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rocksdb/tools/block_cache_analyzer/block_cache_trace_analyzer_...

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#!/usr/bin/env python3
import csv
import math
import os
import random
import sys
import matplotlib
matplotlib.use("Agg")
import matplotlib.backends.backend_pdf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
# Make sure a legend has the same color across all generated graphs.
def get_cmap(n, name="hsv"):
"""Returns a function that maps each index in 0, 1, ..., n-1 to a distinct
RGB color; the keyword argument name must be a standard mpl colormap name."""
return plt.cm.get_cmap(name, n)
color_index = 0
bar_color_maps = {}
colors = []
n_colors = 360
linear_colors = get_cmap(n_colors)
for i in range(n_colors):
colors.append(linear_colors(i))
# Shuffle the colors so that adjacent bars in a graph are obvious to differentiate.
random.shuffle(colors)
def num_to_gb(n):
one_gb = 1024 * 1024 * 1024
if float(n) % one_gb == 0:
return "{}".format(n / one_gb)
# Keep two decimal points.
return "{0:.2f}".format(float(n) / one_gb)
def plot_miss_stats_graphs(
csv_result_dir, output_result_dir, file_prefix, file_suffix, ylabel, pdf_file_name
):
miss_ratios = {}
for file in os.listdir(csv_result_dir):
if not file.startswith(file_prefix):
continue
if not file.endswith(file_suffix):
continue
print("Processing file {}/{}".format(csv_result_dir, file))
mrc_file_path = csv_result_dir + "/" + file
with open(mrc_file_path, "r") as csvfile:
rows = csv.reader(csvfile, delimiter=",")
for row in rows:
cache_name = row[0]
num_shard_bits = int(row[1])
ghost_capacity = int(row[2])
capacity = int(row[3])
miss_ratio = float(row[4])
config = "{}-{}-{}".format(cache_name, num_shard_bits, ghost_capacity)
if config not in miss_ratios:
miss_ratios[config] = {}
miss_ratios[config]["x"] = []
miss_ratios[config]["y"] = []
miss_ratios[config]["x"].append(capacity)
miss_ratios[config]["y"].append(miss_ratio)
fig = plt.figure()
for config in miss_ratios:
plt.plot(
miss_ratios[config]["x"], miss_ratios[config]["y"], label=config
)
plt.xlabel("Cache capacity")
plt.ylabel(ylabel)
plt.xscale("log", basex=2)
plt.ylim(ymin=0)
plt.title("{}".format(file))
plt.legend()
fig.savefig(
output_result_dir + "/{}.pdf".format(pdf_file_name), bbox_inches="tight"
)
def plot_miss_stats_diff_lru_graphs(
csv_result_dir, output_result_dir, file_prefix, file_suffix, ylabel, pdf_file_name
):
miss_ratios = {}
for file in os.listdir(csv_result_dir):
if not file.startswith(file_prefix):
continue
if not file.endswith(file_suffix):
continue
print("Processing file {}/{}".format(csv_result_dir, file))
mrc_file_path = csv_result_dir + "/" + file
with open(mrc_file_path, "r") as csvfile:
rows = csv.reader(csvfile, delimiter=",")
for row in rows:
cache_name = row[0]
num_shard_bits = int(row[1])
ghost_capacity = int(row[2])
capacity = int(row[3])
miss_ratio = float(row[4])
config = "{}-{}-{}".format(cache_name, num_shard_bits, ghost_capacity)
if config not in miss_ratios:
miss_ratios[config] = {}
miss_ratios[config]["x"] = []
miss_ratios[config]["y"] = []
miss_ratios[config]["x"].append(capacity)
miss_ratios[config]["y"].append(miss_ratio)
if "lru-0-0" not in miss_ratios:
return
fig = plt.figure()
for config in miss_ratios:
diffs = [0] * len(miss_ratios["lru-0-0"]["x"])
for i in range(len(miss_ratios["lru-0-0"]["x"])):
for j in range(len(miss_ratios[config]["x"])):
if miss_ratios["lru-0-0"]["x"][i] == miss_ratios[config]["x"][j]:
diffs[i] = (
miss_ratios[config]["y"][j] - miss_ratios["lru-0-0"]["y"][i]
)
break
plt.plot(miss_ratios["lru-0-0"]["x"], diffs, label=config)
plt.xlabel("Cache capacity")
plt.ylabel(ylabel)
plt.xscale("log", basex=2)
plt.title("{}".format(file))
plt.legend()
fig.savefig(
output_result_dir + "/{}.pdf".format(pdf_file_name), bbox_inches="tight"
)
def sanitize(label):
