Redis VS PostgreSQL

To make data retrieval efficient and minimize the need to request data from the API on every page load, I implemented a caching strategy using the ioredis package, which is a popular redis client for Node.js for interacting with a Redis database.

5. ioredis

These are the objects I pass to ioredis to create my Redis instances (one for sessions and one as the main one):

In order to try and mitigate the OOMs. I read the Background Jobs in Node.JS with Redis blog post and implemented Heroku Data For Redis with ioredis, BullMQ and Throng,

Redis.Redis and Redis.Cluster at the end of the rule actually refer to the ioredis constructors, so to set them up, we must use the ioredis constructors.

ioredis is a popular Node.js client, providing a robust support for Redis Cluster.

To connect to Redis from your application, you will need a library that can perform that for you (Otherwise you have to reinvent the wheel). While I've been using IORedis for a nodeJS application in this demo, if you have been using a different language, you will have to look for different connectors like Lettuce for Java or perhaps go-redis for Go.

The Redis client I'm going to use today is ioredis, because its API is intuitive, very robust and with good performance.

Just create your own wrapper service around one of the existing nodejs redis clients, I used the following client https://github.com/luin/ioredis

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)