15 August 2019 0 comments Javascript
I'm working on a CLI in Node. What the CLI does it that it takes one set of .json files, compute some stuff, and spits out a different set of .json files. But what it does is not important. I wanted the CLI to feel flexible and powerful but also quite forgiving. And if you typo something, it should bubble up an error rather than redirecting it to something like console.error("not a valid file!")
.
Basically, you use it like this:
node index.js /some/directory
# or
node index.js /some/directory /some/other/directory
# or
node index.js /some/directory/specificfile.json
# or
node index.js /some/directory/specificfile.json /some/directory/otherfile.json
# or
node index.js "/some/directory/*.json"
# or
node index.js "/some/directory/**/*.json"
(Note that when typing patterns in the shell you have quote them, otherwise the shell will do the expansion for you)
Or, any combination of all of these:
node index.js "/some/directory/**/*.json" /other/directory /some/specific/file.json
Whatever you use, with patterns, in particular, it has to make the final list of found files distinct and ordered by the order of the initial arguments.
Here's what I came up with:
import fs from "fs";
import path from "path";
// https://www.npmjs.com/package/glob
import glob from "glob";
/** Given an array of "things" return all distinct .json files.
*
* Note that these "things" can be a directory, a file path, or a
* pattern.
* Only if each thing is a directory do we search for *.json files
* in there recursively.
*/
function expandFiles(directoriesPatternsOrFiles) {
function findFiles(directory) {
const found = glob.sync(path.join(directory, "*.json"));
fs.readdirSync(directory, { withFileTypes: true })
.filter(dirent => dirent.isDirectory())
.map(dirent => path.join(directory, dirent.name))
.map(findFiles)
.forEach(files => found.push(...files));
return found;
}
const filePaths = [];
directoriesPatternsOrFiles.forEach(thing => {
let files = [];
if (thing.includes("*")) {
// It's a pattern!
files = glob.sync(thing);
} else {
const lstat = fs.lstatSync(thing);
if (lstat.isDirectory()) {
files = findFiles(thing);
} else if (lstat.isFile()) {
files = [thing];
} else {
throw new Error(`${thing} is neither file nor directory`);
}
}
files.forEach(p => filePaths.includes(p) || filePaths.push(p));
});
return filePaths;
}
This is where I'm bracing myself for comments that either point out something obvious that Node experts know or some awesome npm package that already does this but better.
If you have a typo, you get an error thrown that looks something like this:
Error: ENOENT: no such file or directory, lstat 'mydirectorrry'
(assuming mydirectory exists but mydirectorrry is a typo)
24 July 2019 0 comments Javascript, Web Performance, ReactJS, Web development
tl;dr; The previous (React) total JavaScript bundle size was: 36.2K Brotli compressed. The new (Preact) JavaScript bundle size was: 5.9K. I.e. 6 times smaller. Also, it appears to load faster in WebPageTest.
I have this page that is a Django server-side rendered page that has on it a form that looks something like this:
<div id="root">
<form action="https://songsear.ch/q/">
<input type="search" name="term" placeholder="Type your search here..." />
<button>Search</button>
</form>
</div>
It's a simple search form. But, to make it a bit better for users, I wrote a React widget that renders, into this document.querySelector('#root'), a near-identical <form> but with autocomplete functionality that displays suggestions as you type.
Anyway, I built that React bundle using create-react-app. I use the yarn run build command that generates...
css/main.83463791.chunk.css - 1.4Kjs/main.ec6364ab.chunk.js - 9.0K (gzip 2.8K, br 2.5K)js/runtime~main.a8a9905a.js - 1.5K (gzip 754B, br 688B)js/2.b944397d.chunk.js - 119K (gzip 36K, br 33K)
Then, in Python, a piece of post-processing code copies the files from the build/static/ directory and inserts it into the rendered HTML file. The CSS gets injected as an inline <style>
tag.
