Continuous Delivery and software distributors

Back in 2010 the continuous delivery meme was just grabbing traction. Today its extremely well established… except in F/LOSS projects.

I want that to change, so I’m going to try and really bring together a technical view on how that could work – which may require multiple blog posts – and if it gets traction I’ll put my fingers where my thoughts are and get into specifics with any project that wants to do this.

This is however merely a worked model today: it may be possible to do things quite differently, and I welcome all discussion about the topic!


Pick a service discovery mechanism (e.g. environment variables), write two small APIs – one for flag delivery, with streaming updates, and one for telemetry, with an optional aggressive data hiding proxy, then use those to feed enough data to drive a true CI/CD cycle back to upstream open source projects.

Who is in?


(This assumes you know what C/D is – if you don’t, go read the link above, maybe wikipedia etc, then come back.)

Consider a typical SaaS C/D pipeline:

git -> build -> test -> deploy

Here all stages are owned by the one organisation. Once deployed, the build is usable by users – its basically the simplest pipeline around.

Now consider a typical on-premise C/D pipeline:

git -> build -> test -> expose -> install

Here the last stage, the install stage, takes place in the users context, but it may be under the control of the create, or it may be under the control of the user. For instance, Google play updates on an Android phone: when one selects ‘Update Now’, the install phase is triggered. Leaving the phone running with power and Wi-Fi will trigger it automatically, and security updates can be pushed anytime. Continuing the use of Google Play as an example, the expose step here is an API call to upload precompiled packages, so while there are three parties, the distributor – Google – isn’t performing any software development activities (they do gatekeep, but not develop).

Where it gets awkward is when there are multiple parties doing development in the pipeline.

Distributing and C/D

Lets consider an OpenStack cloud underlay circa 2015: an operating system, OpenStack itself, some configuration management tool (or tools), a log egress tool, a metrics egress handler, hardware mgmt vendor binaries. And lets say we’re working on something reasonably standalone. Say horizon.

OpenStack for most users is something obtained from a vendor. E.g. Cisco or Canonical or RedHat. And the model here is that the vendor is responsible for what the user receives; so security fixes – in particular embargoed security fixes – cannot be published publically and the slowly propogate. They must reach users very quickly. Often, ideally, before the public publication.

Now we have something like this:

upstream ends with distribution, then vendor does an on-prem pipeline

Can we not just say ‘the end of the C/D pipeline is a .tar.gz of horizon at the distribute step? Then every organisation can make their own decisions?


Why C/D?

  • Lower risk upgrades (smaller changes that can be reasoned about better; incremental enablement of new implementations to limit blast radius, decoupling shipping and enablement of new features)
  • Faster delivery of new features (less time dealing with failed upgrades == more time available to work on new features; finished features spend less time in inventory before benefiting users).
  • Better code hygiene (the same disciplines needed to make C/D safe also make more aggressive refactoring and tidiness changes safer to do, so it gets done more often).

1. If the upstream C/D pipeline stops at a tar.gz file, the lower-risk upgrade benefit is reduced or lost: the pipeline isn’t able to actually push all the to installation, and thus we cannot tell when a particular upgrade workaround is no longer needed.

But Robert, that is the vendors problem!

I wish it was: in OpenStack so many vendors had the same problem they created shared branches to work on it, then asked for shared time from the project to perform C/I on those branches. The benefit is only realise when the developer who is responsible for creating the issue can fix it, and can be sure that the fix has been delivered; this means either knowing that every install will install transiently every intermediary version, or that they will keep every workaround for every issue for some minimum time period; or that there will be a pipeline that can actually deliver the software.

2. .tar.gz files are not installed and running systems. A key characteristic of a C/D pipeline is that is exercises the installation and execution of software; the ability to run a component up is quite tightly coupled to the component itself, for all the the ‘this is a process’ interface is very general, the specific ‘this is server X’ or ‘this is CLI utility Y’ interfaces are very concrete. Perhaps a container based approach, where a much narrower interface in many ways can be defined, could be used to mitigate this aspect. Then even if different vendors use different config tools to do last mile config, the dev cycle knows that configuration and execution works. We need to make sure that we don’t separate the teams and their products though: the pipeline upstream must only test code that is relevant to upstream – and downstream likewise. We may be able to find a balance here, but I think more work articulating what that looks like it needed.

