A blog and website by Peter Bengtsson

How much HTML is too much for optimal web performance

17 October 2018 0 comments   Web Performance, Web development

Right off the bat; I don't know. All I know is that it's complicated.

I have this page which is just a blog post page. It's entirely rendered on the server, comments and all. At the time of writing, the total size of the HTML document is 119KB (30KB gzipped). If you remove all the comments, which makes up the bulk of the HTML it reduces down to 31KB (7KB gzipped). Fair enough. That's 23KB less to download. But, does it matter (much)?


First of all, I noticed this:

WebPagetest with iPhone 6, 4G on the same US coast as the datacenter

That's a WebPagetest using iPhone 6 on 4G and, lemme emphasize this, it took 126ms to download the HTML document. If you subtract "DNS Lookup" (283ms), "Initial Connection" (1013ms), and "SSL Negotiation" (733ms) it took 684ms serve the file, download it, and parse it. Remember, this is all on 4G. Pretty fast. In conclusion, it's probably not too much HTML in that page to download. This downloadingness is fraction of the total "web performance cost". Let's dig deeper.

Note! With WebPagetest all those numbers like DNS Lookup, Initial Connection and SSL Negotiation are wildly unpredictable between tests. Chances are, the numbers are very different the next time you run a test using the exact same input. Who knows. Deep internet plumbings beyond the control of WebPagetest.

Note! I ran it one more time with the exact same parameters and this time it was 535ms (instead of 684ms) to serve, download, and parse.

Parsing & layout

Parsing is hard to measure but here's what I found when using the Google Chrome dev tools:

Google Chrome Performance devtools
Google Chrome Performance devtools

It says it took...

That's half a second just loading and rendering. Definitely sucks. But note, this test uses 4x CPU slowdown and 3G simulation. So perhaps it's not so bad.

Let's try again with a smaller HTML document

So I butchered up a hybrid version that has almost the same HTML except all but 1 of those 166'ish div.comment DOM nodes. It's now 31KB (7KB gzipped´) to download instead of 119KB (30KB gzipped).

Same WebPagetest parameters but now this this smaller HTML document:

WebPagetest with a much smaller HTML footprint
WebPagetest with a much smaller HTML footprint

Now it says it only took 39ms to download and 232ms (it was 684ms before) to serve the file, download it, and parse it. Interesting!

Note! I ran it one more time with exact same parameters and this time it was 237ms (instead of 232ms) to serve, download, and parse.

Clearly it's working. The smaller the HTML document the faster it performs. No surprise. But stick around for the conclusion.

Parsing & layout with a smaller HTML document

Check this out:

Google Chrome Performance devtools (smaller HTML document)
Google Chrome Performance devtools (smaller HTML document)

It says it took...

Mind you, all of these numbers are at the mercy of what my laptop is up to at the moment as it can affect Chrome's rendering if it has, at that moment, less (or more) access to CPU and memory caching.

Either way, it parses + layout in 126ms instead of 523ms for the larger HTML document.


The best test to see how much faster the smaller HTML document variant is, is to compare them side-by-side. It looks like this:

Visual comparison on WebPagetest (using 4G)

Two major takeaways from this:

  1. The smaller HTML version starts rendering half a second before the original one.
  2. The complete time favors the smaller HTML version by 2.5 seconds but that's possibly influenced by the ads that load more than any slow layout rendering.
  3. This is using 4G which isn't unheard of but definitely much less common than better speeds.

Here they are compared on "Desktop" which appears to give the smaller HTML version a 0.2 second advantage:

Visual comparison on WebPagetest (using "Desktop")
Visual comparison on WebPagetest (using "Desktop")

And here are the Lighthouse reports side-by-side:

Side-by-side using Lighthouse


The above concludes rather unsurprisingly that a smaller HTML footprint downloads, parses and lays out quicker.

The killer reason that page is so large, with all those comments rendered in the original HTML is simple: SEO. Google loves comments because comments indicate that the page is thriving and a place where people go, spend time, and stick around. I've experimented with this in the past and found that if I make the HTML document smaller (or loading the rest after document load) the SEO takes a big hit. Yes, Google's bot renders with JavaScript but not always and even if it does, I assume it's smart enough to appreciate that content that is loaded (async or post-DOMContentLoad) is less important and thus not what the page is about.

