Deploying the Egress Service

When multiple Egress workers are deployed, they will automatically load-balance and ensure requests are distributed across worker instances.

Certain kinds of Egress operations can be resource-intensive. We recommend giving each Egress instance at least 4 CPUs and 4 GB of memory.

An Egress worker may process one or more jobs at once, depending on their resource requirements. For example, a TrackEgress job consumes minimal resources because it doesn't need to transcode. Consequently, hundreds of simulteneous TrackEgress jobs can run on a single instance.

The Egress service takes a yaml config file:

# required fields
api_key: livekit server api key. LIVEKIT_API_KEY env can be used instead
api_secret: livekit server api secret. LIVEKIT_API_SECRET env can be used instead
ws_url: livekit server websocket url. LIVEKIT_WS_URL can be used instead
redis:
  address: must be the same redis address used by your livekit server
  username: redis username
  password: redis password
  db: redis db

# optional fields
health_port: if used, will open an http port for health checks
prometheus_port: port used to collect prometheus metrics. Used for autoscaling
log_level: debug, info, warn, or error (default info)
template_base: can be used to host custom templates (default https://egress-composite.livekit.io/#)
insecure: can be used to connect to an insecure websocket (default false)

# file upload config - only one of the following. Can be overridden per-request
s3:
  access_key: AWS_ACCESS_KEY_ID env can be used instead
  secret: AWS_SECRET_ACCESS_KEY env can be used instead
  region: AWS_DEFAULT_REGION env can be used instead
  endpoint: optional custom endpoint
  bucket: bucket to upload files to
azure:
  account_name: AZURE_STORAGE_ACCOUNT env can be used instead
  account_key: AZURE_STORAGE_KEY env can be used instead
  container_name: container to upload files to
gcp:
  credentials_json: GOOGLE_APPLICATION_CREDENTIALS env can be used instead
  bucket: bucket to upload files to

The config file can be added to a mounted volume with its location passed in the EGRESS_CONFIG_FILE env var, or its body can be passed in the EGRESS_CONFIG_BODY env var.

These changes are not recommended for a production setup.

To run against a local livekit server, you'll need to do the following:

• open /usr/local/etc/redis.conf and comment out the line that says bind 127.0.0.1
• change protected-mode yes to protected-mode no in the same file
• find your IP as seen by docker
• ws_url needs to be set using the IP as Docker sees it
• on linux, this should be 172.17.0.1
• on mac or windows, run docker run -it --rm alpine nslookup host.docker.internal and you should see something like Name: host.docker.internal Address: 192.168.65.2

These changes allow the service to connect to your local redis instance from inside the docker container.

Create a directory to mount. In this example, we will use ~/livekit-egress.

Create a config.yaml in the above directory.

• redis and ws_url should use the above IP instead of localhost
• insecure should be set to true

log_level: debug
api_key: your-api-key
api_secret: your-api-secret
ws_url: ws://192.168.65.2:7880
insecure: true
redis:
  address: 192.168.65.2:6379

Then to run the service:

docker run --rm \
    -e EGRESS_CONFIG_FILE=/out/config.yaml \
    -v ~/livekit-egress:/out \
    livekit/egress

You can then use our cli to submit recording requests to your server.

If you already deployed the server using our Helm chart, jump to helm install below.

Ensure Helm is installed on your machine.

Add the LiveKit repo

$ helm repo add livekit https://helm.livekit.io

Create a values.yaml for your deployment, using egress-sample.yaml as a template. Each instance can record one room at a time, so be sure to either enable autoscaling, or set replicaCount >= the number of rooms you'll need to simultaneously record.

Then install the chart

$ helm install <instance_name> livekit/egress --namespace <namespace> --values values.yaml

We'll publish new version of the chart with new egress releases. To fetch these updates and upgrade your installation, perform

$ helm repo update
$ helm upgrade <instance_name> livekit/egress --namespace <namespace> --values values.yaml

Room Composite egress is limited to one per instance, and typically uses 2.5-3 CPUs. For this reason, it is recommended to use pods with 4 CPUs if you will be using room composite egress.

The livekit_egress_available Prometheus metric is also provided to support autoscaling. prometheus_port must be defined in your config. With this metric, each instance looks at its own CPU utilization and decides whether it is available to accept incoming requests. This can be more accurate than using average CPU or memory utilization, because requests are long-running and are resource intensive.

To keep at least 3 instances available:

sum(livekit_egress_available) > 3

To keep at least 30% of your egress instances available:

sum(livekit_egress_available)/sum(kube_pod_labels{label_project=~"^.*egress.*"}) > 0.3

There are 3 options for autoscaling: targetCPUUtilizationPercentage, targetMemoryUtilizationPercentage, and custom.

autoscaling:
  enabled: false
  minReplicas: 1
  maxReplicas: 5
#  targetCPUUtilizationPercentage: 60
#  targetMemoryUtilizationPercentage: 60
#  custom:
#    metricName: my_metric_name
#    targetAverageValue: 70

To use custom, you'll need to install the prometheus adapter. You can then create a kubernetes custom metric based off the livekit_egress_available prometheus metric.

You can find an example on how to do this here.