.sql.SparkSession.builder \
|
|
.appName("task_slowdown") \
|
|
.config("spark.local.dir", "/run/tmpfiles.d/spark") \
|
|
.config("spark.driver.memory", "124g") \
|
|
.getOrCreate()
|
|
sc = spark.sparkContext
|
|
|
|
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()
|
|
|
|
non = sc.accumulator(0)
|
|
tot = sc.accumulator(0)
|
|
|
|
|
|
def for_each_task(ts):
|
|
global none
|
|
global tot
|
|
|
|
ts = sorted(ts, key=lambda x: x["time"])
|
|
|
|
last_term = None
|
|
priority = None
|
|
responding = False
|
|
|
|
resp_burst_start = None
|
|
resp_burst_type = None
|
|
|
|
resp_time = []
|
|
resp_time_last = 0
|
|
|
|
for i,t in enumerate(ts):
|
|
if t["priority"] is not None and priority is None:
|
|
priority = t["priority"]
|
|
if responding:
|
|
resp_burst_type.append(t["type"])
|
|
if t["type"] >= 4 and t["type"] <= 8:
|
|
last_term = t["type"]
|
|
if responding:
|
|
# This response time interval has ended, so record the time delta and term
|
|
resp_time.append((t["time"] - resp_burst_start, resp_burst_type))
|
|
responding = False
|
|
if (not responding) and (t["type"] < 4 or t["type"] > 8):
|
|
resp_burst_start = t["time"]
|
|
resp_burst_type = [t["type"]]
|
|
responding = True
|
|
|
|
|
|
tot.add(1)
|
|
if last_term != 6:
|
|
non.add(1)
|
|
return (priority, resp_time) if last_term == 5 else None
|
|
|
|
|
|
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": 0 if x.priority is None else int(x.priority)
|
|
}
|
|
|
|
|
|
df2 = df.rdd \
|
|
.filter(lambda x: x.collection_type is None or x.collection_type == 0) \
|
|
.filter(lambda x: x.type is not None and 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) \
|
|
.filter(lambda x: x[1] is not None) \
|
|
.map(lambda x: x[1]) \
|
|
.groupBy(lambda x: x[0]) \
|
|
.mapValues(lambda x: [e[1] for e in x])
|
|
|
|
a = {"val": df2.collect(), "tot": tot.value, "non": non.value}
|
|
with open(cluster + "_state_changes.json", "w") as out:
|
|
json.dump(a, out)
|