# matplotlib cannot plot legends that is prefixed with "_"
# so we need to remove them here.
index = 0
for i in range(len(label)):
if label[i] == "_":
index += 1
else:
break
data = label[index:]
# The value of uint64_max in c++.
if "18446744073709551615" in data:
return "max"
return data
# Read the csv file vertically, i.e., group the data by columns.
def read_data_for_plot_vertical(csvfile):
x = []
labels = []
label_stats = {}
csv_rows = csv.reader(csvfile, delimiter=",")
data_rows = []
for row in csv_rows:
data_rows.append(row)
# header
for i in range(1, len(data_rows[0])):
labels.append(sanitize(data_rows[0][i]))
label_stats[i - 1] = []
for i in range(1, len(data_rows)):
for j in range(len(data_rows[i])):
if j == 0:
x.append(sanitize(data_rows[i][j]))
continue
label_stats[j - 1].append(float(data_rows[i][j]))
return x, labels, label_stats
# Read the csv file horizontally, i.e., group the data by rows.
def read_data_for_plot_horizontal(csvfile):
x = []
labels = []
label_stats = {}
csv_rows = csv.reader(csvfile, delimiter=",")
data_rows = []
for row in csv_rows:
data_rows.append(row)
# header
for i in range(1, len(data_rows)):
labels.append(sanitize(data_rows[i][0]))
label_stats[i - 1] = []
for i in range(1, len(data_rows[0])):
x.append(sanitize(data_rows[0][i]))
for i in range(1, len(data_rows)):
for j in range(len(data_rows[i])):
if j == 0:
# label
continue
label_stats[i - 1].append(float(data_rows[i][j]))
return x, labels, label_stats
def read_data_for_plot(csvfile, vertical):
if vertical:
return read_data_for_plot_vertical(csvfile)
return read_data_for_plot_horizontal(csvfile)
def plot_line_charts(
csv_result_dir,
output_result_dir,
filename_prefix,
filename_suffix,
pdf_name,
xlabel,
ylabel,
title,
vertical,
legend,
):
global color_index, bar_color_maps, colors
pdf = matplotlib.backends.backend_pdf.PdfPages(output_result_dir + "/" + pdf_name)
for file in os.listdir(csv_result_dir):
if not file.endswith(filename_suffix):
continue
if not file.startswith(filename_prefix):
continue
print("Processing file {}/{}".format(csv_result_dir, file))
with open(csv_result_dir + "/" + file, "r") as csvfile:
x, labels, label_stats = read_data_for_plot(csvfile, vertical)
if len(x) == 0 or len(labels) == 0:
continue
# plot figure
fig = plt.figure()
for label_index in label_stats:
# Assign a unique color to this label.
if labels[label_index] not in bar_color_maps:
bar_color_maps[labels[label_index]] = colors[color_index]
color_index += 1
plt.plot(
[int(x[i]) for i in range(len(x) - 1)],
label_stats[label_index][:-1],
label=labels[label_index],
color=bar_color_maps[labels[label_index]],
)
# Translate time unit into x labels.
if "_60" in file:
plt.xlabel("{} (Minute)".format(xlabel))
if "_3600" in file:
plt.xlabel("{} (Hour)".format(xlabel))
plt.ylabel(ylabel)
plt.title("{} {}".format(title, file))
if legend:
plt.legend()
pdf.savefig(fig)
pdf.close()
def plot_stacked_bar_charts(
csv_result_dir,
output_result_dir,
filename_suffix,
pdf_name,
xlabel,
ylabel,
title,
vertical,
x_prefix,
):
global color_index, bar_color_maps, colors
pdf = matplotlib.backends.backend_pdf.PdfPages(
"{}/{}".format(output_result_dir, pdf_name)
)
for file in os.listdir(csv_result_dir):
if not file.endswith(filename_suffix):
continue
with open(csv_result_dir + "/" + file, "r") as csvfile:
print("Processing file {}/{}".format(csv_result_dir, file))
x, labels, label_stats = read_data_for_plot(csvfile, vertical)
if len(x) == 0 or len(label_stats) == 0:
continue
# Plot figure
fig = plt.figure()
ind = np.arange(len(x)) # the x locations for the groups
width = 0.5 # the width of the bars: can also be len(x) sequence
bars = []
bottom_bars = []
for _i in label_stats[0]:
bottom_bars.append(0)
for i in range(0, len(label_stats)):
