191 lines
6.3 KiB
Python
191 lines
6.3 KiB
Python
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#!/usr/bin/env python3
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# coding: utf-8
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import os
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import json
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import pandas as pd
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import findspark
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findspark.init()
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import pyspark
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import pyspark.sql
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import sys
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import gzip
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from pyspark import AccumulatorParam
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from pyspark.sql.functions import lit
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from pyspark.sql import Window
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from pyspark.sql.types import *
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from decimal import *
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if len(sys.argv) is not 4:
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print(sys.argv[0] + " {cluster} {tmpdir} {maxram}")
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sys.exit()
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cluster=sys.argv[1]
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spark = pyspark.sql.SparkSession.builder \
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.appName("task_slowdown") \
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.config("spark.driver.maxResultSize", "128g") \
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.config("spark.local.dir", sys.argv[2]) \
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.config("spark.driver.memory", sys.argv[3]) \
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.getOrCreate()
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sc = spark.sparkContext
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#
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# READING INSTANCE EVENTS DATA
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#
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dfepath = "/home/claudio/google_2019/instance_events/" + cluster + "/" + cluster + "_instance_events*.json.gz"
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df = spark.read.json(dfepath)
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df = df.limit(10000)
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# READING MACHINE EVENTS DATA, sort them and save them as broadcast variable
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print("Starting to read machine events...")
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dfm = pd.read_csv("~/google_2019/machine_events/" + cluster + "_machine_events.csv", converters={
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'time': lambda x: -1 if x == '' else int(x),
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'machine_id': lambda x: str(x),
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'capacity.cpus': lambda x: -1 if x == '' else Decimal(x),
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'capacity.memory': lambda x: -1 if x == '' else Decimal(x)})
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print("Dropping remove events...")
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dfm = dfm[(dfm.type!=2)&(dfm.time!=-1)&(dfm["capacity.cpus"]!=-1)&(dfm["capacity.memory"]!=-1)]
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print("Dropping missing data events...")
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dfm = dfm[dfm.missing_data_reason.isnull()]
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print("Projecting on useful columns...")
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dfm = dfm[["time", "machine_id", "capacity.cpus", "capacity.memory"]]
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print("Sorting by time...")
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dfm = dfm.sort_values(by=["machine_id", "time"])
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print("Converting to broadcast variable...")
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dfm = sc.broadcast([tuple(r) for r in dfm.to_numpy()])
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print("Done with machine events.")
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df = dfe.rdd \
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.filter(lambda x: x.time is not None and x.type is not None and x.machine_id is not None and
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x.instance_index is not None and x.collection_id is not None and x.resource_request is not None and
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x.resource_request.cpus is not None and x.resource_request.memory is not None) \
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.map(lambda x: [tabid(x), int(x.time), int(x.type),
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Decimal(x.resource_request.cpus), Decimal(x.resource_request.memory), x.machine_id]) \
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.toDF(["id", "time", "type", "rcpu", "rram", "mid"])
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def get_machine_time_resources(machine_id, time):
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def aux(i, j):
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if i == j:
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return dfm.value[i] if dfm.value[i][1] == machine_id else None
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elif i + 1 == j:
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if dfm.value[i][1] == machine_id:
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return dfm.value[i]
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elif dfm.value[j][1] == machine_id:
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return dfm.value[j]
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else:
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return None
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mid = (i + j) // 2
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if dfm.value[mid][1] > machine_id:
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return aux(i, mid - 1)
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elif dfm.value[mid][1] < machine_id:
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return aux(mid + 1, j)
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elif dfm.value[mid][0] > time:
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return aux(i, mid)
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elif dfm.value[mid][0] < time:
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return aux(mid, j)
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else:
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return mid
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return aux(0, len(dfm.value)-1)
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def increment_reserv_bucket(bucket, taskid, value):
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if value < 0:
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idx = 0
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else:
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idx = 40 if value >= 1 else (int(value * 40) + 1)
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if taskid not in bucket:
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bucket[taskid] = [0] * (len(ceils) + 1)
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bucket[taskid][idx] += 1
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def bucket_sum_per_termination(bucket, last_term_by_id):
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result = {-1: None, 4: None, 5: None, 6: None, 7: None, 8: None}
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for tid, vs in bucket.items():
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term = last_term_by_id[tid]
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if result[term] is None:
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result[term] = vs
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else:
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result[term] = [sum(x) for x in zip(result[term], vs)]
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return result
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def for_each_joined(x):
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machine_id = x[0]
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ts = x[1]
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ts = sorted(ts, key=lambda x: x.time)
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last_req_by_id = {} # map taskid -> last known req [cpu, ram] (data removed when task terminates)
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reserv_ceils = [0]
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cpu_reservs_by_id = {}
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ram_reservs_by_id = {}
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last_term_by_id = {} # map taskid -> last termination
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start = get_machine_time_resources(machine_id, 0)
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end = get_machine_time_resources(machine_id, 6_000_000_000_000)
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machine_logs = None if start is None or end is None else dfm.value[start:(end+1)]
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for i, t in enumerate(ts):
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if machine_logs is not None and len(machine_logs) > 1 and machine_logs[1][0] >= t.time:
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machine_logs.pop(0)
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if t.id not in last_term_by_id:
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last_term_by_id[t.id] = -1
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if t.rcpu is not None and t.rram is not None:
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last_req_by_id[t.id] = (t.rcpu, t.rram)
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# 8b
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tot_req = [sum(x) for x in zip(*last_req_by_id.values())]
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if machine_logs is not None:
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reserv_cpu = tot_req[0] / machine_logs[0][2]
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reserv_ram = tot_req[1] / machine_logs[0][3]
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else:
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reserv_cpu = -1
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reserv_ram = -1
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increment_reserv_bucket(cpu_reservs_by_id, t.id, reserv_cpu)
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increment_reserv_bucket(ram_reservs_by_id, t.id, reserv_ram)
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if t.type >= 4 and t.type <= 8:
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last_term_by_id[t.id] = t.type
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resobj = {'rscpu': cpu_reservs_by_id, 'rsram': ram_reservs_by_id}
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for k, v in resobj.items():
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resobj[k] = bucket_sum_per_termination(v, last_term_by_id)
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return resobj
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def fold_resobjs(ro1, ro2):
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if ro1 is None:
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return ro2
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elif ro2 is None:
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return ro1
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else:
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for k in ro1.keys():
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for kk in ro1[k].keys():
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if ro1[k][kk] is None:
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ro1[k][kk] = ro2[k][kk]
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elif ro2[k][kk] is None:
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continue
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else:
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ro1[k][kk] = [sum(x) for x in zip(ro1[k][kk], ro2[k][kk])]
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return ro1
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# TODO: partition by id and in the for-each-row
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# function implement lookup to dfm.value to understand its memory capacity
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import random
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result = df.rdd \
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.groupBy(lambda x: x.mid) \
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.partitionBy(10000, lambda x: random.randint(0, 10000-1)) \
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.map(for_each_joined) \
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.fold(None, fold_resobjs)
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d = os.path.dirname(os.path.realpath(__file__))
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with open(d + "/" + cluster + "_figure8.json", "w") as f:
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json.dump(result, f)
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# vim: set ts=4 sw=4 et tw=120:
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