Latest posts of series Transilience
Systemd containers with unittest
This is part of a series of posts on ideas for an ansible-like provisioning system, implemented in Transilience.
Unit testing some parts of Transilience, like the apt and systemd actions, or remote Mitogen connections, can really use a containerized system for testing.
To have that, I reused my work on nspawn-runner. to build a simple and very fast system of ephemeral containers, with minimal dependencies, based on systemd-nspawn and btrfs snapshots:
Setup
To be able to use systemd-nspawn --ephemeral, the chroots needs to be btrfs
subvolumes. If you are not running on a btrfs filesystem, you can create one to
run the tests, even on a file:
fallocate -l 1.5G testfile
/usr/sbin/mkfs.btrfs testfile
sudo mount -o loop testfile test_chroots/
I created a script to setup the test environment, here is an extract:
mkdir -p test_chroots
cat << EOF > "test_chroots/CACHEDIR.TAG"
Signature: 8a477f597d28d172789f06886806bc55
# chroots used for testing transilience, can be regenerated with make-test-chroot
EOF
btrfs subvolume create test_chroots/buster
eatmydata debootstrap --variant=minbase --include=python3,dbus,systemd buster test_chroots/buster
CACHEDIR.TAG is a nice trick to tell backup software not to bother backing up
the contents of this directory, since it can be easily regenerated.
eatmydata is optional, and it speeds up debootstrap quite a bit.
Running unittest with sudo
Here's a simple helper to drop root as soon as possible, and regain it only
when needed. Note that it needs $SUDO_UID and $SUDO_GID, that are set by
sudo, to know which user to drop into:
class ProcessPrivs:
"""
Drop root privileges and regain them only when needed
"""
def __init__(self):
self.orig_uid, self.orig_euid, self.orig_suid = os.getresuid()
self.orig_gid, self.orig_egid, self.orig_sgid = os.getresgid()
if "SUDO_UID" not in os.environ:
raise RuntimeError("Tests need to be run under sudo")
self.user_uid = int(os.environ["SUDO_UID"])
self.user_gid = int(os.environ["SUDO_GID"])
self.dropped = False
def drop(self):
"""
Drop root privileges
"""
if self.dropped:
return
os.setresgid(self.user_gid, self.user_gid, 0)
os.setresuid(self.user_uid, self.user_uid, 0)
self.dropped = True
def regain(self):
"""
Regain root privileges
"""
if not self.dropped:
return
os.setresuid(self.orig_suid, self.orig_suid, self.user_uid)
os.setresgid(self.orig_sgid, self.orig_sgid, self.user_gid)
self.dropped = False
@contextlib.contextmanager
def root(self):
"""
Regain root privileges for the duration of this context manager
"""
if not self.dropped:
yield
else:
self.regain()
try:
yield
finally:
self.drop()
@contextlib.contextmanager
def user(self):
"""
Drop root privileges for the duration of this context manager
"""
if self.dropped:
yield
else:
self.drop()
try:
yield
finally:
self.regain()
privs = ProcessPrivs()
privs.drop()
As soon as this module is loaded, root privileges are dropped, and can be regained for as little as possible using a handy context manager:
with privs.root():
subprocess.run(["systemd-run", ...], check=True, capture_output=True)
Using the chroot from test cases
The infrastructure to setup and spin down ephemeral machine is relatively simple, once one has worked out the nspawn incantations:
class Chroot:
"""
Manage an ephemeral chroot
"""
running_chroots: Dict[str, "Chroot"] = {}
def __init__(self, name: str, chroot_dir: Optional[str] = None):
self.name = name
if chroot_dir is None:
self.chroot_dir = self.get_chroot_dir(name)
else:
self.chroot_dir = chroot_dir
self.machine_name = f"transilience-{uuid.uuid4()}"
def start(self):
"""
Start nspawn on this given chroot.
The systemd-nspawn command is run contained into its own unit using
systemd-run
"""
unit_config = [
'KillMode=mixed',
'Type=notify',
'RestartForceExitStatus=133',
'SuccessExitStatus=133',
'Slice=machine.slice',
'Delegate=yes',
'TasksMax=16384',
'WatchdogSec=3min',
]
cmd = ["systemd-run"]
for c in unit_config:
cmd.append(f"--property={c}")
cmd.extend((
"systemd-nspawn",
"--quiet",
"--ephemeral",
f"--directory={self.chroot_dir}",
f"--machine={self.machine_name}",
"--boot",
"--notify-ready=yes"))
log.info("%s: starting machine using image %s", self.machine_name, self.chroot_dir)
log.debug("%s: running %s", self.machine_name, " ".join(shlex.quote(c) for c in cmd))
with privs.root():
subprocess.run(cmd, check=True, capture_output=True)
log.debug("%s: started", self.machine_name)
self.running_chroots[self.machine_name] = self
def stop(self):
"""
Stop the running ephemeral containers
"""
cmd = ["machinectl", "terminate", self.machine_name]
log.debug("%s: running %s", self.machine_name, " ".join(shlex.quote(c) for c in cmd))
with privs.root():
subprocess.run(cmd, check=True, capture_output=True)
log.debug("%s: stopped", self.machine_name)
del self.running_chroots[self.machine_name]
@classmethod
def create(cls, chroot_name: str) -> "Chroot":
"""
Start an ephemeral machine from the given master chroot
"""
res = cls(chroot_name)
res.start()
return res
@classmethod
def get_chroot_dir(cls, chroot_name: str):
"""
Locate a master chroot under test_chroots/
"""
chroot_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "test_chroots", chroot_name))
if not os.path.isdir(chroot_dir):
raise RuntimeError(f"{chroot_dir} does not exists or is not a chroot directory")
return chroot_dir
# We need to use atextit, because unittest won't run
# tearDown/tearDownClass/tearDownModule methods in case of KeyboardInterrupt
# and we need to make sure to terminate the nspawn containers at exit
@atexit.register
def cleanup():
# Use a list to prevent changing running_chroots during iteration
for chroot in list(Chroot.running_chroots.values()):
chroot.stop()
And here's a TestCase
mixin that starts a containerized systems and opens a Mitogen
connection to it:
class ChrootTestMixin:
"""
Mixin to run tests over a setns connection to an ephemeral systemd-nspawn
container running one of the test chroots
"""
chroot_name = "buster"
@classmethod
def setUpClass(cls):
super().setUpClass()
import mitogen
from transilience.system import Mitogen
cls.broker = mitogen.master.Broker()
cls.router = mitogen.master.Router(cls.broker)
cls.chroot = Chroot.create(cls.chroot_name)
with privs.root():
cls.system = Mitogen(
cls.chroot.name, "setns", kind="machinectl",
python_path="/usr/bin/python3",
container=cls.chroot.machine_name, router=cls.router)
@classmethod
def tearDownClass(cls):
super().tearDownClass()
cls.system.close()
cls.broker.shutdown()
cls.chroot.stop()
Running tests
Once the tests are set up, everything goes on as normal, except one needs to
run nose2 with sudo:
sudo nose2-3
Spin up time for containers is pretty fast, and the tests drop root as soon as possible, and only regain it for as little as needed.
