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table_iii/table-iv-tasks-h.csv/part-00000-4b92304b-a712-417f-9734-5ff862685298-c000.csv create mode 100755 table_iii/tableIII.py create mode 100755 table_iii/tableIV-tasks.py create mode 100755 table_iii/tableIV.py diff --git a/.gitignore b/.gitignore index 88e42ca9..29d66952 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,8 @@ **/.ipynb_checkpoints/ task_slowdown/?_state_changes.json.gz **/*.gz + +figure_9/*.parquet/ +figure_9/?_task_count/ +figure_9/?_machine_count/ +table_iii/*.parquet/ diff --git a/figure_9/9a-total.sh b/figure_9/9a-total.sh new file mode 100755 index 00000000..d663c43e --- /dev/null +++ b/figure_9/9a-total.sh @@ -0,0 +1,7 @@ +#!/bin/sh + +i=$1 + +cut ${i}_task_count/part-00000 -d, -f2-3 | \ + sort | uniq -c | sed 's/^\s*//' | tr ' ' ',' | \ + awk 'BEGIN {print "count,term,task_count"} {print $0}' > ${i}_term_task_count.csv diff --git a/figure_9/9c-total.sh b/figure_9/9c-total.sh new file mode 100755 index 00000000..e25cf360 --- /dev/null +++ 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+1,8,21 +1,8,244 +1,8,50000 diff --git a/figure_9/figure9a-join.py b/figure_9/figure9a-join.py new file mode 100755 index 00000000..6448888b --- /dev/null +++ b/figure_9/figure9a-join.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python3 +# coding: utf-8 + +import json +import pandas +import findspark +import pyspark +import pyspark.sql +import sys +import gzip +import os +from pyspark import AccumulatorParam +from pyspark.sql.functions import lit +from pyspark.sql import Window +from pyspark.sql.types import ByteType +from pyspark.sql.functions import col + +if len(sys.argv) is not 4: + print(sys.argv[0] + " {cluster} {tmpdir} {maxram}") + sys.exit() + +cluster=sys.argv[1] +findspark.init() + +DIR = os.path.dirname(__file__) +NAME = cluster + "_figure9a.csv" + +spark = pyspark.sql.SparkSession.builder \ + .appName("figure_9a_join") \ + .config("spark.driver.maxResultSize", "128g") \ + .config("spark.local.dir", sys.argv[2]) \ + .config("spark.driver.memory", sys.argv[3]) \ + .getOrCreate() +sc = spark.sparkContext + +dfepath="/home/claudio/google_2019/collection_events/" + cluster + "/" + cluster + "_collection_events*.json.gz" +#dfepath="/home/claudio/google_2019/collection_events/" + cluster + "/" + cluster + "_test.json" + +df = spark.read.json(dfepath) +df = df.withColumnRenamed("collection_id", "jobid") \ + .select("jobid", "collection_type", "time", "type") + +try: + df["collection_type"] = df["collection_type"].cast(ByteType()) +except: + df = df.withColumn("collection_type", lit(None).cast(ByteType())) + +df = df.filter(((col("collection_type").isNull()) | (col("collection_type") == + "0")) & (col("time").isNotNull())) + +jpath = DIR + "/figure-9a-task-count-" + cluster + ".parquet" +dfj = spark.read.parquet(jpath) + +df = df.join(dfj, 'jobid', 'left') + +MICROS = 1000000 + +def for_each_job(data): + jobid = data[0] + ts = data[1] + global non + + ts = sorted(ts, key=lambda x: x["time"]) + + l = len(ts) + last_term = -1 + task_count = 0 + + for i,t in enumerate(ts): + if t["type"] >= 4 and t["type"] <= 8: + last_term = t["type"] + task_count = t["task_count"] + + return str(jobid) + "," + str(last_term) + "," + str(task_count) + +def cleanup(x): + return { + "time": int(x.time), + "type": 0 if x.type is None else int(x.type), + "id": x.jobid, + "task_count": -1 if x.task_count is None else int(x.task_count) + } + +df2 = df.rdd \ + .map(cleanup) \ + .groupBy(lambda x: x["id"]) \ + .repartition(100) \ + .map(for_each_job) \ + .coalesce(1) \ + .saveAsTextFile(cluster + "_task_count") + +# vim: set ts=4 sw=4 et tw=80: diff --git a/figure_9/figure9a.py b/figure_9/figure9a.py index f565fdd8..ba43e51d 100755 --- a/figure_9/figure9a.py +++ b/figure_9/figure9a.py @@ -15,6 +15,7 @@ from pyspark.sql.functions