Ethan Dennis
105 lines
4.7 KiB
Markdown
105 lines
4.7 KiB
Markdown
# Forecast potential build runner usage
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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.
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## Prerequisites
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1. Followed the steps [here](./readme.md#configure-your-codespace) to set up your GitHub Codespaces environment and bootstrap an Azure DevOps project.
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2. Completed the [configure lab](./1-configure.md#configuring-credentials).
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## Perform a forecast
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Answer the following questions before running the `forecast` command:
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1. What is the Azure DevOps organization name that you want to audit?
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- __:organization__. This should be the same organization used in the setup steps [here](./readme.md#bootstrap-your-azure-devops-organization)
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2. What is the Azure DevOps project name that you want to audit?
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- __:project__. This should be the same project name used in the setup steps [here](./readme.md#bootstrap-your-azure-devops-organization)
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3. Where do you want to store the results?
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- `tmp/forecast`
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### Steps
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1. Navigate to the codespace terminal.
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2. Run the following command from the root directory:
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```bash
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gh actions-importer forecast azure-devops --output-dir tmp/forecast
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```
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__Note__: The Azure DevOps organization and project name can be omitted from the `forecast` command because they were persisted in the `.env.local` file in the [configure lab](./1-configure.md). You can optionally provide these arguments on the command line with the `--azure-devops-organization` and `--azure-devops-project` CLI options.
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3. The command will output a message that says "No jobs found" because no jobs have been executed in your bootstrapped project.
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4. If you inspect the help menu using the `gh actions-importer forecast --help` command, you will see a `--source-file-path` option. You can use this option to perform a `forecast` using json files that are already present on the filesystem. These labs come bundled with sample json files located [here](./bootstrap/jobs.json).
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5. Run the following `forecast` command while specifying the path to the sample json files:
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```bash
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gh actions-importer forecast azure-devops --output-dir tmp/forecast --source-file-path azure_devops/bootstrap/jobs.json
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```
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6. The command will list all the files written to disk when the command succeeds.
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## Review the forecast report
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The forecast report, logs, and completed job data will be located within the `tmp/forecast` folder.
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1. Find the `forecast_report.md` file in the file explorer.
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2. Right-click the `forecast_report.md` file and select `Open Preview`.
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3. This file contains metrics used to forecast potential GitHub Actions usage.
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### Total
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The `Total` section of the forecast report contains high level statistics related to all the jobs completed after the `--start-date` CLI option:
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```md
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- Job count: **84**
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- Pipeline count: **32**
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- Execution time
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- Total: **82 minutes**
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- Median: **0 minutes**
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- P90: **2 minutes**
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- Min: **0 minutes**
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- Max: **4 minutes**
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- Queue time
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- Median: **0 minutes**
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- P90: **1 minutes**
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- Min: **0 minutes**
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- Max: **5 minutes**
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- Concurrent jobs
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- Median: **0**
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- P90: **0**
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- Min: **0**
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- Max: **5**
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```
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Here are some key terms of items defined in the forecast report:
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- The `Job count` is the total number of completed jobs.
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- The `Pipeline count` is the number of unique pipelines used.
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- `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.
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- 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. You can use the [Actions pricing calculator](https://github.com/pricing/calculator) to estimate a dollar amount.
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- `Queue time` metrics describe the amount of time a job spent waiting for a runner to be available to execute it.
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- `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.
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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.
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## Next steps
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[Perform a dry-run migration of an Azure DevOps pipeline](4-dry-run.md)
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