EJS VS PostgreSQL

Templating language - ejs

Its Github repository is https://github.com/mde/ejs and it Complies with the Express view system but along with pros there are some cons of EJS like it is very difficult to read its code, especially for designers and HTML people who don't focus on Backend languages and one more con is that it has no support for block by default.

With our Yugabyte database setup, let’s install the dependencies for our application. Install typeorm,pg, and ejs with the command below:

We need a frontend client to interact with authorization server. For this purpose we are going to use koa and EJS in combination. EJS is a template engine. It gives us the ability to write HTML files with more flexibility.

ejs to generate HTML template views

EJS: https://github.com/mde/ejs

You can use striate, a superset of ejs, to control which code gets generated within the files. The answers given to the prompts in yogini.json are available as variables within the scope of your template files.

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)