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In TypeScript, how to combine known and unknown keys to an object

July 3, 2024
0 comments JavaScript

More than happy to be informed of a better solution here! But this came up in a real-work situation and I "stumbled" on the solution by more or less guessing.

In plain JavaScript, you have an object which you know you set certain keys on. But because this object is (ab)used for a templating engine, we also put keys/values on it that are not known in advance. In our use case, these keys and booleans came from parsing a .yml file which. It looks something like this:


// Code simplified for the sake of the example

const context = {
  currentVersion: "3.12", 
  currentLanguage: "en",
  activeDate: someDateObject,
  // ... other things that are values of type number, bool, Date, and string
  // ...
}
if (someCondition()) {
  context.hasSomething = true
}

for (const [featureFlag, truth] of Object.entries(parseYamlFile('features.yml')) {
  context[featureFlag] = truth
}

const rendered = render(template: { context })

I don't like this design where you "combine" an object with known keys with a spread of unknown keys coming from an external source. But here we are and we have to convert this to TypeScript, the clock's ticking!

Truncated! Read the rest by clicking the link below.

Simple object lookup in TypeScript

June 14, 2024
2 comments JavaScript

Ever got this error:

Element implicitly has an 'any' type because expression of type 'string' can't be used to index type '{ foo: string; bar: string; }'. No index signature with a parameter of type 'string' was found on type '{ foo: string; bar: string; }'.(7053)

Yeah, me too. What used to be so simple in JavaScript suddenly feels hard in TypeScript.

In JavaScript,


const greetings = {
  good: "Excellent",
  bad: "Sorry to hear",
}
const answer = prompt("How are you?")
if (typeof answer === "string") {
  alert(greetings[answer] || "OK")
}

To see it in action, I put it into a CodePen.

Now, port that to TypeScript,

Truncated! Read the rest by clicking the link below.

How do you thousands-comma AND whitespace format a f-string in Python

March 17, 2024
1 comment Python

For some reason, I always forget how to do this. Tired of that. Let's blog about it so it sticks.

To format a number with thousand-commas you do:


>>> n = 1234567
>>> f"{n:,}"
'1,234,567'

To add whitespace to a string you do:


>>> name="peter"
>>> f"{name:<20}"
'peter               '

How to combine these in one expression, you do:


>>> n = 1234567
>>> f"{n:<15,}"
'1,234,567      '

Leibniz formula for π in Python, JavaScript, and Ruby

March 14, 2024
0 comments Python, JavaScript

Officially, I'm one day behind, but here's how you can calculate the value of π using the Leibniz formula.

Leibniz formula

Python


import math

sum = 0
estimate = 0
i = 0
epsilon = 0.0001
while abs(estimate - math.pi) > epsilon:
    sum += (-1) ** i / (2 * i + 1)
    estimate = sum * 4
    i += 1
print(
    f"After {i} iterations, the estimate is {estimate} and the real pi is {math.pi} "
    f"(difference of {abs(estimate - math.pi)})"
)

Outputs:

After 10000 iterations, the estimate is 3.1414926535900345 and the real pi is 3.141592653589793 (difference of 9.99999997586265e-05)

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Notes on porting a Next.js v14 app from Pages to App Router

March 2, 2024
0 comments React, JavaScript

Unfortunately, the app I ported from using the Pages Router to using App Router, is in a private repo. It's a Next.js static site SPA (Single Page App).

It's built with npm run build and then exported so that the out/ directory is the only thing I need to ship to the CDN and it just works. There's a home page and a few dynamic routes whose slugs depend on an SQL query. So the SQL (PostgreSQL) connection, using knex, has to be present when running npm run build.