Also, dependencies for all this are minimal and available on most systems, and the setup instructions seem pretty straightforward
Building a Transilience playbook in a zipapp
This is part of a series of posts on ideas for an ansible-like provisioning system, implemented in Transilience.
Mitogen is a great library, but scarily complicated, and I've been wondering how hard it would be to make alternative connection methods for Transilience.
Here's a wild idea: can I package a whole Transilience playbook, plus dependencies, in a zipapp, then send the zipapp to the machine to be provisioned, and run it locally?
It turns out I can.
Creating the zipapp
This is somewhat hackish, but until I can rely on Python 3.9's improved
importlib.resources
module, I cannot think of a better way:
def zipapp(self, target: str, interpreter=None):
"""
Bundle this playbook into a self-contained zipapp
"""
import zipapp
import jinja2
import transilience
if interpreter is None:
interpreter = sys.executable
if getattr(transilience.__loader__, "archive", None):
# Recursively iterating module directories requires Python 3.9+
raise NotImplementedError("Cannot currently create a zipapp from a zipapp")
with tempfile.TemporaryDirectory() as workdir:
# Copy transilience
shutil.copytree(os.path.dirname(__file__), os.path.join(workdir, "transilience"))
# Copy jinja2
shutil.copytree(os.path.dirname(jinja2.__file__), os.path.join(workdir, "jinja2"))
# Copy argv[0] as __main__.py
shutil.copy(sys.argv[0], os.path.join(workdir, "__main__.py"))
# Copy argv[0]/roles
role_dir = os.path.join(os.path.dirname(sys.argv[0]), "roles")
if os.path.isdir(role_dir):
shutil.copytree(role_dir, os.path.join(workdir, "roles"))
# Turn everything into a zipapp
zipapp.create_archive(workdir, target, interpreter=interpreter, compressed=True)
Since the zipapp contains not just the playbook, the roles, and the roles' assets, but also Transilience and Jinja2, it can run on any system that has a Python 3.7+ interpreter, and nothing else!
I added it to the standard set of playbook command line options, so any Transilience playbook can turn itself into a self-contained zipapp:
$ ./provision --help
usage: provision [-h] [-v] [--debug] [-C] [--local LOCAL]
[--ansible-to-python role | --ansible-to-ast role | --zipapp file.pyz]
[...]
--zipapp file.pyz bundle this playbook in a self-contained executable
python zipapp
Loading assets from the zipapp
I had to create ZipFile varieties of some bits of infrastructure in Transilience, to load templates, files, and Ansible yaml files from zip files.
You can see above a way to detect if a module is loaded from a zipfile: check
if the module's __loader__ attribute has an archive attribute.
Here's a Jinja2 template loader that looks into a zip:
class ZipLoader(jinja2.BaseLoader):
def __init__(self, archive: zipfile.ZipFile, root: str):
self.zipfile = archive
self.root = root
def get_source(self, environment: jinja2.Environment, template: str):
path = os.path.join(self.root, template)
with self.zipfile.open(path, "r") as fd:
source = fd.read().decode()
return source, None, lambda: True
I also created a FileAsset
abstract interface to represent a local file, and had Role.lookup_file return
an appropriate instance:
def lookup_file(self, path: str) -> str:
"""
Resolve a pathname inside the place where the role assets are stored.
Returns a pathname to the file
"""
if self.role_assets_zipfile is not None:
return ZipFileAsset(self.role_assets_zipfile, os.path.join(self.role_assets_root, path))
else:
return LocalFileAsset(os.path.join(self.role_assets_root, path))
An interesting side effect of having smarter local file accessors is that I can
cache the contents of small files and transmit them to the remote host together
with the other action parameters, saving a potential network round trip for
each builtin.copy action that has a small source.
The result
The result is kind of fun:
$ time ./provision --zipapp test.pyz
real 0m0.203s
user 0m0.174s
sys 0m0.029s
$ time scp test.pyz root@test:
test.pyz 100% 528KB 388.9KB/s 00:01
real 0m1.576s
user 0m0.010s
sys 0m0.007s
And on the remote:
# time ./test.pyz --local=test
2021-06-29 18:05:41,546 test: [connected 0.000s]
[...]
2021-06-29 18:12:31,555 test: 88 total actions in 0.00ms: 87 unchanged, 0 changed, 1 skipped, 0 failed, 0 not executed.
real 0m0.979s
user 0m0.783s
sys 0m0.172s
Compare with a Mitogen run:
$ time PYTHONPATH=../transilience/ ./provision
2021-06-29 18:13:44 test: [connected 0.427s]
[...]
2021-06-29 18:13:46 test: 88 total actions in 2.50s: 87 unchanged, 0 changed, 1 skipped, 0 failed, 0 not executed.
real 0m2.697s
user 0m0.856s
sys 0m0.042s
From a single test run, not a good benchmark, it's 0.203 + 1.576 + 0.979 =
2.758s with the zipapp and 2.697s with Mitogen. Even if I've been lucky,
it's a similar order of magnitude.
