275 lines
10 KiB
Python
Executable file
275 lines
10 KiB
Python
Executable file
#!/usr/bin/env python3
|
|
# coding: utf-8
|
|
|
|
import os
|
|
import json
|
|
import pandas as pd
|
|
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 *
|
|
from decimal import *
|
|
|
|
TESTDATA = True
|
|
|
|
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("task_slowdown") \
|
|
.config("spark.driver.maxResultSize", "128g") \
|
|
.config("spark.local.dir", sys.argv[2]) \
|
|
.config("spark.driver.memory", sys.argv[3]) \
|
|
.getOrCreate()
|
|
sc = spark.sparkContext
|
|
|
|
#
|
|
# READING INSTANCE EVENTS DATA
|
|
#
|
|
dfepath = "/home/claudio/google_2019/instance_events/" + cluster
|
|
dfepath += "/" + cluster + ("_instance_events00000000000?.json.gz" if TESTDATA else "_instance_events*.json.gz")
|
|
dfe = spark.read.json(dfepath)
|
|
try:
|
|
dfe["collection_type"] = dfe["collection_type"].cast(ByteType())
|
|
except:
|
|
dfe = dfe.withColumn("collection_type", lit(None).cast(ByteType()))
|
|
|
|
#
|
|
# READING INSTANCE USAGE DATA
|
|
#
|
|
dfupath = "/home/claudio/google_2019/instance_usage/" + cluster
|
|
dfupath += "/" + cluster + ("_instance_usage00000000000?.csv.gz" if TESTDATA else "_instance_usage*.csv.gz")
|
|
usage_schema = StructType() \
|
|
.add("start_time", LongType(), True) \
|
|
.add("end_time", LongType(), True) \
|
|
.add("collection_id", StringType(), True) \
|
|
.add("instance_index", StringType(), True) \
|
|
.add("machine_id", StringType(), True) \
|
|
.add("alloc_collection_id", LongType(), True) \
|
|
.add("alloc_instance_index", StringType(), True) \
|
|
.add("collection_type", ByteType(), True) \
|
|
.add("average_usage_cpus", DoubleType(), True) \
|
|
.add("average_usage_memory", DoubleType(), True) \
|
|
.add("maximum_usage_cpus", DoubleType(), True) \
|
|
.add("maximum_usage_memory", DoubleType(), True) \
|
|
.add("random_sample_usage_cpus", DoubleType(), True) \
|
|
.add("random_sample_usage_memory", DoubleType(), True) \
|
|
.add("assigned_memory", DoubleType(), True) \
|
|
.add("page_cache_memory", DoubleType(), True) \
|
|
.add("cycles_per_instruction", DoubleType(), True) \
|
|
.add("memory_accLesses_per_instruction", DoubleType(), True) \
|
|
.add("sample_rate", DoubleType(), True) \
|
|
.add("cpu_usage_dist_00", DoubleType(), True) \
|
|
.add("cpu_usage_dist_10", DoubleType(), True) \
|
|
.add("cpu_usage_dist_20", DoubleType(), True) \
|
|
.add("cpu_usage_dist_30", DoubleType(), True) \
|
|
.add("cpu_usage_dist_40", DoubleType(), True) \
|
|
.add("cpu_usage_dist_50", DoubleType(), True) \
|
|
.add("cpu_usage_dist_60", DoubleType(), True) \
|
|
.add("cpu_usage_dist_70", DoubleType(), True) \
|
|
.add("cpu_usage_dist_80", DoubleType(), True) \
|
|
.add("cpu_usage_dist_90", DoubleType(), True) \
|
|
.add("cpu_usage_dist_91", DoubleType(), True) \
|
|
.add("cpu_usage_dist_92", DoubleType(), True) \
|
|
.add("cpu_usage_dist_93", DoubleType(), True) \
|
|
.add("cpu_usage_dist_94", DoubleType(), True) \
|
|
.add("cpu_usage_dist_95", DoubleType(), True) \
|
|
.add("cpu_usage_dist_96", DoubleType(), True) \
|
|
.add("cpu_usage_dist_97", DoubleType(), True) \
|
|
.add("cpu_usage_dist_98", DoubleType(), True) \
|
|
.add("cpu_usage_dist_99", DoubleType(), True)
|
|
|
|
dfu = spark.read.format("csv") \
|
|
.option("header", False) \
|
|
.schema(usage_schema) \
|
|
.load(dfupath)
|
|
|
|
# READING MACHINE EVENTS DATA, sort them and save them as broadcast variable
|
|
print("Starting to read machine events...")
