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docs: provide hardware recommendations for reference architectures #12534

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docs: hardware recommendations for reference architectures
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mtojek committed Mar 15, 2024
commit bb268004c765e81d2299bf784c86e5784d89e1a7
4 changes: 2 additions & 2 deletions docs/admin/architectures/2k-users.md
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Expand Up @@ -10,8 +10,8 @@ clusters.

**Target load**: API: up to 300 RPS

**High Availability**: The mode is _disabled_, but administrators may consider
enabling it for deployment reliability.
**High Availability**: The mode is _enabled_; multiple replicas provide higher
deployment reliability under load.

## Hardware recommendations

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3 changes: 2 additions & 1 deletion docs/admin/architectures/3k-users.md
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Expand Up @@ -26,7 +26,8 @@ purposes.
**Footnotes**:

- An external provisioner is deployed as Kubernetes pod.
- It is strongly discouraged to run provisioner daemons on `coderd` nodes.
- It is strongly discouraged to run provisioner daemons on `coderd` nodes at
this level of scale.
- Separate provisioners into different namespaces in favor of zero-trust or
multi-cloud deployments.

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8 changes: 5 additions & 3 deletions docs/admin/architectures/index.md
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@@ -1,7 +1,7 @@
# Reference Architectures

This document provides prescriptive solutions and reference architectures to
support successful deployments of up to 2000 users and outlines at a high-level
support successful deployments of up to 3000 users and outlines at a high-level
the methodology currently used to scale-test Coder.

## General concepts
Expand Down Expand Up @@ -168,7 +168,7 @@ Provisionerd:
Database:

- Median CPU utilization is 80%, with a significant portion dedicated to writing
metadata.
workspace agent metadata.
- Memory utilization averages at 40%.
- `write_ops_count` between 6.7 and 8.4 operations per second.

Expand Down Expand Up @@ -215,7 +215,9 @@ When determining scaling requirements, consider the following factors:
- API latency/response time: Monitor API latency and response times to ensure
optimal performance under varying loads.
- Average number of HTTP requests: Track the average number of HTTP requests to
gauge system usage and identify potential bottlenecks.
gauge system usage and identify potential bottlenecks. The number of proxied
connections: For a very high number of proxied connections, more memory is
required.

**HTTP API latency**

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