pino VS PostgreSQL

Learn more about logging in Fastify and how to customize the Pino logger.

The Fastify framework includes the Pino logger by default (a really great logger with lots of cool features that doesn't compromise on performance). The framework itself allows a lot of really cool stuff, like controlling the level of logs at runtime.

Fastify provides a built-in logging mechanism based on Pino that allows you to capture various events in your applications. Once enabled, Fastify logs all incoming requests to the server and errors that occur while processing said requests. It also provides a convenient way to log custom messages through the log() method on the Fastify instance or the request object.

1. pino

Vitest is for frontend. Jest is not good for backend (I don’t like it for frontend either), take a look at this issue.

Have a look at https://github.com/pinojs/pino

NodeJS -> Pino, Winston, Bunyan, Npmlog, e.t.c.

Use a configurable logger like pino

Using pino as a logger, for every request on the _server_ , a unique ID generated client side in the headers, so a log may be something like:

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)