github.com/badrootd/nibiru-cometbft@v0.37.5-0.20240307173500-2a75559eee9b/docs/qa/method.md

---
order: 1
parent:
  title: Method
  order: 1
---

# Method

This document provides a detailed description of the QA process.
It is intended to be used by engineers reproducing the experimental setup for future tests of CometBFT.

The (first iteration of the) QA process as described [in the RELEASES.md document][releases]
was applied to version v0.34.x in order to have a set of results acting as benchmarking baseline.
This baseline is then compared with results obtained in later versions.

Out of the testnet-based test cases described in [the releases document][releases] we focused on two of them:
_200 Node Test_, and _Rotating Nodes Test_.

[releases]: https://github.com/cometbft/cometbft/blob/v0.37.x/RELEASES.md#large-scale-testnets

## Software Dependencies

### Infrastructure Requirements to Run the Tests

* An account at Digital Ocean (DO), with a high droplet limit (>202)
* The machine to orchestrate the tests should have the following installed:
    * A clone of the [testnet repository][testnet-repo]
        * This repository contains all the scripts mentioned in the reminder of this section
    * [Digital Ocean CLI][doctl]
    * [Terraform CLI][Terraform]
    * [Ansible CLI][Ansible]

[testnet-repo]: https://github.com/cometbft/qa-infra
[Ansible]: https://docs.ansible.com/ansible/latest/index.html
[Terraform]: https://www.terraform.io/docs
[doctl]: https://docs.digitalocean.com/reference/doctl/how-to/install/

### Requirements for Result Extraction

* Matlab or Octave
* [Prometheus][prometheus] server installed
* blockstore DB of one of the full nodes in the testnet
* Prometheus DB

[prometheus]: https://prometheus.io/

## 200 Node Testnet

### Running the test

This section explains how the tests were carried out for reproducibility purposes.

1. [If you haven't done it before]
   Follow steps 1-4 of the `README.md` at the top of the testnet repository to configure Terraform, and `doctl`.
2. Copy file `testnets/testnet200.toml` onto `testnet.toml` (do NOT commit this change)
3. Set the variable `VERSION_TAG` in the `Makefile` to the git hash that is to be tested.
   * If you are running the base test, which implies an homogeneous network (all nodes are running the same version),
     then make sure makefile variable `VERSION2_WEIGHT` is set to 0
   * If you are running a mixed network, set the variable `VERSION_TAG2` to the other version you want deployed
     in the network. The, adjust the weight variables `VERSION_WEIGHT` and `VERSION2_WEIGHT` to configure the
     desired proportion of nodes running each of the two configured versions.
4. Follow steps 5-10 of the `README.md` to configure and start the 200 node testnet
    * WARNING: Do NOT forget to run `make terraform-destroy` as soon as you are done with the tests (see step 9)
5. As a sanity check, connect to the Prometheus node's web interface and check the graph for the `COMETBFT_CONSENSUS_HEIGHT` metric.
   All nodes should be increasing their heights.
6. You now need to start the load runner that will produce transaction load
    * If you don't know the saturation load of the version you are testing, you need to discover it.
        * `ssh` into the `testnet-load-runner`, then copy script `script/200-node-loadscript.sh` and run it from the load runner node.
        * Before running it, you need to edit the script to provide the IP address of a full node.
          This node will receive all transactions from the load runner node.
        * This script will take about 40 mins to run.
        * It is running 90-seconds-long experiments in a loop with different loads.
    * If you already know the saturation load, you can simply run the test (several times) for 90 seconds with a load somewhat
      below saturation:
        * set makefile variables `ROTATE_CONNECTIONS`, `ROTATE_TX_RATE`, to values that will produce the desired transaction load.
        * set `ROTATE_TOTAL_TIME` to 90 (seconds).
        * run "make runload" and wait for it to complete. You may want to run this several times so the data from different runs can be compared.
7. Run `make retrieve-data` to gather all relevant data from the testnet into the orchestrating machine
    * Alternatively, you may want to run `make retrieve-prometheus-data` and `make retrieve-blockstore` separately.
      The end result will be the same.
    * `make retrieve-blockstore` accepts the following values in makefile variable `RETRIEVE_TARGET_HOST`
        * `any`: (which is the default) picks up a full node and retrieves the blockstore from that node only.
        * `all`: retrieves the blockstore from all full nodes; this is extremely slow, and consumes plenty of bandwidth,
           so use it with care.
        * the name of a particular full node (e.g., `validator01`): retrieves the blockstore from that node only.
8. Verify that the data was collected without errors
    * at least one blockstore DB for a CometBFT validator
    * the Prometheus database from the Prometheus node
    * for extra care, you can run `zip -T` on the `prometheus.zip` file and (one of) the `blockstore.db.zip` file(s)
9. **Run `make terraform-destroy`**
    * Don't forget to type `yes`! Otherwise you're in trouble.

