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Mercurial Automation
This directory contains code and utilities for building and testing Mercurial on remote machines.
The automation.py Script
automation.py is an executable Python script (requires Python 3.5+) that serves as a driver to common automation tasks.
When executed, the script will bootstrap a virtualenv in <source-root>/build/venv-automation then re-execute itself using that virtualenv. So there is no need for the caller to have a virtualenv explicitly activated. This virtualenv will be populated with various dependencies (as defined by the requirements.txt file).
To see what you can do with this script, simply run it:
$ ./automation.py
Local State
By default, local state required to interact with remote servers is stored in the ~/.hgautomation directory.
We attempt to limit persistent state to this directory. Even when performing tasks that may have side-effects, we try to limit those side-effects so they don't impact the local system. e.g. when we SSH into a remote machine, we create a temporary directory for the SSH config so the user's known hosts file isn't updated.
AWS Integration
Various automation tasks integrate with AWS to provide access to resources such as EC2 instances for generic compute.
This obviously requires an AWS account and credentials to work.
We use the boto3 library for interacting with AWS APIs. We do not employ any special functionality for telling boto3 where to find AWS credentials. See https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html for how boto3 works. Once you have configured your environment such that boto3 can find credentials, interaction with AWS should just work.
Hint
Typically you have a ~/.aws/credentials file containing AWS credentials. If you manage multiple credentials, you can override which profile to use at run-time by setting the AWS_PROFILE environment variable.
Resource Management
Depending on the task being performed, various AWS services will be accessed. This of course requires AWS credentials with permissions to access these services.
The following AWS services can be accessed by automation tasks:
- EC2
- IAM
- Simple Systems Manager (SSM)
Various resources will also be created as part of performing various tasks. This also requires various permissions.
The following AWS resources can be created by automation tasks:
- EC2 key pairs
- EC2 security groups
- EC2 instances
- IAM roles and instance profiles
- SSM command invocations
When possible, we prefix resource names with hg- so they can easily be identified as belonging to Mercurial.
Important
We currently assume that AWS accounts utilized by us are single tenancy. Attempts to have discrete users of automation.py (including sharing credentials across machines) using the same AWS account can result in them interfering with each other and things breaking.
Cost of Operation
automation.py tries to be frugal with regards to utilization of remote resources. Persistent remote resources are minimized in order to keep costs in check. For example, EC2 instances are often ephemeral and only live as long as the operation being performed.
Under normal operation, recurring costs are limited to:
- Storage costs for AMI / EBS snapshots. This should be just a few pennies per month.
When running EC2 instances, you'll be billed accordingly. Default instance types vary by operation. We try to be respectful of your money when choosing defaults. e.g. for Windows instances which are billed per hour, we use e.g. t3.medium instances, which cost ~$0.07 per hour. For operations that scale well to many CPUs like running Linux tests, we may use a more powerful instance like c5.9xlarge. However, since Linux instances are billed per second and the cost of running an e.g. c5.9xlarge for half the time of a c5.4xlarge is roughly the same, the choice is justified.
Note
When running Windows EC2 instances, AWS bills at the full hourly cost, even if the instance doesn't run for a full hour (per-second billing doesn't apply to Windows AMIs).
Managing Remote Resources
Occassionally, there may be an error purging a temporary resource. Or you may wish to forcefully purge remote state. Commands can be invoked to manually purge remote resources.
To terminate all EC2 instances that we manage:
$ automation.py terminate-ec2-instances
To purge all EC2 resources that we manage:
$ automation.py purge-ec2-resources
Remote Machine Interfaces
The code that connects to a remote machine and executes things is theoretically machine agnostic as long as the remote machine conforms to an interface. In other words, to perform actions like running tests remotely or triggering packaging, it shouldn't matter if the remote machine is an EC2 instance, a virtual machine, etc. This section attempts to document the interface that remote machines need to provide in order to be valid targets for remote execution. These interfaces are often not ideal nor the most flexible. Instead, they have often evolved as the requirements of our automation code have evolved.
Linux
Remote Linux machines expose an SSH server on port 22. The SSH server must allow the hg user to authenticate using the SSH key generated by the automation code. The hg user should be part of the hg group and it should have sudo access without password prompting.
The SSH channel must support SFTP to facilitate transferring files from client to server.
/bin/bash must be executable and point to a bash shell executable.
The /hgdev directory must exist and all its content owned by hg::hg.
The /hgdev/pyenv directory should contain an installation of pyenv. Various Python distributions should be installed. The exact versions shouldn't matter. pyenv global should have been run so /hgdev/pyenv/shims/ is populated with redirector scripts that point to the appropriate Python executable.
The /hgdev/venv-bootstrap directory must contain a virtualenv with Mercurial installed. The /hgdev/venv-bootstrap/bin/hg executable is referenced by various scripts and the client.
The /hgdev/src directory MUST contain a clone of the Mercurial source code. The state of the working directory is not important.
In order to run tests, the /hgwork directory will be created. This may require running various mkfs.* executables and mount to provision a new filesystem. This will require elevated privileges via sudo.
Various dependencies to run the Mercurial test harness are also required. Documenting them is beyond the scope of this document. Various tests also require other optional dependencies and missing dependencies will be printed by the test runner when a test is skipped.
Releasing Windows Artifacts
The automation.py script can be used to automate the release of Windows artifacts:
$ ./automation.py build-all-windows-packages --revision 5.1.1 $ ./automation.py publish-windows-artifacts 5.1.1
The first command will launch an EC2 instance to build all Windows packages and copy them into the dist directory relative to the repository root. The second command will then attempt to upload these files to PyPI (via twine) and to mercurial-scm.org (via SSH).
Uploading to PyPI requires a PyPI account with write access to the Mercurial package. You can skip PyPI uploading by passing --no-pypi.
Uploading to mercurial-scm.org requires an SSH account on that server with windows group membership and for the SSH key for that account to be the default SSH key (e.g. ~/.ssh/id_rsa) or in a running SSH agent. You can skip mercurial-scm.org uploading by passing --no-mercurial-scm-org.