Peterbe.com

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

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.

tl;dr git restore -- .

I can't believe I didn't know this! Maybe, at one point, I did, but, since forgotten.

You're in a Git repo and you have edited 4 files and run git status and see this:

❯ git status
On branch main
Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
    modified:   four.txt
    modified:   one.txt
    modified:   three.txt
    modified:   two.txt

no changes added to commit (use "git add" and/or "git commit -a")

Suppose you realize; "Oh no! I didn't mean to make those changes in three.txt" You can restore that file by mentioning it by name:

tl;dr; Not very slow.

At work, we have some very large .json that get included in a Docker image. The Node server then opens these files at runtime and displays certain data from that. To make the Docker image not too large, we compress these .json files at build-time. We compress the .json files with Brotli to make a .json.br file. Then, in the Node server code, we read them in and decompress them at runtime. It looks something like this:

export function readCompressedJsonFile(xpath) {
  return JSON.parse(brotliDecompressSync(fs.readFileSync(xpath)))
}

The advantage of compressing them first, at build time, which is GitHub Actions, is that the Docker image becomes smaller which is advantageous when shipping that image to a registry and asking Azure App Service to deploy it. But I was wondering, is this a smart trade-off? In a sense, why compromise on runtime (which faces users) to save time and resources at build-time, which is mostly done away from the eyes of users? The question was; how much overhead is it to have to decompress the files after its data has been read from disk to memory?

The benchmark

The files I test with are as follows:

❯ ls -lh pageinfo*
-rw-r--r--  1 peterbe  staff   2.5M Jan 19 08:48 pageinfo-en-ja-es.json
-rw-r--r--  1 peterbe  staff   293K Jan 19 08:48 pageinfo-en-ja-es.json.br
-rw-r--r--  1 peterbe  staff   805K Jan 19 08:48 pageinfo-en.json
-rw-r--r--  1 peterbe  staff   100K Jan 19 08:48 pageinfo-en.json.br

There are 2 groups:

1. Only English (en)
2. 3 times larger because it has English, Japanese, and Spanish

And for each file, you can see the effect of having compressed them with Brotli.

1. The smaller JSON file compresses 8x
2. The larger JSON file compresses 9x

Here's the benchmark code:

import fs from "fs";
import { brotliDecompressSync } from "zlib";
import { Bench } from "tinybench";

const JSON_FILE = "pageinfo-en.json";
const BROTLI_JSON_FILE = "pageinfo-en.json.br";
const LARGE_JSON_FILE = "pageinfo-en-ja-es.json";
const BROTLI_LARGE_JSON_FILE = "pageinfo-en-ja-es.json.br";

function f1() {
  const data = fs.readFileSync(JSON_FILE, "utf8");
  return Object.keys(JSON.parse(data)).length;
}

function f2() {
  const data = brotliDecompressSync(fs.readFileSync(BROTLI_JSON_FILE));
  return Object.keys(JSON.parse(data)).length;
}

function f3() {
  const data = fs.readFileSync(LARGE_JSON_FILE, "utf8");
  return Object.keys(JSON.parse(data)).length;
}

function f4() {
  const data = brotliDecompressSync(fs.readFileSync(BROTLI_LARGE_JSON_FILE));
  return Object.keys(JSON.parse(data)).length;
}

console.assert(f1() === 2633);
console.assert(f2() === 2633);
console.assert(f3() === 7767);
console.assert(f4() === 7767);

const bench = new Bench({ time: 100 });
bench.add("f1", f1).add("f2", f2).add("f3", f3).add("f4", f4);
await bench.warmup(); // make results more reliable, ref: https://github.com/tinylibs/tinybench/pull/50
await bench.run();

console.table(bench.table());

Here's the output from tinybench:

┌─────────┬───────────┬─────────┬────────────────────┬──────────┬─────────┐
│ (index) │ Task Name │ ops/sec │ Average Time (ns)  │  Margin  │ Samples │
├─────────┼───────────┼─────────┼────────────────────┼──────────┼─────────┤
│    0    │   'f1'    │  '179'  │  5563384.55941942  │ '±6.23%' │   18    │
│    1    │   'f2'    │  '150'  │ 6627033.621072769  │ '±7.56%' │   16    │
│    2    │   'f3'    │  '50'   │ 19906517.219543457 │ '±3.61%' │   10    │
│    3    │   'f4'    │  '44'   │ 22339166.87965393  │ '±3.43%' │   10    │
└─────────┴───────────┴─────────┴────────────────────┴──────────┴─────────┘

Note, this benchmark is done on my 2019 Intel MacBook Pro. This disk is not what we get from the Apline Docker image (running inside Azure App Service). To test that would be a different story. But, at least we can test it in Docker locally.

