71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
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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 pandas
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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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import os
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import pyspark.sql.functions as F
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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 StructType, LongType, StringType, ByteType
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def main():
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global DIR
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global NAME
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global launched
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if len(sys.argv) != 4:
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print(sys.argv[0] + " {cluster} {tmpdir} {maxram}")
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sys.exit()
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DIR = os.path.dirname(__file__)
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NAME = "table-iv-tasks-all"
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if os.path.exists(DIR + "/" + NAME + "-working") or os.path.exists(DIR + "/" + NAME + ".parquet"):
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print("already launched")
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launched = True
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sys.exit()
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os.system("touch " + DIR + "/" + NAME + "-working")
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spark = pyspark.sql.SparkSession.builder \
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.appName(NAME) \
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.config("spark.driver.maxResultSize", "32g") \
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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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usage_schema = StructType() \
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.add("jobid", StringType(), False) \
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.add("job_term", LongType(), False) \
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.add("task_count", LongType(), False)
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df = spark.read.format("csv") \
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.option("header", False) \
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.schema(usage_schema) \
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.load("/home/claudio/google_2019/thesis_queries/figure_9/?_task_count")
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df = df.groupBy("job_term").agg( \
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F.expr("avg(task_count)").alias('mean_tc'),
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F.expr("percentile(task_count, array(0.95))")[0].alias('%95_tc'))
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df.repartition(1).write.csv(DIR + "/" + NAME + ".csv")
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if __name__ == "__main__":
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launched = False
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try:
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main()
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finally:
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if not launched:
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os.system("rm -v " + DIR + "/" + NAME + "-working")
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# vim: set ts=4 sw=4 et tw=120:
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