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chore: adopt markdownlint and markdown-table-formatter for *.md (#15831)
Co-authored-by: Edward Angert <EdwardAngert@users.noreply.github.com>
This commit is contained in:
Muhammad Atif Ali
co-authored by
Edward Angert
Edward Angert
parent
08463c27d8
commit
94f5d52fdc
@@ -40,12 +40,12 @@ Our scale tests include the following stages:
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The scale tests runner can distribute the workload to overlap single scenarios
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based on the workflow configuration:
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| | T0 | T1 | T2 | T3 | T4 | T5 | T6 |
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| -------------------- | --- | --- | --- | --- | --- | --- | --- |
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| SSH connections | X | X | X | X | | | |
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| Web Terminal (PTY) | | X | X | X | X | | |
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| Workspace apps | | | X | X | X | X | |
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| Dashboard (headless) | | | | X | X | X | X |
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| | T0 | T1 | T2 | T3 | T4 | T5 | T6 |
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|----------------------|----|----|----|----|----|----|----|
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| SSH connections | X | X | X | X | | | |
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| Web Terminal (PTY) | | X | X | X | X | | |
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| Workspace apps | | | X | X | X | X | |
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| Dashboard (headless) | | | | X | X | X | X |
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This pattern closely reflects how our customers naturally use the system. SSH
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connections are heavily utilized because they're the primary communication
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@@ -137,7 +137,7 @@ When determining scaling requirements, consider the following factors:
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connections: For a very high number of proxied connections, more memory is
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required.
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**HTTP API latency**
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#### HTTP API latency
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For a reliable Coder deployment dealing with medium to high loads, it's
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important that API calls for workspace/template queries and workspace build
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@@ -152,7 +152,7 @@ between users and the load balancer. Fortunately, the latency can be improved
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with a deployment of Coder
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[workspace proxies](../networking/workspace-proxies.md).
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**Node Autoscaling**
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#### Node Autoscaling
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We recommend disabling the autoscaling for `coderd` nodes. Autoscaling can cause
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interruptions for user connections, see
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@@ -186,7 +186,7 @@ When determining scaling requirements, consider the following factors:
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provisioners are free/available, the more concurrent workspace builds can be
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performed.
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**Node Autoscaling**
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#### Node Autoscaling
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Autoscaling provisioners is not an easy problem to solve unless it can be
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predicted when a number of concurrent workspace builds increases.
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@@ -219,7 +219,7 @@ When determining scaling requirements, consider the following factors:
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running Coder agent and occasional CPU and memory bursts for building
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projects.
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**Node Autoscaling**
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#### Node Autoscaling
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Workspace nodes can be set to operate in autoscaling mode to mitigate the risk
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of prolonged high resource utilization.
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@@ -17,7 +17,7 @@ Learn more about [Coder’s architecture](./architecture.md) and our
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> hardware sizing recommendations.
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| Environment | Coder CPU | Coder RAM | Coder Replicas | Database | Users | Concurrent builds | Concurrent connections (Terminal/SSH) | Coder Version | Last tested |
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| ---------------- | --------- | --------- | -------------- | ----------------- | ----- | ----------------- | ------------------------------------- | ------------- | ------------ |
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|------------------|-----------|-----------|----------------|-------------------|-------|-------------------|---------------------------------------|---------------|--------------|
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| Kubernetes (GKE) | 3 cores | 12 GB | 1 | db-f1-micro | 200 | 3 | 200 simulated | `v0.24.1` | Jun 26, 2023 |
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| Kubernetes (GKE) | 4 cores | 8 GB | 1 | db-custom-1-3840 | 1500 | 20 | 1,500 simulated | `v0.24.1` | Jun 27, 2023 |
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| Kubernetes (GKE) | 2 cores | 4 GB | 1 | db-custom-1-3840 | 500 | 20 | 500 simulated | `v0.27.2` | Jul 27, 2023 |
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@@ -48,14 +48,14 @@ specified template and extra parameters.
