Threads share the memory of their parent process. Different processes allocate their own memory, though they share some memory via copy-on-write. A larger number of threads maxes out your CPU first, and a larger number of processes maxes out your RAM first.
The streaming API handles long-lived HTTP and WebSockets connections, through which clients receive real-time updates. The following environment variables control it:
-`STREAMING_API_BASE_URL` controls the base URL of the streaming API
-`PORT` controls the port the streaming server will listen on, by default 4000. The `BIND` and `SOCKET` environment variables are also able to be used.
The streaming API can use a different subdomain if you want to by setting `STREAMING_API_BASE_URL`. This allows you to have one load balancer for streaming and one for web/API requests. However, this also requires applications to correctly request the streaming URL from the [instance endpoint](/methods/instance/#v2), instead of assuming that it's hosted on the same host as the Web API.
One process of the streaming server can handle a reasonably high number of connections and throughput, but if you find that a single process isn't handling your instance's load, you can run multiple processes by varying the `PORT` number of each, and then using nginx to load balance traffic to each of those instances. For example, a community of about 50,000 accounts with 10,000-20,000 monthly active accounts, you'll typically have an average concurrent load of about 800-1200 streaming connections.
The streaming server also exposes a [Prometheus](https://prometheus.io/) endpoint on `/metrics` with a lot of metrics to help you understand the current load on your mastodon streaming server, some key metrics are:
*`mastodon_streaming_connected_clients`: This is the number of connected clients, tagged by client type (websocket or eventsource)
*`mastodon_streaming_connected_channels`: This is the number of "channels" that are currently subscribed (note that this is much higher than connected clients due to how our internal "system" channels currently work)
*`mastodon_streaming_messages_sent_total`: This is the total number of messages sent to clients since last restart.
*`mastodon_streaming_redis_messages_received_total`: This is the number of messages received from Redis pubsub, and intended to complement [monitoring Redis directly](https://sysdig.com/blog/redis-prometheus/).
The more streaming server processes that you run, the more database connections will be consumed on PostgreSQL, so you'll likely want to use PgBouncer, as documented below.
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An example nginx configuration to route traffic to three different processes on `PORT` 4000, 4001, and 4002 is as follows:
By default, `sudo systemctl start mastodon-streaming` starts just one process on port 4000, equivalent to running `sudo systemctl start mastodon-streaming@4000.service`.
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Previous versions of Mastodon had a `STREAMING_CLUSTER_NUM` environment variable that made the streaming server use clustering, which started mulitple workers processes and used node.js to load balance them.
This interacted with the other settings in ways which made capacity planning difficult, especially when it comes to database connections and CPU resources. By default the streaming server would consume resources on all available CPUs which could cause contention with other software running on that server. Another common issue was that misconfiguring the `STREAMING_CLUSTER_NUM` would exhaust your database connections by opening up a connection pool per cluster worker process, so a `STREAMING_CLUSTER_NUM` of `5` and `DB_POOL` of `10` would potentially consume 50 database connections.
Now a single streaming server process will only use at maximum `DB_POOL` PostgreSQL connections, and scaling is handled by running more instances of the streaming server.
Many tasks in Mastodon are delegated to background processing to ensure the HTTP requests are fast, and to prevent HTTP request aborts from affecting the execution of those tasks. Sidekiq is a single process, with a configurable number of threads.
While the amount of threads in the web process affects the responsiveness of the Mastodon instance to the end-user, the amount of threads allocated to background processing affects how quickly posts can be delivered from the author to anyone else, how soon e-mails are sent out, etc.
The number of threads is not regulated by an environment variable, but rather through a command line argument when invoking Sidekiq, as shown in the following example:
This would initiate the Sidekiq process with 15 threads. It's important to note that each thread requires a database connection, necessitating a sufficiently large database pool. The size of this pool is managed by the DB_POOL environment variable, which should be set to a value at least equal to the number of threads.
