https://github.com/revsys/local-compose-loki-prometheus-vector

Example Docker Compose configuration for using Loki, Prometheus, and Grafana is a local development setting to better consume logs and metrics
https://github.com/revsys/local-compose-loki-prometheus-vector

docker-compose grafana loki prometheus vector

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Example Docker Compose configuration for using Loki, Prometheus, and Grafana is a local development setting to better consume logs and metrics

• Host: GitHub
• URL: https://github.com/revsys/local-compose-loki-prometheus-vector
• Owner: revsys
• License: bsd-3-clause
• Created: 2024-03-17T11:17:00.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-03-17T11:49:42.000Z (8 months ago)
• Last Synced: 2024-04-16T10:32:34.872Z (7 months ago)
• Topics: docker-compose, grafana, loki, prometheus, vector
• Homepage:
• Size: 4.88 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Using Loki and Prometheus for Local Development with Docker Compose

## Why?

As developers, we are often asked to include robust logging (hopefully structured)

and metrics for later use in our deployed environment for operations reasons.

However, we don't have great ways to consume them ourselves or even verify

our work.

This git repository gives you a *mostly working* example of how to do this

without a ton of configuration.

## Stack

### Loki

[Loki](https://grafana.com/oss/loki/) is an efficient log aggregation system.

Its role is the equivalent of ElasticSearch in the typical ELK/EFK stacks you

may be familiar with.

### Vector

[Vector](https://vector.dev/) is a logging agent. It collects logs from all of

our containers and sends them to Loki.

It is configured via [docker/vector.yaml](docker/vector.yaml).

### Prometheus

[Prometheus](https://prometheus.io/) is the most popular time series metrics

storage.

It is configured via [docker/prometheus.yml](docker/prometheus.yml).

### Grafana

[Grafana](https://grafana.com/grafana/) is the most popular visualization tool

used by operations teams.

With this set up you can quickly explore the metrics and logs that are being

collected.  You can also create dashboards of information that can be useful

during local development and debugging.

Another reason you may want to create a Dashboard is to verify it's set up

and then export it for use in your deployed environments.

## FAQ

**Q: Doesn't this take up a bunch of resources on my laptop?**

I too was worried about this and was pleasantly surprised how lightweight it

ended up being.  I'm on an M1 Macbook Pro (2021) and it ends up taking up about

200-300 MBs of RAM and 1-2% of CPU.  I would imagine the CPU would be a bit

higher on x86 architectures than arm, but wouldn't imagine it would even

approach 5%.

**Q: What do I need to do to log in my own code?**

All you need to do is have your code emit logging data to stdout, preferably

in a structured format like JSON.  This makes it far easier to search and filter

your log data in meaningful ways.

## Customizing

You can adjust the Vector, Prometheus, and/or Grafana configurations defined

in their respective YAML files and `compose.yml` itself. For example, you

may need to add a [transform](https://vector.dev/guides/level-up/transformation/)

to your Vector configuration to help filter or shape your log data before

sending it on to Loki.