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```shell
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coder exp scaletest create-workspaces \
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--retry 5 \
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--count "${SCALETEST_PARAM_NUM_WORKSPACES}" \
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--template "${SCALETEST_PARAM_TEMPLATE}" \
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--concurrency "${SCALETEST_PARAM_CREATE_CONCURRENCY}" \
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--timeout 5h \
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--job-timeout 5h \
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--no-cleanup \
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--output json:"${SCALETEST_RESULTS_DIR}/create-workspaces.json"
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--retry 5 \
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--count "${SCALETEST_PARAM_NUM_WORKSPACES}" \
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--template "${SCALETEST_PARAM_TEMPLATE}" \
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--concurrency "${SCALETEST_PARAM_CREATE_CONCURRENCY}" \
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--timeout 5h \
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--job-timeout 5h \
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--no-cleanup \
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--output json:"${SCALETEST_RESULTS_DIR}/create-workspaces.json"
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# Run `coder exp scaletest create-workspaces --help` for all usage
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```
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@@ -79,14 +79,14 @@ Terminal against those workspaces.
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```shell
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# Produce load at about 1000MB/s (25MB/40ms).
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coder exp scaletest workspace-traffic \
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--template "${SCALETEST_PARAM_GREEDY_AGENT_TEMPLATE}" \
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--bytes-per-tick $((1024 * 1024 * 25)) \
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--tick-interval 40ms \
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--timeout "$((delay))s" \
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--job-timeout "$((delay))s" \
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--scaletest-prometheus-address 0.0.0.0:21113 \
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--target-workspaces "0:100" \
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--trace=false \
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--template "${SCALETEST_PARAM_GREEDY_AGENT_TEMPLATE}" \
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--bytes-per-tick $((1024 * 1024 * 25)) \
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--tick-interval 40ms \
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--timeout "$((delay))s" \
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--job-timeout "$((delay))s" \
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--scaletest-prometheus-address 0.0.0.0:21113 \
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--target-workspaces "0:100" \
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--trace=false \
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--output json:"${SCALETEST_RESULTS_DIR}/traffic-${type}-greedy-agent.json"
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```
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@@ -114,8 +114,8 @@ wish to clean up all workspaces, you can run the following command:
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```shell
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coder exp scaletest cleanup \
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--cleanup-job-timeout 2h \
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--cleanup-timeout 15min
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--cleanup-job-timeout 2h \
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--cleanup-timeout 15min
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```
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This will delete all workspaces and users with the prefix `scaletest-`.
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@@ -168,7 +168,7 @@ that operators can deploy depending on the traffic projections.
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There are a few cluster options available:
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| Workspace size | vCPU | Memory | Persisted storage | Details |
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| -------------- | ---- | ------ | ----------------- | ----------------------------------------------------- |
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|----------------|------|--------|-------------------|-------------------------------------------------------|
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| minimal | 1 | 2 Gi | None | |
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| small | 1 | 1 Gi | None | |
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| medium | 2 | 2 Gi | None | Medium-sized cluster offers the greedy agent variant. |
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@@ -13,7 +13,7 @@ tech startups, educational units, or small to mid-sized enterprises.
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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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|-------------|---------------------|---------------------|-----------------|------------|-------------------|
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| Up to 1,000 | 2 vCPU, 8 GB memory | 1-2 / 1 coderd each | `n1-standard-2` | `t3.large` | `Standard_D2s_v3` |
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**Footnotes**:
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@@ -24,7 +24,7 @@ tech startups, educational units, or small to mid-sized enterprises.
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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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|-------------|----------------------|--------------------------------|------------------|--------------|-------------------|
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| Up to 1,000 | 8 vCPU, 32 GB memory | 2 nodes / 30 provisioners each | `t2d-standard-8` | `t3.2xlarge` | `Standard_D8s_v3` |
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**Footnotes**:
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@@ -34,7 +34,7 @@ tech startups, educational units, or small to mid-sized enterprises.
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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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|-------------|----------------------|-------------------------|------------------|--------------|-------------------|
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| Up to 1,000 | 8 vCPU, 32 GB memory | 64 / 16 workspaces each | `t2d-standard-8` | `t3.2xlarge` | `Standard_D8s_v3` |
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**Footnotes**:
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@@ -47,5 +47,5 @@ tech startups, educational units, or small to mid-sized enterprises.