In no particular order, let's look at some differences

Build times

With caching

After running next build a bunch of times, the rough averages are:

  • Pages Router: 20.5 seconds
  • App Router: 19.5 seconds

Without caching

After running rm -fr .next && next build a bunch of times, the rough averages are:

  • Pages Router: 28.5 seconds
  • App Router: 31 seconds

Note

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How to avoid a count query in Django if you can

February 14, 2024
1 comment Django, Python

Suppose you have a complex Django QuerySet query that is somewhat costly (in other words slow). And suppose you want to return:

  1. The first N results
  2. A count of the total possible results

So your implementation might be something like this:


def get_results(queryset, fields, size):
    count = queryset.count()
    results = []
    for record in queryset.values(*fields)[:size]
        results.append(record)
    return {"count": count, "results": results}

That'll work. If there are 1,234 rows in your database table that match those specific filters, what you might get back from this is:


>>> results = get_results(my_queryset, ("name", "age"), 5)
>>> results["count"]
1234
>>> len(results["results"])
5

Or, if the filters would only match 3 rows in your database table:


>>> results = get_results(my_queryset, ("name", "age"), 5)
>>> results["count"]
3
>>> len(results["results"])
3

Between your Python application and your database you'll see:

query 1: SELECT COUNT(*) FROM my_database WHERE ...
query 2: SELECT name, age FROM my_database WHERE ... LIMIT 5

The problem with this is that, in the latter case, you had to send two database queries when all you needed was one.
If you knew it would only match a tiny amount of records, you could do this:


def get_results(queryset, fields, size):
-   count = queryset.count()
    results = []
    for record in queryset.values(*fields)[:size]:
        results.append(record)
+   count = len(results)
    return {"count": count, "results": results}

But that is wrong. The count would max out at whatever the size is.

The solution is to try to avoid the potentially unnecessary .count() query.


def get_results(queryset, fields, size):
    count = 0
    results = []
    for i, record in enumerate(queryset.values(*fields)[: size + 1]):
        if i == size:
            # Alas, there are more records than the pagination
            count = queryset.count()
            break
        count = i + 1
        results.append(record)
    return {"count": count, "results": results}

This way, you only incur one database query when there wasn't that much to find, but if there was more than what the pagination called for, you have to incur that extra database query.

Comparing different efforts with WebP in Sharp

October 5, 2023
0 comments Node, JavaScript

When you, in a Node program, use sharp to convert an image buffer to a WebP buffer, you have an option of effort. The higher the number the longer it takes but the image it produces is smaller on disk.

I wanted to put some realistic numbers for this, so I wrote a benchmark, run on my Intel MacbookPro.

The benchmark

It looks like this:


async function e6() {
  return await f("screenshot-1000.png", 6);
}
async function e5() {
  return await f("screenshot-1000.png", 5);
}
async function e4() {
  return await f("screenshot-1000.png", 4);
}
async function e3() {
  return await f("screenshot-1000.png", 3);
}
async function e2() {
  return await f("screenshot-1000.png", 2);
}
async function e1() {
  return await f("screenshot-1000.png", 1);
}
async function e0() {
  return await f("screenshot-1000.png", 0);
}

async function f(fp, effort) {
  const originalBuffer = await fs.readFile(fp);
  const image = sharp(originalBuffer);
  const { width } = await image.metadata();
  const buffer = await image.webp({ effort }).toBuffer();
  return [buffer.length, width, { effort }];
}

Then, I ran each function in serial and measured how long it took. Then, do that whole thing 15 times. So, in total, each function is executed 15 times. The numbers are collected and the median (P50) is reported.

A 2000x2000 pixel PNG image

1. e0: 191ms                   235KB
2. e1: 340.5ms                 208KB
3. e2: 369ms                   198KB
4. e3: 485.5ms                 193KB
5. e4: 587ms                   177KB
6. e5: 695.5ms                 177KB
7. e6: 4811.5ms                142KB

What it means is that if you use {effort: 6} the conversion of a 2000x2000 PNG took 4.8 seconds but the resulting WebP buffer became 142KB instead of the least effort which made it 235 KB.

Comparing effort, time and size

This graph demonstrates how the (blue) time goes up the more effort you put in. And how the final size (red) goes down the more effort you put in.