import lit from pyspark.sql import Window from pyspark.sql.types import * from decimal import * +import os if len(sys.argv) is not 5: print(sys.argv[0] + " {cluster} {tmpdir} {maxram} {basedir}") @@ -22,6 +23,12 @@ if len(sys.argv) is not 5: cluster=sys.argv[1] +if os.path.exists("/home/claudio/google_2019/thesis_queries/figure_9/figure-9a-task-count-" + cluster + "-working"): + print("already done") + sys.exit() + +os.system("touch /home/claudio/google_2019/thesis_queries/figure_9/figure-9a-task-count-" + cluster + "-working") + spark = pyspark.sql.SparkSession.builder \ .appName("figure_9a") \ .config("spark.driver.maxResultSize", "128g") \ @@ -44,9 +51,10 @@ df = df.rdd.filter(lambda x: x.collection_id is not None and x.instance_index is (x.collection_type == 0 or x.collection_type is None)) \ .map(lambda x: (x.collection_id, x.instance_index)) \ .groupBy(lambda x: x[0]) \ - .map(lambda x: [x[0], len(x[1])]) \ + .map(lambda x: [x[0], len(set(x[1]))]) \ .toDF(["jobid", "task_count"]) df.write.parquet("/home/claudio/google_2019/thesis_queries/figure_9/figure-9a-task-count-" + cluster + ".parquet") +os.system("rm /home/claudio/google_2019/thesis_queries/figure_9/figure-9a-task-count-" + cluster + "-working") # vim: set ts=4 sw=4 et tw=120: diff --git a/figure_9/figure9c-join.py b/figure_9/figure9c-join.py new file mode 100755 index 00000000..90d76574 --- /dev/null +++ b/figure_9/figure9c-join.py @@ -0,0 +1,92 @@ +#!/usr/bin/env python3 +# coding: utf-8 + +import json +import pandas +import findspark +import pyspark +import pyspark.sql +import sys +import gzip +import os +from pyspark import AccumulatorParam +from pyspark.sql.functions import lit +from pyspark.sql import Window +from pyspark.sql.types import ByteType +from pyspark.sql.functions import col + +if len(sys.argv) is not 4: + print(sys.argv[0] + " {cluster} {tmpdir} {maxram}") + sys.exit() + +cluster=sys.argv[1] +findspark.init() + +DIR = os.path.dirname(__file__) +NAME = cluster + "_figure9a.csv" + +spark = pyspark.sql.SparkSession.builder \ + .appName("figure_9a_join") \ + .config("spark.driver.maxResultSize", "128g") \ + .config("spark.local.dir", sys.argv[2]) \ + .config("spark.driver.memory", sys.argv[3]) \ + .getOrCreate() +sc = spark.sparkContext + +dfepath="/home/claudio/google_2019/collection_events/" + cluster + "/" + cluster + "_collection_events*.json.gz" +#dfepath="/home/claudio/google_2019/collection_events/" + cluster + "/" + cluster + "_test.json" + +df = spark.read.json(dfepath) +df = df.withColumnRenamed("collection_id", "jobid") \ + .select("jobid", "collection_type", "time", "type") + +try: + df["collection_type"] = df["collection_type"].cast(ByteType()) +except: + df = df.withColumn("collection_type", lit(None).cast(ByteType())) + +df = df.filter(((col("collection_type").isNull()) | (col("collection_type") == + "0")) & (col("time").isNotNull())) + +jpath = DIR + "/figure-9c-machine-count-" + cluster + ".parquet" +dfj = spark.read.parquet(jpath) + +df = df.join(dfj, 'jobid', 'left') + +MICROS = 1000000 + +def for_each_job(data): + jobid = data[0] + ts = data[1] + global non + + ts = sorted(ts, key=lambda x: x["time"]) + + l = len(ts) + last_term = -1 + machine_count = 0 + + for i,t in enumerate(ts): + if t["type"] >= 4 and t["type"] <= 8: + last_term = t["type"] + machine_count = t["machine_count"] + + return str(jobid) + "," + str(last_term) + "," + str(machine_count) + +def cleanup(x): + return { + "time": int(x.time), + "type": 0 if x.type is None else int(x.type), + "id": x.jobid, + "machine_count": -1 if x.machine_count is None else int(x.machine_count) + } + +df2 = df.rdd \ + .map(cleanup) \ + .groupBy(lambda x: x["id"]) \ + .repartition(100) \ + .map(for_each_job) \ + .coalesce(1) \ + .saveAsTextFile(cluster + "_machine_count") + +# vim: set ts=4 sw=4 et tw=80: diff --git a/figure_9/figure9c.py b/figure_9/figure9c.py new file mode 100755 index 00000000..3b92472d --- /dev/null +++ b/figure_9/figure9c.