### Result Extraction

The method for extracting the results described here is highly manual (and exploratory) at this stage.
The CometBFT team should improve it at every iteration to increase the amount of automation.

#### Steps

1. Unzip the blockstore into a directory
2. Extract the latency report and the raw latencies for all the experiments. Run these commands from the directory containing the blockstore
    * ```bash
       mkdir results
       go run github.com/cometbft/cometbft/test/loadtime/cmd/report@f1aaa436d --database-type goleveldb --data-dir ./ > results/report.txt`
       go run github.com/cometbft/cometbft/test/loadtime/cmd/report@f1aaa436d --database-type goleveldb --data-dir ./ --csv results/raw.csv`
       ```
3. File `report.txt` contains an unordered list of experiments with varying concurrent connections and transaction rate
    * If you are looking for the saturation point
        * Create files `report01.txt`, `report02.txt`, `report04.txt` and, for each experiment in file `report.txt`,
          copy its related lines to the filename that matches the number of connections, for example
          ```bash
          for cnum in 1 2 3 4; do echo "$cnum"; grep "Connections: $cnum" results/report.txt -B 2 -A 10 > results/report$cnum.txt;  done
          ```

        * Sort the experiments in `report01.txt` in ascending tx rate order. Likewise for `report02.txt` and `report04.txt`.
    * Otherwise just keep `report.txt`, and skip step 4.
4. Generate file `report_tabbed.txt` by showing the contents `report01.txt`, `report02.txt`, `report04.txt` side by side
    * This effectively creates a table where rows are a particular tx rate and columns are a particular number of websocket connections.
5. Extract the raw latencies from file `raw.csv` using the following bash loop. This creates a `.csv` file and a `.dat` file per experiment.
   The format of the `.dat` files is amenable to loading them as matrices in Octave.
     * Adapt the values of the for loop variables according to the experiments that you ran (check `report.txt`).
     * Adapt `report*.txt` to the files you produced in step 3.

    ```bash
    uuids=($(cat report01.txt report02.txt report04.txt | grep '^Experiment ID: ' | awk '{ print $3 }'))
    c=1
    rm -f *.dat
    for i in 01 02 04; do
      for j in 0025 0050 0100 0200; do
        echo $i $j $c "${uuids[$c]}"
        filename=c${i}_r${j}
        grep ${uuids[$c]} raw.csv > ${filename}.csv
        cat ${filename}.csv | tr , ' ' | awk '{ print $2, $3 }' >> ${filename}.dat
        c=$(expr $c + 1)
      done
    done
    ```

6. Enter Octave
7. Load all `.dat` files generated in step 5 into matrices using this Octave code snippet

    ```octave
    conns =  { "01"; "02"; "04" };
    rates =  { "0025"; "0050"; "0100"; "0200" };
    for i = 1:length(conns)
      for j = 1:length(rates)
        filename = strcat("c", conns{i}, "_r", rates{j}, ".dat");
        load("-ascii", filename);
      endfor
    endfor
    ```

8. Set variable release to the current release undergoing QA

    ```octave
    release = "v0.34.x";
    ```

9. Generate a plot with all (or some) experiments, where the X axis is the experiment time,
   and the y axis is the latency of transactions.
   The following snippet plots all experiments.