What can I use this for?
This was mostly a fun hack.
It could however be the basis for a Fabric-based connector, or a clusterssh-based connector, or for bundling a Transilience playbook into an installation image, or to add a provisioning script to the boot partition of a Raspberry Pi. It looks like an interesting trick to have up one's sleeve.
One could even build an Ansible-based connector(!) in which a simple Ansible playbook, with no facts gathering, is used to build the zipapp, push it to remote systems and run it. That would be the wackiest way of speeding up Ansible, ever!
Next: using Systemd containers with unittest, for Transilience's test suite.
Ansible conditionals in Transilience
This is part of a series of posts on ideas for an ansible-like provisioning system, implemented in Transilience.
I thought a lot of what I managed to do so far with Transilience would be impossible, but then here I am. How about Ansible conditionals? Those must be impossible, right?
Let's give it a try.
A quick recon of Ansible sources
Looking into Ansible's sources, when expressions are
lists of strings
AND-ed together.
The expressions are Jinja2 expressions that Ansible pastes into a mini-template, renders, and checks the string that comes out.
A quick recon of Jinja2
Jinja2 has a convenient function (jinja2.Environment.compile_expression)
that compiles a template snippet into a Python function.
It can also parse a template into an AST that can be inspected in various ways.
Evaluating Ansible conditionals in Python
Environment.compile_expression seems to really do precisely what we need for
this, straight out of the box.
There is an issue with the concept of "defined": for Ansible it seems to mean
"the variable is present in the template context". In Transilience instead, all
variables are fields in the Role dataclass, and can be None when not set.
This means that we need to remove variables that are set to None before
passing the parameters to the compiled Jinjae expression:
class Conditional:
"""
An Ansible conditional expression
"""
def __init__(self, engine: template.Engine, body: str):
# Original unparsed expression
self.body: str = body
# Expression compiled to a callable
self.expression: Callable = engine.env.compile_expression(body)
def evaluate(self, ctx: Dict[str, Any]):
ctx = {name: val for name, val in ctx.items() if val is not None}
return self.expression(**ctx)
Generating Python code
Transilience does not only support running Ansible roles, but also converting them to Python code. I can keep this up by traversing the Jinja2 AST generating Python expressions.
The code is straightforward enough that I can throw in a bit of pattern matching to make some expressions more idiomatic for Python:
class Conditional:
def __init__(self, engine: template.Engine, body: str):
...
parser = jinja2.parser.Parser(engine.env, body, state='variable')
self.jinja2_ast: nodes.Node = parser.parse_expression()
def get_python_code(self) -> str:
return to_python_code(self.jinja2_ast
def to_python_code(node: nodes.Node) -> str:
if isinstance(node, nodes.Name):
if node.ctx == "load":
return f"self.{node.name}"
else:
raise NotImplementedError(f"jinja2 Name nodes with ctx={node.ctx!r} are not supported: {node!r}")
elif isinstance(node, nodes.Test):
if node.name == "defined":
return f"{to_python_code(node.node)} is not None"
elif node.name == "undefined":
return f"{to_python_code(node.node)} is None"
else:
raise NotImplementedError(f"jinja2 Test nodes with name={node.name!r} are not supported: {node!r}")
elif isinstance(node, nodes.Not):
if isinstance(node.node, nodes.Test):
# Special case match well-known structures for more idiomatic Python
if node.node.name == "defined":
return f"{to_python_code(node.node.node)} is None"
elif node.node.name == "undefined":
return f"{to_python_code(node.node.node)} is not None"
elif isinstance(node.node, nodes.Name):
return f"not {to_python_code(node.node)}"
return f"not ({to_python_code(node.node)})"
elif isinstance(node, nodes.Or):
return f"({to_python_code(node.left)} or {to_python_code(node.right)})"
elif isinstance(node, nodes.And):
return f"({to_python_code(node.left)} and {to_python_code(node.right)})"
else:
raise NotImplementedError(f"jinja2 {node.__class__} nodes are not supported: {node!r}")
Scanning for variables
Lastly, I can implement scanning conditionals for variable references to add as fields to the Role dataclass:
class FindVars(jinja2.visitor.NodeVisitor):
def __init__(self):
self.found: Set[str] = set()
def visit_Name(self, node):
if node.ctx == "load":
self.found.add(node.name)
class Conditional:
...
def list_role_vars(self) -> Sequence[str]:
fv = FindVars()
fv.visit(self.jinja2_ast)
return fv.found
The result in action
Take this simple Ansible task:
---
- name: Example task
file:
state: touch
path: /tmp/test
when: (is_test is defined and is_test) or debug is defined
Run it through ./provision --ansible-to-python test and you get:
from __future__ import annotations
from typing import Any
from transilience import role
from transilience.actions import builtin, facts
@role.with_facts([facts.Platform])
class Role(role.Role):
# Role variables used by templates
debug: Any = None
is_test: Any = None
def all_facts_available(self):
if ((self.is_test is not None and self.is_test)
or self.debug is not None):
self.add(
builtin.file(path='/tmp/test', state='touch'),
name='Example task')
Besides one harmless set of parentheses too much, what I wasn't sure would be possible is there, right there, staring at me with a mischievous grin.
Parsing YAML
This is part of a series of posts on ideas for an Ansible-like provisioning system, implemented in Transilience.
The time has come for me to try and prototype if it's possible to load some Transilience roles from Ansible's YAML instead of Python.
The data models of Transilience and Ansible are not exactly the same. Some of the differences that come to mind:
- Ansible has a big pot of global variables; Transilience has a well defined set of role-specific variables.
- Roles in Ansible are little more than a chunk of playbook that one includes; Roles in Transilience are self-contained and isolated, support pipelined batches of tasks, and can use full Python logic.
- Transilience does not have a
templateaction: the equivalent is acopyaction that uses the Role's rendering engine to render the template. - Handlers in Ansible are tasks identified by a name in a global namespace; handlers in Transilience are Roles, identified by their Python classes.