Sidekiq uses different queues for tasks of varying importance, where importance is defined by how much it would impact the user experience of your server’s local users if the queue wasn’t working, in order of descending importance:
The default queues and their priorities are stored in [config/sidekiq.yml](https://github.com/mastodon/mastodon/blob/main/config/sidekiq.yml), but can be overridden by the command-line invocation of Sidekiq, e.g.:
Sidekiq processes queues by first checking for tasks in the first queue, and if it finds none, it then checks the subsequent queue. Consequently, if the first queue is overfilled, tasks in the other queues may experience delays.
As a solution, it is possible to start different Sidekiq processes for the queues to ensure truly parallel execution, by e.g. creating multiple systemd services for Sidekiq with different arguments.
If you start running out of available PostgreSQL connections (the default is 100) then you may find PgBouncer to be a good solution. This document describes some common gotchas as well as good configuration defaults for Mastodon.
User roles with `DevOps` permissions in Mastodon can monitor the current usage of PostgreSQL connections through the PgHero link in the Administration view. Generally, the number of connections open is equal to the total threads in Puma, Sidekiq, and the streaming API combined.
As long as you specify a user/password in pgbouncer.ini later, the values in userlist.txt do _not_ have to correspond to real PostgreSQL roles. You can arbitrarily define users and passwords, but you can reuse the “real” credentials for simplicity’s sake. Add the `mastodon` user to the `userlist.txt`:
```text
"mastodon" "md5d75bb2be2d7086c6148944261a00f605"
```
Here we’re using the md5 scheme, where the md5 password is just the md5sum of `password + username` with the string `md5` prepended. For instance, to derive the hash for user `mastodon` with password `password`, you can do:
```bash
# ubuntu, debian, etc.
echo -n "passwordmastodon" | md5sum
# macOS, openBSD, etc.
md5 -s "passwordmastodon"
```
Then just add `md5` to the beginning of that.
You’ll also want to create a `pgbouncer` admin user to log in to the PgBouncer admin database. So here’s a sample `userlist.txt`:
Add a line under `[databases]` listing the PostgreSQL databases you want to connect to. Here we’ll just have PgBouncer use the same username/password and database name to connect to the underlying PostgreSQL database:
Mastodon requires a different pooling mode than the default session-based one. Specifically, it needs a transaction-based pooling mode. This means that a PostgreSQL connection is established at the start of a transaction and terminated upon its completion. Therefore, it's essential to change the `pool_mode` setting from `session` to `transaction`:
Next up, `max_client_conn` defines how many connections PgBouncer itself will accept, and `default_pool_size` puts a limit on how many PostgreSQL connections will be opened under the hood. (In PgHero the number of connections reported will correspond to `default_pool_size` because it has no knowledge of PgBouncer.)
You cannot use PgBouncer to perform `db:migrate` tasks. But this is easy to work around. If your PostgreSQL and PgBouncer are on the same host, it can be as simple as defining `DB_PORT=5432` together with `RAILS_ENV=production` when calling the task, for example: `RAILS_ENV=production DB_PORT=5432 bundle exec rails db:migrate` (you can specify `DB_HOST` too if it’s different, etc)
Redis is used widely throughout the application, but some uses are more important than others. Home feeds, list feeds, and Sidekiq queues as well as the streaming API are backed by Redis and that’s important data you wouldn’t want to lose (even though the loss can be survived, unlike the loss of the PostgreSQL database - never lose that!). However, Redis is also used for volatile cache. If you are at a stage of scaling up where you are worried about whether your Redis can handle everything, you can use a different Redis database for the cache. In the environment, you can specify `CACHE_REDIS_URL` or individual parts like `CACHE_REDIS_HOST`, `CACHE_REDIS_PORT` etc. Unspecified parts fallback to the same values as without the cache prefix.
Additionally, Redis is used for volatile caching. If you're scaling up and concerned about Redis's capacity to handle the load, you can allocate a separate Redis database specifically for caching. To do this, set `CACHE_REDIS_URL` in the environment, or define individual components such as `CACHE_REDIS_HOST`, `CACHE_REDIS_PORT`, etc.
Unspecified components will default to their values without the cache prefix.