Regarding SEO, we know that Google loves fast sites. Especially for mobile. But content is still king my gut tells me. Left as an exercise to the reader to take a stand on this.

Another problem with lazy loading the comments (or whatever else might be applicable to your site) is that it might cause "flicker". I put that word in quote because sometimes flicker is literally visual flicker and sometimes it's moments of browser sluggishness. The XHR request and the subsequent post-rendering will cause a bunch of work that strains the browser and might make it unpleasant when your eyes and brain is in the midst of committing to consuming it.

Basically, there are significant real benefits of not trying to squeeze every little millisecond out by making the HTML smaller upfront. Remember the fact that the "smaller HTML" version in this test is drastic. I butchered it from 119KB to 31KB which might be so drastic that it's not necessarily applicable at all. In other words, had I reduced the HTML size by just 20% it might not even register on the performance graph but could be significant in terms SEO keywords.


The majority of the time spend making a web page useful to a user is a sum of all sorts of metrics. The size of the HTML document does matter but remember that it's just one of multiple aspects to watch out for.

In conclusion, it's complicated and depends on your needs and context. I hope you can benefit a little bit from the metrics above.

Switching from AWS S3 (boto3) to Google Cloud Storage (google-cloud-storage) in Python

12 October 2018 0 comments   Python

I'm in the midst of rewriting a big app that currently uses AWS S3 and will soon be switched over to Google Cloud Storage. This blog post is a rough attempt to log various activities in both Python libraries:

Disclaimer: I'm manually copying these snippets from a real project and I have to manually scrub the code clean of unimportant quirks, hacks, and other unrelated things that would just add noise.



$ pip install boto3
$ emacs ~/.aws/credentials


$ pip install google-cloud-storage
$ cat ./google_service_account.json

Note: You need to create a service account and then that gives you a .json file which you download and make sure you pass its path when you create a client.

I suspect there are more/other ways to do this with environment variables alone but I haven't got there yet.

Making a "client"


Note, there are easier shortcuts for this but with this pattern you can have full control over things like like read_timeout, connect_timeout, etc. with that confi_params keyword.

import boto3
from botocore.config import Config

def get_s3_client(region_name=None, **config_params):
    options = {"config": Config(**config_params)}
    if region_name:
        options["region_name"] = region_name
    session = boto3.session.Session()
    return session.client("s3", **options)


from import storage

def get_gcs_client():
    return storage.Client.from_service_account_json(

Checking if a bucket exists and if you have access to it

boto3 (for s3_client here, see above)

from botocore.exceptions import ClientError, EndpointConnectionError


except ClientError as exception:
    if exception.response["Error"]["Code"] in ("403", "404"):
        raise BucketHardError(
            f"Unable to connect to bucket={bucket_name!r} "
            f"ClientError ({exception.response!r})"
except EndpointConnectionError:
    raise BucketSoftError(
        f"Unable to connect to bucket={!r} "
    print("It exists and we have access to it.")


from google.api_core.exceptions import BadRequest

except BadRequest as exception:
    raise BucketHardError(
        f"Unable to connect to bucket={bucket_name!r}, "
        f"because bucket not found due to {exception}"
    print("It exists and we have access to it.")

Checking if an object exists


from botocore.exceptions import ClientError

def key_existing(client, bucket_name, key):
    """return a tuple of (
        key's size if it exists or 0,
        S3 key metadata
    If the object doesn't exist, return None for the metadata.
        response = client.head_object(Bucket=bucket_name, Key=key)
        return response["ContentLength"], response.get("Metadata")
    except ClientError as exception:
        if exception.response["Error"]["Code"] == "404":
            return 0, None

Note, if you do this a lot and often find that the object doesn't exist the using list_objects_v2 is probably faster.


def key_existing(client, bucket_name, key):
    """return a tuple of (
        key's size if it exists or 0,
        S3 key metadata
    If the object doesn't exist, return None for the metadata.
    bucket = client.get_bucket(bucket_name)
    blob = bucket.get_blob(key)
    if blob:
        return blob.size, blob.metadata
    return 0, None

