105 lines
2.8 KiB
Python
Executable File
105 lines
2.8 KiB
Python
Executable File
#!/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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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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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 ByteType
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from pyspark.sql.functions import col
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import math
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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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findspark.init()
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DIR = os.path.dirname(__file__)
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spark = pyspark.sql.SparkSession.builder \
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.appName("figure_9a_join") \
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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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dfepath="/home/claudio/google_2019/collection_events/" + cluster + "/" + cluster + "_collection_events*.json.gz"
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#dfepath="/home/claudio/google_2019/collection_events/" + cluster + "/" + cluster + "_test.json"
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df = spark.read.json(dfepath)
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df = df.withColumnRenamed("collection_id", "jobid") \
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.select("jobid", "collection_type", "time", "type")
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try:
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df["collection_type"] = df["collection_type"].cast(ByteType())
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except:
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df = df.withColumn("collection_type", lit(None).cast(ByteType()))
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df = df.filter(((col("collection_type").isNull()) | (col("collection_type") ==
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"0")) & (col("time").isNotNull()))
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jpath = DIR + "/figure-9c-machine-count-" + cluster + ".parquet"
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dfj = spark.read.parquet(jpath)
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jjpath = DIR + "/figure-9a-task-count-" + cluster + ".parquet"
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dfjj = spark.read.parquet(jjpath)
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df = df.join(dfj, 'jobid', 'left')
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df = df.join(dfjj, "jobid")
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MICROS = 1000000
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def round_down(n, decimals=0):
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multiplier = 10 ** decimals
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return math.floor(n * multiplier) / multiplier
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def for_each_job(data):
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jobid = data[0]
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ts = data[1]
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global non
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ts = sorted(ts, key=lambda x: x["time"])
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l = len(ts)
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last_term = -1
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machine_count = 0
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for i,t in enumerate(ts):
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if t["type"] >= 4 and t["type"] <= 8:
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last_term = t["type"]
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if t["task_count"] <= 0 or t["machine_count"] <= 0:
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machine_count = -1
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else:
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machine_count = round_down(t["machine_count"] / t["task_count"], 1)
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return str(jobid) + "," + str(last_term) + "," + str(machine_count)
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def cleanup(x):
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return {
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"time": int(x.time),
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"type": 0 if x.type is None else int(x.type),
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"id": x.jobid,
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"task_count": -1 if x.task_count is None else int(x.task_count),
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"machine_count": -1 if x.machine_count is None else int(x.machine_count)
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}
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df2 = df.rdd \
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.map(cleanup) \
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.groupBy(lambda x: x["id"]) \
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.repartition(100) \
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.map(for_each_job) \
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.coalesce(1) \
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.saveAsTextFile(cluster + "_machine_locality")
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# vim: set ts=4 sw=4 et tw=80:
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