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export.py
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export.py
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import json
import math
import pandas as pd
import sys
import git
from pathlib import Path
from inspect import cleandoc
from plotly.subplots import make_subplots
import plotly.express as px
import plotly.graph_objects as go
import plotly
from pretty_html_table import build_table
# plot colors
pal = px.colors.qualitative.Plotly
color_sequence = ["#BBB", "#777", "#111", pal[9], pal[4], pal[6], pal[1], pal[0], "#58a2c4", pal[5], pal[2], pal[7], pal[8], pal[3]]
# plot labels
plot_labels = dict(
cpu_time='ns per key',
data_elem_count='dataset size',
table_bits_per_key='total bits per key',
point_lookup_percent='percentage of point queries')
file = "results.json" if len(sys.argv) < 2 else sys.argv[1]
with open(file) as data_file:
data = json.load(data_file)
# convert json results to dataframe
df = pd.json_normalize(data, 'benchmarks')
# augment additional computed columns
# augment plotting datasets
def magnitude(x):
l = math.log(x, 10)
rem = round(x/pow(10, l), 2)
exp = int(round(l, 0))
#return f'${rem} \cdot 10^{{{exp}}}$'
return f'{rem}e-{exp}'
df["method"] = df["label"].apply(lambda x : x.split(":")[0])
df["dataset"] = df["label"].apply(lambda x : x.split(":")[1])
df["elem_magnitude"] = df.apply(lambda x : magnitude(x["data_elem_count"]), axis=1)
# prepare datasets for plotting & augment dataset specific columns
lt_df = df[df["name"].str.lower().str.contains("probe")].copy(deep=True)
ct_df = df[df["name"].str.lower().str.contains("construction")].copy(deep=True)
mw_df = df[df["name"].str.lower().str.contains("mixed")].copy(deep=True)
su_df = ct_df.copy(deep=True)
# subset specific filtering & augmentation
lt_df["probe_distribution"] = lt_df["label"].apply(lambda x : x.split(":")[2] if len(x.split(":")) > 2 else "-")
lt_df["probe_size"] = lt_df["name"].apply(lambda x : int(x.split(",")[1].split(">")[0]))
ct_df["cpu_time_per_key"] = ct_df.apply(lambda x : x["cpu_time"] / x["data_elem_count"], axis=1)
ct_df["throughput"] = ct_df.apply(lambda x : 10**9/x["cpu_time_per_key"], axis=1)
ct_df = ct_df[ct_df["data_elem_count"] > 9 * 10**7]
mw_df["_sort_name"] = mw_df["label"].apply(lambda x : x.split(":")[0] if len(x.split(":")) > 0 else "-")
mw_df["probe_distribution"] = mw_df["label"].apply(lambda x : x.split(":")[2] if len(x.split(":")) > 2 else "-")
mw_df = mw_df.sort_values(["_sort_name", "point_lookup_percent"], ascending=True)
# ensure export output folder exists
results_path = "docs" if len(sys.argv) < 3 else sys.argv[2]
Path(results_path).mkdir(parents=True, exist_ok=True)
def convert_to_html(fig):
return fig.to_html(full_html=False, include_plotlyjs=False)
def plot_lookup_times(probe_size):
data = lt_df[lt_df["probe_size"] == probe_size]
fig = px.line(
data,
x="data_elem_count",
y="cpu_time",
color="method",
facet_row="probe_distribution",
facet_col="dataset",
category_orders={"dataset": ["seq", "gap_10", "uniform", "normal", "wiki", "osm", "fb"]},
markers=True,
log_x=True,
labels=plot_labels,
color_discrete_sequence=color_sequence,
height=1000,
title=f"Probe (size: {probe_size}) - ns per key"
)
# hide prefetched results by default
fig.for_each_trace(lambda trace: trace.update(visible="legendonly")
if trace.name.startswith("Prefetched") else ())
return fig
def plot_mixed():
data = mw_df[mw_df["data_elem_count"] == 10**8]
fig = px.line(
data,
x="point_lookup_percent",
y="cpu_time",
color="method",
facet_row="probe_distribution",
facet_col="dataset",
category_orders={"dataset": ["seq", "gap_10", "uniform", "normal", "wiki", "osm", "fb"]},
markers=True,
log_x=False,
labels=plot_labels,
color_discrete_sequence=color_sequence,
height=1000,
title=f"Mixed workload - ns per key"
)
# hide prefetched results by default
fig.for_each_trace(lambda trace: trace.update(visible="legendonly")
if trace.name.startswith("Prefetched") else ())
return fig
def plot_construction_times():
fig = px.bar(
ct_df,
x="elem_magnitude",
y="throughput",
color="method",
barmode="group",
facet_col="dataset",
category_orders={"dataset": ["seq", "gap_10", "uniform", "normal", "wiki", "osm", "fb"]},
labels=plot_labels,
color_discrete_sequence=color_sequence,
height=500,
title=f"Construction time - keys per second"
)
# hide prefetched results by default
fig.for_each_trace(lambda trace: trace.update(visible="legendonly")
if trace.name.startswith("Prefetched") else ())
return fig
def plot_space_usage():
fig = px.line(
su_df,
x="data_elem_count",
y="table_bits_per_key",
color="method",
facet_col="dataset",
category_orders={"dataset": ["seq", "gap_10", "uniform", "normal", "wiki", "osm", "fb"]},
markers=True,
log_x=True,
labels=plot_labels,
color_discrete_sequence=color_sequence,
height=500,
title=f"Total space usage - bits per key"
)
# hide prefetched results by default
fig.for_each_trace(lambda trace: trace.update(visible="legendonly")
if trace.name.startswith("Prefetched") else ())
return fig
def plot_pareto_lookup_vs_space(probe_size):
filtered = lt_df[(lt_df["probe_size"] == probe_size) & (lt_df["data_elem_count"] > 9 * 10**7)]
fig = px.scatter(
filtered,
x="cpu_time",
y="table_bits_per_key",
color="method",
facet_row="probe_distribution",
facet_col="dataset",
category_orders={"dataset": ["seq", "gap_10", "uniform", "normal", "wiki", "osm", "fb"]},
labels=plot_labels,
color_discrete_sequence=color_sequence,
height=1000,
title=f"Pareto - lookup ({probe_size} elems in ns) vs space (total in bits/key)"
)
# hide prefetched results by default
fig.for_each_trace(lambda trace: trace.update(visible="legendonly")
if trace.name.startswith("Prefetched") else ())
return fig
outfile_name = "index.html" if len(sys.argv) < 4 else sys.argv[3]
with open(f'{results_path}/{outfile_name}', 'w') as readme:
readme.write(cleandoc(f"""
<!doctype html>
<html>
<head>
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
</head>
<body>
{convert_to_html(plot_lookup_times(0))}
{convert_to_html(plot_lookup_times(1))}
{convert_to_html(plot_lookup_times(10))}
{convert_to_html(plot_lookup_times(20))}
{convert_to_html(plot_space_usage())}
{convert_to_html(plot_pareto_lookup_vs_space(0))}
{convert_to_html(plot_pareto_lookup_vs_space(1))}
{convert_to_html(plot_pareto_lookup_vs_space(10))}
{convert_to_html(plot_pareto_lookup_vs_space(20))}
{convert_to_html(plot_construction_times())}
{convert_to_html(plot_mixed())}
</body>
</html>
"""))