Uploading a file with a special Content-Encoding

Note: You have to use your imagination with regards to the source. In this example, I'm assuming that the source is a file on disk and that it might have already been compressed with gzip.


def upload(file_path, bucket_name, key_name, metadata=None, compressed=False):
    content_type = get_key_content_type(key_name)

    metadata = metadata or {}

    # boto3 will raise a botocore.exceptions.ParamValidationError
    # error if you try to do something like:
    #  s3.put_object(Bucket=..., Key=..., Body=..., ContentEncoding=None)
    # ...because apparently 'NoneType' is not a valid type.
    # We /could/ set it to something like '' but that feels like an
    # actual value/opinion. Better just avoid if it's not something
    # really real.
    extras = {}
    if content_type:
        extras["ContentType"] = content_type
    if compressed:
        extras["ContentEncoding"] = "gzip"
    if metadata:
        extras["Metadata"] = metadata

     with open(file_path, "rb") as f:
         s3_client.put_object(Bucket=bucket_name, Key=key_name, Body=f, **extras)


def upload(file_path, bucket_name, key_name, metadata=None, compressed=False):
    content_type = get_key_content_type(key_name)

    metadata = metadata or {}
    bucket = gcs_client.get_bucket(bucket_name)
    blob = bucket.blob(key_name)

    if content_type:
        blob.content_type = content_type
    if compressed:
        blob.content_encoding = "gzip"
    blob.metadata = metadata

Downloading and uncompressing a gzipped object


from io import BytesIO
from gzip import GzipFile
from botocore.exceptions import ClientError

from .utils import iter_lines

def get_stream(bucket_name, key_name):
        response = source.s3_client.get_object(
            Bucket=bucket_name, Key=key
    except ClientError as exception:
        if exception.response["Error"]["Code"] == "NoSuchKey":
            raise KeyHardError("key not in bucket")

    stream = response["Body"]
    # But if the content encoding is gzip we have re-wrap the stream.
    if response.get("ContentEncoding") == "gzip":
        body = response["Body"].read()
        bytestream = BytesIO(body)
        stream = GzipFile(None, "rb", fileobj=bytestream)

    for line in iter_lines(stream):
        yield line.decode("utf-8")


from io import BytesIO
from gzip import GzipFile
from botocore.exceptions import ClientError

from .utils import iter_lines

def get_stream(bucket_name, key_name):
    bucket = gcs_client.get_bucket(bucket_name)
    blob = bucket.get_blob(key)
    if blob is None:
        raise KeyHardError("key not in bucket")

    bytestream = BytesIO()

    for line in iter_lines(bytestream):
        yield line.decode("utf-8")

Note That here blob.download_to_file works a bit like requests.get() in that it automatically notices the Content-Encoding metadata and does the gunzip on the fly.


It's not fair to compare them on style because I think boto3 came out of boto which probably started back in the day when Google was just web search and web emails.

I wanted to include a section about how to unit test against these. Especially how to mock them. But what I had for a draft was getting ugly. Yes, it works for the testing needs I have in my app but it's very personal taste (aka. appropriate for the context) and admittedly quite messy.

Fancy linkifying of text with Bleach and domain checks (with Python)

10 October 2018 2 comments   Web development, Python

Bleach is awesome. Thank you for it @willkg! It's a Python library for sanitizing text as well as "linkifying" text for HTML use. For example, consider this:

>>> import bleach
>>> bleach.linkify("Here is some text with a")
'Here is some text with a <a href="" rel="nofollow"></a>.'

Note that sanitizing is separate thing, but if you're curious, consider this example:

>>> bleach.linkify(bleach.clean("Here is <script> some text with a"))
'Here is &lt;script&gt; some text with a <a href="" rel="nofollow"></a>.'

With that output you can confidently template interpolate that string straight into your HTML.

Getting fancy

That's a great start but I wanted a more. For one, I don't always want the rel="nofollow" attribute on all links. In particular for links that are within the site. Secondly, a lot of things look like a domain but isn't. For example This is a the start which would naively become...:

>>> bleach.linkify("This is a the start")
'This is a <a href="" rel="nofollow"></a> the start'

...because looks like a domain.

