88 lines
2.5 KiB
Python
88 lines
2.5 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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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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DIR = os.path.dirname(__file__)
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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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cluster=sys.argv[1]
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if os.path.exists(DIR + "/bigtable-" + cluster + "-working") or os.path.exists(DIR + "/bigtable-" + cluster + ".parquet"):
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print("already launched")
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sys.exit()
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os.system("touch " + DIR + "/bigtable-" + cluster + "-working")
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spark = pyspark.sql.SparkSession.builder \
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.appName("bigtable") \
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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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df = spark.read.json("/home/claudio/google_2019/instance_events/" + cluster + "/" + cluster + "_instance_events*.json.gz")
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#df = spark.read.json("/home/claudio/google_2019/instance_events/" + cluster + "/" + cluster + "_test.json")
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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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def for_each_task(data):
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rowids = data[0].split("-")
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jobid = rowids[0]
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taskid = rowids[1]
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ts = data[1]
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ts = sorted(ts, key=lambda x: x["time"])
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last_term = -1
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counts = [0] * 11
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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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counts[t["type"]] += 1
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return (jobid, taskid, last_term, *counts)
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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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df = 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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.repartition(1000) \
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.map(for_each_task) \
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.toDF(["jobid", "taskid", "task_term", *["count_" + str(x) for x in range(0,11)]])
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df.write.parquet(DIR + "/bigtable-" + cluster + ".parquet")
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os.system("rm " + DIR + "/bigtable-" + cluster + "-working")
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