92 lines
2.8 KiB
Python
92 lines
2.8 KiB
Python
|
#!/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
|
||
|
|
import gzip
|
||
|
|
|
||
|
|
from pyspark import AccumulatorParam
|
||
|
|
from pyspark.sql.functions import lit
|
||
|
|
from pyspark.sql import Window
|
||
|
|
from pyspark.sql.types import ByteType
|
||
|
|
|
||
|
|
cluster=sys.argv[1]
|
||
|
|
|
||
|
|
spark = pyspark.sql.SparkSession.builder \
|
||
|
|
.appName("task_slowdown") \
|
||
|
|
.config("spark.driver.maxResultSize", "32g") \
|
||
|
|
.config("spark.local.dir", "/run/tmpfiles.d/spark") \
|
||
|
|
.config("spark.driver.memory", "75g") \
|
||
|
|
.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")
|
||
|
|
|
||
|
|
try:
|
||
|
|
df["collection_type"] = df["collection_type"].cast(ByteType())
|
||
|
|
except:
|
||
|
|
df = df.withColumn("collection_type", lit(None).cast(ByteType()))
|
||
|
|
|
||
|
|
RUN = set([(3,1), (3,4), (3,5), (3,6), (3,7), (3,8), (3,10), (10,1), (10,4), (10,5), (10,6), (10,7), (10,8), (10,10)])
|
||
|
|
|
||
|
|
def is_res_none(tres):
|
||
|
|
return tres is None or tres["cpus"] is None or tres["memory"] is None
|
||
|
|
|
||
|
|
def for_each_task(ts):
|
||
|
|
ts = sorted(ts, key=lambda x: x["time"])
|
||
|
|
last_term = None
|
||
|
|
last_resources = None
|
||
|
|
prev = None
|
||
|
|
cpu = 0
|
||
|
|
ram = 0
|
||
|
|
|
||
|
|
for i,t in enumerate(ts):
|
||
|
|
if t["type"] >= 4 and t["type"] <= 8:
|
||
|
|
last_term = t["type"]
|
||
|
|
if prev is not None:
|
||
|
|
if (prev["type"], t["type"]) in RUN:
|
||
|
|
if is_res_none(last_resources):
|
||
|
|
last_resources = t["res"]
|
||
|
|
if not is_res_none(last_resources):
|
||
|
|
delta = t["time"] - prev["time"]
|
||
|
|
cpu += round(delta * last_resources["cpus"])
|
||
|
|
ram += round(delta * last_resources["memory"])
|
||
|
|
prev = t
|
||
|
|
if not is_res_none(last_resources):
|
||
|
|
last_resources = t["res"]
|
||
|
|
|
||
|
|
return [("cpu-" + str(last_term), cpu), ("ram-" + str(last_term), ram)]
|
||
|
|
|
||
|
|
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,
|
||
|
|
"res": x.resource_request
|
||
|
|
}
|
||
|
|
|
||
|
|
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) \
|
||
|
|
.flatMap(lambda x: x[1]) \
|
||
|
|
.groupBy(lambda x: x[0]) \
|
||
|
|
.mapValues(lambda xs: sum(n for _, n in xs)) \
|
||
|
|
.collect()
|
||
|
|
|
||
|
|
result = {}
|
||
|
|
for pair in df2:
|
||
|
|
result[pair[0]] = pair[1]
|
||
|
|
|
||
|
|
with open(cluster + "_res_micros_requested.json", "w") as out:
|
||
|
|
json.dump(result, out, separators=(',', ':'))
|