What are logs, when are they useful1:23
Learn what logs are and why they are useful for back-end visibility, including capturing exceptions, keep-alive signals, and analytics, with guidance on filtering by log levels.
The log levels2:46
Explore log levels, including debug, information, warning, error, and critical, and learn how to apply each level's purpose and conventions for end-to-end logging.
Choose the correct log level
Course Project2:47
build a server-side stock price app that retrieves stock prices on user request and learn end-to-end logging from docker to grafana using gelf logs, logstash, mysql, and alerts.
Environment setup0:07
For this course you'll need to install Docker, C# and VSCode.
Bellow are the links for everything you'll need.
Feel free to jump into the forum if you find any issues!

Creating an API application - Part 11:25
Creating an API application - Part 22:20
Using ILogger - In Controller1:17
Using ILogger - In Program.cs1:46
Obtain an ILogger from the DI container in program.cs via app.services.getService and log that the application has started before app.run, noting its blocking behavior.
Using ILogger - Changing the log level while running2:19
Using nlog - Install and use nuget6:04

Create a Dockerfile3:35
Wrap your code in a docker container by creating a dockerfile and dockerignore, build a lean image, map ports to the host, and view logs via stdout.
Create a docker-compose file0:56
Create a Docker Compose file to run multiple containers and connect them, and learn how Docker Compose builds and runs the app with the image, showing logs in the terminal.
How Docker logging mechanism works2:25
Explore how Docker logs work by shipping container stdout to the host and to a GELF destination, without changing the software, using Docker Compose and log configuration.
"docker logs" command1:26
Some changes in the logs0:03

Using Filter2:29
Add a filter in Logstash to parse the message as JSON, enabling per-attribute handling, and test with HTTP input and curl to verify keys appear in the stream.
Using Filter - Continued1:55
Refine Logstash filters by using prune and whitelist JSON field names, align variables with explicit JSON names, and test changes with curl to view only the selected keys.
Adding categories for better control3:18
Using Tags to control the flow1:48

Preparing the environment - Install Grafana Docker2:03
Add Grafana as a service to the logging Docker Compose, expose port 3000, and mount storage to enable dashboards, tables, graphs, and alerts from multiple data sources after admin/admin login.
Preparing the environment - Setup data source1:22
Visualizations - Table visualization to see “raw” data3:59
Create an alert subscription5:13
Create an alert for high response time in a Grafana panel, configure last as the reduction, set evaluation every 10 seconds, and test firing with thresholds and email notifications.

Requirements

Basic knowledge in C# .Net. I’ll try to cover the basics, but you should know how to program in C# (or any other high level language)
REST API - This concept should be familiar with you
Basic Docker knowledge. We’ll run all required applications using docker containers and docker compose. Everything will be explained, but some basic knowledge will help here.
Some SQL knowledge. Highly recommended to have at least some knowledge on databases, tables and queries. Either way, you won’t get stuck if you aren’t familiar with this.

Description

Hi there! My name is Javier

I’ve been working as a software engineer for more than ten years.

I have a sweet spot for logging and I think it’s a very important aspect of software that mostly gets overseen.

In the last few years I’ve been deep diving into logging, and learned how to correctly write, deliver and analyze them.

At the end of this course you’ll have a working .Net API Server shipping Logs to a Logstash pipeline and being displayed using Grafana Dashboards.

Let’s break this down.

In this course you’ll learn:

How to write logs in C# .Net
Even if C# isn’t your daily programming language, everything I’ll show will be easily projectable to any other programming language and framework.
Handling logs when working with Docker Containers - We’ll see how Docker handles the standard output (stdout), how to use docker commands to see the logs and how to configure Docker to ship the logs to wherever we want to.
Working with Logstash - A processing pipeline for logs. We’ll see what Logstash is, how we can run it and configure it.
Finally, we’ll learn how to use Grafana to display the data, creating dashboards and panels.
The data will come from a SQL database that will be popularized using Logstash.
We’ll see how easy it is to create alerts on Grafana to notify when something happened according to our logs!