Ciro Santilli @cirosantilli  40

This doesn't do a hole lot. Ciro Santilli wouldn't really call it a web framework. It's more like a middleware. Real web frameworks are built on top of it.

Examples under: nodejs/express:

nodejs/express/min.js: minimal example. Visit localhost:3000 and it shows hello world. It is a bit wrong because the headers say HTML but we return plaintext.
nodejs/express/index.js: example dump with automated tests where possible. The automated tests are run at startup after the server launches. Then the server keeps running so you can interact with it.

A live example on Heroku can be seen at: github.com/cirosantilli/heroku-node-min

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FeathersJS Updated 2025-07-16

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Looks interesting.

It seems to abstract the part about the client messaging the backend, which focuses on being able to easily plug in a number of Front-end web framework to manage client state.

Has the "main web API is the same as the REST API" focus, which is fundamental 2020-nowadays.

Uses Socket.IO, which allows the client Javascript to register callbacks when data is updated to achieve Socket.IO, e.g. their default chat app does:

client.service('messages').on('created', addMessage);

so that message appear immediately as they are sent.

Their standard template from feathers generate app on @feathersjs/cli@4.5.0 includes:

several authentication methods, including OAuth
testing
backend database with one of several object-relational mapping! However, they don't abstract across them. E.g., the default Chat example uses NeDB, but a real app will likely use Sequelize, and a port is needed

which looks promising! They don't have a default template for a Front-end web framework however unfortunately: docs.feathersjs.com/guides/frameworks.html#the-feathers-chat lists a few chat app versions, which is their hello world:

Front-end web framework: not built-in on generator, but there are some sample repos pointed from the documentation, and they did work out-of-box:
- feathers-chat-react

But it is in itself a completely boring app with a single splash page, and no database interaction, so not a good showcase. The actual showcase app is feathersjs/feathers-chat.

And there is no official example of the chat app that is immediately deployable to Heroku: FeathersJS Heroku deployment, all setups require thinking.

Global source entry point: determine on package.json as usual, defaults to src/index.js.

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Meteor (web framework) Updated 2025-07-16

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github.com/meteor/meteor

The idea is cool. It really unifies front-and back end.

But Ciro Santilli feels the approach proposed by FeathersJS of being a glue between bigger third-party Front-end web frameworks like React and backend (object-relational mapping) is more promising and flexible.

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Nest.js Updated 2025-07-16

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Sails.js Updated 2025-07-16

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github.com/balderdashy/sails

Front-end web framework integration: no native one:

React:
- github.com/markmur/sails-react-webpack
- github.com/sepineda/sails-react-webpack Last updated as of 2021: 2018
- stackoverflow.com/questions/28331032/sails-js-with-react-js-how-to-do-it-correctly
Vue.js:
- github.com/mikermcneil/ration Issue tracker disabled...
  - live at: ration.io/
  - selling a course at: courses.platzi.com/courses/sails-js/
- platzi.com/cursos/javascript-pro/ non-free and in Spanish pointed to from official README...
- Nuxt.js:
  - github.com/AngelMunoz/sails-nuxt

TODO server-side rendering anyone??

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Node.js standard library Updated 2025-07-16

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Node.js database bindings Updated 2025-07-16

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Node.js SQLite bindings

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Node.js ORM library Updated 2025-07-16

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JavaScript memory usage benchmark Updated 2025-07-16

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In this section we will use the file nodejs/bench_mem.js, tests are run on Node.js v16.14.2 from NVM, Ubuntu 21.10, on Lenovo ThinkPad P51 (2017) which has 32 GB RAM.

First using topp from stackoverflow.com/questions/1221555/retrieve-cpu-usage-and-memory-usage-of-a-single-process-on-linux/40576129#40576129 let's observe the memory usage of some baseline cases.

A C hello world with an infinite loop at the end has:

2.7 MB
770 KB

For a Node.js infinite loop nodejs/infinite_loop.js

topp infinite_loop.js

This gives approximately:

RSS: 20 MB
VSZ: 230 MB

Adding a single hello world to it as in nodejs/infinite_hello.js and running:

topp infinite_hello.js

leads to:

RSS: 26 MB
VSZ: 580 MB

We understand that Node.js preallocates VSZ wildly. No big deal, but it does mean that VSZ is a useless measure for Node.js.