So here is how I use it here on to linkify blog comments:

def custom_nofollow_maker(attrs, new=False):
    href_key = (None, u"href")

    if href_key not in attrs:
        return attrs

    if attrs[href_key].startswith(u"mailto:"):
        return attrs

    p = urlparse(attrs[href_key])
    if p.netloc not in settings.NOFOLLOW_EXCEPTIONS:
        # Before we add the `rel="nofollow"` let's first check that this is a
        # valid domain at all.
        root_url = p.scheme + "://" + p.netloc
            response = requests.head(root_url)
            if response.status_code == 301:
                redirect_p = urlparse(response.headers["location"])
                # If the only difference is that it redirects to https instead
                # of http, then amend the href.
                if (
                    redirect_p.scheme == "https"
                    and p.scheme == "http"
                    and p.netloc == redirect_p.netloc
                    attrs[href_key] = attrs[href_key].replace("http://", "https://")

        except ConnectionError:
            return None

        rel_key = (None, u"rel")
        rel_values = [val for val in attrs.get(rel_key, "").split(" ") if val]
        if "nofollow" not in [rel_val.lower() for rel_val in rel_values]:
        attrs[rel_key] = " ".join(rel_values)

    return attrs

html = bleach.linkify(text, callbacks=[custom_nofollow_maker])

This basically taking the default nofollow callback and extending it a bit.

By the way, here is the complete code I use for sanitizing and linkifying blog comments here on this site: render_comment_text.


This is slow because it requires network IO every time a piece of text needs to be linkified (if it has domain looking things in it) but that's best alleviated by only doing it once and either caching it or persistently storing the cleaned and rendered output.

Also, the check uses try: requests.head() except requests.exceptions.ConnectionError: as the method to see if the domain works. I considered doing a whois lookup or something but that felt a little wrong because just because a domain exists doesn't mean there's a website there. Either way, it could be that the domain/URL is perfectly fine but in that very unlucky instant you checked your own server's internet or some other DNS lookup thing is busted. Perhaps wrapping it in a retry and doing try: requests.head() except requests.exceptions.RetryError: instead.

Lastly, the business logic I chose was to rewrite all http:// to https:// only if the URL http://domain does a 301 redirect to https://domain. So if the original link was it leaves it as is. Perhaps a fancier version would be to look at the domain name ending. For example HEAD 301 redirects to so you could use the fact that "".endswith("").

UPDATE Oct 10 2018

Moments after publishing this, I discovered a bug where it would fail badly if the text contained a URL with an ampersand in it. Turns out, it was a known bug in Bleach. It only happens when you try to pass a filter to the bleach.Cleaner() class.

So I simplified my code and now things work. Apparently, using bleach.Cleaner(filters=[...]) is faster so I'm losing that. But, for now, that's OK in my context.

Also, in another later fix, I improved the function some more by avoiding non-HTTP links (with the exception of mailto: and tel:). Otherwise it would attempt to run requests.head('ssh://') which doesn't make sense.

The ideal number of workers in Jest

08 October 2018 0 comments   ReactJS, Python

tl;dr; Use --runInBand when running jest in CI and use --maxWorkers=3 on your laptop.

Running out of memory on CircleCI

We have a test suite that covers 236 tests across 68 suites and runs mainly a bunch of enzyme rendering of React component but also some plain old JavaScript function tests. We hit a problem where tests utterly failed in CircleCI due to running out of memory. Several individual tests, before it gave up or failed, reported to take up to 45 seconds.
Turns out, jest tried to use 36 workers because the Docker OS it was running was reporting 36 CPUs.

> circleci@9e4c489cf76b:~/repo$ node
> var os = require('os')
> os.cpus().length

After forcibly setting --maxWorkers=2 to the jest command, the tests passed and it took 20 seconds. Yay!

But that got me thinking, what is the ideal number of workers when I'm running the suite here on my laptop? To find out, I wrote a Python script that would wrap the call CI=true yarn run test --maxWorkers=%(WORKERS) repeatedly and report which number is ideal for my laptop.

