body-parser VS PostgreSQL

Using body-parser you can set the limit on the size of the payload

body-parser is a middleware that parses the body of incoming requests, and exposes the resulting object on req.body

Now that our app can send us messages, can we send messages back to it? Let's implement the slash command, which will allow us to ask our app for some of its important stats and info. This time, Slack will send an HTTP POST to our app. If we take a look at the Slack docs again, we notice that Slack will send the slash command instruction to the URL we specified in the command set up earlier. We can also see that the POST payload is in the format application/x-www-form-urlencoded. We can set up a body-parser to interpret this data.

Our server application is a very simple one. We just render a simple HTML page with a form and input field to search for the user name. And we are using pug as our template engine for the express app and the body-parser library which is Node.js body parsing middleware to parse incoming request bodies in a middleware before your handlers, available under the req.body property.

Body-parser

pg doesn't do too well with serverless, dead connections are left in the pool (or something)

https://github.com/brianc/node-postgres/issues/2112

If you don't want to use ORM then there are some other packages as well! For PostgreSQL we have node-postgres

Interface with PostgreSQL database

(Instead of the following with pg.)

So far every JS framework that uses https://node-postgres.com works great and so no reason to think Drizzle wouldn't.

One thing that keeps coming up is that SQL equals low productivity. I don't think this is true. I think the culprit is that most developers are using to heavily abstracting SQL using ORMs like Prisma that hides the database and SQL logic.

Since building a SQL generator (https://aihelperbot.com) as a side project, I have become much more proficient in SQL and even though I am also locked into Prisma, I use the `queryRaw` all the time to execute raw SQL queries. You can understand the code without knowing Prisma API. It is more performant. For more complex SQL queries, I use the SQL generator for initial suggestions and adapt if needed.

For the next projects I build I want to use the minimal Postgres client (https://github.com/brianc/node-postgres) combined with a lightweight migration library.

For newer projects I use the small Postgres client. Initially my leap into SQL was lead by AI but as I refreshed and relearned SQL, I now use a mixture of AI and self-written SQL queries. Something like this is just easier to have AI do the grunt work and then adjustment as needed.

This was a known issue for pg developers, and they managed to fix it a long time ago (at the pg level), but the knowledge of this problem didn't reach Knex maintainers.

Integrate the database into your application itself with a postgres client library allowing your applications to make infrastructure changes (like provisioning sharded resources for a client that wants isolation, or using a more accurate forecasting model to pre-allocate more resources before the storm hits).

node-postgres (raw sql, without ORM)