52 lines
1.5 KiB
Python
Executable file
52 lines
1.5 KiB
Python
Executable file
#!/usr/bin/env python3
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# coding: utf-8
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import os
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import json
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import pandas as pd
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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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if len(sys.argv) is not 5:
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print(sys.argv[0] + " {cluster} {tmpdir} {maxram} {basedir}")
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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_9a") \
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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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# READING INSTANCE EVENTS DATA
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dfepath = sys.argv[4] + "/" + cluster + "/" + cluster + "_instance_events*.json.gz"
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#dfepath = sys.argv[4] + "/" + 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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df = df.rdd.filter(lambda x: x.collection_id is not None and x.instance_index is not None and
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(x.collection_type == 0 or x.collection_type is None)) \
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.map(lambda x: (x.collection_id, x.instance_index)) \
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.groupBy(lambda x: x[0]) \
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.map(lambda x: [x[0], len(x[1])]) \
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.toDF(["jobid", "task_count"])
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df.write.parquet("/home/claudio/google_2019/thesis_queries/figure_9/figure-9a-task-count-" + cluster + ".parquet")
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# vim: set ts=4 sw=4 et tw=120:
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