3. it will break the feedback cycle if the running metrics are not receive upstream; yes we need to be careful of privacy aspects, but basic telemetry: the upgrade worked, the upgrade failed, here is a crash dump – these are the tools for sifting through failure at scale, and a number of open source projects like firefox, Ubuntu and chromium have adopted them, with great success. Notably all three have direct delivery models: their preference is to own the relationship with the user and gather such telemetry directly.

C/D and technical debt

Sidebar: ignoring public APIs and external dependencies, because they form the contract that installations and end users interact with, which we can reasonably expect to be quite sticky, the rest of a system should be entirely up to the maintainers right? Refactor the DB; Switch frameworks, switch languages. Cleanup classes and so on. With microservices there is a grey area: APIs that other microservices use which are not publically supported.

The grey area is crucial, because it is where development drag comes in: anything internal to the system can be refactored in a single commit, or in a series of small commits that is rolled up into one, or variations on this theme.

But some aspect that another discrete component depends upon, with its own delivery cycle: that cannot be fixed, and unless it was built with the same care public APIs were, it may well have poor scaling or performance characteristics that making fixing it very important.

Given two C/D’d components A and B, where A wants to remove some private API B uses, A cannot delete that API from its git repo until all B’s everywhere that receive A via C/D have been deployed with a version that does not use the private API.

That is, old versions of B place technical debt on A across the interfaces of A that they use. And this actually applies to public interfaces too – even if they are more sticky, we can expect the components of an ecosystem to update to newer APIs that are cheaper to serve, and laggards hold performance back, keep stale code alive in the codebase for longer and so on.

This places a secondary requirement on the telemetry: we need to be able to tell whether the fleet is upgraded or not.

So what does a working model look like?

I think we need a different diagram than the pipeline; the pipeline talks about the things most folk doing an API or some such project will have directly in hand, but its not actually the full story. The full story is rounded out with two additional features. Feature flags and telemetry. And since we want to protect our users, and distributors probably will simply refuse to provide insights onto actual users, lets assume a near-zero-trust model around both.

Feature flags

As I discussed in my previous blog post, feature flags can be used for fairly arbitrary purposes, but in this situation, where trust is limited, I think we need to identify the crucial C/D enabling use cases, and design for them.

I think that those can be reduce to soft launches – decoupling activating new code paths from getting them shipped out onto machines, and kill switches – killing off flawed / faulty code paths when they start failing in advance of a massive cascade failure; which we can implement with essentially the same thing: some identifier for a code path and then a percentage of the deployed base to enable it on. If we define this API with efficient streaming updates and a consistent service discovery mechanism for the flag API, then this could be replicated by vendors and other distributors or even each user, and pull the feature API data downstream in near real time.


The difficulty with telemetry APIs is that they can egress anything. OTOH this is open source code, so malicious telemetry would be visible. But we can structure it to make it harder to violate privacy.

What does the C/D cycle need from telemetry, and what privacy do we need to preserve?

This very much needs discussion with stakeholders, but at a first approximation: the C/D cycle depends on knowing what versions are out there and whether they are working. It depends on known what feature flags have actually activated in the running versions. It doesn’t depend on absolute numbers of either feature flags or versions

Using Google Play again as an example, there is prior art – – but I want to think truely minimally, because the goal I have is to enable C/D in situations with vastly different trust levels than Google play has. However, perhaps this isn’t enough, perhaps we do need generic events and the ability to get deeper telemetry to enable confidence.

That said, let us sketch what an API document for that might look like:

- name:

If that was reported by every deployed instance of a project, once per hour, maybe with a dependencies version list added to deal with variation in builds, it would trivially reveal the cardinality of reporters. Many reporters won’t care (for instance QA testbeds). Many will.

If we aggregate through a cardinality hiding proxy, then that vector is addressed – something like this:

- project:
  - name:
- project: ...

Because this data is really only best effort, such a proxy could be backed by memcache or even just an in-memory store, depending on what degree of ‘cloud-nativeness’ we want to offer. It would receive accurate data, then deduplicate to get relative weights, round those to (say) 5% as a minimum to avoid disclosing too much about long tail situations (and yes, the sum of 100 1% reports would exceed 100 :)), and then push that up.

Open Questions

  • Should library projects report, or are they only used in the context of an application/service?
    • How can we help library projects answer questions like ‘has every user stopped using feature Y so that we can finally remove it’ ?
  • Would this be enough to get rid of the fixation on using stable branches everyone seems to have?
    • If not why not?
  • What have I forgotten?


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