slonik VS PostgreSQL

And sometimes invest instead in learning a technology rather than hide it: for example slonik encourages you to write normal SQL queries by making SQL templating easier and safer. In turn, your IDE would be able to understand those queries and give you support based on the database schemas you actually have.

I'd push you to consider using postgres, slonik or similar for database queries. With these libraries, you just write SQL, but they perform input sanitization for you. So you can safely write:

You can already use postgres with Slonik.

https://github.com/gajus/slonik#user-content-slonik-how-are-...

It is not going to be the default because it is way slower.

https://github.com/gajus/slonik/actions/runs/6616647651

Test node_version:18 test_only:postgres-integration is taking 3 minutes.

Test node_version:18 test_only:pg-integration is taking 38 seconds.

For those uninitiated, Slonik is a battle-tested SQL query building and execution library for Node.js. Its primary goal is to allow you to write and compose SQL queries in a safe and convenient way. Now, let's see how it pairs with Express.js.

I am the maintainer of Slonik and I am trying to understand what portion of this sub-users are using Slonik vs other libraries, and if they are using anything else – what are their reasons for it.

It's nice that they implemented string templates essentially exactly the same way Javascript template literals and tag functions work. They even give an example of using it to create a prepared statement (e.g. DB."SELECT * FROM foo WHERE bar = \{inputParam}") which is exactly what many NodeJS libraries due, e.g. Slonik https://github.com/gajus/slonik, like sql`SELECT * FROM foo WHERE bar = ${inputParam}`;

I've found libraries like Zod useful when interacting with external data sources like a database. Slonik[1] uses Zod to define the types expected from a SQL query and then performs runtime validation on the data to ensure that the query is yielding the expected type.

I don't think it's necessary to use Zod/runtime validation everywhere, but it's a nice tool to have on hand.

[1]https://github.com/gajus/slonik

Demonstrate how easily and accidentally one can make an SQL injection with these:

https://github.com/porsager/postgres

https://github.com/gajus/slonik

Have you tried Slonik (https://github.com/gajus/slonik)? It won't generate types from queries automatically, but it encourages writing SQL vs. a query builder and allows type annotations of queries with Zod. Query results are validated at runtime to ensure the queries are typed correctly.

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)