To simplify the work, I'll start from loading a single role out of Ansible, not an entire playbook.
TL;DR: scroll to the bottom of the post for the conclusion!
Loading tasks
The first problem of loading an Ansible task is to figure out which of the keys is the module name. I have so far failed to find precise reference documentation about what keyboards are used to define a task, so I'm going by guesswork, and if needed a look at Ansible's sources.
My first attempt goes by excluding all known non-module keywords:
candidates = []
for key in task_info.keys():
if key in ("name", "args", "notify"):
continue
candidates.append(key)
if len(candidates) != 1:
raise RoleNotLoadedError(f"could not find a known module in task {task_info!r}")
modname = candidates[0]
if modname.startswith("ansible.builtin."):
name = modname[16:]
else:
name = modname
This means that Ansible keywords like when or with will break the parsing,
and it's fine since they are not supported yet.
args seems to carry arguments to the module, when the module main argument is
not a dict, as may happen at least with the command module.
Task parameters
One can do all sorts of chaotic things to pass parameters to Ansible tasks: for example string lists can be lists of strings or strings with comma-separated lists, and they can be preprocesed via Jinja2 templating, and they can be complex data structures that might contain strings that need Jinja2 preprocessing.
I ended up mapping the behaviours I encountered in an AST-like class hierarchy which includes recursive complex structures.
Variables
Variables look hard: Ansible has a big free messy cauldron of global variables, and Transilience needs a predefined list of per-role variables.
However, variables are mainly used inside Jinja2 templates, and Jinja2 can parse to an Abstract Syntax Tree and has useful methods to examine its AST.
Using that, I managed with resonable effort to scan an Ansible role and
generate a list of all the variables it uses! I can then use that list,
filter out facts-specific names like ansible_domain, and use them to add
variable definition to the Transilience roles. That is exciting!
Handlers
Before loading tasks, I load handlers as one-action roles, and index them by name. When an Ansible task notifies a handler, I can then loop up by name the roles I generated in the earlier pass, and I have all that I need.
Parsed Abstract Syntax Tree
Most of the results of all this parsing started looking like an AST, so I changed the rest of the prototype to generate an AST.
This means that, for a well defined subset of Ånsible's YAML, there exists now a tool that is able to parse it into an AST and raeson with it.
Transilience's playbooks gained a --ansible-to-ast option to parse an Ansible
role and dump the resulting AST as JSON:
$ ./provision --help
usage: provision [-h] [-v] [--debug] [-C] [--ansible-to-python role]
[--ansible-to-ast role]
Provision my VPS
optional arguments:
[...]
-C, --check do not perform changes, but check if changes would be
needed
--ansible-to-ast role
print the AST of the given Ansible role as understood
by Transilience
The result is extremely verbose, since every parameter is itself a node in the tree, but I find it interesting.
Here is, for example, a node for an Ansible task which has a templated parameter:
{
"node": "task",
"action": "builtin.blockinfile",
"parameters": {
"path": {
"node": "parameter",
"type": "scalar",
"value": "/etc/aliases"
},
"block": {
"node": "parameter",
"type": "template_string",
"value": "root: {{postmaster}}\n{% for name, dest in aliases.items() %}\n{{name}}: {{dest}}\n{% endfor %}\n"
}
},
"ansible_yaml": {
"name": "configure /etc/aliases",
"blockinfile": {},
"notify": "reread /etc/aliases"
},
"notify": [
"RereadEtcAliases"
]
},
Here's a node for an Ansible template task converted to Transilience's model:
{
"node": "task",
"action": "builtin.copy",
"parameters": {
"dest": {
"node": "parameter",
"type": "scalar",
"value": "/etc/dovecot/local.conf"
},
"src": {
"node": "parameter",
"type": "template_path",
"value": "dovecot.conf"
}
},
"ansible_yaml": {
"name": "configure dovecot",
"template": {},
"notify": "restart dovecot"
},
"notify": [
"RestartDovecot"
]
},
Executing
The first iteration of prototype code for executing parsed Ansible roles is a little execise in closures and dynamically generated types:
def get_role_class(self) -> Type[Role]:
# If we have handlers, instantiate role classes for them
handler_classes = {}
for name, ansible_role in self.handlers.items():
handler_classes[name] = ansible_role.get_role_class()
# Create all the functions to start actions in the role
start_funcs = []
for task in self.tasks:
start_funcs.append(task.get_start_func(handlers=handler_classes))
# Function that calls all the 'Action start' functions
def role_main(self):
for func in start_funcs:
func(self)
if self.uses_facts:
role_cls = type(self.name, (Role,), {
"start": lambda host: None,
"all_facts_available": role_main
})
role_cls = dataclass(role_cls)
role_cls = with_facts(facts.Platform)(role_cls)
else:
role_cls = type(self.name, (Role,), {
"start": role_main
})
role_cls = dataclass(role_cls)
return role_cls
Now that the parsed Ansible role is a proper AST, I'm considering redesigning that using a generic Role class that works as an AST interpreter.
Generating Python
I maintain a library that can turn an invoice into Python code, and I have a convenient AST. I can't not generate Python code out of an Ansible role!
$ ./provision --help
usage: provision [-h] [-v] [--debug] [-C] [--ansible-to-python role]
[--ansible-to-ast role]
Provision my VPS
optional arguments:
[...]
--ansible-to-python role
print the given Ansible role as Transilience Python
code
--ansible-to-ast role
print the AST of the given Ansible role as understood
by Transilience
And will you look at this annotated extract:
$ ./provision --ansible-to-python mailserver
from __future__ import annotations
from typing import Any
from transilience import role
from transilience.actions import builtin, facts
# Role classes generated from Ansible handlers!
class ReloadPostfix(role.Role):
def start(self):
self.add(
builtin.systemd(unit='postfix', state='reloaded'),
name='reload postfix')
class RestartDovecot(role.Role):
def start(self):
self.add(
builtin.systemd(unit='dovecot', state='restarted'),
name='restart dovecot')
# The role, including a standard set of facts
@role.with_facts([facts.Platform])
class Role(role.Role):
# These are the variables used by Jinja2 template files and strings. I need
# to use Any, since Ansible variables are not typed
aliases: Any = None
myhostname: Any = None
postmaster: Any = None
virtual_domains: Any = None
def all_facts_available(self):
...