# Assign a unique color to this label.
if labels[i] not in bar_color_maps:
bar_color_maps[labels[i]] = colors[color_index]
color_index += 1
p = plt.bar(
ind,
label_stats[i],
width,
bottom=bottom_bars,
color=bar_color_maps[labels[i]],
)
bars.append(p[0])
for j in range(len(label_stats[i])):
bottom_bars[j] += label_stats[i][j]
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.xticks(
ind, [x_prefix + x[i] for i in range(len(x))], rotation=20, fontsize=8
)
plt.legend(bars, labels)
plt.title("{} filename:{}".format(title, file))
pdf.savefig(fig)
pdf.close()
def plot_heatmap(csv_result_dir, output_result_dir, filename_suffix, pdf_name, title):
pdf = matplotlib.backends.backend_pdf.PdfPages(
"{}/{}".format(output_result_dir, pdf_name)
)
for file in os.listdir(csv_result_dir):
if not file.endswith(filename_suffix):
continue
csv_file_name = "{}/{}".format(csv_result_dir, file)
print("Processing file {}/{}".format(csv_result_dir, file))
corr_table = pd.read_csv(csv_file_name)
corr_table = corr_table.pivot("label", "corr", "value")
fig = plt.figure()
sns.heatmap(corr_table, annot=True, linewidths=0.5, fmt=".2")
plt.title("{} filename:{}".format(title, file))
pdf.savefig(fig)
pdf.close()
def plot_timeline(csv_result_dir, output_result_dir):
plot_line_charts(
csv_result_dir,
output_result_dir,
filename_prefix="",
filename_suffix="access_timeline",
pdf_name="access_time.pdf",
xlabel="Time",
ylabel="Throughput",
title="Access timeline with group by label",
vertical=False,
legend=True,
)
def convert_to_0_if_nan(n):
if math.isnan(n):
return 0.0
return n
def plot_correlation(csv_result_dir, output_result_dir):
# Processing the correlation input first.
label_str_file = {}
for file in os.listdir(csv_result_dir):
if not file.endswith("correlation_input"):
continue
csv_file_name = "{}/{}".format(csv_result_dir, file)
print("Processing file {}/{}".format(csv_result_dir, file))
corr_table = pd.read_csv(csv_file_name)
label_str = file.split("_")[0]
label = file[len(label_str) + 1 :]
label = label[: len(label) - len("_correlation_input")]
output_file = "{}/{}_correlation_output".format(csv_result_dir, label_str)
if output_file not in label_str_file:
f = open("{}/{}_correlation_output".format(csv_result_dir, label_str), "w+")
label_str_file[output_file] = f
f.write("label,corr,value\n")
f = label_str_file[output_file]
f.write(
"{},{},{}\n".format(
label,
"LA+A",
convert_to_0_if_nan(
corr_table["num_accesses_since_last_access"].corr(
corr_table["num_accesses_till_next_access"], method="spearman"
)
),
)
)
f.write(
"{},{},{}\n".format(
label,
"PA+A",
convert_to_0_if_nan(
corr_table["num_past_accesses"].corr(
corr_table["num_accesses_till_next_access"], method="spearman"
)
),
)
)
f.write(
"{},{},{}\n".format(
label,
"LT+A",
convert_to_0_if_nan(
corr_table["elapsed_time_since_last_access"].corr(
corr_table["num_accesses_till_next_access"], method="spearman"
)
),
)
)
f.write(
"{},{},{}\n".format(
label,
"LA+T",
convert_to_0_if_nan(
corr_table["num_accesses_since_last_access"].corr(
corr_table["elapsed_time_till_next_access"], method="spearman"
)
),
)
)
f.write(
"{},{},{}\n".format(
label,
"LT+T",
convert_to_0_if_nan(
corr_table["elapsed_time_since_last_access"].corr(
corr_table["elapsed_time_till_next_access"], method="spearman"
)
),
)
)
f.write(
"{},{},{}\n".format(
label,
"PA+T",
convert_to_0_if_nan(
corr_table["num_past_accesses"].corr(