After leaving it running for a while it spits out this result:

3 8.47s
4 8.59s
6 9.12s
5 9.18s
2 9.51s
7 10.14s
8 10.59s
1 13.80s

The conclusion is vague. There is some benefit to using some small number greater than 1. If you attempt a bigger number it might backfire and take longer than necessary and if you do do that your laptop is likely to crawl and cough.

Notes and conclusions

Inline scripts in create-react-app 2.0 and CSP hashes

05 October 2018 0 comments   ReactJS, Javascript, Web development


My understanding of how to generate the CSP nonces was wrong. What I initially posted was a confusion between nonces and hashes. Sorry. The blog post has been updated to use hashing.


Shortly after publishing this I changed my mind entirely. I decided I don't want any inline scripts no matter how small. Reasons are: 1) with HTTP2 it's cheap to send another file and thus that critical precious first HTML document becomes smaller and 2) when you load it as an external you have the power to load it async if it's applicable.

Check out this new script, it's hackish but works: uninline_scripts.js

UPDATE (Oct 18, 2018)

If you use INLINE_RUNTIME_CHUNK=false yarn run build no scripts, independent of size, are inlined. See this pull request for details.


I have an app that is hosted on github-pages and because I can't control Content Security Policy HTTP headers I have to do it with a <meta http-equiv="Content-Security-Policy" content="${csp}"> tag in the HTML. That's working fine and the way I do it is that I have a script that looks like this:

#!/usr/bin/env node
const fs = require("fs");
const crypto = require("crypto");

const CSP_TEMPLATE = `
default-src 'none';
connect-src 'self';
img-src 'self' https://*;
script-src 'self'%SCRIPT_HASHES%;
style-src 'self' 'unsafe-inline';
font-src 'self' data:;
manifest-src 'self'

const htmlFile = process.argv[2];
if (!htmlFile) throw new Error("missing file argument");
let html = fs.readFileSync(htmlFile, "utf8");

let hashes = "";
let csp = CSP_TEMPLATE;
const matches = html.match(/<script>.*<\/script>/g);
if (matches) {
  matches.forEach(scriptTag => {
    const hash = crypto.createHash("sha256");
    hash.update(scriptTag.replace(/<script>/, "").replace("</script>", ""));
    const digest = hash.digest("hex");
    hashes += ` 'sha256-${digest.toString("base64")}'`;
csp = csp.replace(/%SCRIPT_HASHES%/, hashes);

const metatag = `
  <meta http-equiv="Content-Security-Policy" content="${csp}">
  .replace(/\n/g, "")
if ( > -1)
  throw new Error("already has CSP metatag in HTML");
const anchor = '<meta charset="utf-8">';
const newHtml = html.replace(anchor, `${anchor}${metatag}`);
fs.writeFileSync(htmlFile, newHtml, "utf8");

Laugh all you like at my hurried node scripting but it works. It finds any <script>ANYTHING</script> tags (which means it disregards any <script src="... tags), calculates a sha256 hash string out of it and then puts that into the CSP block.

The output becomes something like this:

<!DOCTYPE html>
<html lang="en">
    <meta charset="utf-8">
      content="default-src 'none';script-src 'self' 'sha256-bb84aa7f904e73495b9e99f08531053f3a86f3c1b2e232e3abbac252bf723f1f';">

I don't know if I've done it right but at least what didn't use to work now works; the page loads in my browsers now.

An awesome snippet to web performance test a page programmatically

01 October 2018 0 comments   Web Performance, Javascript, Web development

I found this in an issue discussing measuring page performance with puppeteer and it's pure gold. Especially because it's so accessible and easy to use.

Here's the code:

const puppeteer = require('puppeteer');

async function run() {
  const browser = await puppeteer.launch();
  const page = await browser.newPage();

  await page.goto('');

  console.log('\n==== performance.getEntries() ====\n');
    await page.evaluate(() =>
      JSON.stringify(performance.getEntries(), null, '  ')

  console.log('\n==== performance.toJSON() ====\n');
    await page.evaluate(() => JSON.stringify(performance.toJSON(), null, '  '))

  console.log('\n==== page.metrics() ====\n');
  const perf = await page.metrics();
  console.log(JSON.stringify(perf, null, '  '));



Network waterfall Google Chrome

When you run it you get this output:
To run it you need to have a decently up-to-date version of puppeteer installed.