    ```octave
    legends = {};
    hold off;
    for i = 1:length(conns)
      for j = 1:length(rates)
        data_name = strcat("c", conns{i}, "_r", rates{j});
        l = strcat("c=", conns{i}, " r=", rates{j});
        m = eval(data_name); plot((m(:,1) - min(m(:,1))) / 1e+9, m(:,2) / 1e+9, ".");
        hold on;
        legends(1, end+1) = l;
      endfor
    endfor
    legend(legends, "location", "northeastoutside");
    xlabel("experiment time (s)");
    ylabel("latency (s)");
    t = sprintf("200-node testnet - %s", release);
    title(t);
    ```

10. Consider adjusting the axis, in case you want to compare your results to the baseline, for instance

    ```octave
    axis([0, 100, 0, 30], "tic");
    ```

11. Use Octave's GUI menu to save the plot (e.g. as `.png`)

12. Repeat steps 9 and 10 to obtain as many plots as deemed necessary.

13. To generate a latency vs throughput plot, using the raw CSV file generated
    in step 2, follow the instructions for the [`latency_throughput.py`] script.
    This plot is useful to visualize the saturation point.

[`latency_throughput.py`]: ../../scripts/qa/reporting/README.md#Latency-vs-Throughput-Plotting

14. Alternatively,  follow the instructions for the [`latency_plotter.py`] script.
    This script generates a series of plots per experiment and configuration that my
    help with visualizing Latency vs Throughput variation.

[`latency_plotter.py`]: ../../scripts/qa/reporting/README.md#Latency-vs-Throughput-Plotting-version-2

#### Extracting Prometheus Metrics

1. Stop the prometheus server if it is running as a service (e.g. a `systemd` unit).
2. Unzip the prometheus database retrieved from the testnet, and move it to replace the
   local prometheus database.
3. Start the prometheus server and make sure no error logs appear at start up.
4. Identify the time window you want to plot in your graphs.
5. Execute the [`prometheus_plotter.py`] script for the time window.

[`prometheus_plotter.py`]: ../../scripts/qa/reporting/README.md#prometheus-metrics

## Rotating Node Testnet

### Running the test

This section explains how the tests were carried out for reproducibility purposes.

1. [If you haven't done it before]
   Follow steps 1-4 of the `README.md` at the top of the testnet repository to configure Terraform, and `doctl`.
2. Copy file `testnet_rotating.toml` onto `testnet.toml` (do NOT commit this change)
3. Set variable `VERSION_TAG` to the git hash that is to be tested.
4. Run `make terraform-apply EPHEMERAL_SIZE=25`
    * WARNING: Do NOT forget to run `make terraform-destroy` as soon as you are done with the tests
5. Follow steps 6-10 of the `README.md` to configure and start the "stable" part of the rotating node testnet
6. As a sanity check, connect to the Prometheus node's web interface and check the graph for the `tendermint_consensus_height` metric.
   All nodes should be increasing their heights.
7. On a different shell,
    * run `make runload ROTATE_CONNECTIONS=X ROTATE_TX_RATE=Y`
    * `X` and `Y` should reflect a load below the saturation point (see, e.g.,
      [this paragraph](CometBFT-QA-34.md#finding-the-saturation-point) for further info)
8. Run `make rotate` to start the script that creates the ephemeral nodes, and kills them when they are caught up.
    * WARNING: If you run this command from your laptop, the laptop needs to be up and connected for full length
      of the experiment.
9. When the height of the chain reaches 3000, stop the `make rotate` script
10. When the rotate script has made two iterations (i.e., all ephemeral nodes have caught up twice)
    after height 3000 was reached, stop `make rotate`
11. Run `make retrieve-data` to gather all relevant data from the testnet into the orchestrating machine
12. Verify that the data was collected without errors
    * at least one blockstore DB for a CometBFT validator
    * the Prometheus database from the Prometheus node
    * for extra care, you can run `zip -T` on the `prometheus.zip` file and (one of) the `blockstore.db.zip` file(s)
13. **Run `make terraform-destroy`**

Steps 8 to 10 are highly manual at the moment and will be improved in next iterations.

### Result Extraction

In order to obtain a latency plot, follow the instructions above for the 200 node experiment, but:

* The `results.txt` file contains only one experiment
* Therefore, no need for any `for` loops

As for prometheus, the same method as for the 200 node experiment can be applied.