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Marty Oehme 2025-10-01 14:42:07 +02:00
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import marimo
__generated_with = "0.16.2"
app = marimo.App(width="medium")
with app.setup:
# Initialization code that runs beimpofore all other cells
import re
from pathlib import Path
import lets_plot as lp
import marimo as mo
import polars as pl
LIMIT_ROWS = 50_000
DATA_RAW_DIR = "data/raw"
DATA_CLEAN_DIR = "data/cleaned"
DATA_PARQUET_DIR = "data/parquet"
@app.cell(hide_code=True)
def _():
mo.md(r"""# Void Linux 'Popcorn' package repository stat analysis
This notebook analyses the daily package repository statistics files,
colloquially known as 'popcorn' files, that are generated by the Void Linux
package manager `xbps` and uploaded by users who have opted in to share.
""")
return
# run data prep
@app.cell
def _():
import clean
clean.json_to_daily_pkg(
Path(DATA_RAW_DIR) / "daily", Path(DATA_CLEAN_DIR) / "daily", force=False
)
clean.json_to_unique_csv(
Path(DATA_RAW_DIR) / "daily", Path(DATA_CLEAN_DIR), force=False
)
clean.json_to_daily_kernel_csv(
Path(DATA_RAW_DIR) / "daily", Path(DATA_CLEAN_DIR) / "kernels", force=False
)
@app.cell
def _():
def parse_size(size_str):
try:
return float(re.search(r"(\d+.?\d+) kB", size_str).group(1)) # pyright: ignore[reportOptionalMemberAccess]
except AttributeError:
return None
sizes_df_raw = (
pl.read_csv(f"{DATA_CLEAN_DIR}/file_sizes.csv")
.with_columns(
pl.col("name")
.str.replace(r"data/(\d{4}-\d{2}-\d{2}).json", "${1}")
.str.to_date()
.alias("date"),
pl.col("size")
.map_elements(lambda x: parse_size(x), return_dtype=pl.Float32)
.alias("size_num"),
)
.select(["date", "size_num", "size", "modified"])
)
sizes_df = sizes_df_raw.filter(pl.col("size_num").is_not_null())
return sizes_df, sizes_df_raw
@app.cell(hide_code=True)
def _():
mo.md(
r"""
## Daily statistics file size
The simplest operation we can do is look at the overall file size for each of the daily
statistics files over time. The files consist of a long list of packages which have been checked
from the repositories that day, along with the number of package instances. It also consists of
the same list separated by specifically installed versions of packages, so if somebody has
v0.9.1 and somebody else v0.9.3 instead this would count both packages separately.
Another count is the number of different Kernels that have been used on that day, with their
exact kernel name including major version, minor version and any suffix.
These are the major things that will lead to size increases in the file, but not just for an
increased amount of absolute users, packages or uploads --- we will get to those shortly.
No, an increase in file size here mainly suggests an increase in the 'breadth' of files on offer
in the repository, whether that be a wider variety of program versions or more different
packages that people are interested in, and those that the community chooses to use.
So while the overall amount of packages gives a general estimate of the interest in the
distribution, this can show a more 'distributor'-aligned view on how many different aisles of
the buffet people are eating from.
"""
)
return
@app.cell
def _(sizes_df):
(
lp.ggplot(sizes_df, lp.aes(x="date", y="size"))
+ lp.geom_point()
+ lp.geom_smooth(method="lm")
+ lp.labs(
title="Size growth",
subtitle="Size of daily popcorn statistics files over time",
caption="Raw json file size, without any formatting, removal of markers, characters or newlines.",
)
)
return
@app.cell(hide_code=True)
def _():
mo.md(
r"""
As we can see, the difference over time is massive. Especially early on, between 2019 and the
start of 2021, the amount of different packages and package versions used grew rapidly, with the
pace picking up once again starting 2023.
There are a few outlier days with a size of 0 kB, which we will remove from the data. In all
likelihood, those days were not reported correctly or there was some kind of issue on the
backend so the stats for those days are lost.
There are also a few days where the modification date of the file does not correspond to the
represented statistical date but those are kept. This rather points to certain times when the
files have been moved on the backend, or recreated externally but does not mean the data are
bad.
"""
)
return
@app.cell
def _():
df_pkg_lazy = (
pl.scan_csv(
f"{DATA_CLEAN_DIR}/daily/*.csv",
include_file_paths="file",
schema={
"date": pl.Date,
"package": pl.String,
"downloads": pl.UInt16,
},
)
.drop("file")
.fill_null(0)
)
if LIMIT_ROWS: # NOTE: this is only for debugging purposes
df_pkg_lazy = df_pkg_lazy.head(LIMIT_ROWS)
# give small df preview
df_pkg_lazy.head(100).collect(engine="streaming")
return
@app.cell(hide_code=True)
def _():
mo.md(
r"""
## Package statistics
Now that we have an idea of how the overall interest in the distribution has changed over time,
let's look at the actual package statistics.
The popcorn files contain two main pieces of information: the number of installs per package
(e.g. how many people have rsync installed) and the number of unique installs (i.e. unique
machines providing statistics). We will look at both of these in turn.