When configuring the Redis database for caching, it's possible to disable background saving to disk, as data loss on restart is not critical in this context, and this can save some disk I/O. Additionally, consider setting a maximum memory limit and implementing a key eviction policy. For more details on these configurations, refer to this guide:[Using Redis as an LRU cache](https://redis.io/topics/lru-cache)
Redis is used in Sidekiq to keep track of its locks and queue. Although in general the performance gain is not that big, some instances may benefit from having a seperate Redis instance for Sidekiq.
In the environment file, you can specify `SIDEKIQ_REDIS_URL` or individual parts like `SIDEKIQ_REDIS_HOST`, `SIDEKIQ_REDIS_PORT` etc. Unspecified parts fallback to the same values as without the `SIDEKIQ_` prefix.
Creating a seperate Redis instance for Sidekiq is relatively simple:
Start by making a copy of the default redis systemd service:
In this `redis-sidekiq.conf` file, change the following values:
```bash
port 6479
pidfile /var/run/redis/redis-server-sidekiq.pid
logfile /var/log/redis/redis-server-sidekiq.log
dir /var/lib/redis-sidekiq
```
Before starting the new Redis instance, create a data directory:
```bash
mkdir /var/lib/redis-sidekiq
chown redis /var/lib/redis-sidekiq
```
Start the new Redis instance:
```bash
systemctl enable --now redis-sidekiq
```
Update your environment, add the following line:
```bash
SIDEKIQ_REDIS_URL=redis://127.0.0.1:6479/
```
Restart Mastodon to use the new Redis instance, make sure to restart both web and Sidekiq (otherwise, one of them will still be working from the wrong instance):
To reduce the load on your PostgreSQL server, you may wish to set up hot streaming replication (read replica). [See this guide for an example](https://cloud.google.com/community/tutorials/setting-up-postgres-hot-standby).
### Mastodon >= 4.2
Mastodon has built-in replica support starting with version 4.2. You can use the same configuration for every service (Sidekiq included), and some queries will be directed to your read-only replica, when possible, using Rails's built-in replica support. If your replica is lagging behind for more than a few seconds, then the app will stop sending it queries until it catches up.
To configure it, use the following environment variables:
```
REPLICA_DB_HOST
REPLICA_DB_PORT
REPLICA_DB_NAME
REPLICA_DB_USER
REPLICA_DB_PASS
```
Alternatively, you can also use `REPLICA_DATABASE_URL` if you want to configure them all using the same variable.
Once done, this is all good and you should start seeing requests against your replica server!
### Mastodon <= 4.1
For Mastodon versions before 4.2, you can make use of the replica in Mastodon in these ways:
* The streaming API server does not issue writes at all, so you can connect it straight to the replica (it is not querying the database very often anyway, so the impact of this is small).
* Use the Makara driver in the web and Sidekiq processes, so that writes go to the master database, while reads go to the replica. Let’s talk about that.
Make sure the URLs point to wherever your PostgreSQL servers are. You can add multiple replicas. You could have a locally installed PgBouncer with a configuration to connect to two different servers based on the database name, e.g. “mastodon” going to the primary, “mastodon_replica” going to the replica, so in the file above both URLs would point to the local PgBouncer with the same user, password, host and port, but different database name. There are many possibilities how this could be set up! For more information on Makara, [see their documentation](https://github.com/taskrabbit/makara#databaseyml).
Cloud providers like DigitalOcean, AWS, Hetzner, etc., offer virtual load balancing solutions that distribute network traffic across multiple servers, but provide a single public IP address.
Scaling your deployment to provision multiple web/Puma servers behind one of these virtual load balancers can help provide more consistent performance by reducing the risk that a single server may become overwhelmed by user traffic, and decrease downtime when performing maintenance or upgrades. You should consult your provider documentation on how to setup and configure a load balancer, but consider that you need to configure your load balancer to monitor the health of the backend web/Puma nodes, otherwise you may send traffic to a service that is not responsive.
The following endpoints are available to monitor for this purpose:
- **Web/Puma:** `/health`
- **Streaming API:** `/api/v1/streaming/health`
These endpoints should both return an HTTP status code of 200, and the text `OK` as a result.
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You can also use these endpoints for health checks with a third-party monitoring/alerting utility.