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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 Bitbucket.

Prerequisites

  1. Followed the steps here to set up your GitHub Codespaces environment.
  2. Completed the configure lab.

Perform a forecast

Answer the following questions before running the forecast command:

  1. What workspace do you want to run the forecast for?
    • actions-importer
  2. What is the date you want to start forecasting from?
    • 2023-08-03. By default, the value is set to one week ago, but it's recommended that you choose a start date that provides a representative view of typical usage.
  3. Where do you want to store the results?
    • tmp/forecast

Steps

  1. Navigate to your codespace terminal

  2. Run the following command from the root directory:

    gh actions-importer forecast bitbucket --workspace actions-importer --start-date 2023-08-03 --output-dir tmp/forecast --source-file-path bitbucket/**/source_files/*.json

    Note: The --source-file-path option is not required and is used throughout this lab to convert files that are stored locally. This can be omitted and GitHub Actions Importer will programmatically fetch historical pipeline runs using the Bitbucket REST APIs.

  3. The command will list all the files written to disk when the command succeeds.

    [2023-09-07 18:26:22] Logs: 'tmp/audit/log/valet-20230907-182622.log'
[2023-09-07 18:26:22] Forecasting 'https://bitbucket.org/actions-importer'
[2023-09-07 18:26:22] Output file(s):
[2023-09-07 18:26:22]   tmp/forecast/forecast_report.md

Review the forecast report

The forecast report, logs, and completed job data will be located within the tmp/forecast 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:

- Job count: **8**
- Pipeline count: **8**

- Execution time

  - Total: **11 minutes**
  - Median: **1 minutes**
  - P90: **4 minutes**
  - Min: **1 minutes**
  - Max: **4 minutes**

- Concurrent jobs

  - Median: **0**
  - P90: **0**
  - Min: **0**
  - Max: **1**

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. You can use the Actions pricing calculator to estimate a dollar amount.
  • 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 by hosted and self-hosted runners.

Forecasting multiple providers

You can examine the available options for the forecast command by running gh actions-importer forecast --help. When you do this you will see the --source-file-path option:

$ gh actions-importer forecast -h
Options:
  --source-file-path <source-file-path> (REQUIRED)  The file path(s) to existing jobs data.
  -o, --output-dir <output-dir> (REQUIRED)          The location for any output files.
  --start-date <start-date>                         The start date of the forecast analysis in YYYY-MM-DD format. [default: 9/12/2022 12:42:39 PM]
  --time-slice <time-slice>                         The time slice in seconds to use for computing concurrency metrics. [default: 60]
  --credentials-file <credentials-file>             The file containing the credentials to use.
  --no-telemetry                                    Boolean value to disallow telemetry.
  --no-ssl-verify                                   Disable ssl certificate verification.
  --no-http-cache                                   Disable caching of http responses.
  -?, -h, --help                                    Show help and usage information

You can use the --source-file-path CLI option to combine data from multiple reports into a single report. This becomes useful if you use multiple CI/CD providers and want to get a holistic view of the runner usage. This works by using the .json files generated by forecast commands as space-delimited values for the --source-file-path CLI option. Optionally, this value could be a glob pattern to dynamically specify the list of files (e.g. **/*.json).

Below is a example command that would generate a report for all files matching tmp/**/jobs/*.json:

gh actions-importer forecast --source-file-path tmp/**/jobs/*.json --output-dir tmp/forecast-combined

Next steps

Perform a dry-run migration of a Bitbucket pipeline