"""
)
return
@app.cell
def _(df_pkg_lazy: pl.LazyFrame):
def _():
weekly_packages = (
df_pkg_lazy.sort("date")
.group_by_dynamic("date", every="1w")
.agg(pl.col("downloads").sum())
.sort("date")
)
return (
lp.ggplot(weekly_packages.collect(engine="streaming"), lp.aes("date", "downloads"))
+ lp.geom_line()
+ lp.geom_smooth(method="loess")
+ lp.labs(
title="Weekly package ownership",
caption="Count of all installed packages aggregated for each week",
)
)
_()
return
@app.cell(hide_code=True)
def _():
mo.md(
r"""
The amount of packages installed on all machines increases strongly over time.
"""
)
return
@app.cell
def _(df_pkg_lazy: pl.LazyFrame):
def _():
weekday_downloads = df_pkg_lazy.sort("date").with_columns(
pl.col("date")
.dt.weekday()
.sort()
.replace_strict(
{
1: "Mon",
2: "Tue",
3: "Wed",
4: "Thu",
5: "Fri",
6: "Sat",
7: "Sun",
}
)
.alias("weekday")
)
return (
lp.ggplot(weekday_downloads.collect(engine="streaming"), lp.aes("weekday", "downloads"))
+ lp.geom_bar()
+ lp.labs(
title="Weekday downloads",
caption="Downloads aggregated per day of the week they took place.",
)
)
_()
return
@app.cell
def _(df_pkg_lazy: pl.LazyFrame):
def _():
month_agg_downloads = df_pkg_lazy.sort("date").with_columns(
pl.col("date").dt.month().alias("month")
)
return (
lp.ggplot(month_agg_downloads.collect(engine="streaming"), lp.aes("month", "downloads"))
+ lp.geom_bar()
+ lp.labs(
title="Monthwise downloads",
caption="Downloads aggregated per month of the year.",
)
)
_()
return
@app.cell
def _():
(
lp.ggplot(
pl.read_csv(
f"{DATA_CLEAN_DIR}/unique_installs.csv",
schema={"date": pl.Date, "unique": pl.UInt16},
),
lp.aes("date", "unique"),
)
+ lp.geom_line()
+ lp.geom_smooth()
+ lp.labs(
title="Unique daily uploads",
caption="Daily number of unique providers for package update statistics opting in to popcorn.",
)
)
return
@app.cell
def _(df_pkg_lazy: pl.LazyFrame):
df_pkg_dl = df_pkg_lazy.group_by("package").agg(pl.col("downloads").sum())
def _():
DISPLAY_LIMIT = 20
return lp.gggrid(
[
lp.ggplot(
df_pkg_dl.sort("downloads", descending=True)
.head(DISPLAY_LIMIT)
.collect(engine="streaming"),
lp.aes("package", "downloads"),
)
+ lp.geom_bar(stat="identity")
+ lp.labs(
title="Top packages",
caption="Most installed packages over all time",
),
lp.ggplot(
df_pkg_dl.sort("downloads", descending=False)
# this seems arbitrary but gives a better result?
.head(DISPLAY_LIMIT)
.collect(engine="streaming"),
lp.aes("package", "downloads"),
)
+ lp.geom_bar(stat="identity")
+ lp.labs(
title="Rare packages",
caption="Least often installed packages",
),
],
ncol=1,
)
_()
return
@app.cell(hide_code=True)
def _(df_pkg_dl: pl.LazyFrame):
def _():
return (
lp.ggplot(df_pkg_dl.collect(engine="streaming"), lp.aes("downloads"))
+ lp.geom_freqpoly(stat="bin")
+ lp.labs(
title="Package installation count distribution",
)
)
_()
return
@app.cell(hide_code=True)
def _(df_pkg_dl: pl.LazyFrame):
def _():
def get_num(df: pl.LazyFrame) -> int:
return df.count().collect(engine="streaming").item(0, 0)
one_install = df_pkg_dl.sort("downloads", descending=False).filter(
pl.col("downloads") == 1
)
two_installs = df_pkg_dl.sort("downloads", descending=False).filter(
(pl.col("downloads") >= 2) & (pl.col("downloads") < 10)
)
three_installs = df_pkg_dl.sort("downloads", descending=False).filter(
(pl.col("downloads") >= 10) & (pl.col("downloads") < 20)
)
# TODO: Fix for new filters above
return mo.md(rf"""
There are {get_num(one_install)} packages which have exactly a single
installation in the data, {get_num(two_installs)} packages with exactly
two installations, and {get_num(three_installs)} packages with exactly
three.