It's a simple little widget. No need for any service-workers or react-router or any global state stuff. (Actually, it only has 1 single runtime dependency outside the framework) I thought, how about moving this to Preact?
In comes preact-cli
The app used a couple of React hooks but they were easy to transform into class components. Now I just needed to run:
npx preact create --yarn widget name-of-my-preact-project
cd name-of-my-preact-project
mkdir src
cp ../name-of-React-project/src/App.js src/
code src/App.js
Then, I slowly moved over the src/App.js from the create-react-app project and slowly by slowly I did the various little things that you need to do. For example, to learn to build with preact build --no-prerender --no-service-worker and how I can override the default template.
Long story short, the new built bundles look like this:
style.82edf.css - 1.4Kbundle.d91f9.js - 18K (gzip 6.4K, br 5.9K)polyfills.9168d.js - 4.5K (gzip 1.8K, br 1.6K)
(The polyfills.9168d.js gets injected as a script tag if window.fetch is falsy)
Unfortunately, when I did the move from React to Preact I did make some small fixes. Doing the "migration" I noticed a block of code that was never used so that gives the build bundle from Preact a slight advantage. But I think it's nominal.
In conclusion: The previous total JavaScript bundle size was: 36.2K (Brotli compressed). The new JavaScript bundle size was: 5.9K (Brotli compressed). I.e. 6 times smaller. But if you worry about the total amount of JavaScript to parse and execute, the size difference uncompressed
was 129K vs. 18K. I.e. 7 times smaller. I can only speculate but I do suspect you need less CPU/battery to process 18K instead of 129K if CPU/batter matters more (or closer to) than network I/O.
Rendering speed difference
Rendering speed is so darn hard to measure on the web because the app is so small. Plus, there's so much else going on that matters.
However, using WebPageTest I can do a visual comparison with the "Mobile - Slow 3G" preset. It'll be a somewhat decent measurement of the total time of downloading, parsing and executing. Thing is, the server-side rended HTML form has a button. But the React/Preact widget that takes over the DOM hides that submit button. So, using the screenshots that WebPageTest provides, I can deduce that the Preact widget completes 0.8 seconds faster than the React widget. (I.e. instead of 4.4s it became 3.9s)
Truth be told, I'm not sure how predictable or reproducible is. I ran that WebPageTest visual comparison more than once and the results can vary significantly. I'm not even sure which run I'm referring to here (in the screenshot) but the React widget version was never faster.
Conclusion and thoughts
Unsurprisingly, Preact is smaller because you simply get less from that framework. E.g. synthetic events. I was lucky. My app uses onChange
which I could easily "migrate" to onInput and I managed to get it to work pretty easily. I'm glad the widget app was so small and that I don't depend on any React specific third-party dependencies.
But! In WebPageTest Visual Comparison it was on "Mobile - Slow 3G" which only represents a small portion of the traffic. Mobile is a huge portion of the traffic but "Slow 3G" is not. When you do a Desktop comparison the difference is roughtly 0.1s.
Also, in total, that page is made up of 3 major elements
- The server-side rendered HTML
- The progressive JavaScript widget (what this blog post is about)
- A piece of JavaScript initiated banner ad
That HTML controls the "First Meaningful Paint" which takes 3 seconds. And the whole shebang, including the banner ad, takes a total of about 9s. So, all this work of rewriting a React app to Preact saved me 0.8s out of the total of 9s.
Web performance is hard and complicated. Every little counts, but keep your eye on the big ticket items assuming there's something you can do about them.
At the time of writing, preact-cli uses Preact 8.2 and I'm eager to see how Preact X feels. Apparently, since April 2019, it's in beta. Looking forward to giving it a try!
13 July 2019 0 comments Javascript, Web development
I use localhost:3000 for a lot of different projects. It's the default port on create-react-app's dev server. The browser profile remains but projects come and go. There's a lot of old stuff in there that I have no longer any memory of adding.
