more results for 7c

figure_7/f_figure7c.csv

@@ -0,0 +1,625 @@
+term,n_exec,count
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figure_8/figure8-cd-only.py

@@ -16,12 +16,25 @@ from pyspark.sql import Window
 from pyspark.sql.types import *
 from decimal import *
 
+CHECKDIR = "/home/claudio/google_2019/thesis_queries/figure_8/" 
+
 if len(sys.argv) is not 4:
     print(sys.argv[0] + " {cluster} {tmpdir} {maxram}")
     sys.exit()
 
 cluster=sys.argv[1]
 
+if os.path.exists(CHECKDIR + cluster + "_figure8cd.json"):
+    print("already computed")
+    sys.exit()
+
+if os.path.exists(CHECKDIR + cluster + "_figure8cd_working"):
+    print("already in execution")
+    sys.exit()
+
+os.system("touch " + CHECKDIR + cluster + "_figure8cd_working")
+
+
 spark = pyspark.sql.SparkSession.builder \
   .appName("task_slowdown") \
   .config("spark.driver.maxResultSize", "128g") \
@@ -30,12 +43,15 @@ spark = pyspark.sql.SparkSession.builder \
   .getOrCreate()
 sc = spark.sparkContext
 
+def tabid(x):
+    return Decimal(x.collection_id) + Decimal(x.instance_index) / Decimal(2**64)
+
 #
 # READING INSTANCE EVENTS DATA
 #
 dfepath = "/home/claudio/google_2019/instance_events/" + cluster + "/" + cluster + "_instance_events*.json.gz"
+#dfepath = "/home/claudio/google_2019/instance_events/" + cluster + "/" + cluster + "_test.json"
 df = spark.read.json(dfepath)
-df = df.limit(10000)
 
 # READING MACHINE EVENTS DATA, sort them and save them as broadcast variable
 print("Starting to read machine events...")
@@ -56,7 +72,7 @@ print("Converting to broadcast variable...")
 dfm = sc.broadcast([tuple(r) for r in dfm.to_numpy()])
 print("Done with machine events.")
 
-df = dfe.rdd \
+df = df.rdd \
     .filter(lambda x: x.time is not None and x.type is not None and x.machine_id is not None and
             x.instance_index is not None and x.collection_id is not None and x.resource_request is not None and
             x.resource_request.cpus is not None and x.resource_request.memory is not None) \
@@ -68,12 +84,12 @@ df = dfe.rdd \
 def get_machine_time_resources(machine_id, time):
     def aux(i, j):
         if i == j:
-            return dfm.value[i] if dfm.value[i][1] == machine_id else None
+            return i if dfm.value[i][1] == machine_id else None
         elif i + 1 == j:
             if dfm.value[i][1] == machine_id:
-                return dfm.value[i]
+                return i
             elif dfm.value[j][1] == machine_id:
-                return dfm.value[j]
+                return j
             else:
                 return None
 
@@ -170,20 +186,19 @@ def fold_resobjs(ro1, ro2):
                     ro1[k][kk] = [sum(x) for x in zip(ro1[k][kk], ro2[k][kk])]
         return ro1
  
-# TODO: partition by id and in the for-each-row 
-# function implement lookup to dfm.value to understand its memory capacity
-
 import random
 
 result = df.rdd \
     .groupBy(lambda x: x.mid) \
-    .partitionBy(10000, lambda x: random.randint(0, 10000-1)) \
+    .partitionBy(1000, lambda x: random.randint(0, 1000-1)) \
     .map(for_each_joined) \
     .fold(None, fold_resobjs)
 
 d = os.path.dirname(os.path.realpath(__file__))
 
-with open(d + "/" + cluster + "_figure8.json", "w") as f:
+with open(d + "/" + cluster + "_figure8cd.json", "w") as f:
     json.dump(result, f)
 
+os.system("rm " + CHECKDIR + cluster + "_figure8cd_working")
+
 # vim: set ts=4 sw=4 et tw=120: