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Domains
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=======
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One of our key design features in Takahē is that we support multiple different
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domains for ActivityPub users to be under.
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As a server administrator, you do this by specifying one or more Domains on
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your server that users can make Identities (posting accounts) under.
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Domains can take two forms:
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* **Takahē lives on and serves the domain**. In this case, you just set the domain
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to point to Takahē and ensure you have a matching domain record; ignore the
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"service domain" setting.
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* **Takahē handles accounts under the domain but does not live on it**. For
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example, you wanted to service the ``@andrew@aeracode.org`` handle, but there
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is already a site on ``aeracode.org``, and Takahē instead must live elsewhere
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(e.g. ``fedi.aeracode.org``).
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In this second case, you need to have a *service domain* - a place where
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Takahē and the Actor URIs for your users live, but which is different to your
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main domain you'd like the account handles to contain.
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To set this up, you need to:
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* Choose a service domain and point it at Takahē. *You cannot change this
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domain later without breaking everything*, so choose very wisely.
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* On your primary domain, forward the URLs ``/.well-known/webfinger``,
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``/.well-known/nodeinfo`` and ``/.well-known/host-meta`` to Takahē.
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* Set up a domain with these separate primary and service domains in its
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record.
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Technical Details
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-----------------
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At its core, ActivityPub is a system built around URIs; the
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``@username@domain.tld`` format is actually based on Webfinger, a different
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standard, and merely used to discover the Actor URI for someone.
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Making a system that allows any Webfinger handle to be accepted is relatively
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easy, but unfortunately this is only how users are discovered via mentions
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and search; when an incoming Follow comes in, or a Post is boosted onto your
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timeline, you have to discover the user's Webfinger handle
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*from their Actor URI* and this is where it gets tricky.
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Mastodon, and from what we can tell most other implementations, do this by
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taking the ``preferredUsername`` field from the Actor object, the domain from
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the Actor URI, and webfinger that combination of username and domain. This
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means that the domain you serve the Actor URI on must uniquely map to a
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Webfinger handle domain - they don't need to match, but they do need to be
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translatable into one another.
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Takahē handles all this internally, however, with a concept of Domains. Each
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domain has a primary (display) domain name, and an optional "service" domain;
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the primary domain is what we will use for the user's Webfinger handle, and
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the service domain is what their Actor URI is served on.
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We look at ``HOST`` headers on incoming requests to match users to their
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domains, though for Actor URIs we ensure the domain is in the URI anyway.
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@ -15,4 +15,5 @@ in alpha. For more information about Takahē, see
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:caption: Contents:
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installation
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principles
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domains
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stator
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@ -14,6 +14,7 @@ Prerequisites
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* SSL support (Takahē *requires* HTTPS)
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* Something that can run Docker/OCI images
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* A PostgreSQL 14 (or above) database
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* Hosting/reverse proxy that passes the ``HOST`` header down to Takahē
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* One of these to store uploaded images and media:
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* Amazon S3
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@ -28,7 +29,7 @@ This means that a "serverless" platform like AWS Lambda or Google Cloud Run is
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not enough by itself; while you can use these to serve the web pages if you
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like, you will need to run the Stator runner somewhere else as well.
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The flagship Takahē instance, [takahe.social](https://takahe.social), runs
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The flagship Takahē instance, `takahe.social <https://takahe.social>`_, runs
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inside of Kubernetes, with one Deployment for the webserver and one for the
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Stator runner.
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@ -1,59 +0,0 @@
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Design Principles
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=================
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Takahē is somewhat opinionated in its design goals, which are:
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* Simplicity of maintenance and operation
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* Multiple domain support
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* Asychronous Python core
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* Low-JS user interface
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These are explained more below, but it's important to stress the one thing we
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are not aiming for - scalability.
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If we wanted to build a system that could handle hundreds of thousands of
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accounts on a single server, it would be built very differently - queues
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everywhere as the primary communication mechanism, most likely - but we're
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not aiming for that.
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Our final design goal is for around 10,000 users to work well, provided you do
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some PostgreSQL optimisation. It's likely the design will work beyond that,
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but we're not going to put any specific effort towards it.
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After all, if you want to scale in a federated system, you can always launch
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more servers. We'd rather work towards the ability to share moderation and
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administration workloads across servers rather than have one giant big one.
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Simplicity Of Maintenance
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-------------------------
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It's important that, when running a social networking server, you have as much
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time to focus on moderation and looking after your users as you can, rather
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than trying to be an SRE.
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To this end, we use our deliberate design aim of "small to medium size" to try
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and keep the infrastructure simple - one set of web servers, one set of task
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runners, and a PostgreSQL database.
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The task system (which we call Stator) is not based on a task queue, but on
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a state machine per type of object - which have retry logic built in. The
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system continually examines every object to see if it can progress its state
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by performing an action, which is not quite as *efficient* as using a queue,
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but recovers much more easily and doesn't get out of sync.
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Multiple Domain Support
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-----------------------
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TODO
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Asynchronous Python
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-------------------
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TODO
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Low-JS User Interface
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---------------------
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Stator
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======
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Takahē's background task system is called Stator, and rather than being a
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transitional task queue, it is instead a *reconciliation loop* system; the
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workers look for objects that could have actions taken, try to take them, and
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update them if successful.
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As someone running Takahē, the most important aspects of this are:
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* You have to run at least one Stator worker to make things like follows,
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posting, and timelines work.
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* You can run as many workers as you want; there is a locking system to ensure
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they can coexist.
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* You can get away without running any workers for a few minutes; the server
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will continue to accept posts and follows from other servers, and will
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process them when a worker comes back up.
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* There is no separate queue to run, flush or replay; it is all stored in the
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main database.
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* If all your workers die, just restart them, and within a few minutes the
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existing locks will time out and the system will recover itself and process
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everything that's pending.
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You run a worker via the command ``manage.py runstator``. It will run forever
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until it is killed; send SIGINT (Ctrl-C) to it once to have it enter graceful
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shutdown, and a second time to force exiting immediately.
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Technical Details
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-----------------
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Each object managed by Stator has a set of extra columns:
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* ``state``, the name of a state in a state machine
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* ``state_ready``, a boolean saying if it's ready to have a transition tried
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* ``state_changed``, when it entered into its current state
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* ``state_attempted``, when a transition was last attempted
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* ``state_locked_until``, when the entry is locked by a worker until
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They also have an associated state machine which is a subclass of
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``stator.graph.StateGraph``, which will define a series of states, the
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possible transitions between them, and handlers that run for each state to see
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if a transition is possible.
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An object becoming ready for execution happens first:
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* If it's just entered into a new state, or just created, it is marked ready.
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* If ``state_attempted`` is far enough in the past (based on the ``try_interval``
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of the current state), a small scheduling loop marks it as ready.
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Then, in the main fast loop of the worker, it:
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* Selects an item with ``state_ready`` that is in a state it can handle (some
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states are "externally progressed" and will not have handlers run)
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* Fires up a coroutine for that handler and lets it run
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* When that coroutine exits, sees if it returned a new state name and if so,
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transitions the object to that state.
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* If that coroutine errors or exits with ``None`` as a return value, it marks
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down the attempt and leaves the object to be rescheduled after its ``try_interval``.
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