Mercurial > hg
view contrib/automation/README.rst @ 43057:c5c502bd1f70
automation: add a command to submit to a Try server
The CI code for running the Try Server requires more thorough review.
Let's add just the client-side bits for submitting to Try so others
can start using it.
Differential Revision: https://phab.mercurial-scm.org/D6983
author | Gregory Szorc <gregory.szorc@gmail.com> |
---|---|
date | Sat, 05 Oct 2019 11:21:39 -0400 |
parents | f71b3c561b93 |
children |
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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. Try Server ========== There exists a *Try Server* which allows automation to run against an arbitrary Mercurial changeset and displays results via the web. .. note:: The *Try Server* is still experimental infrastructure. To use the *Try Server*:: $ ./automation.py try With a custom AWS profile:: $ AWS_PROFILE=hg contrib/automation/automation.py try By default, the ``.`` revision is submitted. **Any uncommitted changes are not submitted.** To switch which revision is used:: $ ./automation.py try -r abcdef Access to the *Try Server* requires access to a special AWS account. This account is currently run by Gregory Szorc. Here is the procedure for accessing the *Try Server*: 1. Email Gregory Szorc at gregory.szorc@gmail.com and request a username. This username will be stored in the public domain. 2. Wait for an email reply containing your temporary AWS credentials. 3. Log in at https://gregoryszorc-hg.signin.aws.amazon.com/console and set a new, secure password. 4. Go to https://console.aws.amazon.com/iam/home?region=us-west-2#/security_credentials 5. Under ``Access keys for CLI, SDK, & API access``, click the ``Create access key`` button. 6. See the ``AWS Integration`` section for instructions on configuring your local client to use the generated credentials. 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*. To configure ``boto3``, you can use the ``aws configure`` command to write out configuration files. (The ``aws`` command is typically provided by an ``awscli`` package available in your package manager, including ``pip``.) Alternatively, you can write out files in ``~/.aws/`` directly. e.g.:: # ~/.aws/config [default] region = us-west-2 # ~/.aws/credentials [default] aws_access_key_id = XXXX aws_secret_access_key = YYYY If you have multiple AWS accounts, you can name the profile something different from ``default``. e.g. ``hg``. You can influence which profile is used by ``boto3`` by setting the ``AWS_PROFILE`` environment variable. e.g. ``AWS_PROFILE=hg``. 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`.