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https://github.com/migalabs/eth-client-hw-analysis

Repo compiling all the images that came out from our Ethereum Consensus Client's hardware resource analysis
https://github.com/migalabs/eth-client-hw-analysis

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Repo compiling all the images that came out from our Ethereum Consensus Client's hardware resource analysis

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README 

        
# Ethereum Consensus Clients Hardware Analysis V3

This repository collects all the scripts and generates the figures used in the Ethereum Consensus Client's hardware resource analysis. 



## The Analysis

This Analysis was performed during the months of February 2024 and May 2024.

It consisted of running the 6 main clients (Prysm, Lighthouse, Teku, Nimbus, Lodestar and Grandine)



## Running the clients



Inside this repository you may find several files with which you launch the nodes you intend to analyze.

The setup includes an execution node (always Nethermind), a consensus node (Lighthouse, Prysm, Teku, Nimbus, Lodestar, Grandine) and some monitoring tools (such as Prometheus, Node Exporter or CAdvisor)



### Server



It is possible to run a metrics server, which should aborb all the generated metrics into a single prometheus instance.

This is very useful when the experiment involves several clients and machines. This way, analyzing the data is faster and easier, as everything is contained in a single instance.



Caddy is a Proxy server which will receive all the incoming traffic and redirect it to the metrics server.



1. Copy `Caddyfile.sample` into `Caddyfile`

2. You may configure some credentials if needed.

3. Copy `prometheus-template.yml` into `prometheus.yml` (prometheus folder). You may comment all the scrapes as this prometheus will be a server receiving the data.

4. Run `docker-compose up -d prometheus victoriametrics caddy` 

5. You may now use `http://user:password@yourIP/promhttp/api/v1/write` and `http://user:password@yourIP/victoria/api/v1/write` as remote write



### Clients



1. Decide which client to run

2. Copy `.env.sample` into `.env` and fill the variables. Tags refer to the client version (docker image).

3. Copy `prometheus-template.yml` into `prometheus.yml` (prometheus folder). See remote write above if needed, comment it otherwise.

4. You may configure which clients to scrape and the remote write (see server options above).

4. Run `docker-compose up -d nethermind  prometheus cadvisor node-exporter`.



## Data Collection



Data has been collected using a Jupyter Notebook and a Prometheus. Please refer to the `report.ipynb` file, where you may find a page containing the data collection details.

After the execution, you may find several CSV files have been downloaded (under `{network}/csv/`).



## Data Plotting



Data has been plotted using the same Jupyter Notebook `report.ipynb`. The last page contains details about the plots. These plots are built from the CSV files downloaded previously with the same document.



### Generating metrics



First of all we must place ourselves in the `analysis` folder



It is important to load the requirements into a venv



Most of the figures can be generated by running `download_csvs.py` and then running `plot.py`



In those files, one must edit the `main()` to select which plots and csvs to generate.



Before all, the `base.py` contains a section called `run1` and another `run2`. These define the times at which to download and plot the data, and which files to use. By commenting and uncommenting `run2` section, we can select which phase to download and plot. By default, we have commented `run1`



Maintained by [MigaLabs](http://migalabs.io)