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python3 +# coding: utf-8 + +import os +import json +import pandas as pd +import findspark +findspark.init() +import pyspark +import pyspark.sql +import sys +import gzip +from pyspark import AccumulatorParam +from pyspark.sql.functions import lit +from pyspark.sql import Window +from pyspark.sql.types import * +from decimal import * +import os + +if len(sys.argv) is not 5: + print(sys.argv[0] + " {cluster} {tmpdir} {maxram} {basedir}") + sys.exit() + +cluster=sys.argv[1] + +DIR = os.path.dirname(__file__) +NAME = "figure-9c-machine-count-" + cluster + +if os.path.exists(DIR + "/" + NAME + "-working"): + print("already done") + sys.exit() + +os.system("touch " + DIR + "/" + NAME + "-working") + +spark = pyspark.sql.SparkSession.builder \ + .appName(NAME) \ + .config("spark.driver.maxResultSize", "128g") \ + .config("spark.local.dir", sys.argv[2]) \ + .config("spark.driver.memory", sys.argv[3]) \ + .getOrCreate() +sc = spark.sparkContext + +# READING INSTANCE EVENTS DATA +dfepath = sys.argv[4] + "/" + cluster + "/" + cluster + "_instance_events*.json.gz" +#dfepath = sys.argv[4] + "/" + cluster + "/" + cluster + "_test.json" +df = spark.read.json(dfepath) + +try: + df["collection_type"] = df["collection_type"].cast(ByteType()) +except: + df = df.withColumn("collection_type", lit(None).cast(ByteType())) + +df = df.rdd.filter(lambda x: x.collection_id is not None and x.machine_id is not None and + int(x.machine_id) is not -1 and int(x.machine_id) is not 0 and + (x.collection_type == 0 or x.collection_type is None)) \ + .map(lambda x: (x.collection_id, int(x.machine_id))) \ + .groupBy(lambda x: x[0]) \ + .map(lambda x: [x[0], len(set(x[1]))]) \ + .toDF(["jobid", "machine_count"]) + +df.write.parquet(DIR + "/" + NAME + ".parquet") + +os.system("rm " + DIR + "/" + NAME + "-working") +# vim: set ts=4 sw=4 et tw=120: diff --git a/figure_9/g_priority_exectime/._SUCCESS.crc b/figure_9/g_priority_exectime/._SUCCESS.crc new file mode 100644 index 0000000000000000000000000000000000000000..3b7b044936a890cd8d651d349a752d819d71d22c GIT binary patch literal 8 PcmYc;N@ieSU}69O2$TUk literal 0 HcmV?d00001 diff --git a/figure_9/g_priority_exectime/.part-00000.crc 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Spark was used to execute a series of +queries to perform various sums and aggregations over the entire dataset +provided by Google. + +In general, each query follows a general Map-Reduce template, where traces are +first read, parsed, filtered by performing selections, projections and computing +new derived fields. Then, the trace records are often grouped by one of their +fields, clustering related data toghether before a reduce or fold operation is +applied to each grouping. + +Most input data is in JSONL format and adheres to a schema Google profided in +the form of a protobuffer specification located here: + +https://github.com/google/cluster-data/blob/master/clusterdata_trace_format_v3.proto + +On of the main quirks in the traces is that fields that have a "zero" value +(i.e. a value like 0 or the empty string) are often omitted in the JSON object +records. When reading the traces in Apache Spark is therefore necessary to check +for this possibility and populate those zero fields when omitted. + +Most queries use only two or three fields in each trace records, while the +original records often are made of a couple of dozen fields. In order to save +memory during the query, a projection is often applied to the data by the means +of a .map() operation over the entire trace set, performed using Spark's RDD +API. + +Another operation that is often necessary to perform prior to the Map-Reduce core of +each query is a record filtering