pgx VS prometheus

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

It's like Prometheus, but for logs. Okay it's not really to do with the Norse or Greek gods, instead Loki is a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by the open source project Prometheus. Built by Grafana Labs, Loki is designed for ease of use. Instead of indexing the contents of the logs, Loki provides a set of labels for each log stream. The latest update includes query acceleration with Bloom filters, native OTel support, Helm charts, and more. Check out the changelog for all the major changes and deprecations.

WriteRequest::timeseries is a vector (https://github.com/prometheus/prometheus/blob/main/prompb/re...) and

Developers widely use Prometheus as a system for operational monitoring and alerting for their projects. Here is a list of tools for monitoring frontend services with Prometheus.

Just to give an example of the power of Go for CLI builds, you may have already used or at least heard of Docker, Kubernetes, Prometheus, Terraform, but what do they all have in common? They all have a large part of their usability via CLI and are developed in Go 🐿.

Distributed system administrators need mechanisms and tools for monitoring individual nodes in order to analyze the system and promptly detect anomalies. Developers also need effective mechanisms for analyzing, diagnosing issues, and identifying bugs in protocol implementations. Logging, tracing, and collecting metrics are common observability techniques to allow monitoring and obtaining diagnostic information from the system; most of the explored code bases use these techniques. OpenTelemetry and Prometheus are popular open-source monitoring solutions, which are used in many of the explored code bases.

Setting up monitoring for a system, especially one involving GRPC communication, provides crucial visibility into its operations. In this guide, we walked through the steps to instrument both a GRPC server and client with Prometheus metrics, exposed those metrics via an HTTP endpoint, and visualized them using Grafana. The Docker-Compose setup simplified the deployment of both Prometheus and Grafana, ensuring a streamlined process.

Alerting and Notification: Select a tool with flexible alerting mechanisms to proactively detect anomalies or deviations from defined thresholds. Consider asking questions like "Does this tool offer customizable alerting options and support notification channels that suit our team's communication preferences?" A tool like Prometheus provides robust alerting capabilities.

Prometheus

Prometheus is an open-source monitoring and alerting toolkit. It is designed to monitor highly dynamic containerized systems, making it an excellent choice for monitoring Docker containers and Kubernetes clusters.