# A Jinja2 string inside a string list!
self.add(
builtin.command(
argv=[
'certbot', 'certonly', '-d',
self.render_string('mail.{{ansible_domain}}'), '-n',
'--apache'
],
creates=self.render_string(
'/etc/letsencrypt/live/mail.{{ansible_domain}}/fullchain.pem'
)),
name='obtain mail.* letsencrypt certificate')
# A converted template task!
self.add(
builtin.copy(
dest='/etc/dovecot/local.conf',
src=self.render_file('templates/dovecot.conf')),
name='configure dovecot',
# Notify referring to the corresponding Role class!
notify=RestartDovecot)
# Referencing a variable collected from a fact!
self.add(
builtin.copy(dest='/etc/mailname', content=self.ansible_domain),
name='configure /etc/mailname',
notify=ReloadPostfix)
...
Conclusion
Transilience can load a (growing) subset of Ansible syntax, one role at a time, which contains:
- All actions defined in
Transilience's
builtin.*namespace - Ansible's template module (without
block_start_string,block_end_string,lstrip_blocks,newline_sequence,output_encoding,trim_blocks,validate,variable_end_string,variable_start_string) - Jinja2 templates in string parameters, even when present inside lists and dicts and nested lists and dicts
- Variables from facts provided by
transilience.actions.facts.Platform - Variables used in jitsi templates, both in strings and in files, provided by host vars, group vars, role parameters, and facts
- Notify using handlers defined within the role. Notifying handlers from other roles is not supported, since roles in Transilience are self-contained
The role loader in Transilience now looks for YAML when it does not find a Python module, and runs it pipelined and fast!
There is code to generate Python code from an Ansible module: you can take an Ansible role, convert it to Python, and then work on it to add more complex logic, or clean it up for adding it to a library of reusable roles!
Next: Ansible conditionals
Transilience check mode
This is part of a series of posts on ideas for an ansible-like provisioning system, implemented in Transilience.
I added check mode to Transilience, to do everything except perform changes, like Ansible does:
$ ./provision --help
usage: provision [-h] [-v] [--debug] [-C] [--to-python role]
Provision my VPS
optional arguments:
-h, --help show this help message and exit
-v, --verbose verbose output
--debug verbose output
-C, --check do not perform changes, but check if changes would be ← NEW!
needed ← NEW!
It was quite straightforwad to add a new field to the base Action class, and
tweak the implementations not to perform changes if it is True:
# Shortcut function to annotate dataclass fields with documentation metadata
def doc(default: Any, doc: str, **kw):
return field(default=default, metadata={"doc": doc})
@dataclass
class Action:
...
check: bool = doc(False, "when True, check if the action would perform changes, but do nothing")
Like with Ansible, check mode takes about the same time as a normal run which does not perform changes.
Unlike Ansible, with Transilience this is actually pretty fast! ;)
Next step: parsing YAML!
Playbooks, host vars, group vars
This is part of a series of posts on ideas for an ansible-like provisioning system, implemented in Transilience.
Host variables
Ansible allows to specify per-host variables, and I like that. Let's try to model a host as a dataclass:
@dataclass
class Host:
"""
A host to be provisioned.
"""
name: str
type: str = "Mitogen"
args: Dict[str, Any] = field(default_factory=dict)
def _make_system(self) -> System:
cls = getattr(transilience.system, self.type)
return cls(self.name, **self.args)
This should have enough information to create a connection to the host, and can be subclassed to add host-specific dataclass fields.
Host variables can then be provided as default constructor arguments when instantiating Roles:
# Add host/group variables to role constructor args
host_fields = {f.name: f for f in fields(host)}
for field in fields(role_cls):
if field.name in host_fields:
role_kwargs.setdefault(field.name, getattr(host, field.name))
role = role_cls(**role_kwargs)
Group variables
It looks like I can model groups and group variables by using dataclasses as mixins:
@dataclass
class Webserver:
server_name: str = "www.example.org"
@dataclass
class Srv1(Webserver):
...
Doing things like filtering all hosts that are members of a given group can be
done with a simple isinstance or issubclass test.
Playbooks
So far Transilience is executing on one host at a time, and Ansible can execute on a whole host inventory.
Since the most part of running a playbook is I/O bound, we can parallelize hosts using threads, without worrying too much about the performance impact of GIL.
Let's introduce a Playbook class as the main entry point for a playbook:
class Playbook:
def setup_logging(self):
...
def make_argparser(self):
description = inspect.getdoc(self)
if not description:
description = "Provision systems"
parser = argparse.ArgumentParser(description=description)
parser.add_argument("-v", "--verbose", action="store_true",
help="verbose output")
parser.add_argument("--debug", action="store_true",
help="verbose output")
return parser
def hosts(self) -> Sequence[Host]:
"""
Generate a sequence with all the systems on which the playbook needs to run
"""
return ()
def start(self, runner: Runner):
"""
Start the playbook on the given runner.
This method is called once for each system returned by systems()
"""
raise NotImplementedError(f"{self.__class__.__name__}.start is not implemented")
def main(self):
parser = self.make_argparser()
self.args = parser.parse_args()
self.setup_logging()
# Start all the runners in separate threads
threads = []
for host in self.hosts():
runner = Runner(host)
self.start(runner)
t = threading.Thread(target=runner.main)
threads.append(t)
t.start()
# Wait for all threads to complete
for t in threads:
t.join()
And an actual playbook will now look like something like this:
from dataclasses import dataclass
import sys
from transilience import Playbook, Host
@dataclass
class MyServer(Host):
srv_root: str = "/srv"
site_admin: str = "enrico@enricozini.org"
class VPS(Playbook):
"""
Provision my VPS
"""
def hosts(self):
yield MyServer(name="server", args={
"method": "ssh",
"hostname": "host.example.org",
"username": "root",
})
def start(self, runner):
runner.add_role("fail2ban")
runner.add_role("prosody")
runner.add_role(
"mailserver",
postmaster="enrico",
myhostname="mail.example.org",
aliases={...})
if __name__ == "__main__":
sys.exit(VPS().main())
It looks quite straightforward to me, works on any number of hosts, and has a proper command line interface:
./provision --help
usage: provision [-h] [-v] [--debug]
Provision my VPS
optional arguments:
-h, --help show this help message and exit
-v, --verbose verbose output
--debug verbose output
Next step: check mode!
