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Edward Angert
e5ba8b7912
from @jatcod3r on Slack: > for the AWS recs on our [validated arch](https://coder.com/docs/admin/infrastructure/validated-architectures/1k-users) docs, should we be referencing customers to use non-T type instances? > Once you've exceeded EC2's [CPU credits](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/burstable-performance-instances.html) Coder starts performing poorly. > We do suggest to [scale for peak demand](https://coder.com/docs/tutorials/best-practices/scale-coder#scaling-3), so does recommending something from the [cpu](https://aws.amazon.com/ec2/instance-types/#Compute_Optimized) or [memory optimized](https://aws.amazon.com/ec2/instance-types/#Memory_Optimized) types make sense? [preview](https://coder.com/docs/@aws-ec2-arch/admin/infrastructure/validated-architectures#aws-instance-types) --------- Co-authored-by: EdwardAngert <17991901+EdwardAngert@users.noreply.github.com>
59 lines
2.7 KiB
Markdown
59 lines
2.7 KiB
Markdown
# Reference Architecture: up to 1,000 users
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The 1,000 users architecture is designed to cover a wide range of workflows.
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Examples of subjects that might utilize this architecture include medium-sized
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tech startups, educational units, or small to mid-sized enterprises.
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**Target load**: API: up to 180 RPS
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**High Availability**: non-essential for small deployments
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## Hardware recommendations
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### Coderd nodes
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| Users | Node capacity | Replicas | GCP | AWS | Azure |
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|-------------|---------------------|--------------------------|-----------------|------------|-------------------|
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| Up to 1,000 | 2 vCPU, 8 GB memory | 1-2 nodes, 1 coderd each | `n1-standard-2` | `m5.large` | `Standard_D2s_v3` |
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**Footnotes**:
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- For small deployments (ca. 100 users, 10 concurrent workspace builds), it is
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acceptable to deploy provisioners on `coderd` nodes.
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### Provisioner nodes
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| Users | Node capacity | Replicas | GCP | AWS | Azure |
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|-------------|----------------------|-------------------------------|------------------|--------------|-------------------|
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| Up to 1,000 | 8 vCPU, 32 GB memory | 2 nodes, 30 provisioners each | `t2d-standard-8` | `c5.2xlarge` | `Standard_D8s_v3` |
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**Footnotes**:
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- An external provisioner is deployed as Kubernetes pod.
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### Workspace nodes
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| Users | Node capacity | Replicas | GCP | AWS | Azure |
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|-------------|----------------------|------------------------------|------------------|--------------|-------------------|
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| Up to 1,000 | 8 vCPU, 32 GB memory | 64 nodes, 16 workspaces each | `t2d-standard-8` | `m5.2xlarge` | `Standard_D8s_v3` |
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**Footnotes**:
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- Assumed that a workspace user needs at minimum 2 GB memory to perform. We
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recommend against over-provisioning memory for developer workloads, as this my
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lead to OOMKiller invocations.
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- Maximum number of Kubernetes workspace pods per node: 256
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### Database nodes
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| Users | Node capacity | Replicas | Storage | GCP | AWS | Azure |
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|-------------|---------------------|----------|---------|--------------------|---------------|-------------------|
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| Up to 1,000 | 2 vCPU, 8 GB memory | 1 node | 512 GB | `db-custom-2-7680` | `db.m5.large` | `Standard_D2s_v3` |
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**Footnotes for AWS instance types**:
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- For production deployments, we recommend using non-burstable instance types,
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such as `m5` or `c5`, instead of burstable instances, such as `t3`.
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Burstable instances can experience significant performance degradation once
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CPU credits are exhausted, leading to poor user experience under sustained load.
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