Mercurial > hg
annotate contrib/automation/README.rst @ 42024:b05a3e28cf24
automation: perform tasks on remote machines
Sometimes you don't have access to a machine in order to
do something. For example, you may not have access to a Windows
machine required to build Windows binaries or run tests on that
platform.
This commit introduces a pile of code intended to help
"automate" common tasks, like building release artifacts.
In its current form, the automation code provides functionality
for performing tasks on Windows EC2 instances.
The hgautomation.aws module provides functionality for integrating
with AWS. It manages EC2 resources such as IAM roles, EC2
security groups, AMIs, and instances.
The hgautomation.windows module provides a higher-level
interface for performing tasks on remote Windows machines.
The hgautomation.cli module provides a command-line interface to
these higher-level primitives.
I attempted to structure Windows remote machine interaction
around Windows Remoting / PowerShell. This is kinda/sorta like
SSH + shell, but for Windows. In theory, most of the functionality
is cloud provider agnostic, as we should be able to use any
established WinRM connection to interact with a remote. In
reality, we're tightly coupled to AWS at the moment because
I didn't want to prematurely add abstractions for a 2nd cloud
provider. (1 was hard enough to implement.)
In the aws module is code for creating an image with a fully
functional Mercurial development environment. It contains VC9,
VC2017, msys, and other dependencies. The image is fully capable
of building all the existing Mercurial release artifacts and
running tests.
There are a few things that don't work. For example, running
Windows tests with Python 3. But building the Windows release
artifacts does work. And that was an impetus for this work.
(Although we don't yet support code signing.)
Getting this functionality to work was extremely time consuming.
It took hours debugging permissions failures and other wonky
behavior due to PowerShell Remoting. (The permissions model for
PowerShell is crazy and you brush up against all kinds of
issues because of the user/privileges of the user running
the PowerShell and the permissions of the PowerShell session
itself.)
The functionality around AWS resource management could use some
improving. In theory we support shared tenancy via resource
name prefixing. In reality, we don't offer a way to configure
this.
Speaking of AWS resource management, I thought about using a tool
like Terraform to manage resources. But at our scale, writing a
few dozen lines of code to manage resources seemed acceptable.
Maybe we should reconsider this if things grow out of control.
Time will tell.
Currently, emphasis is placed on Windows. But I only started
there because it was likely to be the most difficult to implement.
It should be relatively trivial to automate tasks on remote Linux
machines. In fact, I have a ~1 year old script to run tests on a
remote EC2 instance. I will likely be porting that to this new
"framework" in the near future.
# no-check-commit because foo_bar functions
Differential Revision: https://phab.mercurial-scm.org/D6142
author | Gregory Szorc <gregory.szorc@gmail.com> |
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date | Fri, 15 Mar 2019 11:24:08 -0700 |
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children | 65b3ef162b39 |
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1 ==================== |
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2 Mercurial Automation |
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3 ==================== |
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4 |
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5 This directory contains code and utilities for building and testing Mercurial |
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6 on remote machines. |
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7 |
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8 The ``automation.py`` Script |
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9 ============================ |
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10 |
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11 ``automation.py`` is an executable Python script (requires Python 3.5+) |
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12 that serves as a driver to common automation tasks. |
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13 |
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14 When executed, the script will *bootstrap* a virtualenv in |
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15 ``<source-root>/build/venv-automation`` then re-execute itself using |
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16 that virtualenv. So there is no need for the caller to have a virtualenv |
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17 explicitly activated. This virtualenv will be populated with various |
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18 dependencies (as defined by the ``requirements.txt`` file). |
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19 |
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20 To see what you can do with this script, simply run it:: |
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21 |
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22 $ ./automation.py |
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23 |
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24 Local State |
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25 =========== |
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26 |
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27 By default, local state required to interact with remote servers is stored |
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28 in the ``~/.hgautomation`` directory. |
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29 |
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30 We attempt to limit persistent state to this directory. Even when |
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31 performing tasks that may have side-effects, we try to limit those |
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32 side-effects so they don't impact the local system. e.g. when we SSH |
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33 into a remote machine, we create a temporary directory for the SSH |
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34 config so the user's known hosts file isn't updated. |
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35 |
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36 AWS Integration |
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37 =============== |
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38 |
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39 Various automation tasks integrate with AWS to provide access to |
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40 resources such as EC2 instances for generic compute. |
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41 |
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42 This obviously requires an AWS account and credentials to work. |
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43 |
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44 We use the ``boto3`` library for interacting with AWS APIs. We do not employ |