Working in a recent single page app, I tried to use localStorage as a cache for some XHR requests and got: DOMException: "The quota has been exceeded.".
Wat?! I'm only trying to store a ~250KB JSON string. Surely that's far away from the mythical 5MB limit. Do I really have to lzw compress the string in and out to save room and pay for it in CPU cycles?
Better yet, find out what junk I still have in there.
Paste this into your Web Console (it's safe as milk):
Object.entries(localStorage).forEach(([k,v]) => console.log(k, v.length, (v.length / 1024).toFixed(1) + 'KB'))
The output looks something like this:
Or, sorted and filtered a bit:
Object.entries(localStorage).sort((a, b) => b[1].length -a[1].length).slice(0,5).forEach(
([k,v]) => console.log(k, v.length, (v.length / 1024).toFixed(1) + 'KB'));
Looks like this:
And for the record, summed total in kilobytes:
(Object.values(localStorage).map(x => x.length).reduce((a, b) => a + b) / 1024).toFixed(1) + 'KB';
Wrapping up
Seems my Firefox browser's localStorage limit is still 5MB.
Also, you can do the loop using localStorage.length and localStorage.key(n) and localStorage.getItem(localStorage.key(n)).length but using Object.entries(localStorage) seems neater.
I guess this means I can still use localStorage in my app. It seems I just need to localStorage.removeItem('massive-list:items') which sounds like an experiment, from eons ago, for seeing how much I can stuff in there.
11 July 2019 0 comments Redis, Nginx, Python, Django
By analyzing my Nginx logs, I've concluded that SongSearch's autocomplete JSON API now gets about 2.2 requests per second. I.e. these are XHR requests to /api/search/autocomplete?q=....
Roughly, 1.8 requests per second goes back to the Django/Elasticsearch backend. That's a hit ratio of 16%. These Django/Elasticsearch requests take roughly 200ms on average. I suspect about 150-180ms of that time is spent querying Elasticsearch, the rest being Python request/response and JSON "paperwork".
Caching strategy
Caching is hard because the queries are so vastly different over time. Had I put a Redis cache decorator on the autocomplete Django view function I'd quickly bloat Redis memory and cause lots of evictions.
What I used to do was something like this:
def search_autocomplete(request):
q = request.GET.get('q')
cache_key = None
if len(q) < 10:
cache_key = 'autocomplete:' + q
results = cache.get(cache_key)
if results is not None:
return http.JsonResponse(results)
results = _do_elastisearch_query(q)
if cache_key:
cache.set(cache_key, results, 60 * 60)
return http.JsonResponse(results)
However, after some simple benchmarking it was clear that using Nginx' uwsgi_cache it was much faster to let the cacheable queries terminate already at Nginx. So I changed the code to something like this:
def search_autocomplete(request):
q = request.GET.get('q')
results = _do_elastisearch_query(q)
response = http.JsonResponse(results)
if len(q) < 10:
patch_cache_control(response, public=True, max_age=60 * 60)
return response
The only annoying thing about Nginx caching is that purging is hard unless you go for that Nginx Plus (or whatever their enterprise version is called). But more annoying, to me, is that fact that I can't really see what this means for my server. When I was caching with Redis I could just use redis-cli and...
> INFO
...
# Memory
used_memory:123904288
used_memory_human:118.16M
...
Nginx Amplify
My current best tool for keeping an eye on Nginx is Nginx Amplify. It gives me some basic insights about the state of things. Here are some recent screenshots:
Thoughts and conclusion
Caching is hard. But it's also fun because it ties directly into performance work.
In my business logic, I chose that autocomplete queries that are between 1 and 9 characters are cacheable. And I picked a TTL of 60 minutes. At this point, I'm not sure exactly why I chose that logic but I remember doing some back-of-envelope calculations about what the hit ratio would be and roughly what that would mean in bytes in RAM. I definitely remember picking 60 minutes because I was nervous about bloating Nginx's memory usage. But as of today, I'm switching that up to 24 hours and let's see what that does to my current 16% Nginx cache hit ratio. At the moment, /var/cache/nginx-cache/ is only 34MB which isn't much.