Forcing garbage collection as in nodejs/infinite_hello.js brings it down to 20 MB however:

topp node --expose-gc infinite_hello_gc.js

Finally let's see a baseline for process.memoryUsage nodejs/infinite_memoryusage.js:

node --expose-gc infinite_memoryusage.js

which gives initially:

{
  rss: 23851008,
  heapTotal: 6987776,
  heapUsed: 3674696,
  external: 285296,
  arrayBuffers: 10422
}

but after a few seconds randomly jumps to:

{
  rss: 26005504,
  heapTotal: 9084928,
  heapUsed: 3761240,
  external: 285296,
  arrayBuffers: 10422
}

so we understand that

heapUsed seems constant at 3.7 MB
heapTotal is a very noisy, as it starts at 7 MB, but randomly jumps to 9 MB at one point without apparent reason

Now let's run our main test program.

First a baseline case with an array of length 1:

node --expose-gc bench_mem.js n 1

This gives the same results as node --expose-gc infinite_memoryusage.js. The same result is obtained by doing:

a = undefined

with:

node --expose-gc bench_mem.js dealloc

Not let's vary the size of n a bit with:

node --expose-gc bench_mem.js n N

which gives:

N	`heapUsed`	`heapTotal`	`rss`	`heapUsed` per elem	`rss` per elem
1 M	14 MB	48 MB	56 MB	10	30
10 M	122 MB	157 MB	176 MB	18	15
100 M	906 MB	940 MB	960 MB	9	9.3

"rss per elem" is calculated as: rss - 26 MB, where 26 MB is the baseline RSS seen on n 1.

Similarly "heapUsed per elem" deduces the 4 MB (approximation of the above 3.7 MB) seen on n 1.

Note that to reach MAX_SAFE_INTEGER we would need 8 bytes per elem worst case.

Everything below 100 million (8) is therefore very memory wasteful in terms of RSS.

If we use Int32Array typed array buffers instead of a simple Array:

node --expose-gc bench_mem.js array-buffer n N

we see that the memory is now, unsurprisingly, accounted for under arrayBuffers, e.g. for N 1 million:

{
  rss: 31776768,
  heapTotal: 6463488,
  heapUsed: 3674520,
  external: 4285296,
  arrayBuffers: 4010422
}

Results for different N:

|| N
|| `arrayBuffers`
|| `rss`
|| `rss` per elem

| 1 M
| 4 MB
| 31 MB
| 5

| 10 M
| 40 MB
| 67 MB
| 4.6

| 100 M
| 40 MB
| 427 MB
| 4

We see therefore that typed arrays are much closer to what they advertise (4 bytes per element), even for smaller element counts, as expected.

Now let's try one million objects of type { a: 1, b: -1 }:

node --expose-gc bench_mem.js obj

gives:

{
  rss: 138969088,
  heapTotal: 105246720,
  heapUsed: 70103896,
  external: 285296,
  arrayBuffers: 10422
}

Disaster! Memory usage is up to 70 MB! Why?? We were expecting only about 24, 4 baseline + 2 * 10 for each million int?!

And now an equivalent version using class:

node --expose-gc bench_mem.js class

gives the same result.

Let's try Array:

node --expose-gc bench_mem.js arr

is even worse at 78 MB!! OMG why.

{
  rss: 164597760,
  heapTotal: 129363968,
  heapUsed: 78117008,
  external: 285296,
  arrayBuffers: 10422
}

Let's change the number of fields on the object? First as a sanity check:

node --expose-gc bench_mem.js obj 2

produces as expected the smae result as:

node --expose-gc bench_mem.js obj

so adding properties one by one doesn't change anything from creating the literal all at once. Good.

Now:

node --expose-gc bench_mem.js obj N

gives heapUsed:

1: 70M
2: 70M
3: 70M
4: 70M
5: 110M
6: 110M
7: 110M
8: 134M
9: 134M
10: 134M
11: 158M

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Caenorhabditis elegans Updated 2025-07-16

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www.cell.com/cell-systems/fulltext/S2405-4712(16)30120-X
www.cell.com/cell-systems/fulltext/S2405-4712(16)30151-X A Genome-Scale Database and Reconstruction of Caenorhabditis elegans Metabolism Gebauer, Juliane et al. Cell Systems , Volume 2 , Issue 5 , 312 - 322

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A Three-Dimensional Model of the Myoglobin Molecule Obtained by X-Ray Analysis (1958) Updated 2025-07-16

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www.nature.com/articles/181662a0 on Nature. Paywalled as of 2022.

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Money creation vs tax Updated 2025-07-16

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politics.stackexchange.com/questions/73498/how-is-printing-money-different-from-taxing-people

To Ciro Santilli, a key observation is:

The rich are more easily able to avoid the harm than poor and middle-class people [...] they are more likely to have large amounts of non-cash assets to shield themselves from inflation.

Clearly the rich will be much, much more shielded by keeping large parts of their wealth in shares... from this point of view, it is insane to print money!!! Tax the rich instead...

Mentions:

Erik Finman: twitter.com/erikfinman/status/1446853065320112130

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Geocentric model Updated 2025-07-16

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