I don't claim (far from it actually!) to understand all the metrics points in there but I believe this is basically what the Network panel in the Google Chrome Dev tools is built upon. But some details and facts are easy to figure out and use in your analysis. For example, the fact that the getEntries() lists all the resources that had to be downloaded in the order they were downloaded. Also, at the end of getEntries() you get the first-paint which is often a useful metric.

Anyway, give it a spin. Wrap this up in a platform and see if you can build something really simple and really tailored to your web projects web performance testing.

Comparing KeyCDN and DigitalOcean's new Spaces CDN

28 September 2018 0 comments   Web development

The News

This week DigitalOcean added a free CDN option for retrieving public files on their Spaces product. If you haven't heard about it, Spaces is like AWS S3 but Digital Ocean instead. You use the same tools as you use for S3, like s3cmd. It's super easy to use in the admin control panel and you can do all sorts of neat and nifty things via the web. And the documentation about how to use setup and use s3cmd is very good.

If we just focus on the CDN functionality, it's just a URL to a distributed resource that can be reached and retrieved faster because the server you get it from is hopefully geographically as near to you as possible. That's also what AWS CloudFront, Akamai CDN, and KeyCDN do. However, what you can do with the likes of KeyCDN is that you can have what's called a "pull zone". You basically host the files on your regular (aka. "origin server") and the CDN will automatically pick it up from there.

With DigitalOcean Spaces CDN...

1) GET
2) CDN doesn't have it because it's never been requested before
3) CDN does GET and serves it
4) If it didn't exist on the client gets a 404 Not Found.

With KeyCDN CDN...

1) GET
2) CDN doesn't have it because it's never been requested before
3) CDN does GET and serves it
4) If it didn't exist the client gets a 404 Not Found

The critical differences is that with DigitalOcean Spaces CDN, it won't go and check your web server. On your web server it's not enough to have the files on disk. You have to upload them to the Spaces.

That's often not a problem because disks are not something you want to rely on anyway. It's better to store all files to something like Spaces or S3 and then feel free to just throw away the web server and recreate it. However, it's an important distinction.

On Performance

It's hard to measure these things but when shopping around for a CDN provider/solution you want to make sure you get one that is fast and reliable. There are lots of sites that compare CDNs, like but I often find that they either don't have the CDN I could chose or there's something else weird about the comparison.

So I set up a test using I created a URL that uses KeyCDN and a URL that uses DigitalOcean Spaces CDN. It's the same 32KB image JPEG. Then, I created two monitors on Hyperping, both from San Francisco, New York, London, Frankfurt, Mumbai, São Paulo and Sydney.

Now they've been doing GET requests to these respective URLs for about 24h and the results so far are as follows:

Side by side

I don't know how much response times are "skewed" because of the long tail of response times for Mumbai and Sydney.
But if you take an average of the times for San Francisco, New York, London and Frankfurt you get:

KeyCDN on Hyperping

DigitalOcean Spaces on Hyperping

In Conclusion

Being able to offload all the files from disk and put them somewhere safe is an important feature. In my side project Song Search I actually, currently, host about 7GB of images (~500k files), directly on disk and it's making me nervous. I need to move them off to something like Spaces. But it's a cumbersome to have to make sure every single file generated on disk is correctly synced. Especially if post-processing is happening with the file using the local file system. (This project runs mozjpeg and guetzli on every time).

Either way, this is a non-trivial dev-ops topic with many angles and opinions. I just thought I'd share about my quick research and performance testing.

And last but not least, the difference between 20ms and 30ms isn't important if 90+% of your visitors are from US. All of these considerations depend on your context.

UPDATE - Oct 1, 2018

I'll continue to take the risk of looking like an idiot who doesn't understand networks and the science of data analysis.

I wrote a Python script which downloads random images from each CDN URL. You leave it running for a long time and hopefully the median will start to even out and be less affected by your own network saturation.

I ran it on my laptop; from South Carolina, USA:

DOMAIN                                             MEDIAN     MEAN         0.140s     0.335s                          0.165s     0.250s
2,705 iterations.

