276 lines
10 KiB
Python
Executable File
276 lines
10 KiB
Python
Executable File
#!/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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TESTDATA = True
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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
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dfepath += "/" + cluster + ("_instance_events00000000000?.json.gz" if TESTDATA else "_instance_events*.json.gz")
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dfe = spark.read.json(dfepath)
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try:
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dfe["collection_type"] = dfe["collection_type"].cast(ByteType())
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except:
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dfe = dfe.withColumn("collection_type", lit(None).cast(ByteType()))
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#
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# READING INSTANCE USAGE DATA
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#
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dfupath = "/home/claudio/google_2019/instance_usage/" + cluster
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dfupath += "/" + cluster + ("_instance_usage00000000000?.csv.gz" if TESTDATA else "_instance_usage*.csv.gz")
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usage_schema = StructType() \
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.add("start_time", LongType(), True) \
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.add("end_time", LongType(), True) \
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.add("collection_id", StringType(), True) \
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.add("instance_index", StringType(), True) \
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.add("machine_id", StringType(), True) \
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.add("alloc_collection_id", LongType(), True) \
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.add("alloc_instance_index", StringType(), True) \
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.add("collection_type", ByteType(), True) \
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.add("average_usage_cpus", DoubleType(), True) \
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.add("average_usage_memory", DoubleType(), True) \
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.add("maximum_usage_cpus", DoubleType(), True) \
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.add("maximum_usage_memory", DoubleType(), True) \
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.add("random_sample_usage_cpus", DoubleType(), True) \
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.add("random_sample_usage_memory", DoubleType(), True) \
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.add("assigned_memory", DoubleType(), True) \
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.add("page_cache_memory", DoubleType(), True) \
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.add("cycles_per_instruction", DoubleType(), True) \
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.add("memory_accLesses_per_instruction", DoubleType(), True) \
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.add("sample_rate", DoubleType(), True) \
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.add("cpu_usage_dist_00", DoubleType(), True) \
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.add("cpu_usage_dist_10", DoubleType(), True) \
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.add("cpu_usage_dist_20", DoubleType(), True) \
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.add("cpu_usage_dist_30", DoubleType(), True) \
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.add("cpu_usage_dist_40", DoubleType(), True) \
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.add("cpu_usage_dist_50", DoubleType(), True) \
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.add("cpu_usage_dist_60", DoubleType(), True) \
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.add("cpu_usage_dist_70", DoubleType(), True) \
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.add("cpu_usage_dist_80", DoubleType(), True) \
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.add("cpu_usage_dist_90", DoubleType(), True) \
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.add("cpu_usage_dist_91", DoubleType(), True) \
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.add("cpu_usage_dist_92", DoubleType(), True) \
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.add("cpu_usage_dist_93", DoubleType(), True) \
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.add("cpu_usage_dist_94", DoubleType(), True) \
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.add("cpu_usage_dist_95", DoubleType(), True) \
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.add("cpu_usage_dist_96", DoubleType(), True) \
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.add("cpu_usage_dist_97", DoubleType(), True) \
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.add("cpu_usage_dist_98", DoubleType(), True) \
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.add("cpu_usage_dist_99", DoubleType(), True)
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dfu = spark.read.format("csv") \
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.option("header", False) \
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.schema(usage_schema) \
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.load(dfupath)
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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")
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print("Dropping remove events...")
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dfm = dfm[(dfm.type==1)|(dfm.type==3)]
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print("Dropping missing data events...")
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dfm = dfm[dfm.missing_data_reason.notnull()]
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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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def tabid(x):
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return Decimal(x.collection_id) + Decimal(x.instance_index) / Decimal(2**64)
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# interpolate machine data by extending each last machine report before a gap to cover it
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def clean_usage(x):
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return [tabid(x), Decimal(x.average_usage_cpus), Decimal(x.average_usage_memory), int(x.start_time),
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int(x.end_time), x.machine_id]
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def interpolate_usage(ts):
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ts = sorted(ts, key=lambda x: x[3])
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l = len(ts)
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for i in range(1, l-1):
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if ts[i+1][3] > ts[i][4]:
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ts[i][4] = ts[i+1][3]
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return ts
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dfu = dfu.rdd \
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.filter(lambda x: x.start_time is not None and x.end_time is not None and
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x.instance_index is not None and x.collection_id is not None and
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x.machine_id is not None) \
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.map(clean_usage).groupBy(lambda x: x[0]) \
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.flatMap(lambda x: interpolate_usage(x[1])).toDF(["id", "acpu", "aram", "start", "end", "mid"])
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dfe = 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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df = dfe.join(dfu, [dfe.id == dfu.id, dfe.mid == dfu.mid, dfe.time >= dfu.start, dfe.time < dfu.end])
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def get_machine_time_resources(machine_id):
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def aux(i, j):
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mid = (i + j) // 2
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print(i, j, mid)
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print(dfm.value[mid])
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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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else:
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start = mid
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while dfm.value[start][1] == machine_id and start >= 0: # once found, search for oldest log for machine
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start -= 1
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start += 1
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end = mid
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while dfm.value[end][1] == machine_id and end < len(dfm.value):
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end += 1
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return dfm.value[start:end]
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return aux(0, len(dfm.value)-1)
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def increment_reserv_bucket(bucket, ceils, taskid, reserv):
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idx = 0
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while ceils[idx] < reserv and idx < len(ceils):
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idx += 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 = [0.2, 0.4, 0.6, 0.8, 1]
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cpu_reservs_by_id = {} # map taskid -> [a, b, c, d, e, f] where:
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# a: count of event with res. reserv. <0.2
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# b: count of event with res. reserv. [0.2, 0.4)
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# c: count of event with res. reserv. [0.4, 0.6)
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# d: count of event with res. reserv. [0.6, 0.8)
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# e: count of event with res. reserv. [0.8, 0.1)
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# f: count of event with res. reserv. >=1
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ram_reservs_by_id = {}
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request_ceils = [0.025, 0.05, 0.075]
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cpu_request_by_id = {} # map taskid -> [a, b, c, d] where <0.025, [0.025, 0.05), [0.05,0.075), >=0.075
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ram_request_by_id = {}
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util_ceils = reserv_ceils
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cpu_util_by_id = {}
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ram_util_by_id = {}
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last_term_by_id = {} # map taskid -> last termination
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machine_logs = get_machine_time_resources(machine_id)
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for i, t in enumerate(ts):
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if 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.type >= 4 and t.type <= 8:
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last_term_by_id[t.id] = t.type
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del last_req_by_id[t.id]
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else:
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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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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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increment_reserv_bucket(cpu_reservs_by_id, reserv_ceils, t.id, reserv_cpu)
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increment_reserv_bucket(ram_reservs_by_id, reserv_ceils, t.id, reserv_ram)
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# 8a
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increment_reserv_bucket(cpu_request_by_id, request_ceils, t.id, t.rcpu)
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increment_reserv_bucket(ram_request_by_id, request_ceils, t.id, t.rram)
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# 8c
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increment_reserv_bucket(cpu_util_by_id, util_ceils, t.id, t.acpu)
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increment_reserv_bucket(ram_util_by_id, util_ceils, t.id, t.aram)
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resobj = {'rcpu': cpu_request_by_id, 'rram': ram_request_by_id, 'rscpu': cpu_reservs_by_id,
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'rsram': ram_reservs_by_id, 'ucpu': cpu_util_by_id, 'uram': ram_util_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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result = df.rdd \
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.groupBy(lambda x: x.mid) \
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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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