Reimagining Ansible variables
This is part of a series of posts on ideas for an ansible-like provisioning system, implemented in Transilience.
While experimenting with Transilience, I've been giving some thought about Ansible variables.
My gripes
I like the possibility to define host and group variables, and I like to have a set of variables that are autodiscovered on the target systems.
I do not like to have everything all blended in a big bucket of global variables.
Let's try some more prototyping.
My fiddlings
First, Role classes could become dataclasses, too, and declare the variables and
facts that they intend to use (typed, even!):
class Role(role.Role):
"""
Postfix mail server configuration
"""
# Postmaster username
postmaster: str = None
# Public name of the mail server
myhostname: str = None
# Email aliases defined on this mail server
aliases: Dict[str, str] = field(default_factory=dict)
Using dataclasses.asdict()
I immediately gain context variables for rendering Jinja2 templates:
class Role:
# [...]
def render_file(self, path: str, **kwargs):
"""
Render a Jinja2 template from a file, using as context all Role fields,
plus the given kwargs.
"""
ctx = asdict(self)
ctx.update(kwargs)
return self.template_engine.render_file(path, ctx)
def render_string(self, template: str, **kwargs):
"""
Render a Jinja2 template from a string, using as context all Role fields,
plus the given kwargs.
"""
ctx = asdict(self)
ctx.update(kwargs)
return self.template_engine.render_string(template, ctx)
I can also model results from fact gathering into dataclass members:
# From ansible/module_utils/facts/system/platform.py
@dataclass
class Platform(Facts):
"""
Facts from the platform module
"""
ansible_system: Optional[str] = None
ansible_kernel: Optional[str] = None
ansible_kernel: Optional[str] = None
ansible_kernel_version: Optional[str] = None
ansible_machine: Optional[str] = None
# [...]
ansible_userspace_architecture: Optional[str] = None
ansible_machine_id: Optional[str] = None
def summary(self):
return "gather platform facts"
def run(self, system: transilience.system.System):
super().run(system)
# ... collect facts
I like that this way, one can explicitly declare what variables a Facts
action will collect, and what variables a Role needs.
At this point, I can add machinery to allow a Role to declare what Facts it
needs, and automatically have the fields from the Facts class added to the
Role class. Then, when facts are gathered, I can make sure that their fields
get copied over to the Role classes that use them.
In a way, variables become role-scoped, and Facts subclasses can be used like
some kind of Role mixin, that contributes only field members:
# Postfix mail server configuration
@role.with_facts([actions.facts.Platform])
class Role(role.Role):
# Postmaster username
postmaster: str = None
# Public name of the mail server
myhostname: str = None
# Email aliases defined on this mail server
aliases: Dict[str, str] = field(default_factory=dict)
# All fields from actions.facts.Platform are inherited here!
def have_facts(self, facts):
# self.ansible_domain comes from actions.facts.Platform
self.add(builtin.command(
argv=["certbot", "certonly", "-d", f"mail.{self.ansible_domain}", "-n", "--apache"],
creates=f"/etc/letsencrypt/live/mail.{self.ansible_domain}/fullchain.pem"
), name="obtain mail.* certificate")
# the template context will have the Role variables, plus the variables
# of all the Facts the Role uses
with self.notify(ReloadPostfix):
self.add(builtin.copy(
dest="/etc/postfix/main.cf",
content=self.render_file("roles/mailserver/templates/main.cf"),
), name="configure /etc/postfix/main.cf")
One can also fill in variables when instantiating Roles, making parameterized generic Roles possible and easy:
runner.add_role(
"mailserver",
postmaster="enrico",
myhostname="mail.enricozini.org",
aliases={
"me": "enrico",
},
)
Outcomes
I like where this is going: having well defined variables for facts and roles, means that the variables that get into play can be explicitly defined, well known, and documented.
I think this design lends itself quite well to role reuse:
- Roles can use variables without risking interfering with each other.
- Variables from facts can have well defined meanings across roles.
- Roles are classes, and can easily be made inheritable.
I have a feeling that, this way, it may be much easier to create generic libraries of Roles that one can reuse to compose complex playbooks.
Since roles are just Python modules, we even already know how to package and distribute them!
Next step: Playbooks, host vars, group vars.
Pipelining
This is part of a series of posts on ideas for an ansible-like provisioning system, implemented in Transilience.
Running actions on a server is nice, but a network round trip for each action is not very efficient. If I need to run a linear sequence of actions, I can stream them all to the server, and then read replies streamed from the server as they get executed.
This technique is called pipelining and one can see it used, for example, in Redis, or Mitogen.
Roles
Ansible has the concept of "Roles" as a series of related tasks: I'll play with that. Here's an example role to install and setup fail2ban:
class Role(role.Role):
def main(self):
self.add(builtin.apt(
name=["fail2ban"],
state="present",
))
self.add(builtin.copy(
content=inline("""
[postfix]
enabled = true
[dovecot]
enabled = true
"""),
dest="/etc/fail2ban/jail.local",
owner="root",
group="root",
mode=0o644,
), name="configure fail2ban")
I prototyped roles as classes, with methods that push actions down the pipeline. If an action fails, all further actions for the same role won't executed, and will be marked as skipped.