I created a Dockerfile that contains...

ARG NODE_VERSION=20.10.0

FROM node:${NODE_VERSION}-alpine

and run the same benchmark in there by running docker composite up --build. The results are:

┌─────────┬───────────┬─────────┬────────────────────┬──────────┬─────────┐
│ (index) │ Task Name │ ops/sec │ Average Time (ns)  │  Margin  │ Samples │
├─────────┼───────────┼─────────┼────────────────────┼──────────┼─────────┤
│    0    │   'f1'    │  '151'  │ 6602581.124978315  │ '±1.98%' │   16    │
│    1    │   'f2'    │  '112'  │  8890548.4166656   │ '±7.42%' │   12    │
│    2    │   'f3'    │  '44'   │ 22561206.40002191  │ '±1.95%' │   10    │
│    3    │   'f4'    │  '37'   │ 26979896.599974018 │ '±1.07%' │   10    │
└─────────┴───────────┴─────────┴────────────────────┴──────────┴─────────┘

Analysis/Conclusion

First, focussing on the smaller file: Processing the .json is 25% faster than the .json.br file

Then, the larger file: Processing the .json is 16% faster than the .json.br file

So that's what we're paying for a smaller Docker image. Depending on the size of the .json file, your app runs ~20% slower at this operation. But remember, as a file on disk (in the Docker image), it's ~8x smaller.

I think, in conclusion: It's a small price to pay. It's worth doing. Your context depends.
Keep in mind the numbers there to process that 300KB pageinfo-en-ja-es.json.br file, it was able to do that 37 times in one second. That means it took 27 milliseconds to process that file!

The caveats

To repeat, what was mentioned above: This was run in my Intel MacBook Pro. It's likely to behave differently in a real Docker image running inside Azure.

The thing that I wonder the most about is arguably something that actually doesn't matter. 🙃
When you ask it to read in a .json.br file, there's less data to ask from the disk into memory. That's a win. You lose on CPU work but gain on disk I/O. But only the end net result matters so in a sense that's just an "implementation detail".

Admittedly, I don't know if the macOS or the Linux kernel does things with caching the layer between the physical disk and RAM for these files. The benchmark effectively asks "Hey, hard disk, please send me a file called ..." and this could be cached in some layer beyond my knowledge/comprehension. In a real production server, this only happens once because once the whole file is read, decompressed, and parsed, it won't be asked for again. Like, ever. But in a benchmark, perhaps the very first ask of the file is slower and all the other runs are unrealistically faster.

Feel free to clone https://github.com/peterbe/reading-json-files and mess around to run your own tests. Perhaps see what effect async can have. Or perhaps try it with Bun and it's file system API.

Do you use rg (ripgrep) all the time on the command line? Yes, so do I. An annoying problem with it is that, by default, it does not search hidden directories.

"A file or directory is considered hidden if its base name starts with a dot character (.)."

One such directory, that is very important in my git/GitHub-based projects (which is all of mine by the way) is the .github directory. So I cd into a directory and it finds nothing:

cd ~/dev/remix-peterbecom
rg actions/setup-node
# Empty! I.e. no results

It doesn't find anything because the file .github/workflows/test.yml is part of a hidden directory.

The quick solution to this is to use --hidden:

❯ rg --hidden actions/setup-node
.github/workflows/test.yml
20:        uses: actions/setup-node@v4

I find it very rare that I would not want to search hidden directories. So I added this to my ~/.zshrc file:

alias rg='rg --hidden'

Now, this happens:

❯ rg actions/setup-node
.github/workflows/test.yml
20:        uses: actions/setup-node@v4

With that being set, it's actually possible to "undo" the behavior. You can use --no-hidden

❯ rg --no-hidden actions/setup-node

And that can useful if there is a hidden directory that is not git ignored yet. For example .download-cache/.

I used nvm so that when I cd into a different repo, it would automatically load the appropriate version of node (and npm). Simply by doing cd ~/dev/remix-peterbecom, for example, it would make the node executable to become whatever the value of the optional file ~/dev/remix-peterbecom/.nvmrc's content. For example v18.19.0.
And nvm helps you install and get your hands on various versions of node to be able to switch between. Much more fine-tuned than brew install node20.

The problem with all of this is that it's horribly slow. Opening a new terminal is annoyingly slow because that triggers the entering of a directory and nvm slowly does what it does.

The solution is to ditch it and go for fnm instead. Please, if you're an nvm user, do consider making this same jump in 2024.

