104 lines
3.8 KiB
Python
104 lines
3.8 KiB
Python
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# vim: set ts=4 sw=4 et tw=80:
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#!/usr/bin/env python3
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# coding: utf-8
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import json
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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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cluster=sys.argv[1]
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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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spark = pyspark.sql.SparkSession.builder \
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.appName("task_slowdown") \
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.config("spark.driver.maxResultSize", "256g") \
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.config("spark.local.dir", "/home/claudio/tmp") \
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.config("spark.driver.memory", "120g") \
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.getOrCreate()
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sc = spark.sparkContext
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path = "/home/claudio/" + cluster + "/" + cluster
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path += "_instance_usage*.csv.gz"
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#path += "_test.csv"
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df = spark.read.format("csv") \
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.option("header", False) \
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.schema(schema) \
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.load(path)
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def compute_res_s(x):
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delta = int(x.end_time) - int(x.start_time);
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return [x.collection_id + "-" + x.instance_index,
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Decimal(x.average_usage_cpus) * Decimal(delta),
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Decimal(x.average_usage_memory) * Decimal(delta)]
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def sum_res(xs):
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cpu = Decimal(0)
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ram = Decimal(0)
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for x in xs:
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cpu += x[1]
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ram += x[2]
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return ['{0:f}'.format(cpu), '{0:f}'.format(ram)]
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filename = "/home/claudio/p620/google_2019/thesis_queries" + \
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"/spatial_resource_waste/" + cluster + "_res_micros_actual_per_job"
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df2 = df.rdd \
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.filter(lambda x: x.collection_type is None or x.collection_type == 0) \
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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) \
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.map(compute_res_s) \
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.groupBy(lambda x: x[0]) \
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.mapValues(sum_res) \
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.map(lambda x: x[0] + "," + x[1][0] + "," + x[1][1]) \
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.saveAsTextFile(filename, "org.apache.hadoop.io.compress.GzipCodec")
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