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Scaling synapse via workers
---------------------------
Synapse has experimental support for splitting out functionality into
multiple separate python processes, helping greatly with scalability. These
processes are called 'workers', and are (eventually) intended to scale
horizontally independently.
All processes continue to share the same database instance, and as such, workers
only work with postgres based synapse deployments (sharing a single sqlite
across multiple processes is a recipe for disaster, plus you should be using
postgres anyway if you care about scalability).
The workers communicate with the master synapse process via a synapse-specific
HTTP protocol called 'replication' - analogous to MySQL or Postgres style
database replication; feeding a stream of relevant data to the workers so they
can be kept in sync with the main synapse process and database state.
To enable workers, you need to add a replication listener to the master synapse, e.g.::
listeners:
- port: 9092
bind_address: ''
type: http
tls: false
x_forwarded: false
resources:
- names: [replication]
compress: false
You then create a set of configs for the various worker processes. These should be
worker configuration files should be stored in a dedicated subdirectory, to allow
synctl to manipulate them.
The current available worker applications are:
* synapse.app.pusher - handles sending push notifications to sygnal and email
* synapse.app.synchrotron - handles /sync endpoints. can scales horizontally through multiple instances.
* synapse.app.appservice - handles output traffic to Application Services
* synapse.app.federation_reader - handles receiving federation traffic (including public_rooms API)
* synapse.app.media_repository - handles the media repository.
Each worker configuration file inherits the configuration of the main homeserver
configuration file. You can then override configuration specific to that worker,
e.g. the HTTP listener that it provides (if any); logging configuration; etc.
You should minimise the number of overrides though to maintain a usable config.
You must specify the type of worker application (worker_app) and the replication
endpoint that it's talking to on the main synapse process (worker_replication_url).
For instance::
worker_app: synapse.app.synchrotron
# The replication listener on the synapse to talk to.
worker_replication_url: http://127.0.0.1:9092/_synapse/replication
worker_listeners:
- type: http
port: 8083
resources:
- names:
- client
worker_daemonize: True
worker_pid_file: /home/matrix/synapse/synchrotron.pid
worker_log_config: /home/matrix/synapse/config/synchrotron_log_config.yaml
...is a full configuration for a synchotron worker instance, which will expose a
plain HTTP /sync endpoint on port 8083 separately from the /sync endpoint provided
by the main synapse.
Obviously you should configure your loadbalancer to route the /sync endpoint to
the synchotron instance(s) in this instance.
Finally, to actually run your worker-based synapse, you must pass synctl the -a
commandline option to tell it to operate on all the worker configurations found
in the given directory, e.g.::
synctl -a $CONFIG/workers start
Currently one should always restart all workers when restarting or upgrading
synapse, unless you explicitly know it's safe not to. For instance, restarting
synapse without restarting all the synchotrons may result in broken typing
notifications.
To manipulate a specific worker, you pass the -w option to synctl::
synctl -w $CONFIG/workers/synchotron.yaml restart
All of the above is highly experimental and subject to change as Synapse evolves,
but documenting it here to help folks needing highly scalable Synapses similar
to the one running matrix.org!