A python script to plot graphs for cvs files generated by block_cache_trace_analyzer

Summary: Pull Request resolved: https://github.com/facebook/rocksdb/pull/5563

Test Plan: Manually run the script on files generated by block_cache_trace_analyzer.

Differential Revision: D16214400

Pulled By: HaoyuHuang

fbshipit-source-id: 94485eed995e9b2b63e197c5dfeb80129fa7897f

tools/block_cache_trace_analyzer_plot.py

+#!/usr/bin/env python3
+import csv
+import os
+import random
+import sys
+
+import matplotlib.backends.backend_pdf
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+# 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 = 60
+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_ratio_graphs(csv_result_dir, output_result_dir):
+    mrc_file_path = csv_result_dir + "/mrc"
+    if not os.path.exists(mrc_file_path):
+        return
+    miss_ratios = {}
+    print("Processing file {}".format(mrc_file_path))
+    with open(mrc_file_path, "r") as csvfile:
+        rows = csv.reader(csvfile, delimiter=",")
+        is_header = False
+        for row in rows:
+            if not is_header:
+                is_header = True
+                continue
+            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(num_to_gb(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 (GB)")
+    plt.ylabel("Miss Ratio (%)")
+    # plt.xscale('log', basex=2)
+    plt.ylim(ymin=0)
+    plt.title("RocksDB block cache miss ratios")
+    plt.legend()
+    fig.savefig(output_result_dir + "/mrc.pdf", 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_suffix,
+    pdf_name,
+    xlabel,
+    ylabel,
+    title,
+    vertical,
+    legend,
+):
+    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
+        print("Processing file {}".format(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:
+                plt.plot(
+                    [int(x[i]) for i in range(len(x))],
+                    label_stats[label_index],
+                    label=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_access_timeline(csv_result_dir, output_result_dir):
+    plot_line_charts(
+        csv_result_dir,
+        output_result_dir,
+        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 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_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="< ",
+    )
+
+
+if __name__ == "__main__":
+    if len(sys.argv) < 3:
+        print(
+            "Must provide two arguments: 1) The directory that saves a list of "
+            "directories which contain block cache trace analyzer result files "
+            "2) the directory to save plotted graphs."
+        )
+        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_miss_ratio_graphs(csv_abs_dir, result_dir)
+        plot_access_timeline(csv_abs_dir, result_dir)
+        plot_reuse_graphs(csv_abs_dir, result_dir)
+        plot_percentage_access_summary(csv_abs_dir, result_dir)
+        plot_access_count_summary(csv_abs_dir, result_dir)