Another crux with using uwsgi_cache (or proxy_cache) is that you can't control the cache key very well. When it was all in Python I was able to decide about the cache key myself. A plausible implementation is cache_key = q.lower().strip()
for example. That means you can protect your Elasticsearch backend from having to do {"q": "A"} and {"q": "a"}. Who knows, perhaps there is a way to hack this in Nginx without compiling in some Lua engine.
The ideal would be some user-friendly diagnostics tool that I can point somewhere, towards Nginx, that says how much my uwsgi_cache is hurting or saving me. Autocomplete is just one of many things going on on this single DigitalOcean server. There's also a big PostgreSQL server, a node-express cluster, a bunch of uwsgi workers, Redis, lots of cron job scripts, and of course a big honking Elasticsearch 6.
UPDATE (July 12 2019)
Currently, and as mentioned above, I only set Cache-Control
headers (which means Nginx snaps it up) for queries that at max 9 characters long. I wanted to appreciate and understand how ratio of all queries are longer than 9 characters so I wrote a report and its output is this:
POINT: 7
Sum show 75646 32.2%
Sum rest 159321 67.8%
POINT: 8
Sum show 83702 35.6%
Sum rest 151265 64.4%
POINT: 9
Sum show 90870 38.7%
Sum rest 144097 61.3%
POINT: 10
Sum show 98384 41.9%
Sum rest 136583 58.1%
POINT: 11
Sum show 106093 45.2%
Sum rest 128874 54.8%
POINT: 12
Sum show 113905 48.5%
Sum rest 121062 51.5%
It means that (independent of time expiry) 38.7% of queries are 9 characters or less.
18 June 2019 3 comments Javascript, Web development
tl;dr; The main JavaScript bundle goes from 29KB to 6KB by switching from JQuery to Cash. Both with Brotli compression.
In Web Performance, every byte counts. Downloading less stuff means faster network operations but for JavaScript it also means less to parse and execute. This site used use JQuery 3.4.1 but now uses Cash 4.1.2. It requires some changes to how you use $ and most noticeable is the lack of animations and $.ajax.
I still stand by the $ function. It's great when you have a regular (static) website that isn't a single page app but still needs a little bit of interactive JavaScript functionality. On this site, I use it for making the commenting work and some various navigation/header stuff.
Switching to Cash means you have to stop doing things like $.getJSON()
and $('.classname').fadeIn(400) which, in a sense, gives Cash an unfair advantage because those bits take up a large portion of the bundle size. Yes, there is a custom build of jQuery without those but check out this size comparison:
| Bundle | Uncompressed (bytes) | Gzipped (bytes) |
|---|
| jQuery 3.4.1 | 88,145 | 30,739 |
| jQuery 3.4.1 Slim | 71,037 | 24,403 |
| Cash 4.1.2 | 14,818 | 5,167 |
I still needed a fadeIn function, which I was relying on from jQuery, but to remedy that I just copied one of these from youmightnotneedjquery.com. It would be better to not do that an use a CSS transform instead but, well, I'm only human.
Before: with jQuery
Another thing you'll need to replace is to switch from $.ajax to fetch but there are good polyfills but I haven't bothered with polyfills because the tiny percentage of visitors I have, without fetch support still get a working site but can't post comments.
I was contemplating doing what GitHub did in 2018 which was to replace jQuery with real vanilla JavaScript code but it didn't seem worth it now that Cash is only 5KB (gzipped) and it's an actively maintained project too.
