80 lines
3.2 KiB
Python
Executable File
80 lines
3.2 KiB
Python
Executable File
#!/usr/bin/env python3
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# coding: utf-8
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# # Temporal impact: machine time waste
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import pandas
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from IPython import display
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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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from pyspark.sql.functions import col, lag, when, concat_ws, last, first
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from pyspark.sql import Window
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from pyspark.sql.types import LongType
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cluster="b"
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spark = pyspark.sql.SparkSession.builder \
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.appName("machine_time_waste") \
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.config("spark.local.dir", "/run/tmpfiles.d/spark") \
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.config("spark.driver.memory", "124g") \
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.getOrCreate()
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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.printSchema()
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df.show()
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# .filter(df.collection_type == 0) \
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df2 = df \
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.withColumn("time", col("time").cast(LongType())) \
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.withColumn("type", col("type").cast(LongType())) \
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.withColumn("type", when(col("type").isNull(), 0).otherwise(col("type"))) \
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.withColumn("id", concat_ws("-", "collection_id", "instance_index")) \
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.where(col("time").isNotNull()) \
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.where(col("type").isNotNull()) \
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.where((col("instance_index").isNotNull()) & (col("collection_id").isNotNull())) \
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.select("time", "type", "id")
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df2.show()
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print("Total: " + str(df.count()))
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print("Filtered: " + str(df2.count()))
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# my_window = Window.partitionBy("machine_id", "id").orderBy(df2.time.asc())
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w2 = Window.partitionBy("id").orderBy(df2.time.asc()).rowsBetween(Window.currentRow, Window.unboundedFollowing)
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# .withColumn("prev_time", lag(df2.time).over(my_window)) \
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# .withColumn("prev_type", lag(df2.type).over(my_window)) \
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df3 = df2 \
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.withColumn("t3_time", when((df2.type != 3), None).otherwise(df2.time)) \
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.withColumn("t45678_time", when((df2.type < 4) | (df2.type > 8), None).otherwise(df2.time)) \
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.withColumn("t45678_type", when((df2.type < 4) | (df2.type > 8), None).otherwise(df2.type)) \
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.withColumn("t01_time", when((df2.type != 0) & (df2.type != 1), None).otherwise(df2.time)) \
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.withColumn("t01_type", when((df2.type != 0) & (df2.type != 1), None).otherwise(df2.type)) \
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.withColumn("next_time", when(df2.type == 3, first(col("t45678_time"), True).over(w2)) \
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.when((df2.type == 0) | (df2.type == 1), first(col("t3_time"), True).over(w2)) \
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.when((df2.type >= 4) | (df2.type <= 8), first(col("t01_time"), True).over(w2)) \
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.otherwise(None)) \
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.withColumn("next_type", when(df2.type == 3, first(col("t45678_type"), True).over(w2)) \
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.when((df2.type == 0) | (df2.type == 1), 3) \
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.when((df2.type >= 4) | (df2.type <= 8), first(col("t01_type"), True).over(w2)) \
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.otherwise(None)) \
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.withColumn("last_term_type", last(col("t45678_type"), True).over(w2)) \
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.withColumn("time_delta", col("next_time") - col("time")) \
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.select("id", "time", "type", "last_term_type", "time_delta", "t01_time", \
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"t01_type", "t3_time", "t45678_time", "t45678_type", "next_time", "next_type")
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df4 = df3.where(df3.next_type.isNotNull()).groupby("type", "next_type", "last_term_type").sum("time_delta")
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# df3.orderBy(df3.machine_id, df3.time).show(n=100)
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# df3.printSchema()
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df4.show(n=1000000)
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df4.write.csv("/home/claudio/google_2019/thesis_queries/machine_time_waste/" + cluster + "_state_change.csv")
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# vim: set ts=2 sw=2 et tw=120:
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