128 lines
3.4 KiB
Python
128 lines
3.4 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# # Task slowdown
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import json
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import sys
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import gzip
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import pandas
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import seaborn as sns
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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from IPython.display import display, HTML
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columns = ["priority", "n_fsh", "n_non", "finished%", "c_zero_end", "s_last", "m_last", "s_all", "m_all", "s_slow", "m_slow"]
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CLUSTERS = "abcdefgh"
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DIR = "/home/claudio/google_2019/thesis_queries/task_slowdown/"
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df = {}
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for cluster in CLUSTERS:
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wc = 0
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tc = {}
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s_slowdown = 0
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# Loading
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print("Loading cluster " + cluster + "...")
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data = None
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with gzip.open(DIR + cluster + "_state_changes.json.gz", "r") as f:
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data = json.load(f)
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print("Done loading")
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# Computation
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priorities = sorted(set(data["val"].keys()).union(set(data["non"].keys())), key=lambda x: int(x))
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table = {}
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for c in columns:
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table[c] = []
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def add(col, val):
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table[col].append(val)
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def empty_row():
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add("m_last", None)
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add("m_all", None)
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add("m_slow", None)
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add("s_last", None)
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add("s_all", None)
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add("s_slow", None)
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add("c_zero_end", None)
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for priority in priorities:
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print("Priority " + priority)
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add("priority", priority)
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n_not_finished = 0 if priority not in data["non"] else data["non"][priority]
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n_finished = 0 if priority not in data["val"] else len(data["val"][priority])
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add("n_fsh", n_finished)
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add("n_non", n_not_finished)
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add("finished%", n_finished / (n_not_finished + n_finished))
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if n_finished == 0:
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empty_row()
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else:
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s = 0
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c = 0
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ss = 0
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sum_zero_end = 0
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for task in data["val"][priority]:
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idx_last = len(task) - 1
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t = 0
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while idx_last >= 0 and task[idx_last][1][-1] != 6:
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idx_last -= 1
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t += 1
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if idx_last < 0:
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#print("weird")
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wc += 1
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n_finished -= 1
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n_not_finished += 1
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continue
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if t in tc:
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tc[t] += 1
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else:
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tc[1] = 1
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last_slice = task[idx_last]
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s += last_slice[0]
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local_sum = 0
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for exec_slice in task:
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local_sum += exec_slice[0]
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c += 1
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if last_slice[0] > 0:
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s_slowdown += (local_sum / last_slice[0])
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else:
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sum_zero_end += 1
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ss += local_sum
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if n_finished == 0:
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empty_row()
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continue
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add("c_zero_end", sum_zero_end)
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add("s_last", s)
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add("m_last", s / n_finished)
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add("s_all", ss)
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add("m_all", ss / c)
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add("s_slow", s_slowdown)
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add("m_slow", s_slowdown / n_finished)
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df[cluster] = pandas.DataFrame(table, columns=columns)
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df[cluster].to_csv(DIR + "/" + cluster + "_slowdown_table.csv")
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print("Done csv")
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