After a long winter break from recreational programming, over the past days I finally built up steam and broke a chunk of new ground on Mitogen, this time growing its puny module forwarder into a bona fide beast, ready to handle almost any network condition and user code thrown at it.

No adversary is a match for the BFG





Recap

Mitogen is a library for executing parts of a Python program in a remote context, primarily over sudo and SSH connections, and establishing bidirectional communication with those parts. Targeting infrastructure applications, it requires no upfront configuration of target machines, aside from an SSH daemon and Python 2.x interpreter, which is the default for almost every Linux machine found on any conceivable network.

The target need not possess a writeable filesystem, code is loaded dynamically on demand, and execution occurs entirely from RAM.

How Import Works

To implement dynamic loading, child Python processes (“contexts”) have a PEP-302 import hook installed that causes attempts to import modules unavailable locally to automatically be served over the network connection to the parent process. For example, in a script like:

import mitogen import requests def get_url(url): return requests.get(url).text @mitogen.main() def main(router): host = router.ssh(hostname='k3') print host.call(get_url, 'https://www.google.com/')

If the requests package is missing on the host k3 , it will automatically be copied and imported in RAM, without requiring upfront configuration, or causing or requiring writes to the remote filesystem.

Kathmandu to Paris via 3G: serious business

So far, so good. Just one hitch

While the loader has served well over the library’s prototypical life (which in real time, is approaching 12 years!), it has always placed severe limits on the structure of the loaded code, as each additional source file introduced one network round-trip to serve it.

Given a relatively small dependency such as Kenneth Reitz’ popular Requests package, comprising 17 submodules, this means 17 additional network round-trips. While that may not mean much over a typical local area network segment where roundtrips are measured in microseconds, it quickly multiplies over even modest wide-area networks, where infrastructure tooling is commonly deployed.

For a library like Requests, 17 round-trips amounts to 340ms latency over a reasonably local 20ms link, which is comfortably within the realms of acceptable, however over common radio and international links of 200ms or more, already this adds at least 3.4 seconds to the startup cost of any Mitogen program, time wasted doing nothing but waiting on the network.

Sadly, Requests is hardly even the biggest dependency Mitogen can expect to encounter. For testing I chose django.db.models as a representative baseline: heavily integrated with all of Django, it transitively imports over 160 modules across numerous subpackages. That means on an international link, over 30 seconds of startup latency spent on one dependency.

It is worth note that Django is not something I’d expect to see in a typical Mitogen program, it’s simply an extraordinarily worst-case target worth hitting. If Mitogen can handle django.db.models , it should cope with pretty much anything.

Combining evils, over an admittedly better-than-average Nepali mobile data network, and an international link to my IRC box mail server in Paris, django.db.models takes almost 60 seconds to load with the old design.

In the real world, this one-file-per-roundtrip characteristic means the current approach sucks almost as much as Ansible does, which calls into doubt my goal of implementing an Ansible-trumping Ansible connection plug-in. Clearly something must give!





50.35 seconds and hundreds of roundtrips spent transferring django.db.models from Kathmandu to Paris via 3G. Despite a fast link, throughput averages 13KiB/sec and never exceeds 45KiB/sec. Well over half of the 989 frames sent are wasted on signalling (Y=0)





Trying harder

Over the years I discarded many approaches for handling this latency nightmare:

Having the user explicitly configure a module list to deliver upfront to new contexts, which sucks and is plainly unmaintainable. Installing a PEP-302 hook in the master in order to observe the import graph, which would be technically exciting, but likely to suck horribly due to fragility and inevitable interference with real PEP-302 hooks, such as py2exe. Observing the import graph caused by a function call in a single context, then using it to preload modules in additional contexts. This seems workable, except the benefit would only be felt by multiple-child Mitogen programs. Single child programs would continue to pay the latency tax. Variants of 2 and 3, except caching the result as intermediate state in the master’s filesystem. Ignoring the fact persistent intermediate state is always evil (a topic for later!), that would require weird and imperfect invalidation rules, which means performance would suck during development and prototyping, and bugs are possible where state gets silently wedged and previously working programs inexplicably slow down.

Finally last year I settled on using static analysis, and restricting preloading at package boundaries. When a dependency is detected in a package external to the one being requested, it is not preloaded until the child has demonstrated, by requesting the top-level package module from its parent, that the child lacks all of the submodules contained by it.

This seems like a good rule: preloading can occur aggressively within a package, but must otherwise wait for a child to signal a package as missing before preemptively wasting time and bandwidth delivering code the child never needed.

As a final safeguard, preloading is restricted to only modules the master itself loaded. It is not sufficient for an import statement to exist: surrounding conditional logic must have caused the module to be loaded by the master. In this manner the semantics of platform, version-specific and lazy imports are roughly preserved.

Syntax tree hell

Quite predictably, after attempting to approach the problem with regexes, I threw my hands up on realizing a single regex may not handle every possible import statement:

import a

import a as b

from a import b

from a import b as c

from a import (b, c, d)

I gleefully thought I’d finally found a use for the compiler and ast modules, and these were the obvious alternative to avoiding the rats nest of multiple regexes. Not quite. You see, across Python releases the grammar has changed, and in lock-step so have the representations exported by the compiler and ast modules.

Adding insult to injury: neither module is supported through every interesting Python version. I have seen Python 2.4 deployed commercially as recently as summer 2016, and therefore consider it mandatory for the kind of library I want on my toolbelt. To support antique and chic Python alike, it was necessary to implement both approaches and select one at runtime. Many might see this is an opportunity to drop 2.4, but “just upgrade lol” is never a good answer while maintaining long shelf-life systems, and should never be a a barrier to applying a trusted Swiss Army Knife.

After some busy days last September, I had a working scanner built around syntax trees, except for a tiny problem: it was ridiculously slow. Parsing the 8KiB mitogen.core module took 12ms on my laptop, which multiplied up is over a second of CPU burnt scanning dependencies for a package like Django. If memory serves, reality was closer to 3 seconds: far exceeding the latency saved while talking to a machine on a LAN.

Sometimes hacking bytecode make perfect sense

I couldn’t stop groaning the day I abandoned ASTs. As is often true when following software industry best practice, we are left holding a decomposing trout that, while technically fulfilling its role, stinks horribly, costs all involved a fortune to support and causes pains worse than those it was intended to relieve. Still hoping to avoid regexes, I went digging for precedent elsewhere in tools dealing with the same problem.

That’s when I discovered the strange and unloved modulefinder buried in the standard library, a forgotten relic from a bygone era, seductively deposited there as a belated Christmas gift to all, on a gloomy New Year’s Eve 2002 by Guido’s own brother. Diving in, I was shocked and mesmerized to find dependencies synthesized by recompiling each module and extracting IMPORT_FROM opcodes from the compiled bytecode. Reimplementing a variant, I was overjoyed to discover django.db.models transitive dependencies enumerated in under 350ms on my laptop. A workable solution!

The solution has some further crazy results: IMPORT_FROM has barely changed since the Python 2.4 days, right through to Python 3.x. The same approach works everywhere, including PyPy, which uses the same format, which makes this more portable than the ast and compiler modules!

Coping with concurrency

Now a mechanism exists to enumerate dependencies, we need a mode of delivery. The approach used is simplistic, and (as seen later), will likely require future improvement.

On receiving a GET_MODULE message from a child, a parent (don’t forget, Mitogen operates recursively!) first tries to satisfy the request from its own cache, before forwarding it upwards towards the master. The master sends LOAD_MODULE messages for all dependencies known to be missing from the child before sending a final message containing the module that was actually requested. Since contexts always cache unsolicited LOAD_MODULE messages from upstream, by the time the message arrives for the requested module, many dependencies should be in RAM and no further network roundtrips requesting them are required.

Meanwhile for each stream connected to any parent, a set of module names ever delivered on that stream are recorded. Each parent is allowed to ignore any GET_MODULE for which a corresponding LOAD_MODULE has already been sent, preventing a race between in-flight requests causing the same module to ever be sent twice.

This places the onus on downstream contexts to ensure the single LOAD_MODULE message received for each distinct module always reaches every interested party. In short, GET_MODULE messages must be deduplicated and synchronized not only for any arriving from a context’s children, but also from its own threads.

Some further gory details are in the docs

Pretty pictures

And finally the result. For my test script, the total number of roundtrips dropped from 166 to 13, one of which is for the script itself, and 3 negative requests for extension modules that cannot be transferred. That leaves, bugs aside, 9 roundtrips to transfer the most obscene dependency I could think of.

One more look at the library’s network profile. Over the same connection as previously, the situation has improved immensely:

570 packets sending Django 7,231km over 3G in 16.47 seconds. Throughput averages 38.6KiB/sec and peaks at 2.7MiB/sec





Not only is performance up, but the number of frames transmitted has dropped by 42%. That’s a 42% fewer changes of connection hang due to crappy WiFi!

One final detail is visible: around the 10 second mark, a tall column of frames is sent with progressively increasing size, almost in the same instant. This is not some bug, it is Path MTU Discovery (PMTUD) in action. PMTUD is a mechanism by which IP subprotocols can learn the maximum frame size tolerated by the path between communicating peers, which in turn maximizes link efficiency by minimizing bandwidth wasted on headers. The size is ramped up until either loss occurs or an intermediary signals error via ICMP.

Just like the network path, PMTUD is dynamic and must restart on any signal indicating network conditions have changed. Comparing this graph with the previous, we see one final improvement as a result of providing the network layer enough data to do its job: PMTUD appears restart much less frequently, and the stream is pegged at the true path MTU for much longer.

Futures

Aside from simple fixes to reduce wasted roundtrips for extension modules that can’t be imported, and optional imports of top-level packages that don’t exist on the master, there are two major niggles remaining in how import works today.

The first is an irritating source of latency present in deep trees: currently it is impossible for intermediary nodes satisfying GET_MODULE requests for children to streamily send preloaded modules towards a child until the final LOAD_MODULE arrives at the intermediary for the module actually requested by the child. That means preloading is artificially serialized at each layer in the tree, when a better design would allow it to progress concurrent to the LOAD_MODULE messages still in-flight from the master.

This will present itself when doing multi-machine hops where links between the machines are slow or suffer high latency. It will also be important to fix before handling hundreds to thousands of children, such as should become practical once asynchronous connect() is implemented.

There are various approaches to tweaking the design so that concurrency is restored, but I would like to let the paint dry a little on the new implementation before destablizing it again.

The second major issue is almost certainly a bug waiting to be discovered, but I’m out of energy to attack it right now. It relates to complex situations where many children have different functions invoked in them, from a complex set of overlapping packages. In such cases, it is possible that a LOAD_MODULE for an unrelated GET_MODULE prematurely delivers the final module from another import, before it has had all requisite modules preloaded into the child.

To fix that, the library must ensure the tree of dependencies for all module requests are sent downstream depth-first, i.e. it is never possible for any module to appear in a LOAD_MODULE before all of its dependencies have first.

Finally there are latency sources buried elsewhere in the library, including at least 2 needless roundtrips during connection setup. Fighting latency is an endless war, but with module loading working efficiently, the most important battle is over.