corr_table["elapsed_time_till_next_access"], method="spearman"
)
),
)
)
for label_str in label_str_file:
label_str_file[label_str].close()
plot_heatmap(
csv_result_dir,
output_result_dir,
"correlation_output",
"correlation.pdf",
"Correlation",
)
def plot_reuse_graphs(csv_result_dir, output_result_dir):
plot_stacked_bar_charts(
csv_result_dir,
output_result_dir,
filename_suffix="avg_reuse_interval_naccesses",
pdf_name="avg_reuse_interval_naccesses.pdf",
xlabel="",
ylabel="Percentage of accesses",
title="Average reuse interval",
vertical=True,
x_prefix="< ",
)
plot_stacked_bar_charts(
csv_result_dir,
output_result_dir,
filename_suffix="avg_reuse_interval",
pdf_name="avg_reuse_interval.pdf",
xlabel="",
ylabel="Percentage of blocks",
title="Average reuse interval",
vertical=True,
x_prefix="< ",
)
plot_stacked_bar_charts(
csv_result_dir,
output_result_dir,
filename_suffix="access_reuse_interval",
pdf_name="reuse_interval.pdf",
xlabel="Seconds",
ylabel="Percentage of accesses",
title="Reuse interval",
vertical=True,
x_prefix="< ",
)
plot_stacked_bar_charts(
csv_result_dir,
output_result_dir,
filename_suffix="reuse_lifetime",
pdf_name="reuse_lifetime.pdf",
xlabel="Seconds",
ylabel="Percentage of blocks",
title="Reuse lifetime",
vertical=True,
x_prefix="< ",
)
plot_line_charts(
csv_result_dir,
output_result_dir,
filename_prefix="",
filename_suffix="reuse_blocks_timeline",
pdf_name="reuse_blocks_timeline.pdf",
xlabel="",
ylabel="Percentage of blocks",
title="Reuse blocks timeline",
vertical=False,
legend=False,
)
def plot_percentage_access_summary(csv_result_dir, output_result_dir):
plot_stacked_bar_charts(
csv_result_dir,
output_result_dir,
filename_suffix="percentage_of_accesses_summary",
pdf_name="percentage_access.pdf",
xlabel="",
ylabel="Percentage of accesses",
title="",
vertical=True,
x_prefix="",
)
plot_stacked_bar_charts(
csv_result_dir,
output_result_dir,
filename_suffix="percent_ref_keys",
pdf_name="percent_ref_keys.pdf",
xlabel="",
ylabel="Percentage of blocks",
title="",
vertical=True,
x_prefix="",
)
plot_stacked_bar_charts(
csv_result_dir,
output_result_dir,
filename_suffix="percent_data_size_on_ref_keys",
pdf_name="percent_data_size_on_ref_keys.pdf",
xlabel="",
ylabel="Percentage of blocks",
title="",
vertical=True,
x_prefix="",
)
plot_stacked_bar_charts(
csv_result_dir,
output_result_dir,
filename_suffix="percent_accesses_on_ref_keys",
pdf_name="percent_accesses_on_ref_keys.pdf",
xlabel="",
ylabel="Percentage of blocks",
title="",
vertical=True,
x_prefix="",
)
def plot_access_count_summary(csv_result_dir, output_result_dir):
plot_stacked_bar_charts(
csv_result_dir,
output_result_dir,
filename_suffix="access_count_summary",
pdf_name="access_count_summary.pdf",
xlabel="Access count",
ylabel="Percentage of blocks",
title="",
vertical=True,
x_prefix="< ",
)
plot_line_charts(
csv_result_dir,
output_result_dir,
filename_prefix="",
filename_suffix="skewness",
pdf_name="skew.pdf",
xlabel="",
ylabel="Percentage of accesses",
title="Skewness",
vertical=True,
legend=False,
)
def plot_miss_ratio_timeline(csv_result_dir, output_result_dir):
plot_line_charts(
csv_result_dir,
output_result_dir,
filename_prefix="",
filename_suffix="3600_miss_ratio_timeline",
pdf_name="miss_ratio_timeline.pdf",
xlabel="Time",
ylabel="Miss Ratio (%)",
title="Miss ratio timeline",
vertical=False,
legend=True,
)
plot_line_charts(
csv_result_dir,
output_result_dir,