A 1000x1000 pixel PNG image

1. e0: 54ms                    70KB
2. e1: 60ms                    66KB
3. e2: 65ms                    61KB
4. e3: 96ms                    59KB
5. e4: 169ms                   53KB
6. e5: 193ms                   53KB
7. e6: 1466ms                  51KB

A 500x500 pixel PNG image

1. e0: 24ms                    23KB
2. e1: 26ms                    21KB
3. e2: 28ms                    20KB
4. e3: 37ms                    19KB
5. e4: 57ms                    18KB
6. e5: 66ms                    18KB
7. e6: 556ms                   18KB

Conclusion

Up to you but clearly, {effort: 6} is to be avoided if you're worried about it taking a huge amount of time to make the conversion.

Perhaps the takeaway is; that if you run these operations in the build step such that you don't have to ever do it again, it's worth the maximum effort. Beyond that, find a sweet spot for your particular environment and challenge.

Introducing hylite - a Node code-syntax-to-HTML highlighter written in Bun

October 3, 2023
0 comments Node, Bun, JavaScript

hylite is a command line tool for syntax highlight code into HTML. You feed it a file or some snippet of code (plus what language it is) and it returns a string of HTML.

Suppose you have:


❯ cat example.py
# This is example.py
def hello():
    return "world"

When you run this through hylite you get:


❯ npx hylite example.py
<span class="hljs-keyword">def</span> <span class="hljs-title function_">hello</span>():
    <span class="hljs-keyword">return</span> <span class="hljs-string">&quot;world&quot;</span>

Now, if installed with the necessary CSS, it can finally render this:


# This is example.py
def hello():
    return "world"

(Note: At the time of writing this, npx hylite --list-css or npx hylite --css don't work unless you've git clone the github.com/peterbe/hylite repo)

How I use it

This originated because I loved how highlight.js works. It supports numerous languages, can even guess the language, is fast as heck, and the HTML output is compact.

Originally, my personal website, whose backend is in Python/Django, was using Pygments to do the syntax highlighting. The problem with that is it doesn't support JSX (or TSX). For example:


export function Bell({ color }: {color: string}) {
  return <div style={{ backgroundColor: color }}>Ding!</div>
}

The problem is that Python != Node so to call out to hylite I use a sub-process. At the moment, I can't use bunx or npx because that depends on $PATH and stuff that the server doesn't have. Here's how I call hylite from Python:


command = settings.HYLITE_COMMAND.split()
assert language
command.extend(["--language", language, "--wrapped"])
process = subprocess.Popen(
    command,
    stdin=subprocess.PIPE,
    stdout=subprocess.PIPE,
    stderr=subprocess.PIPE,
    text=True,
    cwd=settings.HYLITE_DIRECTORY,
)
process.stdin.write(code)
output, error = process.communicate()

The settings are:


HYLITE_DIRECTORY = "/home/django/hylite"
HYLITE_COMMAND = "node dist/index.js"

How I built hylite

What's different about hylite compared to other JavaScript packages and CLIs like this is that the development requires Bun. It's lovely because it has a built-in test runner, TypeScript transpiler, and it's just so lovely fast at starting for anything you do with it.

In my current view, I see Bun as an equivalent of TypeScript. It's convenient when developing but once stripped away it's just good old JavaScript and you don't have to worry about compatibility.

So I use bun for manual testing like bun run src/index.ts < foo.go but when it comes time to ship, I run bun run build (which executes, with bun, the src/build.ts) which then builds a dist/index.js file which you can run with either node or bun anywhere.

