104 lines
2.8 KiB
Python
Executable file
104 lines
2.8 KiB
Python
Executable file
#!/usr/bin/env python3
|
|
# coding: utf-8
|
|
|
|
import json
|
|
import pandas
|
|
import findspark
|
|
import pyspark
|
|
import pyspark.sql
|
|
import sys
|
|
import gzip
|
|
import os
|
|
from pyspark import AccumulatorParam
|
|
from pyspark.sql.functions import lit
|
|
from pyspark.sql import Window
|
|
from pyspark.sql.types import ByteType
|
|
from pyspark.sql.functions import col
|
|
import math
|
|
|
|
if len(sys.argv) is not 4:
|
|
print(sys.argv[0] + " {cluster} {tmpdir} {maxram}")
|
|
sys.exit()
|
|
|
|
cluster=sys.argv[1]
|
|
findspark.init()
|
|
|
|
DIR = os.path.dirname(__file__)
|
|
|
|
spark = pyspark.sql.SparkSession.builder \
|
|
.appName("figure_9a_join") \
|
|
.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)
|
|
df = df.withColumnRenamed("collection_id", "jobid") \
|
|
.select("jobid", "collection_type", "time", "type")
|
|
|
|
try:
|
|
df["collection_type"] = df["collection_type"].cast(ByteType())
|
|
except:
|
|
df = df.withColumn("collection_type", lit(None).cast(ByteType()))
|
|
|
|
df = df.filter(((col("collection_type").isNull()) | (col("collection_type") ==
|
|
"0")) & (col("time").isNotNull()))
|
|
|
|
jpath = DIR + "/figure-9c-machine-count-" + cluster + ".parquet"
|
|
dfj = spark.read.parquet(jpath)
|
|
|
|
jjpath = DIR + "/figure-9a-task-count-" + cluster + ".parquet"
|
|
dfjj = spark.read.parquet(jjpath)
|
|
|
|
df = df.join(dfj, 'jobid', 'left')
|
|
df = df.join(dfjj, "jobid")
|
|
|
|
MICROS = 1000000
|
|
|
|
def round_down(n, decimals=0):
|
|
multiplier = 10 ** decimals
|
|
return math.floor(n * multiplier) / multiplier
|
|
|
|
def for_each_job(data):
|
|
jobid = data[0]
|
|
ts = data[1]
|
|
global non
|
|
|
|
ts = sorted(ts, key=lambda x: x["time"])
|
|
|
|
l = len(ts)
|
|
last_term = -1
|
|
machine_count = 0
|
|
|
|
for i,t in enumerate(ts):
|
|
if t["type"] >= 4 and t["type"] <= 8:
|
|
last_term = t["type"]
|
|
if t["task_count"] <= 0 or t["machine_count"] <= 0:
|
|
machine_count = -1
|
|
else:
|
|
machine_count = round_down(t["machine_count"] / t["task_count"], 1)
|
|
|
|
return str(jobid) + "," + str(last_term) + "," + str(machine_count)
|
|
|
|
def cleanup(x):
|
|
return {
|
|
"time": int(x.time),
|
|
"type": 0 if x.type is None else int(x.type),
|
|
"id": x.jobid,
|
|
"task_count": -1 if x.task_count is None else int(x.task_count),
|
|
"machine_count": -1 if x.machine_count is None else int(x.machine_count)
|
|
}
|
|
|
|
df2 = df.rdd \
|
|
.map(cleanup) \
|
|
.groupBy(lambda x: x["id"]) \
|
|
.repartition(100) \
|
|
.map(for_each_job) \
|
|
.coalesce(1) \
|
|
.saveAsTextFile(cluster + "_machine_locality")
|
|
|
|
# vim: set ts=4 sw=4 et tw=80:
|