103 lines
4.4 KiB
Markdown
103 lines
4.4 KiB
Markdown
# Forecast potential build runner usage
|
|
|
|
In this lab you will use the `forecast` command to forecast potential GitHub Actions usage by computing metrics from completed pipeline runs in your Azure DevOps project.
|
|
|
|
## Prerequisites
|
|
|
|
1. Followed the steps [here](./readme.md#configure-your-codespace) to set up your Codespace environment and bootstrap an Azure DevOps project.
|
|
2. Completed the [configure lab](./1-configure-lab.md#configuring-credentials).
|
|
|
|
## Perform a forecast
|
|
|
|
We will need to answer the following questions before running the `forecast` command:
|
|
|
|
1. What is the Azure DevOps organization name that we want to audit?
|
|
- __:organization__. This should be the same organization used in the setup steps [here](./readme.md#bootstrap-your-azure-devops-organization)
|
|
|
|
2. What is the Azure DevOps project name that we want to audit?
|
|
- __:project__. This should be the same project name used in the setup steps [here](./readme.md#bootstrap-your-azure-devops-organization)
|
|
|
|
3. Where do we want to store the results?
|
|
- `./tmp/forecast_reports`
|
|
|
|
### Steps
|
|
|
|
1. Navigate to the codespace terminal.
|
|
2. Run the following command from the root directory:
|
|
|
|
```bash
|
|
gh valet forecast azure-devops --output-dir ./tmp/forecast_reports --azure-devops-project :project
|
|
```
|
|
|
|
3. The command will output a message that says "No jobs found" because no jobs have been executed in your bootstrapped project.
|
|
|
|
|
|
|
|
4. If you inspect the help menu using the `gh valet forecast --help` command then you will see a `--source-file-path` option. This option can be used to perform a `forecast` command using json files that are already present on the filesystem. These labs come bundled with sample json files located [here](./bootstrap/jobs.json).
|
|
|
|
|
|
|
|
5. Run the following `forecast` command while specifying the path to the sample json files:
|
|
|
|
```bash
|
|
gh valet forecast azure-devops -o ./tmp/forecast_reports --source-file-path azure_devops/bootstrap/jobs.json
|
|
```
|
|
|
|
6. The command will list all the files written to disk when the command succeeds.
|
|
|
|
|
|
|
|
## Review the forecast report
|
|
|
|
The forecast report, logs, and completed job data will be located within the `tmp/forecast_reports` folder.
|
|
|
|
1. Find the `forecast_report.md` file in the file explorer.
|
|
2. Right-click the `forecast_report.md` file and select `Open Preview`.
|
|
3. This file contains metrics used to forecast potential GitHub Actions usage.
|
|
|
|
### Total
|
|
|
|
The `Total` section of the forecast report contains high level statistics related to all the jobs completed after the `--start-date` CLI option:
|
|
|
|
```md
|
|
- Job count: **84**
|
|
- Pipeline count: **32**
|
|
|
|
- Execution time
|
|
|
|
- Total: **82 minutes**
|
|
- Median: **0 minutes**
|
|
- P90: **2 minutes**
|
|
- Min: **0 minutes**
|
|
- Max: **4 minutes**
|
|
|
|
- Queue time
|
|
|
|
- Median: **0 minutes**
|
|
- P90: **1 minutes**
|
|
- Min: **0 minutes**
|
|
- Max: **5 minutes**
|
|
|
|
- Concurrent jobs
|
|
|
|
- Median: **0**
|
|
- P90: **0**
|
|
- Min: **0**
|
|
- Max: **5**
|
|
```
|
|
|
|
Here are some key terms of items defined in the forecast report:
|
|
|
|
- The `Job count` is the total number of completed jobs.
|
|
- The `Pipeline count` is the number of unique pipelines used.
|
|
- `Execution time` describes the amount of time a runner spent on a job. This metric can be used to help plan for the cost of GitHub hosted runners.
|
|
- This metric is correlated to how much you should expect to spend in GitHub Actions. This will vary depending on the hardware used for these minutes and the [Actions pricing calculator](https://github.com/pricing/calculator) should be used to estimate a dollar amount.
|
|
- `Queue time` metrics describe the amount of time a job spent waiting for a runner to be available to execute it.
|
|
- `Concurrent jobs` metrics describe the amount of jobs running at any given time. This metric can be used to define the number of runners a customer should configure.
|
|
|
|
Additionally, these metrics are defined for each queue of runners defined in Azure DevOps. This is especially useful if there are a mix of hosted/self-hosted runners or high/low spec machines to see metrics specific to different types of runners.
|
|
|
|
## Next steps
|
|
|
|
This concludes all labs for migrating Azure DevOps pipelines to Actions with Valet!
|