Before: with jQuery
After: with Cash
01 June 2019 0 comments Python, Django
Suppose that you have so many thousands of pages that you can't just create a single /sitemap.xml
file that has all the URLs (aka <loc>) listed. Then you need to make a /sitemaps.xml that points to the other sitemap files. And if you're in the thousands, you'll need to gzip these files.
The blog post demonstrates how Song Search generates a sitemap file that points to 63 sitemap-{M}-{N}.xml.gz files which spans about 1,000,000 URLs. The context here is Python and the getting of the data is from Django. Python is pretty key here but if you have something other than Django, you can squint and mentally replace that with your own data mapper.
Generate the sitemap .xml.gz file(s)
Here's the core of the work. A generator function that takes a Django QuerySet instance (that is ordered and filtered!) and then starts generating etree trees and dumps them to disk with gzip.
import gzip
from lxml import etree
outfile = "sitemap-{start}-{end}.xml"
batchsize = 40_000
def generate(self, qs, base_url, outfile, batchsize):
# Use `.values` to make the query much faster
qs = qs.values("name", "id", "artist_id", "language")
def start():
return etree.Element(
"urlset", xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"
)
def close(root, filename):
with gzip.open(filename, "wb") as f:
f.write(b'<?xml version="1.0" encoding="utf-8"?>\n')
f.write(etree.tostring(root, pretty_print=True))
root = filename = None
count = 0
for song in qs.iterator():
if not count % batchsize:
if filename: # not the very first loop
close(root, filename)
yield filename
filename = outfile.format(start=count, end=count + batchsize)
root = start()
loc = "{}{}".format(base_url, make_song_url(song))
etree.SubElement(etree.SubElement(root, "url"), "loc").text = loc
count += 1
close(root, filename)
yield filename
The most important lines in terms of lxml.etree and sitemaps are:
root = etree.Element("urlset", xmlns="http://www.sitemaps.org/schemas/sitemap/0.9")
...
etree.SubElement(etree.SubElement(root, "url"), "loc").text = loc
Another important thing is the note about using .values()
. If you don't do that Django will create a model instance for every single row it returns of the iterator. That's expensive. See this blog post.
Another important thing is to use a Django ORM iterator as that's much more efficient than messing around with limits and offsets.
Generate the map of sitemaps
Making the map of maps doesn't need to be gzipped since it's going to be tiny.
def generate_map_of_maps(base_url, outfile):
root = etree.Element(
"sitemapindex", xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"
)
with open(outfile, "wb") as f:
f.write(b'<?xml version="1.0" encoding="UTF-8"?>\n')
files_created = sorted(glob("sitemap-*.xml.gz"))
for file_created in files_created:
sitemap = etree.SubElement(root, "sitemap")
uri = "{}/{}".format(base_url, os.path.basename(file_created))
etree.SubElement(sitemap, "loc").text = uri
lastmod = datetime.datetime.fromtimestamp(
os.stat(file_created).st_mtime
).strftime("%Y-%m-%d")
etree.SubElement(sitemap, "lastmod").text = lastmod
f.write(etree.tostring(root, pretty_print=True))
And that sums it up. On my laptop, it takes about 60 seconds to generate 39 of these files (e.g. sitemap-1560000-1600000.xml.gz) and that's good enough.
Bonus and Thoughts
The bad news is that this is about as good as it gets in terms of performance. The good news is that there are no low-hanging fruit fixes. I know, because I tried. I experimented with not using pretty_print=True and I experimented with not writing with gzip.open and instead gzipping the files on later. Nothing made any significant difference. The lxml.etree part of this, in terms of performance, is order of maginitude marginal in comparison to the cost of actually getting the data out of the database plus later writing to disk. I also experimenting with generating the gzip content with zopfli and it didn't make much of a difference.
I originally wrote this code years ago and when I did, I think I knew more about sitemaps. In my implementation I use a batch size of 40,000 so each file is called something like sitemap-40000-80000.xml.gz and weighs about 800KB. Not sure why I chose 40,000 but perhaps not important.