Since skipping is applied per-role, it means that I can blissfully stream actions for multiple roles to the server down the same pipe, and errors in one role will stop executing that role and not others. Potentially I can get multiple roles going with a single network round-trip:
#!/usr/bin/python3
import sys
from transilience.system import Mitogen
from transilience.runner import Runner
@Runner.cli
def main():
system = Mitogen("my server", "ssh", hostname="server.example.org", username="root")
runner = Runner(system)
# Send roles to the server
runner.add_role("general")
runner.add_role("fail2ban")
runner.add_role("prosody")
# Run until all roles are done
runner.main()
if __name__ == "__main__":
sys.exit(main())
That looks like a playbook, using Python as glue rather than YAML.
Decision making in roles
Besides filing a series of actions, a role may need to take decisions based on the results of previous actions, or on facts discovered from the server. In that case, we need to wait until the results we need come back from the server, and then decide if we're done or if we want to send more actions down the pipe.
Here's an example role that installs and configures Prosody:
from transilience import actions, role
from transilience.actions import builtin
from .handlers import RestartProsody
class Role(role.Role):
"""
Set up prosody XMPP server
"""
def main(self):
self.add(actions.facts.Platform(), then=self.have_facts)
self.add(builtin.apt(
name=["certbot", "python-certbot-apache"],
state="present",
), name="install support packages")
self.add(builtin.apt(
name=["prosody", "prosody-modules", "lua-sec", "lua-event", "lua-dbi-sqlite3"],
state="present",
), name="install prosody packages")
def have_facts(self, facts):
facts = facts.facts # Malkovich Malkovich Malkovich!
domain = facts["domain"]
ctx = {
"ansible_domain": domain
}
self.add(builtin.command(
argv=["certbot", "certonly", "-d", f"chat.{domain}", "-n", "--apache"],
creates=f"/etc/letsencrypt/live/chat.{domain}/fullchain.pem"
), name="obtain chat certificate")
with self.notify(RestartProsody):
self.add(builtin.copy(
content=self.template_engine.render_file("roles/prosody/templates/prosody.cfg.lua", ctx),
dest="/etc/prosody/prosody.cfg.lua",
), name="write prosody configuration")
self.add(builtin.copy(
src="roles/prosody/templates/firewall-ruleset.pfw",
dest="/etc/prosody/firewall-ruleset.pfw",
), name="write prosody firewall")
# ...
This files some general actions down the pipe, with a hook that says: when the
results of this action come back, run self.have_facts().
At that point, the role can use the results to build certbot command lines,
render prosody's configuration from Jinja2 templates, and use the results to
file further action down the pipe.
Note that this way, while the server is potentially still busy installing prosody, we're already streaming prosody's configuration to it.
If anything goes wrong with the installation of prosody's package, the role
will be marked as failed and all further actions of the same role, even those
filed by have_facts() will be skipped.
Notify and handlers
In the previous example self.notify() also appears: that's my attempt to
model the equivalent of Ansible's handlers. If any of the actions inside the
with produce changes, then the RestartProsody role will be executed,
potentially filing more actions ad the end of the playbook.
The runner will take care of collecting all the triggered role classes in a
set,
which discards duplicates, and then running
the main() method of all resulting roles, which will cause more actions to be
filed down the pipe.
Action conditions
Sometimes some actions are only meaningful as consequences of other actions.
Let's take, for example, enabling buster-backports as an extra apt source:
a = self.add(builtin.copy(
owner="root",
group="root",
mode=0o644,
dest="/etc/apt/sources.list.d/debian-buster-backports.list",
content="deb [arch=amd64] https://mirrors.gandi.net/debian/ buster-backports main contrib",
), name="enable backports")
self.add(builtin.apt(
update_cache=True
), name="update after enabling backports",
# Run only if the previous copy changed anything
when={a: ResultState.CHANGED},
)
Here we want to update Apt's cache, which is a slow operation, only after we
actually write /etc/apt/sources.list.d/debian-buster-backports.list. If the
file was already there from a previous run, we can skip downloading the new
package lists.
The when= attributes adds an annotation to the action that is sent town the
pipeline, that says that it should only be run if the state of a previous
action matches the given one.
In this case, when on the remote it's the turn of "update after enabling
backports", it gets skipped unless the state of the previous "enable backports"
action is CHANGED.
Effects of pipelining
I ported enough of Ansible's modules to be able to run the provisioning scripts of my VPS entirely via ansible.
This is the playbook run as plain Ansible:
$ time ansible-playbook vps.yaml
[...]
servername : ok=55 changed=1 unreachable=0 failed=0 skipped=0 rescued=0 ignored=0
real 2m10.072s
user 0m33.149s
sys 0m10.379s
This is the same playbook run with Ansible speeded up via the Mitogen backend, which makes Ansible more bearable:
$ export ANSIBLE_STRATEGY=mitogen_linear
$ time ansible-playbook vps.yaml
[...]
servername : ok=55 changed=1 unreachable=0 failed=0 skipped=0 rescued=0 ignored=0
real 0m24.428s
user 0m8.479s
sys 0m1.894s
This is the same playbook ported to Transilience:
$ time ./provision
[...]
real 0m2.585s
user 0m0.659s
sys 0m0.034s
Doing nothing went from 2 minutes down to 3 seconds!
That's the kind of running time that finally makes me comfortable with maintaining my VPS by editing the playbook only, and never logging in to mess with the system configuration by hand!
Next steps
I'm quite happy with what I have: I can now maintain my VPS with a simple script with quick iterative cycles.
I might use it to develop new playbooks, and port them to ansible only when they're tested and need to be shared with infrastructure that needs to rely on something more solid and battle tested than a prototype provisioning system.
I might also keep working on it as I have more interesting ideas that I'd like to try. I feel like Ansible reached some architectural limits that are hard to overcome without a major redesign, and are in many way hardcoded in its playbook configuration. It's nice to be able to try out new designs without that baggage.
I'd love it if even just the library of Transilience actions could grow, and gain widespread use. Ansible modules standardized a set of management operations, that I think became the way people think about system management, and should really be broadly available outside of Ansible.