process, which is often motivated by the +presence of incomplete data (i.e. records which contain fields whose values is +unknown). This filtering is performed using the .filter() operation of Spark's +RDD API. + +The core of each query is often a groupBy followed by a map() operation on the +aggregated data. The groupby groups the set of all records into several subsets +of records each having something in common. Then, each of this small clusters is +reduced with a .map() operation to a single record. The motivation behind this +computation is often to analyze a time series of several different traces of +programs. 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pyspark import AccumulatorParam +from pyspark.sql.functions import lit +from pyspark.sql import Window +from pyspark.sql.types import ByteType + +DIR = os.path.dirname(__file__) + +if len(sys.argv) != 4: + print(sys.argv[0] + " {cluster} {tmpdir} {maxram}") + sys.exit() + +cluster=sys.argv[1] + +if os.path.exists(DIR + "/bigtable-" + cluster + "-working") or os.path.exists(DIR + "/bigtable-" + cluster + ".parquet"): + print("already launched") + sys.exit() + +os.system("touch " + DIR + "/bigtable-" + cluster + "-working") + +spark = pyspark.sql.SparkSession.builder \ + .appName("bigtable") \ + .config("spark.driver.maxResultSize", "32g") \ + .config("spark.local.dir", sys.argv[2]) \ + .config("spark.driver.memory", sys.argv[3]) \ + .getOrCreate() +sc = spark.sparkContext + +df = spark.read.json("/home/claudio/google_2019/instance_events/" + cluster + "/" + cluster + "_instance_events*.json.gz") +#df = spark.read.json("/home/claudio/google_2019/instance_events/" + cluster + "/" + cluster + "_test.json") + +try: + df["collection_type"] = df["collection_type"].cast(ByteType()) +except: + df = df.withColumn("collection_type", lit(None).cast(ByteType())) + +def for_each_task(data): + rowids = data[0].split("-") + jobid = rowids[0] + taskid = rowids[1] + + ts = data[1] + ts = sorted(ts, key=lambda x: x["time"]) + + last_term = -1 + counts = [0] * 11 + + for i,t in enumerate(ts): + if t["type"] >= 4 and t["type"] <= 8: + last_term = t["type"] + counts[t["type"]] += 1 + + return (jobid, taskid, last_term, *counts) + +def cleanup(x): + return { + "time": int(x.time), + "type": 0 if x.type is None else int(x.type), + "id": x.collection_id + "-" + x.instance_index, + "priority": -1 if x.priority is None else int(x.priority) + } + + +df = df.rdd \ + .filter(lambda x: x.collection_type is None or x.collection_type == 0) \ + .filter(lambda x: x.time is not None and x.instance_index is not None and x.collection_id is not None) \ + .map(cleanup) \ + .groupBy(lambda x: x["id"]) \ + .repartition(1000) \ + .map(for_each_task) \ + .toDF(["jobid", "taskid", "task_term", *["count_" + str(x) for x in range(0,11)]]) + +df.write.parquet(DIR + "/bigtable-" + cluster + ".parquet") +os.system("rm " + DIR + "/bigtable-" + cluster + "-working") + diff --git a/table_iii/table-iii-a.csv/._SUCCESS.crc b/table_iii/table-iii-a.csv/._SUCCESS.crc new file mode 100644 index 0000000000000000000000000000000000000000..3b7b044936a890cd8d651d349a752d819d71d22c GIT binary patch literal 8 PcmYc;N@ieSU}69O2$TUk literal 0 HcmV?d00001 diff --git a/table_iii/table-iii-a.csv/.part-00000-672a3964-a5e1-48b4-ace7-f2dde6a257b4-c000.csv.crc b/table_iii/table-iii-a.csv/.part-00000-672a3964-a5e1-48b4-ace7-f2dde6a257b4-c000.csv.crc new file mode 100644 index 0000000000000000000000000000000000000000..86210d383ebd60f0f70e3f77a1e7c9f735586553 GIT binary patch literal 20 bcmYc;N@ieSU}8wnmARN4IPGTb_Gvi)IQa(- literal 0 HcmV?d00001 diff --git a/table_iii/table-iii-a.csv/_SUCCESS 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python3 +# coding: utf-8 + +import json +import pandas +import findspark +findspark.init() +import pyspark +import pyspark.sql +import sys +import gzip +import os + +import pyspark.sql.functions as F +from pyspark import AccumulatorParam +from pyspark.sql.functions import lit +from pyspark.sql import Window +from pyspark.sql.types import StructType, LongType, StringType, ByteType + +def main(): + global DIR + global NAME + global launched + + if len(sys.argv) != 4: + print(sys.argv[0] + " {cluster} {tmpdir} {maxram}") + sys.exit() + + cluster=sys.argv[1] + DIR = os.path.dirname(__file__) + NAME = "table-iii-" + cluster + + if os.path.exists(DIR + "/" + NAME + "-working") or os.path.exists(DIR + "/" + NAME + ".parquet"): + print("already launched") + launched = True + sys.exit() + + os.system("touch " + DIR + "/" + NAME + "-working") + + spark = pyspark.sql.SparkSession.builder \ + .appName(NAME) \ + .config("spark.driver.maxResultSize", "32g") \ + .config("spark.local.dir", sys.argv[2]) \ + .config("spark.driver.memory", sys.argv[3]) \ + .getOrCreate() + sc = spark.sparkContext + + df = spark.read.parquet(DIR + "/bigtable-" + cluster + ".parquet") + +# df = df.groupBy("jobid").agg( +# F.expr("sum(count_0)").alias('count_0'), +# F.expr("sum(count_1)").alias('count_1'), +# F.expr("sum(count_2)").alias('count_2'), +# F.expr("sum(count_3)").alias('count_3'), +# F.expr("sum(count_4)").alias('count_4'), +# F.expr("sum(count_5)").alias('count_5'), +# F.expr("sum(count_6)").alias('count_6'), +# F.expr("sum(count_7)").alias('count_7'), +# F.expr("sum(count_8)").alias('count_8'), +# F.expr("sum(count_9)").alias('count_9'), +# F.expr("sum(count_10)").alias('count_10')) +# +# usage_schema = StructType() \ +# .add("jobid", StringType(), False) \ +# .add("job_term", LongType(), False) \ +# .add("task_count", LongType(), False) +# dfj = spark.read.format("csv") \ +# .option("header", False) \ +# .schema(usage_schema) \ +# .load("/home/claudio/google_2019/thesis_queries/figure_9/" + cluster + "_task_count") +# dfj = dfj.select(["jobid", "job_term"]) +# +# df = df.join(dfj, "jobid") + df = df.withColumn("count_sum", + df["count_0"] + \ + df["count_1"] + \ + df["count_2"] + \ + df["count_3"] + \ + df["count_4"] + \ + df["count_5"] + \ + df["count_6"] + \ + df["count_7"] + \ + df["count_8"] + \ + df["count_9"] + \ + df["count_10"]) + + df = df.groupBy("task_term") \ + .agg(F.expr("percentile(count_sum, array(0.95))")[0].alias('%95'), + F.expr("avg(count_sum)").alias('mean'), + F.expr("avg(count_0)").alias('avg_count_0'), + F.expr("avg(count_1)").alias('avg_count_1'), + F.expr("avg(count_2)").alias('avg_count_2'), + F.expr("avg(count_3)").alias('avg_count_3'), + F.expr("avg(count_4)").alias('avg_count_4'), + F.expr("avg(count_5)").alias('avg_count_5'), + F.expr("avg(count_6)").alias('avg_count_6'), + F.expr("avg(count_7)").alias('avg_count_7'), + F.expr("avg(count_8)").alias('avg_count_8'), + F.expr("avg(count_9)").alias('avg_count_9'), + F.expr("avg(count_10)").alias('avg_count_10')) + + df.repartition(1).write.option("header", True).csv(DIR + "/" + NAME + ".csv") + +if __name__ == "__main__": + launched = False + try: + main() + finally: + if not launched: + os.system("rm -v " + DIR + "/" + NAME + "-working") + +# vim: set ts=4 sw=4 et tw=120: diff --git a/table_iii/tableIV-tasks.py b/table_iii/tableIV-tasks.py new file mode 100755 index 00000000..5dc00ddd --- /dev/null +++ b/table_iii/tableIV-tasks.py @@ -0,0 +1,71 @@ +#!