Installation

Running curl -fsSL https://fnm.vercel.app/install | bash basically does some brew install and figuring out what shell you have and editing your shell config. By default, it put:

export PATH="/Users/peterbe/Library/Application Support/fnm:$PATH"
eval "`fnm env`"

...into my .zshrc file. But, I later learned you need to edit the last line to:

-eval "`fnm env`"
+eval "$(fnm env --use-on-cd)"

so that it automatically activates immediately after you've cd'ed into a directory.
If you had direnv to do this, get rid of that. fmn does not need direnv.

Now, create a fresh new terminal and it should be set up, including tab completion. You can test it by typing fnm[TAB]. You'll see:

❯ fnm
alias                   -- Alias a version to a common name
completions             -- Print shell completions to stdout
current                 -- Print the current Node.js version
default                 -- Set a version as the default version
env                     -- Print and set up required environment variables for fnm
exec                    -- Run a command within fnm context
help                    -- Print this message or the help of the given subcommand(s)
install                 -- Install a new Node.js version
list         ls         -- List all locally installed Node.js versions
list-remote  ls-remote  -- List all remote Node.js versions
unalias                 -- Remove an alias definition
uninstall               -- Uninstall a Node.js version
use                     -- Change Node.js version

Usage

If you had .nvmrc files sprinkled about from before, fnm will read those. If you cd into a directory, that contains .nvmrc, whose version fnm hasn't installed, yet, you get this:

❯ cd ~/dev/GROCER/groce/
Can't find an installed Node version matching v16.14.2.
Do you want to install it? answer [y/N]:

Neat!

But if you want to set it up from scratch, go into your directory of choice, type:

fnm ls-remote

...to see what versions of node you can install. Suppose you want v20.10.0 in the current directory do these two commands:

fnm install v20.10.0
echo v20.10.0 > .node-version

That's it!

Notes

• I prefer that .node-version convention so I've been going around doing mv .nvmrc .node-version in various projects

• fnm ls is handy to see which ones you've installed already

• Suppose you want to temporarily use a specific version, simply type fnm use v16.20.2 for example

• I heard good things about volta too but got a bit nervous when I found out it gets involved in installing packages and not just versions of node.

• fnm does not concern itself with upgrading your node versions. To get the latest version of node v21.x, it's up to you to check fnm ls-remote and compare that with the output of node --version.

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.

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.

This is not a bug in the age-old zip Linux program. It's maybe a bug in its intuitiveness.

I have a piece of automation that downloads a zip file from a file storage cache (GitHub Actions actions/cache in this case). Then, it unpacks it, and plucks some of the files from it into another fresh new directory. Lastly, it creates a new .zip file with the same name. The same name because that way, when the process is done, it uploads the new .zip file into the file storage cache. But be careful; does it really create a new .zip file?

To demonstrate the surprise:

$ cd /tmp/
$ mkdir somefiles
$ touch somefiles/file1.txt
$ touch somefiles/file2.txt
$ zip -r somefiles.zip somefiles
  adding: somefiles/ (stored 0%)
  adding: somefiles/file1.txt (stored 0%)
  adding: somefiles/file2.txt (stored 0%)

Now we have a somefiles.zip to work with. It has 2 files in it.

Next session. Let's say it's another day and a fresh new /tmp directory and the previous somefiles.txt has been downloaded from the first session. This time we want to create a new somefile directory but in it, only have file2.txt from before and a new file file3.txt.

$ rm -fr somefiles
$ unzip somefiles.zip
Archive:  somefiles.zip
   creating: somefiles/
 extracting: somefiles/file1.txt
 extracting: somefiles/file2.txt
$ rm somefiles/file1.txt
$ touch somefiles/file3.txt
$ zip -r somefiles.zip somefiles
updating: somefiles/ (stored 0%)
updating: somefiles/file2.txt (stored 0%)
  adding: somefiles/file3.txt (stored 0%)

And here comes the surprise, let's peek into the newly zipped up somefiles.txt (which was made from the somefiles/ directory which only contained file2.txt and file3.txt):

$ rm -fr somefiles
$ unzip -l somefiles.zip
Archive:  somefiles.zip
  Length      Date    Time    Name
---------  ---------- -----   ----
        0  2023-10-04 16:06   somefiles/
        0  2023-10-04 16:05   somefiles/file1.txt
        0  2023-10-04 16:06   somefiles/file2.txt
        0  2023-10-04 16:06   somefiles/file3.txt
---------                     -------
        0                     4 files

I did not see that coming! The command zip -r somefiles.zip somefiles/ doesn't create a fresh new .zip file based on recursively walking the somefiles directory. It does an append by default!

The solution is easy. Right before the zip -r somefiles.zip somefiles command, do a rm somefiles.zip.

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)

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.