Peterbe.com

A blog and website by Peter Bengtsson

fastest way to turn HTML into text in Python

08 January 2021 2 comments   Python


tl;dr; selectolax is best for stripping HTML down to plain text.

The problem is that I have 10,000+ HTML snippets that I need to index into Elasticsearch as plain text. (Before you ask, yes I know Elasticsearch has a html_strip text filter but it's not what I want/need to use in this context).
Turns out, stripping the HTML into plain text was actually quite expensive at that scale. So what's the most performant way?

PyQuery

from pyquery import PyQuery as pq

text = pq(html).text()

selectolax

from selectolax.parser import HTMLParser

text = HTMLParser(html).text()

regular expression

import re

regex = re.compile(r'<.*?>')
text = clean_regex.sub('', html)

Results

I wrote a script that iterated through 10,000 files that contains HTML snippets. Note! The snippets aren't complete <html> documents (with a <head> and <body> etc) Just blobs of HTML. The average size is 10,314 bytes (5,138 bytes median).

pyquery
  SUM:    18.61 seconds
  MEAN:   1.8633 ms
  MEDIAN: 1.0554 ms
selectolax
  SUM:    3.08 seconds
  MEAN:   0.3149 ms
  MEDIAN: 0.1621 ms
regex
  SUM:    1.64 seconds
  MEAN:   0.1613 ms
  MEDIAN: 0.0881 ms

I've run it a bunch of times. The results are pretty stable.

Point is: selectolax is ~7 times faster than PyQuery

Regex? Really?

No, I don't think I want to use that. It makes me nervous without even attempting to dig up some examples where it goes wrong. It might work just fine for the most basic blobs of HTML. Actually, if the HTML is <p>Foo &amp; Bar</p>, I expect the plain text transformation should be Foo & Bar, not Foo &amp; Bar.

More pressing, both PyQuery and selectolax supports something very specific but important to my use case. I need to remove certain tags (and its content) before I proceed. For example:

<h4 class="warning">This should get stripped.</h4>
<p>Please keep.</p>
<div style="display: none">This should also get stripped.</div>

That can never be done with a regex.

Version 2.0

So my requirement will probably change but basically, I want to delete certain tags. E.g. <div class="warning"> and <div class="hidden"> and <div style="display: none">. So let's implement that:

PyQuery

from pyquery import PyQuery as pq

_display_none_regex = re.compile(r'display:\s*none')

doc = pq(html)
doc.remove('div.warning, div.hidden')
for div in doc('div[style]').items():
    style_value = div.attr('style')
    if _display_none_regex.search(style_value):
        div.remove()
text = doc.text()

selectolax

from selectolax.parser import HTMLParser

_display_none_regex = re.compile(r'display:\s*none')

tree = HTMLParser(html)
for tag in tree.css('div.warning, div.hidden'):
    tag.decompose()
for tag in tree.css('div[style]'):
    style_value = tag.attributes['style']
    if style_value and _display_none_regex.search(style_value):
        tag.decompose()
text = tree.body.text()

This actually works. When I now run the same benchmark for 10,000 of these are the new results:

pyquery
  SUM:    21.70 seconds
  MEAN:   2.1701 ms
  MEDIAN: 1.3989 ms
selectolax
  SUM:    3.59 seconds
  MEAN:   0.3589 ms
  MEDIAN: 0.2184 ms
regex
  Skip

Again, selectolax beats PyQuery by a factor of ~6.

Conclusion

Regular expressions are fast but weak in power. Makes sense.

This selectolax is very impressive.
I got the inspiration from this blog post which sets out to do something very similar to what I'm doing.

I hope this helps someone. Thank you Artem Golubin of selectolax and @lexborisov for Modest which selectolax is built upon.

Gcm - git checkout master or main

21 December 2020 1 comment   Python


I love git on the command line and I actually never use a GUI to navigate git branches. But sometimes, I need scripting to make abstractions that make life more convenient. What often happens is that I need to go back to the "main" branch. I write main in quotation marks because it's not always called main. Sometimes it's called master. And it's tedious to have to remember which one is the default. So I wrote a script called Gcm.