By the way, the README as a section on Benchmarking. It concludes two things:

  1. node dist/index.js has the same performance as bun run dist/index.js
  2. bunx hylite is 7x times faster than npx hylite but it's bullcrap because bunx doesn't check the network if there's a new version (...until you restart your computer)

Shallow clone vs. deep clone, in Node, with benchmark

September 29, 2023
0 comments Node, JavaScript

A very common way to create a "copy" of an Object in JavaScript is to copy all things from one object into an empty one. Example:


const original = {foo: "Foo"}
const copy = Object.assign({}, original)
copy.foo = "Bar"
console.log([original.foo, copy.foo])

This outputs


[ 'Foo', 'Bar' ]

Obviously the problem with this is that it's a shallow copy, best demonstrated with an example:


const original = { names: ["Peter"] }
const copy = Object.assign({}, original)
copy.names.push("Tucker")
console.log([original.names, copy.names])

This outputs:


[ [ 'Peter', 'Tucker' ], [ 'Peter', 'Tucker' ] ]

which is arguably counter-intuitive. Especially since the variable was named "copy".
Generally, I think Object.assign({}, someThing) is often a red flag because if not today, maybe in some future the thing you're copying might have mutables within.

The "solution" is to use structuredClone which has been available since Node 16. Actually, it was introduced within minor releases of Node 16, so be a little bit careful if you're still on Node 16.

Same example:


const original = { names: ["Peter"] };
// const copy = Object.assign({}, original);
const copy = structuredClone(original);
copy.names.push("Tucker");
console.log([original.names, copy.names]);

This outputs:


[ [ 'Peter' ], [ 'Peter', 'Tucker' ] ]

Another deep copy solution is to turn the object into a string, using JSON.stringify and turn it back into a (deeply copied) object using JSON.parse. It works like structuredClone but full of caveats such as unpredictable precision loss on floating point numbers, and not to mention date objects ceasing to be date objects but instead becoming strings.

Benchmark

Given how much "better" structuredClone is in that it's more intuitive and therefore less dangerous for sneaky nested mutation bugs. Is it fast? Before even running a benchmark; no, structuredClone is slower than Object.assign({}, ...) because of course. It does more! Perhaps the question should be: how much slower is structuredClone? Here's my benchmark code:


import fs from "fs"
import assert from "assert"

import Benchmark from "benchmark"

const obj = JSON.parse(fs.readFileSync("package-lock.json", "utf8"))

function f1() {
  const copy = Object.assign({}, obj)
  copy.name = "else"
  assert(copy.name !== obj.name)
}

function f2() {
  const copy = structuredClone(obj)
  copy.name = "else"
  assert(copy.name !== obj.name)
}

function f3() {
  const copy = JSON.parse(JSON.stringify(obj))
  copy.name = "else"
  assert(copy.name !== obj.name)
}

new Benchmark.Suite()
  .add("f1", f1)
  .add("f2", f2)
  .add("f3", f3)
  .on("cycle", (event) => {
    console.log(String(event.target))
  })
  .on("complete", function () {
    console.log("Fastest is " + this.filter("fastest").map("name"))
  })
  .run()

The results:

❯ node assign-or-clone.js
f1 x 8,057,542 ops/sec ±0.84% (93 runs sampled)
f2 x 37,245 ops/sec ±0.68% (94 runs sampled)
f3 x 37,978 ops/sec ±0.85% (92 runs sampled)
Fastest is f1

In other words, Object.assign({}, ...) is 200 times faster than structuredClone.
By the way, I re-ran the benchmark with a much smaller object (using the package.json instead of the package-lock.json) and then Object.assign({}, ...) is only 20 times faster.

Mind you! They're both ridiculously fast in the grand scheme of things.

If you do this...


for (let i = 0; i < 10; i++) {
  console.time("f1")
  f1()
  console.timeEnd("f1")

  console.time("f2")
  f2()
  console.timeEnd("f2")

  console.time("f3")
  f3()
  console.timeEnd("f3")
}

the last bit of output of that is:

f1: 0.006ms
f2: 0.06ms
f3: 0.053ms

which means that it took 0.06 milliseconds for structuredClone to make a convenient deep copy of an object that is 5KB as a JSON string.

Conclusion

Yes Object.assign({}, ...) is ridiculously faster than structuredClone but structuredClone is a better choice.