filename_prefix="",
filename_suffix="3600_miss_timeline",
pdf_name="miss_timeline.pdf",
xlabel="Time",
ylabel="# of misses ",
title="Miss timeline",
vertical=False,
legend=True,
)
plot_line_charts(
csv_result_dir,
output_result_dir,
filename_prefix="",
filename_suffix="3600_miss_timeline",
pdf_name="miss_timeline.pdf",
xlabel="Time",
ylabel="# of misses ",
title="Miss timeline",
vertical=False,
legend=True,
)
plot_line_charts(
csv_result_dir,
output_result_dir,
filename_prefix="",
filename_suffix="3600_policy_timeline",
pdf_name="policy_timeline.pdf",
xlabel="Time",
ylabel="# of times a policy is selected ",
title="Policy timeline",
vertical=False,
legend=True,
)
plot_line_charts(
csv_result_dir,
output_result_dir,
filename_prefix="",
filename_suffix="3600_policy_ratio_timeline",
pdf_name="policy_ratio_timeline.pdf",
xlabel="Time",
ylabel="Percentage of times a policy is selected ",
title="Policy timeline",
vertical=False,
legend=True,
)
if __name__ == "__main__":
if len(sys.argv) < 3:
print(
"Must provide two arguments: \n"
"1) The directory that saves a list of "
"directories which contain block cache trace analyzer result files. \n"
"2) the directory to save plotted graphs. \n"
)
exit(1)
csv_result_dir = sys.argv[1]
output_result_dir = sys.argv[2]
print(
"Processing directory {} and save graphs to {}.".format(
csv_result_dir, output_result_dir
)
)
for csv_relative_dir in os.listdir(csv_result_dir):
csv_abs_dir = csv_result_dir + "/" + csv_relative_dir
result_dir = output_result_dir + "/" + csv_relative_dir
if not os.path.isdir(csv_abs_dir):
print("{} is not a directory".format(csv_abs_dir))
continue
print("Processing experiment dir: {}".format(csv_relative_dir))
if not os.path.exists(result_dir):
os.makedirs(result_dir)
plot_access_count_summary(csv_abs_dir, result_dir)
plot_timeline(csv_abs_dir, result_dir)
plot_miss_ratio_timeline(csv_result_dir, output_result_dir)
plot_correlation(csv_abs_dir, result_dir)
plot_reuse_graphs(csv_abs_dir, result_dir)
plot_percentage_access_summary(csv_abs_dir, result_dir)
plot_miss_stats_graphs(
csv_abs_dir,
result_dir,
file_prefix="",
file_suffix="mrc",
ylabel="Miss ratio (%)",
pdf_file_name="mrc",
)
plot_miss_stats_diff_lru_graphs(
csv_abs_dir,
result_dir,
file_prefix="",
file_suffix="mrc",
ylabel="Miss ratio (%)",
pdf_file_name="mrc_diff_lru",
)
# The following stats are only available in pysim.
for time_unit in ["1", "60", "3600"]:
plot_miss_stats_graphs(
csv_abs_dir,
result_dir,
file_prefix="ml_{}_".format(time_unit),
file_suffix="p95mb",
ylabel="p95 number of byte miss per {} seconds".format(time_unit),
pdf_file_name="p95mb_per{}_seconds".format(time_unit),
)
plot_miss_stats_graphs(
csv_abs_dir,
result_dir,
file_prefix="ml_{}_".format(time_unit),
file_suffix="avgmb",
ylabel="Average number of byte miss per {} seconds".format(time_unit),
pdf_file_name="avgmb_per{}_seconds".format(time_unit),
)
plot_miss_stats_diff_lru_graphs(
csv_abs_dir,
result_dir,
file_prefix="ml_{}_".format(time_unit),
file_suffix="p95mb",
ylabel="p95 number of byte miss per {} seconds".format(time_unit),
pdf_file_name="p95mb_per{}_seconds_diff_lru".format(time_unit),
)
plot_miss_stats_diff_lru_graphs(
csv_abs_dir,
result_dir,
file_prefix="ml_{}_".format(time_unit),
file_suffix="avgmb",
ylabel="Average number of byte miss per {} seconds".format(time_unit),
pdf_file_name="avgmb_per{}_seconds_diff_lru".format(time_unit),
)