kubebuilder VS pgx

The Spin operator uses the Kubebuilder framework and contains a Spin App Custom Resource Definition (CRD) and controller. It watches Spin App Custom Resources and realizes the desired state in the K8s cluster. Aside from the immediate benefits gained by running Wasm workloads in k8s, additional optimizations such as Horizontal Pod Scaling (HPA) and k8s Event-driven Autoscaling (KEDA) can be achieved in a pinch.

kubebuilder: brew install kubebuilder

The operator that I've been working on is designed to manage the full lifecycle of a QuestDB database instance, including version and hardware upgrades, config changes, backups, and (eventually) recovery from node failure. I used the Operator SDK and kubebuilder frameworks to provide scaffolding and API support.

Recently, I've been spending a lot of time writing a Kubernetes operator using the go operator-sdk, which is built on top of the Kubebuilder framework. This is a list of a few tips and tricks that I've compiled over the past few months working with these frameworks.

Since we built our operator using the Kubebuilder framework, most standard monitoring tasks were handled for us out-of-the-box. Our operator automatically exposes a rich set of Prometheus metrics that measure reconciliation performance, the number of k8s API calls, workqueue statistics, and memory-related metrics. We we were able to ingest these metrics into pre-built dashboards by leveraging the grafana/v1-alpha plugin, which scaffolds two Grafana dashboards to monitor Operator resource usage and performance. All we had to do was add these to our existing Grafana manifests and we were good to go!

I wrote a CSI driver and some operators. I admire K8s, because you can find solution to almost any problem in the source code - API versioning, load balancing, request throttling, optimistic concurrency, security, and much much more. I recommend https://book.kubebuilder.io/ It is similar to Operator SDK, but without Openshift-specific stuff. It gradually introduces you to many k8s concepts, and follows design patterns that k8s uses internally.

Do you mean this: https://book.kubebuilder.io/ ?

https://book.kubebuilder.io/ all you need to know

A better way to write an operator these days is to use kubebuilder [1].

My complaint is that I have seen orgs write operators for random stuff, often reinventing the wheel. Lot of operators in orgs are result of resume driven development. Having said that it often comes handy for complex orchestration.

[1]https://github.com/kubernetes-sigs/kubebuilder

Sometimes, backend developers tend to opt for an ORM library because it provides an abstraction between your app and the database and thus there is little or no need to write raw queries and migrations which is nice. However, if you want to get better at writing queries (SQL for example), you need to learn how to build your repositories without an ORM. To open a database handle, you can either do it directly from the database driver or do it from database/sql with the driver passed into it. I will be opening the connection with database/sql together with pgx which is a driver and toolkit for PostgreSQL. Walk with me.

The infrastructure layer contains the concrete implementation of the repository domain interface ActivityRepository in the struct DbActivityRepository. This repository implementation uses the Postgres driver pgx and plain SQL to store the activity in the database. It uses the database transaction from the context, since the transaction was initiated by the application service.

For building the RESTful Point of Sale service API, I've considered and selected a combination of technologies that would work seamlessly together. For handling HTTP requests and responses, using the Gin HTTP web framework would make sense because I think it seems complete and popular among Go community too. To ensure data integrity and persistence, I'm using PostgreSQL database with pgx as the database driver, the reason I choose PostgreSQL because it is the most popular relational database to use in production and offers efficient Go integration. I'm also implementing caching using Redis with go-redis client library, which provides powerful in-memory data storage capabilities.

If you are willing to commit to working only with Postgres, I highly recommend pgx. Be sure you get the latest version github.com/jackc/pgx/v5. This gives you the full power of interacting with Postgres without going through an intermediate lowest-common-denominator library.

It uses pg package and pgx driver under the hood.

Starting off, is there any reason you're calling out to the CLI, instead of just using a Postgres driver like pgx? Shelling out to the command line should always be a last resort where possible as a software engineer.

For maintaining state I use PostgreSQL. Driver: https://github.com/jackc/pgx (I use the pgxpools) Along with Sqlc for generating database models and allowing me to focus on just building queries in DBeaver. https://sqlc.dev/

An example of what I'm looking for can be found here https://github.com/jackc/pgx/wiki/Getting-started-with-pgx-through-database-sql/c9f798b4d9a500fcf93931df2464af969d68f516

Except pgx recommends using their native interface, not database/sql, for performance and extra features [0], so it's not that simple in practice.

[0]: https://github.com/jackc/pgx#choosing-between-the-pgx-and-da...

pgx is “PostgreSQL driver and toolkit for Go”. Take a look at https://github.com/jackc/pgx