bull VS Redis

To implement the solution with a queue, I used a package called 'Bull' (https://github.com/OptimalBits/bull). It's a library that helps with distributed job control, providing some very useful solutions for this type of work, such as background job processing, queues with priorities (FIFO, LIFO, and others), among other features. 'Bull' uses Redis for queue storage, so if your application crashes for any reason, once it's back online, it will continue executing the processes that are in the queue. In our case, we'll use the FIFO (First in, first out) queue solution, meaning priority based on arrival order.

We use bull-js for our distributed queue and event-driven library.

I'm using the excellent Redis based Bull.js as a job queue on Kubernetes.

For Job Queue, NestJs provides a package named @nestjs/bull as an abstraction/wrapper on top of Bull, a popular, well-supported, high-performance Node. js-based Queue system implementation.

I'm working on a large ETL project that involves handling queues for file integration. Currently, we are using Redis with Bull (https://www.npmjs.com/package/bull) for this purpose. However, to streamline our architecture and address security concerns, we are considering migrating the queue to PostgreSQL.

BullMQ is a pretty solid choice: https://github.com/taskforcesh/bullmq It's the successor of Bull: https://github.com/OptimalBits/bull

For Node specifically, things like Streams, understanding the asynchronous model completely, and the event loop. Also, queues tend to get used a lot in Node, so understanding the basic concepts behind that and how to use something like bull would be useful.

To do this properly, you need to put the task into queue, so if there are 1000 simultaneous uploads it won't kill your server but will be processed one by one. For the queue, see bull. Image hostings like AWS have some functionality for processing on their side, as an option.

This is a memory intensive process though and Heroku is OOM'ing with R14 errors. For this they recommend migrating intensive work like this to a Background Job via Redis, implemented in Bull and Throng

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