122 lines
3.0 KiB
Python
Executable File
122 lines
3.0 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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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 ByteType
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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("figure_9b") \
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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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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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MICROS = 1000000
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def sumrow(l, p, t, c):
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t = t // (MICROS * 60)
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if t < 1:
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t = "<1"
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elif t < 2:
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t = "1-2"
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elif t < 4:
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t = "2-4"
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elif t < 10:
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t = "4-10"
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elif t < 60:
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t = "10-60"
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elif t < 60 * 24:
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t = "60-1d"
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else:
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t = ">=1d"
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return (l, p, t, c)
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def sumid(sr):
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return (sr[0], sr[1], sr[2])
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def for_each_task(ts):
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global non
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ts = sorted(ts, key=lambda x: x["time"])
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in_exec = False
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exec_start = None
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exec_tot = 0
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priority = -1
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l = len(ts)
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last_term = -1
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for i,t in enumerate(ts):
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if t["priority"] is not -1 and priority is -1:
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priority = t["priority"]
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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 in_exec and (t["type"] == 1 or (t["type"] >= 4 and t["type"] <= 8)):
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exec_tot += t["time"] - exec_start
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in_exec = False
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if (not in_exec) and (t["type"] == 3):
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exec_start = t["time"]
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in_exec = True
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return sumrow(last_term, priority, exec_tot, l)
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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.collection_id + "-" + x.instance_index,
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"priority": -1 if x.priority is None else int(x.priority)
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}
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def sum_rows(xs):
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csum = 0
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for x in xs:
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csum += x[3]
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return csum
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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.time is not None and x.instance_index is not None and x.collection_id is not None) \
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.map(cleanup) \
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.groupBy(lambda x: x["id"]) \
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.mapValues(for_each_task) \
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.map(lambda x: x[1]) \
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.groupBy(lambda x: sumid(x)) \
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.mapValues(sum_rows) \
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.map(lambda x: str(x[0][0]) + "," + str(x[0][1]) + "," + str(x[0][2]) + "," + str(x[1])) \
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.coalesce(1) \
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.saveAsTextFile(cluster + "_priority_exectime")
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# vim: set ts=4 sw=4 et tw=80:
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