""")
_()
return
@app.cell(hide_code=True)
def _():
mo.md(r""" ## Kernel Analysis """)
return
# - which kernels have been DL when? (simplified for semver)
@app.cell
def _():
kernel_df_lazy = (
pl.scan_csv(
f"{DATA_CLEAN_DIR}/kernels/*.csv",
schema={
"date": pl.Date,
"kernel": pl.String,
"downloads": pl.UInt16,
},
)
.fill_null(0)
.with_columns(pl.col("kernel").str.replace(r"(\d+\.\d+\.\d+).*", "${1}"))
.with_columns(
pl.col("kernel")
.str.replace(r"(\d+).*", "${1}")
.str.to_integer(dtype=pl.UInt8)
.alias("major_ver"),
pl.col("kernel").str.replace(r"(\d+\.\d+).*", "${1}").alias("minor_ver"),
)
)
kernel_df_v99 = (
kernel_df_lazy.filter(pl.col("major_ver") == 99).collect(engine="streaming").select("date")
)
kernel_df_lazy = kernel_df_lazy.filter(pl.col("major_ver") != 99)
(
lp.ggplot(
kernel_df_lazy.with_columns(pl.col("major_ver").cast(pl.String))
.group_by("major_ver")
.agg(pl.col("downloads").sum())
.sort("major_ver")
.collect(engine="streaming"),
lp.aes("major_ver", "downloads"),
)
+ lp.geom_bar(stat="identity")
+ lp.labs(
title="Kernel versions used",
caption="For each daily download, add up the currently running kernel version",
)
)
return
@app.cell(hide_code=True)
def _(kernel_df_v99: pl.DataFrame):
mo.md(
rf"""
When looking at the kernel versions used, we see a very strong jump between major kernel version
4 and major kernel version 5.
For this analysis we had to exclude {kernel_df_v99.select(pl.len()).item()} rows which were
apparently from the future, as they were running variations of major kernel version 99. In all
likelihood there is a custom kernel version out there which reports its own major version as 99.
The strange version starts appearing on {kernel_df_v99.select("date").row(0)[0]} and shows up
all the way until {kernel_df_v99.select("date").row(-1)[0]}.
"""
)
return
@app.cell
def _(kernel_df_lazy: pl.LazyFrame):
weekly_kernel_df = (
kernel_df_lazy.with_columns(pl.col("major_ver").cast(pl.String))
.select(["date", "major_ver", "downloads"])
.sort("date")
.group_by_dynamic("date", every="1w", group_by="major_ver")
.agg(pl.col("downloads").sum())
.collect(engine="streaming")
)
(
lp.ggplot(
weekly_kernel_df,
lp.aes("date", "downloads", color="major_ver"),
)
+ lp.geom_line()
+ lp.labs(
title="Kernels over time",
caption="For each daily download, count used kernel versions",
)
)
@app.cell(hide_code=True)
def _(weekly_kernel_df: pl.DataFrame):
from datetime import date
last_kernel4: date = weekly_kernel_df.filter(pl.col("major_ver") == "4")[-1][
"date"
].item()
first_kernel5: date = weekly_kernel_df.filter(pl.col("major_ver") == "5")[0][
"date"
].item()
last_kernel5: date = weekly_kernel_df.filter(pl.col("major_ver") == "5")[-1][
"date"
].item()
mo.md(
rf"""
A timeline analysis of the kernels used to report daily downloads shows that people generally
adopt new major kernel versons at roughly the same time. This change is especially stark between
major kernel versions 5 and 6, which seem to have traded place in usage almost over night.
The first time that major version 5 of the kernel shows up is on {first_kernel5}. From here, it
took a long time for the last of the version 4 kernels to disappear, coinciding with the big
switch between major version 5 and 6. The last time a major version 4 is seen is on
{last_kernel4}, while the last major version 5 kernels still pop up.
It would seem, then, that the people still running kernel version 4 used the opportunity of
everybody switching to the stable version of 6 to also upgrade their machines.
"""
)
return
@app.cell(hide_code=True)
def _():
mo.md(
r"""
## Odds and Ends
There are some missing days in the statistics.
"""
)
return
@app.cell
def _(sizes_df_raw):
sizes_df_null = sizes_df_raw.filter(pl.col("size_num").is_null())
sizes_df_null.select(["date", "size"]).style.tab_header(
title="Missing Days",
subtitle="Days with 0B size due to missing on the popcorn server.",
)
return
@app.cell
def _(sizes_df):
def _():
different_modification_date = sizes_df.with_columns(
pl.col("modified")
.str.to_datetime(format="%F %T %:z", strict=False)
.alias("modified_dt"),
).filter(pl.col("date") != pl.col("modified_dt").dt.date())
# This does not work well what are we showing?
# 'true' capture date on X but then what on Y - the
# same date for each? the difference in dt?
return (
lp.ggplot(
different_modification_date,
lp.aes("date", "modified_dt"),
)
+ lp.geom_freqpoly()
)
_()
return
# further ideas:
#
# - daily download habits:
# - are we downloading further spread of versions on specific days
# - are there 'update' days, where things converge? specific weekday/on holidays/etc?
#
# - when did specific kernels enter the repos?
#
# - which arches are/were most prevalent over time?
# - have the arches been mostly even relative to each other?
#
# - what does unique install mean?
#
# - which Packages had the most unique versions, least versions
# - which pkg had the most download of a single version?
# - for which pkg were the version dls the most spread out?
if __name__ == "__main__":
app.run()