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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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|-------------|---------------------|----------|---------|--------------------|---------------|-------------------|
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| Up to 1,000 | 2 vCPU, 8 GB memory | 1 | 512 GB | `db-custom-2-7680` | `db.t3.large` | `Standard_D2s_v3` |
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@@ -18,13 +18,13 @@ deployment reliability under load.
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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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|-------------|----------------------|-------------------------|-----------------|-------------|-------------------|
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| Up to 2,000 | 4 vCPU, 16 GB memory | 2 nodes / 1 coderd each | `n1-standard-4` | `t3.xlarge` | `Standard_D4s_v3` |
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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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|-------------|----------------------|--------------------------------|------------------|--------------|-------------------|
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| Up to 2,000 | 8 vCPU, 32 GB memory | 4 nodes / 30 provisioners each | `t2d-standard-8` | `t3.2xlarge` | `Standard_D8s_v3` |
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**Footnotes**:
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@@ -37,7 +37,7 @@ deployment reliability under load.
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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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|-------------|----------------------|--------------------------|------------------|--------------|-------------------|
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| Up to 2,000 | 8 vCPU, 32 GB memory | 128 / 16 workspaces each | `t2d-standard-8` | `t3.2xlarge` | `Standard_D8s_v3` |
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**Footnotes**:
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@@ -50,7 +50,7 @@ deployment reliability under load.
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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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|-------------|----------------------|----------|---------|---------------------|----------------|-------------------|
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| Up to 2,000 | 4 vCPU, 16 GB memory | 1 | 1 TB | `db-custom-4-15360` | `db.t3.xlarge` | `Standard_D4s_v3` |
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**Footnotes**:
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@@ -19,13 +19,13 @@ continuously improve the reliability and performance of the platform.
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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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|-------------|----------------------|-------------------|-----------------|-------------|-------------------|
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| Up to 3,000 | 8 vCPU, 32 GB memory | 4 / 1 coderd each | `n1-standard-4` | `t3.xlarge` | `Standard_D4s_v3` |
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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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|-------------|----------------------|--------------------------|------------------|--------------|-------------------|
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| Up to 3,000 | 8 vCPU, 32 GB memory | 8 / 30 provisioners each | `t2d-standard-8` | `t3.2xlarge` | `Standard_D8s_v3` |
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**Footnotes**:
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@@ -39,7 +39,7 @@ continuously improve the reliability and performance of the platform.
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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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|-------------|----------------------|--------------------------------|------------------|--------------|-------------------|
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| Up to 3,000 | 8 vCPU, 32 GB memory | 256 nodes / 12 workspaces each | `t2d-standard-8` | `t3.2xlarge` | `Standard_D8s_v3` |
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**Footnotes**:
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@@ -53,7 +53,7 @@ continuously improve the reliability and performance of the platform.
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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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|-------------|----------------------|----------|---------|---------------------|-----------------|-------------------|
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| Up to 3,000 | 8 vCPU, 32 GB memory | 2 | 1.5 TB | `db-custom-8-30720` | `db.t3.2xlarge` | `Standard_D8s_v3` |
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**Footnotes**:
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@@ -23,7 +23,7 @@ This guide targets the following personas. It assumes a basic understanding of
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cloud/on-premise computing, containerization, and the Coder platform.
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| Role | Description |
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| ------------------------- | ------------------------------------------------------------------------------ |
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|---------------------------|--------------------------------------------------------------------------------|
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| Platform Engineers | Responsible for deploying, operating the Coder deployment and infrastructure |
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| Enterprise Architects | Responsible for architecting Coder deployments to meet enterprise requirements |
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| Managed Service Providers | Entities that deploy and run Coder software as a service for customers |
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@@ -31,7 +31,7 @@ cloud/on-premise computing, containerization, and the Coder platform.
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## CVA Guidance
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| CVA provides: | CVA does not provide: |
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| ---------------------------------------------- | ---------------------------------------------------------------------------------------- |
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|------------------------------------------------|------------------------------------------------------------------------------------------|
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| Single and multi-region K8s deployment options | Prescribing OS, or cloud vs. on-premise |
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| Reference architectures for up to 3,000 users | An approval of your architecture; the CVA solely provides recommendations and guidelines |
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| Best practices for building a Coder deployment | Recommendations for every possible deployment scenario |
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