#!/usr/bin/env python
import subprocess


def run(*args):
    default_branch = get_default_branch()
    current_branch = get_current_branch()
    if default_branch != current_branch:
        checkout_branch(default_branch)
    else:
        print(f"Already on {default_branch}")
        return 1


def checkout_branch(branch_name):
    subprocess.run(f"git checkout {branch_name}".split())


def get_default_branch():
    origin_name = "origin"
    res = subprocess.run(
        f"git remote show {origin_name}".split(), check=True, capture_output=True,
    )
    for line in res.stdout.decode("utf-8").splitlines():
        if line.strip().startswith("HEAD branch:"):
            return line.replace("HEAD branch:", "").strip()

    raise NotImplementedError(f"No remote called {origin_name!r}")


def get_current_branch():
    res = subprocess.run("git branch --show-current".split(), capture_output=True)
    for line in res.stdout.decode("utf-8").splitlines():
        return line.strip()

    raise NotImplementedError("Don't know what to do!")


if __name__ == "__main__":
    import sys

    sys.exit(run(*sys.argv[1:]))

It ain't pretty or a spiffy one-liner, but it works. It assumes that the repo has a remote called origin which doesn't matter if it's the upstream or your fork. Put this script into a file called ~/bin/Gcm and run chmox +x ~/bin/Gcm.

Now, whenever I want to go back to the main branch I type Gcm and it takes me there.

Gcm in action

It might seem silly, and it might not be for you, but I love it and use it many times per day. Perhaps by sharing this tip, it'll inspire someone else to set up something similar for themselves.

Why it's spelled with an uppercase G

I have a pattern (or rule?) that all scripts that I write myself are always capitalized like that. It avoids clashes with stuff I install with brew or other bash/zsh aliases.

For example:

ls -l ~/bin/RemoteVSCodePeterbecom.sh
ls -l ~/bin/Cleanupfiles
ls -l ~/bin/RandomString.py

sharp vs. jimp - Node libraries to make thumbnail images

15 December 2020 0 comments   Node, JavaScript


I recently wrote a Google Firebase Cloud function that resizes images on-the-fly and after having published that I discovered that sharp is "better" than jimp. And by better I mean better performance.

To reach this conclusion I wrote a simple trick that loops over a bunch of .png and .jpg files I had lying around and compare how long it took each implementation to do that. Here are the results:

Using jimp

▶ node index.js ~/Downloads
Sum size before: 41.1 MB (27 files)
...
Took: 28.278s
Sum size after: 337 KB

Using sharp

▶ node index.js ~/Downloads
Sum size before: 41.1 MB (27 files)
...
Took: 1.277s
Sum size after: 200 KB

The files are in the region of 100-500KB, a couple that are 1-3MB, and 1 that is 18MB.

So basically: 28 seconds for jimp and 1.3 seconds for sharp

Bonus, the code

Don't ridicule me for my benchmarking code. These are quick hacks. Let's focus on the point.

sharp

function f1(sourcePath, destination) {
  return readFile(sourcePath).then((buffer) => {
    console.log(sourcePath, "is", humanFileSize(buffer.length));
    return sharp(sourcePath)
      .rotate()
      .resize(100)
      .toBuffer()
      .then((data) => {
        const destPath = path.join(destination, path.basename(sourcePath));
        return writeFile(destPath, data).then(() => {
          return stat(destPath).then((s) => s.size);
        });
      });
  });
}

jimp

function f2(sourcePath, destination) {
  return readFile(sourcePath).then((buffer) => {
    console.log(sourcePath, "is", humanFileSize(buffer.length));
    return Jimp.read(sourcePath).then((img) => {
      const destPath = path.join(destination, path.basename(sourcePath));
      img.resize(100, Jimp.AUTO);
      return img.writeAsync(destPath).then(() => {
        return stat(destPath).then((s) => s.size);
      });
    });
  });
}

I test them like this:

console.time("Took");
const res = await Promise.all(files.map((file) => f1(file, destination)));
console.timeEnd("Took");

And just to be absolutely sure, I run them separately so the whole process is dedicated to one implementation.

downloadAndResize - Firebase Cloud Function to serve thumbnails

08 December 2020 0 comments   Web development, That's Groce!, Node, JavaScript


UPDATE 2020-12-30

With sharp after you've loaded the image (sharp(contents)) make sure to add .rotate() so it automatically rotates the image correctly based on EXIF data.