|
|
dfm = pd.read_csv("~/google_2019/machine_events/" + cluster + "_machine_events.csv")
|
|
print("Dropping remove events...")
|
|
dfm = dfm[(dfm.type==1)|(dfm.type==3)]
|
|
print("Dropping missing data events...")
|
|
dfm = dfm[dfm.missing_data_reason.notnull()]
|
|
print("Projecting on useful columns...")
|
|
dfm = dfm[["time", "machine_id", "capacity.cpus", "capacity.memory"]]
|
|
print("Sorting by time...")
|
|
dfm = dfm.sort_values(by=["machine_id", "time"])
|
|
print("Converting to broadcast variable...")
|
|
dfm = sc.broadcast([tuple(r) for r in dfm.to_numpy()])
|
|
print("Done with machine events.")
|
|
|
|
def tabid(x):
|
|
return Decimal(x.collection_id) + Decimal(x.instance_index) / Decimal(2**64)
|
|
|
|
# interpolate machine data by extending each last machine report before a gap to cover it
|
|
def clean_usage(x):
|
|
return [tabid(x), Decimal(x.average_usage_cpus), Decimal(x.average_usage_memory), int(x.start_time),
|
|
int(x.end_time), x.machine_id]
|
|
|
|
def interpolate_usage(ts):
|
|
ts = sorted(ts, key=lambda x: x[3])
|
|
l = len(ts)
|
|
for i in range(1, l-1):
|
|
if ts[i+1][3] > ts[i][4]:
|
|
ts[i][4] = ts[i+1][3]
|
|
return ts
|
|
|
|
dfu = dfu.rdd \
|
|
.filter(lambda x: x.start_time is not None and x.end_time is not None and
|
|
x.instance_index is not None and x.collection_id is not None and
|
|
x.machine_id is not None) \
|
|
.map(clean_usage).groupBy(lambda x: x[0]) \
|
|
.flatMap(lambda x: interpolate_usage(x[1])).toDF(["id", "acpu", "aram", "start", "end", "mid"])
|
|
|
|
dfe = dfe.rdd \
|
|
.filter(lambda x: x.time is not None and x.type is not None and x.machine_id is not None and
|
|
x.instance_index is not None and x.collection_id is not None and x.resource_request is not None and
|
|
x.resource_request.cpus is not None and x.resource_request.memory is not None) \
|
|
.map(lambda x: [tabid(x), int(x.time), int(x.type),
|
|
Decimal(x.resource_request.cpus), Decimal(x.resource_request.memory), x.machine_id]) \
|
|
.toDF(["id", "time", "type", "rcpu", "rram", "mid"])
|
|
|
|
df = dfe.join(dfu, [dfe.id == dfu.id, dfe.mid == dfu.mid, dfe.time >= dfu.start, dfe.time < dfu.end])
|
|
|
|
def get_machine_time_resources(machine_id):
|
|
def aux(i, j):
|
|
mid = (i + j) // 2
|
|
print(i, j, mid)
|
|
print(dfm.value[mid])
|
|
if dfm.value[mid][1] > machine_id:
|
|
return aux(i, mid - 1)
|
|
elif dfm.value[mid][1] < machine_id:
|
|
return aux(mid + 1, j)
|
|
else:
|
|
start = mid
|
|
while dfm.value[start][1] == machine_id and start >= 0: # once found, search for oldest log for machine
|
|
start -= 1
|
|
start += 1
|
|
end = mid
|
|
while dfm.value[end][1] == machine_id and end < len(dfm.value):
|
|
end += 1
|
|
return dfm.value[start:end]
|
|
return aux(0, len(dfm.value)-1)
|
|
|
|
def increment_reserv_bucket(bucket, ceils, taskid, reserv):
|
|
idx = 0
|
|
while ceils[idx] < reserv and idx < len(ceils):
|
|
idx += 1
|
|
|
|
if taskid not in bucket:
|
|
bucket[taskid] = [0] * (len(ceils) + 1)
|
|
bucket[taskid][idx] += 1
|
|
|
|
def bucket_sum_per_termination(bucket, last_term_by_id):
|
|
result = {-1: None, 4: None, 5: None, 6: None, 7: None, 8: None}
|
|
for tid, vs in bucket.items():
|
|
term = last_term_by_id[tid]
|
|
if result[term] is None:
|
|
result[term] = vs
|
|
else:
