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Merged
merged 23 commits into from
Mar 22, 2024
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docs: use scale testing utility #12643

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301cf48
deduplicate knowledge scale.md
mtojek Mar 18, 2024
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Merge branch 'main' into 12426-own-scale-tests
mtojek Mar 19, 2024
fa60b7c
Upload coder templates
mtojek Mar 19, 2024
358f990
make fmt
mtojek Mar 19, 2024
51f9f0c
Use mock Grafana url
mtojek Mar 19, 2024
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READMEs
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More todos
mtojek Mar 20, 2024
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Move scaletests
mtojek Mar 20, 2024
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Move around templates
mtojek Mar 20, 2024
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command
mtojek Mar 20, 2024
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WIP
mtojek Mar 21, 2024
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Clusters
mtojek Mar 21, 2024
e9b7803
no pod monitor
mtojek Mar 21, 2024
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Mention graphs
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Cian's comments
mtojek Mar 22, 2024
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WIP
mtojek Mar 22, 2024
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Noted
mtojek Mar 22, 2024
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Fix
mtojek Mar 22, 2024
ef25826
Merge branch 'main' into 12426-own-scale-tests
mtojek Mar 22, 2024
e9189e3
Use template vars
mtojek Mar 22, 2024
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mtojek Mar 22, 2024
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Fix: nodepool
mtojek Mar 22, 2024
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Try: force make gen
mtojek Mar 22, 2024
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@mtojek
mtojek committed Mar 21, 2024
commit 04fdb5265d58d94046baccf69413347bcffb182a
23 changes: 19 additions & 4 deletions docs/admin/scale.md
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Expand Up @@ -170,16 +170,31 @@ There are a few cluster options

#### Greedy agent

The greedy agent variant is a template modification that forces the Coder agent
to transmit large metadata (size: 4K) while emitting stats. The transmission of
large chunks puts extra overhead on coderd instances and agents while processing
The greedy agent variant is a template modification that makes the Coder agent
transmit large metadata (size: 4K) while reporting stats. The transmission of
large chunks puts extra overhead on coderd instances and agents when handling
and storing the data.

Use this template variant to verify limits of the cluster performance.

### Observability

TODO Grafana and logs
During scale tests, operators can monitor progress using a Grafana dashboard.
Coder offers a comprehensive overview
[dashboard](https://github.com/coder/coder/blob/main/scaletest/scaletest_dashboard.json)
that can seamlessly integrate into the internal Grafana deployment.

This dashboard provides insights into various aspects, including:

- Utilization of resources within the Coder control plane (CPU, memory, pods)
- Database performance metrics (CPU, memory, I/O, connections, queries)
- Coderd API performance (requests, latency, error rate)
- Resource consumption within Coder workspaces (CPU, memory, network usage)
- Internal metrics related to provisioner jobs

It is highly recommended to deploy a solution for centralized log collection and
aggregation. The presence of error logs may indicate an underscaled deployment
of Coder, necessitating action from operators.

## Autoscaling

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