/usr/bin/env python3 +# coding: utf-8 + +import json +import pandas +import findspark +findspark.init() +import pyspark +import pyspark.sql +import sys +import gzip +import os + +import pyspark.sql.functions as F +from pyspark import AccumulatorParam +from pyspark.sql.functions import lit +from pyspark.sql import Window +from pyspark.sql.types import StructType, LongType, StringType, ByteType + +def main(): + global DIR + global NAME + global launched + + if len(sys.argv) != 4: + print(sys.argv[0] + " {cluster} {tmpdir} {maxram}") + sys.exit() + + cluster=sys.argv[1] + DIR = os.path.dirname(__file__) + NAME = "table-iv-tasks-" + cluster + + if os.path.exists(DIR + "/" + NAME + "-working") or os.path.exists(DIR + "/" + NAME + ".parquet"): + print("already launched") + launched = True + sys.exit() + + os.system("touch " + DIR + "/" + NAME + "-working") + + spark = pyspark.sql.SparkSession.builder \ + .appName(NAME) \ + .config("spark.driver.maxResultSize", "32g") \ + .config("spark.local.dir", sys.argv[2]) \ + .config("spark.driver.memory", sys.argv[3]) \ + .getOrCreate() + sc = spark.sparkContext + + usage_schema = StructType() \ + .add("jobid", StringType(), False) \ + .add("job_term", LongType(), False) \ + .add("task_count", LongType(), False) + df = spark.read.format("csv") \ + .option("header", False) \ + .schema(usage_schema) \ + .load("/home/claudio/google_2019/thesis_queries/figure_9/" + cluster + "_task_count") + + df = df.groupBy("job_term").agg( \ + F.expr("avg(task_count)").alias('mean_tc'), + F.expr("percentile(task_count, array(0.95))")[0].alias('%95_tc')) + + df.repartition(1).write.csv(DIR + "/" + NAME + ".csv") + +if __name__ == "__main__": + launched = False + try: + main() + finally: + if not launched: + os.system("rm -v " + DIR + "/" + NAME + "-working") + +# vim: set ts=4 sw=4 et tw=120: diff --git a/table_iii/tableIV.py b/table_iii/tableIV.py new file mode 100755 index 00000000..d0631a07 --- /dev/null +++ b/table_iii/tableIV.py @@ -0,0 +1,85 @@ +#!/usr/bin/env python3 +# coding: utf-8 + +import json +import pandas +import findspark +findspark.init() +import pyspark +import pyspark.sql +import sys +import gzip +import os + +import pyspark.sql.functions as F +from pyspark import AccumulatorParam +from pyspark.sql.functions import lit +from pyspark.sql import Window +from pyspark.sql.types import StructType, LongType, StringType, ByteType + +def main(): + global DIR + global NAME + global launched + + if len(sys.argv) != 4: + print(sys.argv[0] + " {cluster} {tmpdir} {maxram}") + sys.exit() + + cluster=sys.argv[1] + DIR = os.path.dirname(__file__) + NAME = "table-iv-evts-" + cluster + + if os.path.exists(DIR + "/" + NAME + "-working") or os.path.exists(DIR + "/" + NAME + ".parquet"): + print("already launched") + launched = True + sys.exit() + + os.system("touch " + DIR + "/" + NAME + "-working") + + spark = pyspark.sql.SparkSession.builder \ + .appName(NAME) \ + .config("spark.driver.maxResultSize", "32g") \ + .config("spark.local.dir", sys.argv[2]) \ + .config("spark.driver.memory", sys.argv[3]) \ + .getOrCreate() + sc = spark.sparkContext + + df = spark.read.parquet(DIR + "/bigtable-" + cluster + ".parquet") + + usage_schema = StructType() \ + .add("jobid", StringType(), False) \ + .add("job_term", LongType(), False) \ + .add("task_count", LongType(), False) + dfj = spark.read.format("csv") \ + .option("header", False) \ + .schema(usage_schema) \ + .load("/home/claudio/google_2019/thesis_queries/figure_9/" + cluster + "_task_count") + dfj = dfj.select(["jobid", "job_term"]) + + df = df.join(dfj, "jobid") + + df = df.groupBy("job_term").agg( \ + F.expr("avg(count_0)").alias('avg_count_0'), + F.expr("avg(count_1)").alias('avg_count_1'), + F.expr("avg(count_2)").alias('avg_count_2'), + F.expr("avg(count_3)").alias('avg_count_3'), + F.expr("avg(count_4)").alias('avg_count_4'), + F.expr("avg(count_5)").alias('avg_count_5'), + F.expr("avg(count_6)").alias('avg_count_6'), + F.expr("avg(count_7)").alias('avg_count_7'), + F.expr("avg(count_8)").alias('avg_count_8'), + F.expr("avg(count_9)").alias('avg_count_9'), + F.expr("avg(count_10)").alias('avg_count_10')) + + df.repartition(1).write.csv(DIR + "/" + NAME + ".csv") + +if __name__ == "__main__": + launched = False + try: + main() + finally: + if not launched: + os.system("rm -v " + DIR + "/" + NAME + "-working") + +# vim: set ts=4 sw=4 et tw=120: