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Scaling Coder

Scaling Coder

We scale-test Coder with a built-in utility that can be used in your environment for insights into how Coder scales with your infrastructure.

General concepts

Coder runs workspace operations in a queue. The number of concurrent builds will be limited to the number of provisioner daemons across all coderd replicas.

  • coderd: Coder’s primary service. Learn more about Coder’s architecture
  • coderd replicas: Replicas (often via Kubernetes) for high availability, this is an enterprise feature
  • concurrent workspace builds: Workspace operations (e.g. create/stop/delete/apply) across all users
  • concurrent connections: Any connection to a workspace (e.g. SSH, web terminal, coder_app)
  • provisioner daemons: Coder runs one workspace build per provisioner daemon. One coderd replica can host many daemons
  • scaletest: Our scale-testing utility, built into the coder command line.
2 coderd replicas * 30 provisioner daemons = 60 max concurrent workspace builds

Infrastructure recommendations

Note: The below are guidelines for planning your infrastructure. Your mileage may vary depending on your templates, workflows, and users.

When planning your infrastructure, we recommend you consider the following:

  1. CPU and memory requirements for coderd. We recommend allocating 1 CPU core and 2 GB RAM per coderd replica at minimum. See Concurrent users for more details.
  2. CPU and memory requirements for external provisioners, if required. We recommend allocating 1 CPU core and 1 GB RAM per 5 concurrent workspace builds to external provisioners. Note that this may vary depending on the template used. See Concurrent workspace builds for more details. By default, coderd runs 3 integrated provisioners.
  3. CPU and memory requirements for the database used by coderd. We recommend allocating an additional 1 CPU core to the database used by Coder for every 1000 active users.
  4. CPU and memory requirements for workspaces created by Coder. This will vary depending on users' needs. However, the Coder agent itself requires at minimum 0.1 CPU cores and 256 MB to run inside a workspace.

Concurrent users

We recommend allocating 2 CPU cores and 4 GB RAM per coderd replica per 1000 active users. We also recommend allocating an additional 1 CPU core to the database used by Coder for every 1000 active users. Inactive users do not consume Coder resources, although workspaces configured to auto-start will consume resources when they are built.

Users' primary mode of accessing Coder will also affect resource requirements. If users will be accessing workspaces primarily via Coder's HTTP interface, we recommend doubling the number of cores and RAM allocated per user. For example, if you expect 1000 users accessing workspaces via the web, we recommend allocating 4 CPU cores and 8 GB RAM.

Users accessing workspaces via SSH will consume fewer resources, as SSH connections are not proxied through Coder.

Concurrent workspace builds

Workspace builds are CPU-intensive, as it relies on Terraform. Various Terraform providers have different resource requirements. When tested with our kubernetes template, coderd will consume roughly 0.25 cores per concurrent workspace build. For effective provisioning, our helm chart prefers to schedule one coderd replica per-node.

We recommend:

  • Running coderd on a dedicated set of nodes. This will prevent other workloads from interfering with workspace builds. You can use node selectors, or taints and tolerations to achieve this.
  • Disabling autoscaling for coderd nodes. Autoscaling can cause interruptions for users, see Autoscaling for more details.
  • (Enterprise-only) Running external provisioners instead of Coder's built-in provisioners (CODER_PROVISIONER_DAEMONS=0) will separate the load caused by workspace provisioning on the coderd nodes. For more details, see External provisioners.
  • Alternatively, if increasing the number of integrated provisioner daemons in coderd (CODER_PROVISIONER_DAEMONS>3), allocate additional resources to coderd to compensate (approx. 0.25 cores and 256 MB per provisioner daemon).

For example, to support 120 concurrent workspace builds:

  • Create a cluster/nodepool with 4 nodes, 8-core each (AWS: t3.2xlarge GCP: e2-highcpu-8)
  • Run coderd with 4 replicas, 30 provisioner daemons each. (CODER_PROVISIONER_DAEMONS=30)
  • Ensure Coder's PostgreSQL server can use up to 2 cores and 4 GB RAM

Recent scale tests

Note: the below information is for reference purposes only, and are not intended to be used as guidelines for infrastructure sizing.