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45 any special functionality for telling ``boto3`` where to find AWS credentials. See |
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46 https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html |
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47 for how ``boto3`` works. Once you have configured your environment such |
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48 that ``boto3`` can find credentials, interaction with AWS should *just work*. |
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49 |
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50 .. hint:: |
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51 |
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52 Typically you have a ``~/.aws/credentials`` file containing AWS |
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53 credentials. If you manage multiple credentials, you can override which |
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54 *profile* to use at run-time by setting the ``AWS_PROFILE`` environment |
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55 variable. |
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56 |
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57 Resource Management |
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58 ------------------- |
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59 |
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60 Depending on the task being performed, various AWS services will be accessed. |
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61 This of course requires AWS credentials with permissions to access these |
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62 services. |
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63 |
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64 The following AWS services can be accessed by automation tasks: |
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65 |
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66 * EC2 |
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67 * IAM |
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68 * Simple Systems Manager (SSM) |
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69 |
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70 Various resources will also be created as part of performing various tasks. |
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71 This also requires various permissions. |
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72 |
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73 The following AWS resources can be created by automation tasks: |
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74 |
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75 * EC2 key pairs |
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76 * EC2 security groups |
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77 * EC2 instances |
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78 * IAM roles and instance profiles |
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79 * SSM command invocations |
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80 |
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81 When possible, we prefix resource names with ``hg-`` so they can easily |
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82 be identified as belonging to Mercurial. |
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83 |
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84 .. important:: |
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85 |
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86 We currently assume that AWS accounts utilized by *us* are single |
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87 tenancy. Attempts to have discrete users of ``automation.py`` (including |
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88 sharing credentials across machines) using the same AWS account can result |
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89 in them interfering with each other and things breaking. |
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90 |
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91 Cost of Operation |
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92 ----------------- |
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93 |
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94 ``automation.py`` tries to be frugal with regards to utilization of remote |
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95 resources. Persistent remote resources are minimized in order to keep costs |
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96 in check. For example, EC2 instances are often ephemeral and only live as long |
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97 as the operation being performed. |
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98 |
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99 Under normal operation, recurring costs are limited to: |
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100 |
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101 * Storage costs for AMI / EBS snapshots. This should be just a few pennies |
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102 per month. |
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103 |
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104 When running EC2 instances, you'll be billed accordingly. By default, we |
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105 use *small* instances, like ``t3.medium``. This instance type costs ~$0.07 per |
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106 hour. |
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107 |
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108 .. note:: |
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109 |
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110 When running Windows EC2 instances, AWS bills at the full hourly cost, even |
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111 if the instance doesn't run for a full hour (per-second billing doesn't |
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112 apply to Windows AMIs). |
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113 |
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114 Managing Remote Resources |
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115 ------------------------- |
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116 |
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117 Occassionally, there may be an error purging a temporary resource. Or you |
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118 may wish to forcefully purge remote state. Commands can be invoked to manually |
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119 purge remote resources. |
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120 |
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121 To terminate all EC2 instances that we manage:: |
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122 |
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123 $ automation.py terminate-ec2-instances |
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124 |
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125 To purge all EC2 resources that we manage:: |
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126 |
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127 $ automation.py purge-ec2-resources |