|
|
result[term] = [sum(x) for x in zip(result[term], vs)]
|
|
return result
|
|
|
|
def for_each_joined(x):
|
|
machine_id = x[0]
|
|
ts = x[1]
|
|
|
|
ts = sorted(ts, key=lambda x: x.time)
|
|
last_req_by_id = {} # map taskid -> last known req [cpu, ram] (data removed when task terminates)
|
|
|
|
reserv_ceils = [0.2, 0.4, 0.6, 0.8, 1]
|
|
cpu_reservs_by_id = {} # map taskid -> [a, b, c, d, e, f] where:
|
|
# a: count of event with res. reserv. <0.2
|
|
# b: count of event with res. reserv. [0.2, 0.4)
|
|
# c: count of event with res. reserv. [0.4, 0.6)
|
|
# d: count of event with res. reserv. [0.6, 0.8)
|
|
# e: count of event with res. reserv. [0.8, 0.1)
|
|
# f: count of event with res. reserv. >=1
|
|
ram_reservs_by_id = {}
|
|
|
|
request_ceils = [0.025, 0.05, 0.075]
|
|
cpu_request_by_id = {} # map taskid -> [a, b, c, d] where <0.025, [0.025, 0.05), [0.05,0.075), >=0.075
|
|
ram_request_by_id = {}
|
|
|
|
util_ceils = reserv_ceils
|
|
cpu_util_by_id = {}
|
|
ram_util_by_id = {}
|
|
|
|
last_term_by_id = {} # map taskid -> last termination
|
|
machine_logs = get_machine_time_resources(machine_id)
|
|
|
|
for i, t in enumerate(ts):
|
|
if len(machine_logs) > 1 and machine_logs[1][0] >= t.time:
|
|
machine_logs.pop(0)
|
|
if t.id not in last_term_by_id:
|
|
last_term_by_id[t.id] = -1
|
|
if t.type >= 4 and t.type <= 8:
|
|
last_term_by_id[t.id] = t.type
|
|
del last_req_by_id[t.id]
|
|
else:
|
|
if t.rcpu is not None and t.rram is not None:
|
|
last_req_by_id[t.id] = (t.rcpu, t.rram)
|
|
|
|
# 8b
|
|
tot_req = [sum(x) for x in zip(*last_req_by_id.values())]
|
|
reserv_cpu = tot_req[0] / machine_logs[0][2]
|
|
reserv_ram = tot_req[1] / machine_logs[0][3]
|
|
increment_reserv_bucket(cpu_reservs_by_id, reserv_ceils, t.id, reserv_cpu)
|
|
increment_reserv_bucket(ram_reservs_by_id, reserv_ceils, t.id, reserv_ram)
|
|
|
|
# 8a
|
|
increment_reserv_bucket(cpu_request_by_id, request_ceils, t.id, t.rcpu)
|
|
increment_reserv_bucket(ram_request_by_id, request_ceils, t.id, t.rram)
|
|
|
|
# 8c
|
|
increment_reserv_bucket(cpu_util_by_id, util_ceils, t.id, t.acpu)
|
|
increment_reserv_bucket(ram_util_by_id, util_ceils, t.id, t.aram)
|
|
|
|
resobj = {'rcpu': cpu_request_by_id, 'rram': ram_request_by_id, 'rscpu': cpu_reservs_by_id,
|
|
'rsram': ram_reservs_by_id, 'ucpu': cpu_util_by_id, 'uram': ram_util_by_id}
|
|
|
|
for k, v in resobj.items():
|
|
resobj[k] = bucket_sum_per_termination(v, last_term_by_id)
|
|
|
|
return resobj
|
|
|
|
def fold_resobjs(ro1, ro2):
|
|
if ro1 is None:
|
|
return ro2
|
|
elif ro2 is None:
|
|
return ro1
|
|
else:
|
|
for k in ro1.keys():
|
|
for kk in ro1[k].keys():
|
|
if ro1[k][kk] is None:
|
|
ro1[k][kk] = ro2[k][kk]
|
|
elif ro2[k][kk] is None:
|
|
continue
|
|
else:
|
|
ro1[k][kk] = [sum(x) for x in zip(ro1[k][kk], ro2[k][kk])]
|
|
return ro1
|
|
|
|
# TODO: partition by id and in the for-each-row
|
|
# function implement lookup to dfm.value to understand its memory capacity
|
|
|
|
result = df.rdd \
|
|
.groupBy(lambda x: x.mid) \
|
|
.map(for_each_joined) \
|
|
.fold(None, fold_resobjs)
|
|
|
|
d = os.path.dirname(os.path.realpath(__file__))
|
|
|
|
with open(d + "/" + cluster + "_figure8.json", "w") as f:
|
|
json.dump(result, f)
|
|
|
|
# vim: set ts=4 sw=4 et tw=120:
|