EnvironmentCoder CPUCoder RAMCoder ReplicasDatabaseUsersConcurrent buildsConcurrent connections (Terminal/SSH)Coder VersionLast tested
Kubernetes (GKE)3 cores12 GB1db-f1-micro2003200 simulatedv0.24.1Jun 26, 2023
Kubernetes (GKE)4 cores8 GB1db-custom-1-38401500201,500 simulatedv0.24.1Jun 27, 2023
Kubernetes (GKE)2 cores4 GB1db-custom-1-384050020500 simulatedv0.27.2Jul 27, 2023
Kubernetes (GKE)2 cores4 GB2db-custom-2-76801000201000 simulatedv2.2.1Oct 9, 2023

Note: a simulated connection reads and writes random data at 40KB/s per connection.

Scale testing utility

Since Coder's performance is highly dependent on the templates and workflows you support, you may wish to use our internal scale testing utility against your own environments.

Note: This utility is intended for internal use only. It is not subject to any compatibility guarantees, and may cause interruptions for your users. To avoid potential outages and orphaned resources, we recommend running scale tests on a secondary "staging" environment. Run it against a production environment at your own risk.

Workspace Creation

The following command will run our scale test against your own Coder deployment. You can also specify a template name and any parameter values.

coder exp scaletest create-workspaces \
    --count 1000 \
    --template "kubernetes" \
    --concurrency 0 \
    --cleanup-concurrency 0 \
    --parameter "home_disk_size=10" \
    --run-command "sleep 2 && echo hello"

# Run `coder exp scaletest create-workspaces --help` for all usage

The test does the following:

  1. create 1000 workspaces
  2. establish SSH connection to each workspace
  3. run sleep 3 && echo hello on each workspace via the web terminal
  4. close connections, attempt to delete all workspaces
  5. return results (e.g. 998 succeeded, 2 failed to connect)

Concurrency is configurable. concurrency 0 means the scaletest test will attempt to create & connect to all workspaces immediately.

If you wish to leave the workspaces running for a period of time, you can specify --no-cleanup to skip the cleanup step. You are responsible for deleting these resources later.

Traffic Generation

Given an existing set of workspaces created previously with create-workspaces, the following command will generate traffic similar to that of Coder's web terminal against those workspaces.

coder exp scaletest workspace-traffic \
    --byes-per-tick 128 \
    --tick-interval 100ms \
    --concurrency 0

To generate SSH traffic, add the --ssh flag.

Cleanup

The scaletest utility will attempt to clean up all workspaces it creates. If you wish to clean up all workspaces, you can run the following command:

coder exp scaletest cleanup

This will delete all workspaces and users with the prefix scaletest-.

Autoscaling

We generally do not recommend using an autoscaler that modifies the number of coderd replicas. In particular, scale down events can cause interruptions for a large number of users.

Coderd is different from a simple request-response HTTP service in that it services long-lived connections whenever it proxies HTTP applications like IDEs or terminals that rely on websockets, or when it relays tunneled connections to workspaces. Loss of a coderd replica will drop these long-lived connections and interrupt users. For example, if you have 4 coderd replicas behind a load balancer, and an autoscaler decides to reduce it to 3, roughly 25% of the connections will drop. An even larger proportion of users could be affected if they use applications that use more than one websocket.

The severity of the interruption varies by application. Coder's web terminal, for example, will reconnect to the same session and continue. So, this should not be interpreted as saying coderd replicas should never be taken down for any reason.

We recommend you plan to run enough coderd replicas to comfortably meet your weekly high-water-mark load, and monitor coderd peak CPU & memory utilization over the long term, reevaluating periodically. When scaling down (or performing upgrades), schedule these outside normal working hours to minimize user interruptions.

A note for Kubernetes users

When running on Kubernetes on cloud infrastructure (i.e. not bare metal), many operators choose to employ a cluster autoscaler that adds and removes Kubernetes nodes according to load. Coder can coexist with such cluster autoscalers, but we recommend you take steps to prevent the autoscaler from evicting coderd pods, as an eviction will cause the same interruptions as described above. For example, if you are using the Kubernetes cluster autoscaler, you may wish to set cluster-autoscaler.kubernetes.io/safe-to-evict: "false" as an annotation on the coderd deployment.

Troubleshooting

If a load test fails or if you are experiencing performance issues during day-to-day use, you can leverage Coder's prometheus metrics to identify bottlenecks during scale tests. Additionally, you can use your existing cloud monitoring stack to measure load, view server logs, etc.

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