UPDATE 2020-12-13

I discovered that sharp is much better than jimp. It's order of maginitude faster. And it's actually what the Firebase Resize Images extension uses. Code updated below.

I have a Firebase app that uses the Firebase Cloud Storage to upload images. But now I need thumbnails. So I wrote a cloud function that can generate thumbnails on-the-fly.

There's a Firebase Extension called Resize Images which is nicely done but I just don't like that strategy. At least not for my app. Firstly, I'm forced to pick the right size(s) for thumbnails and I can't really go back on that. If I pick 50x50, 1000x1000 as my sizes, and depend on that in the app, and then realize that I actually want it to be 150x150, 500x500 then I'm quite stuck.

Instead, I want to pick any thumbnail sizes dynamically. One option would be a third-party service like imgix, CloudImage, or Cloudinary but these are not free and besides, I'll need to figure out how to upload the images there. There are other Open Source options like picfit which you install yourself but that's not an attractive option with its implicit complexity for a side-project. I want to stay in the Google Cloud. Another option would be this AppEngine function by Albert Chen which looks nice but then I need to figure out the access control between that and my Firebase Cloud Storage. Also, added complexity.

As part of your app initialization in Firebase, it automatically has access to the appropriate storage bucket. If I do:

const storageRef = storage.ref();
uploadTask = storageRef.child('images/photo.jpg').put(file, metadata);
...

...in the Firebase app, it means I can do:

 admin
      .storage()
      .bucket()
      .file('images/photo.jpg')
      .download()
      .then((downloadData) => {
        const contents = downloadData[0];

...in my cloud function and it just works!

And to do the resizing I use Jimp which is TypeScript aware and easy to use. Now, remember this isn't perfect or mature but it works. It solves my needs and perhaps it will solve your needs too. Or, at least it might be a good start for your application that you can build on. Here's the function (in functions/src/index.ts):

interface StorageErrorType extends Error {
  code: number;
}

const codeToErrorMap: Map<number, string> = new Map();
codeToErrorMap.set(404, "not found");
codeToErrorMap.set(403, "forbidden");
codeToErrorMap.set(401, "unauthenticated");

export const downloadAndResize = functions
  .runWith({ memory: "1GB" })
  .https.onRequest(async (req, res) => {
    const imagePath = req.query.image || "";
    if (!imagePath) {
      res.status(400).send("missing 'image'");
      return;
    }
    if (typeof imagePath !== "string") {
      res.status(400).send("can only be one 'image'");
      return;
    }
    const widthString = req.query.width || "";
    if (!widthString || typeof widthString !== "string") {
      res.status(400).send("missing 'width' or not a single string");
      return;
    }
    const extension = imagePath.toLowerCase().split(".").slice(-1)[0];
    if (!["jpg", "png", "jpeg"].includes(extension)) {
      res.status(400).send(`invalid extension (${extension})`);
      return;
    }
    let width = 0;
    try {
      width = parseInt(widthString);
      if (width < 0) {
        throw new Error("too small");
      }
      if (width > 1000) {
        throw new Error("too big");
      }
    } catch (error) {
      res.status(400).send(`width invalid (${error.toString()}`);
      return;
    }

    admin
      .storage()
      .bucket()
      .file(imagePath)
      .download()
      .then((downloadData) => {
        const contents = downloadData[0];
        console.log(
          `downloadAndResize (${JSON.stringify({
            width,
            imagePath,
          })}) downloadData.length=${humanFileSize(contents.length)}\n`
        );

        const contentType = extension === "png" ? "image/png" : "image/jpeg";
        sharp(contents)
          .rotate()
          .resize(width)
          .toBuffer()
          .then((buffer) => {
            res.setHeader("content-type", contentType);
            // TODO increase some day
            res.setHeader("cache-control", `public,max-age=${60 * 60 * 24}`);
            res.send(buffer);
          })
          .catch((error: Error) => {
            console.error(`Error reading in with sharp: ${error.toString()}`);
            res
              .status(500)
              .send(`Unable to read in image: ${error.toString()}`);
          });
      })
      .catch((error: StorageErrorType) => {
        if (error.code && codeToErrorMap.has(error.code)) {
          res.status(error.code).send(codeToErrorMap.get(error.code));
        } else {
          res.status(500).send(error.message);
        }
      });
  });