If you are interesting in playing with Transilience, such as:
- polishing the packaging, adding a
setup.py, publishing to PIP, packaging in Debian - adding example playbooks
- porting more Ansible modules to Transilience actions
- improving the command line interface
- test other ways to feed actions to pipelines
- test other pipeline primitives
- add backends besides Local and Mitogen
- prototype a parser to turn a subsets of YAML playbook syntax into transilience actions
- adopt it into your multinational organization infrastructure to speed up provisioning times by orders of magnitude at the cost of the development time that it takes to turn this prototype into something solid and road tested
- create a startup and get millions in venture capital to disrupt the provisioning ecosystem
do get in touch or send a pull request! :)
Next step: Reimagining Ansible variables.
Use ansible actions in a script
This is part of a series of posts on ideas for an ansible-like provisioning system, implemented in Transilience.
I like many of the modules provided with Ansible: they are convenient, platform-independent implementations of common provisioning steps. They'd be fantastic to have in a library that I could use in normal programs.
This doesn't look easy to do with Ansible code as it is. Also, the code quality of various Ansible modules doesn't fit something I'd want in a standard library of cross-platform provisioning functions.
Modeling Actions
I want to keep the declarative, idempotent aspect of describing actions on a
system. A good place to start could be a hierarchy of
dataclasses that hold
the same parameters as ansible modules, plus a run() method that performs the
action:
@dataclass
class Action:
"""
Base class for all action implementations.
An Action is the equivalent of an ansible module: a declarative
representation of an idempotent operation on a system.
An Action can be run immediately, or serialized, sent to a remote system,
run, and sent back with its results.
"""
uuid: str = field(default_factory=lambda: str(uuid.uuid4()))
result: Result = field(default_factory=Result)
def summary(self):
"""
Return a short text description of this action
"""
return self.__class__.__name__
def run(self, system: transilience.system.System):
"""
Perform the action
"""
self.result.state = ResultState.NOOP
I like that Ansible tasks have names, and I hate having to give names to
trivial tasks like "Create directory /foo/bar", so I added a summary() method
so that trivial tasks like that can take care of naming themselves.
Dataclasses allow to introspect fields and annotate them with extra metadata, and together with docstrings, I can make actions reasonably self-documeting.
I ported some of Ansible's modules over: see complete list in the git repository.
Running Actions in a script
With a bit of glue code I can now run Ansible-style functions from a plain Python script:
#!/usr/bin/python3
from transilience.runner import Script
script = Script()
for i in range(10):
script.builtin.file(state="touch", path=f"/tmp/test{i}")
Running Actions remotely
Dataclasses have an asdict function that makes them trivially serializable. If their members stick to data types that can be serialized with Mitogen and the run implementation doesn't use non-pure, non-stdlib Python modules, then I can trivially run actions on all sorts of remote systems using Mitogen:
#!/usr/bin/python3
from transilience.runner import Script
from transilience.system import Mitogen
script = Script(system=Mitogen("my server", "ssh", hostname="machine.example.org", username="user"))
for i in range(10):
script.builtin.file(state="touch", path=f"/tmp/test{i}")
How fast would that be, compared to Ansible?
$ time ansible-playbook test.yaml
[...]
real 0m15.232s
user 0m4.033s
sys 0m1.336s
$ time ./test_script
real 0m4.934s
user 0m0.547s
sys 0m0.049s
With a network round-trip for each single operation I'm already 3x faster than Ansible, and it can run on nspawn containers, too!
I always wanted to have a library of ansible modules useable in normal scripts, and I've always been angry with Ansible for not bundling their backend code in a generic library. Well, now there's the beginning of one!
Sweet! Next step, pipelining.
My gripes with Ansible
This is part of a series of posts on ideas for an ansible-like provisioning system, implemented in Transilience.
Musing about Ansible
I like infrastructure as code.
I like to be able to represent an entire system as text files in a git repositories, and to be able to use that to recreate the system, from my Virtual Private Server, to my print server and my stereo, to build machines, to other kind of systems I might end up setting up.
I like that the provisioning work I do on a machine can be self-documenting and replicable at will.
The good
For that I quite like Ansible, in principle: simple (in theory) YAML files describe a system in (reasonably) high-level steps, and it can be run on (almost) any machine that happens to have a simple Python interpreter installed.
I also like many of the modules provided with Ansible: they are convenient, platform-independent implementations of common provisioning steps. They'd be fantastic to have in a library that I could use in normal programs.
The bad
Unfortunately, Ansible is slow. Running the playbook on my VPS takes about 3 whole minutes even if I'm just changing a line in a configuration file.
This means that most of the time, instead of changing that line in the playbook and running it, to then figure out after 3 minutes that it was the wrong line, or I made a spelling mistake in the playbook, I end up logging into the server and editing in place.
That defeats the whole purpose, but that level of latency between iterations is just unacceptable to me.
The ugly
I also think that Ansible has outgrown its original design, and the supposedly declarative, idempotent YAML has become a full declarative scripting language in disguise, whose syntax is extremely awkward and verbose.
If I'm writing declarative descriptions, YAML is great. If I'm writing loops and conditionals, I want to write code, not templated YAML.
I also keep struggling trying to use Ansible to provision chroots and nspawn containers.
A personal experiment: Transilience
There's another thing I like in Ansible: it's written in Python, which is a language I'm comfortable with. Compared to other platforms, it's one that I'm more likely to be able to control beyond being a simple user.
What if I can port Ansible modules into a library of high-level provisioning functions, that I can just run via normal Python scripts?
What if I can find a way to execute those scripts remotely and not just locally?
I've started writing some prototype code, and the biggest problem is, of course, finding a name.
Ansible comes from Ursula K. Le Guin's Hainish Cycle novels, where it is a device that allows its users to communicate near-instantaneously over interstellar distances. Traveling, however, is still constrained by the speed of light.
Later in the same universe, the novels A Fisherman of the Inland Sea and The Shobies' Story, talk about experiments with instantaneous interstellar travel, as a science Ursula Le Guin called transilience:
Transilience: n. A leap across or from one thing to another [1913 Webster]
Transilience. I like everything about this name.
Now that the hardest problem is solved, the rest is just a simple matter of implementation details.