And my colleague Mathieu ran it; from Barcelona, Spain:

DOMAIN                                             MEDIAN     MEAN         1.027s     1.168s                          0.574s     0.648s    
134 iterations.

And my colleague Ethan ran it; from New York City, USA:

DOMAIN                                             MEDIAN     MEAN         0.335s     0.595s                          0.066s     0.076s
122 iterations.

I think that if you're in Barcelona you're so far away from the nearest edge location that the latency is "king of the difference". Mathieu found the KeyCDN median download times to be almost twices as good!

If you're in New York, where I suspect DigitalOcean has an edge location and I know KeyCDN has one the latency probably matters less and what matters more is the speed of the CDN web servers. In Ethan's case (only 122 measurement points) the median for KeyCDN is almost five times better! Not sure what that means but it definitely raises thoughts about how this actually matters a lot.

Also, if you're in Barcelona you would definitely want to download the little JPEGs half a second faster if you could.

Merge two arrays without duplicates in JavaScript

20 September 2018 0 comments   Javascript

Here's how you do it if you don't care about the order:

const array1 = [1, 2, 3];
const array2 = [2, 3, 4];
console.log([ Set([...array1, ...array2])]);
// prints [1, 2, 3, 4]

It merges two arrays first. Then it creates a set out of that merged array and lastly convers the set back out to an array.

I searched for a solution and all I found was dated or wrong. This oneliner works and I'm using it to make it possible to add a list of product versions to another list and I don't want to mutate existing arrays because of React state stuff.

If you want to see the ES5 version, check out this Babel repl.

A darn good search filter function in JavaScript

12 September 2018 0 comments   Javascript, Web development

Demo here. The demo uses React and a list of blog post titles that get immediately filtered when you type in a search. I.e. you have the whole list but show less when a search term is entered.

That the demo uses React isn't important. What's important is the search function. It looks like this:

function filterList(q, list) {
  function escapeRegExp(s) {
    return s.replace(/[-/\\^$*+?.()|[\]{}]/g, "\\$&");
  const words = q
    .map(s => s.trim())
    .filter(s => !!s);
  const hasTrailingSpace = q.endsWith(" ");
  const searchRegex = new RegExp(
      .map((word, i) => {
        if (i + 1 === words.length && !hasTrailingSpace) {
          // The last word - ok with the word being "startswith"-like
          return `(?=.*\\b${escapeRegExp(word)})`;
        } else {
          // Not the last word - expect the whole word exactly
          return `(?=.*\\b${escapeRegExp(word)}\\b)`;
      .join("") + ".+",
  return list.filter(item => {
    return searchRegex.test(item.title);

In action
I use this in a single-page content management app. There's a list of records and a search input. Every character you put into the search bar updates the list of records shown.

What it does is that it allows you to search texts based on multiple whole words. But the key feature is that the last word doesn't have to be whole. For example, it will positively match "This is a blog post about JavaScript" if the search is "post javascript" or "post javasc". But it won't match on "pos blog".

The idea is that if a user has typed in a full word followed by a space, all previous words needs to be matched fully. For example if the input is "java " it won't match on "This is a blog post about JavaScript" because the word java, alone, isn't in the search text.

Sure, there are different ways to write this but I think this functionality is good for this kind of filtering search. A different implementation would have a function that returns the regex and then it can be used both for filtering and for highlighting.

Hope it helps.

Replace an item in an array, by number, without mutation in JavaScript (ES6)

23 August 2018 0 comments   Javascript

Suppose you have an array like this:

const items = ["B", "M", "X"];

And now you want to replace that second item ("J" instead of "M") and suppose that you already know it's position as opposed to finding its position by doing an Array.prototype.find.

Here's how you do it:

const index = 1;
const replacementItem = "J";

const newArray = Object.assign([], items, {[index]: replacementItem});

console.log(items); // ["B", "M", "X"]
console.log(newArray); //  ["B", "J", "X"]

Wasn't immediately obvious to me but writing it down will help me remember.


There's a much faster way and that's to use slice and it actually looks nicer too:

function replaceAt(array, index, value) {
  const ret = array.slice(0);
  ret[index] = value;
  return ret;
const newArray = replaceAt(items, index, "J");

See this codepen.