function humanFileSize(size: number): string {
  if (size < 1024) return `${size} B`;
  const i = Math.floor(Math.log(size) / Math.log(1024));
  const num = size / Math.pow(1024, i);
  const round = Math.round(num);
  const numStr: string | number =
    round < 10 ? num.toFixed(2) : round < 100 ? num.toFixed(1) : round;
  return `${numStr} ${"KMGTPEZY"[i - 1]}B`;
}

Here's what a sample URL looks like.

I hope it helps!

I think the next thing for me to consider is to extend this so it uploads the thumbnail back and uses the getDownloadURL() of the created thumbnail as a redirect instead. It would be transparent to the app but saves on repeated views. That'd be a good optimization.

Default food shopping items isn't for everyone

24 November 2020 0 comments   That's Groce!


An old friend of mine, from The Netherlands, contacted me about That's Groce! because the default food word suggestions simple don't make sense to him since they're all in English. American English too, I suspect.

Let's back up a bit. Here's a picture of some of the ~100 default food words that are meant to help you when you start out:

Some of the default food word suggestions

The idea is that until you've started using That's Groce! it'll take a while to get your patterns settled. The assumption is that for most people it makes sense to have some sensible default suggestions. This way, as you start typing ch it can suggest: "Cherries 🍒", "Chilis 🌶", "Chicken 🍗", etc.

Now, you can turn this functionality off. The option looks like this:

Disable default suggestions

What's cool about this new feature is that the feature was borne from feedback. My friend used the "Feedback" form and is actually the first one to ever do so. Thanks Ivo!

Feedback option

Popularity contest for your grocery list

21 November 2020 0 comments   Web development, Mobile, That's Groce!


tl;dr; Up until recently, when you started to type a new entry in your That's Groce shopping list, the suggestions that would appear weren't sorted intelligently. Now they are. They're sorted by popularity.

The whole point with the suggestions that appear is to make it easier for you to not have to type the rest. The first factor that decides which should appear is simply based on what you've typed so far. If you started typing ch we can suggest:

They all contain ch in some form (starting of words only). But space is limited and you can't show every suggestion. So, if you're going to cap it to only show, say, 4 suggestions; which ones should you show first?
I think the solution is to do it by frequency. I.e. items you often put onto the list.

How to calculate the frequency

The way That's Groce now does it is that it knows the exact times a certain item was added to the list. It then takes that list and applies the following algorithm:

For each item...

  1. Discard the dates older than 3 months
  2. Discard any duplicates from clusters (e.g. you accidentally added it and removed it and added it again within minutes)
  3. Calculate the distance (in seconds) between each add
  4. From the last 4 times it was added, take the median value of the distance between

So the frequency becomes a number of seconds. It should feel somewhat realistic. In my family, it actually checks out. We buy bananas every week but sometimes slightly more often than that and in our case, the number comes to ~6 days.

The results

Before sorting by popularity
Before sorting by popularity

After sorting by popularity
After sorting by popularity

Great! The chances of appreciating and picking one of the suggestions is greater if it's more likely to be what you were looking for. And things that have been added frequently in the past are more likely to be added again.

How to debug this

There's now a new page called the "Popularity contest". You get to it from the "List options" button in the upper right-hand corner. On its own, it's fairly "useless" because it just lists them. But it's nice to get a feeling for what your family most frequently add to the list. A lot more can probably be done to this page but for now, it really helps to back up the understanding of how the suggestions are sorted when you're adding new entries.

Popularity contest

If you look carefully at my screenshot here you'll notice two little bugs. There are actually two different entries for "Lemon 🍋" and that was from the early days when that could happen.
Also, another bug is that there's one entry called "Bananas" and one called "Bananas 🍌" which is also something that's being fixed in the latest release. My own family's list predates those fixes.

Hope it helps!