Movatterモバイル変換

[0]ホーム
URL:

Skip to content

Navigation Menu

Sign in
Appearance settings

Search code, repositories, users, issues, pull requests...

Provide feedback

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly

 Sign up
Appearance settings

docs: update reference architecture: glossary, scale tests methodology#12438

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to ourterms of service andprivacy statement. We’ll occasionally send you account related emails.

Already on GitHub?Sign in to your account

Merged
mtojek merged 20 commits into12426-mainfrom12426-build-public-facing-docs
Mar 8, 2024
Merged
Changes from1 commit
Commits
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
PrevPrevious commit
NextNext commit
Cian's feedback
  • Loading branch information
@mtojek
mtojek committedMar 7, 2024
commit4151036d6ef169ee53b093093b3bebde201285e7
46 changes: 21 additions & 25 deletionsdocs/admin/reference-architectures.md
View file
Open in desktop
Original file line numberDiff line numberDiff line change
@@ -1,12 +1,10 @@
# Reference architectures

As Coder evolves to meet the demands of modern development workflows, ensuring
scalability is paramount. Today, we're stress-testing our platform with 2000
concurrent users, preparing for deployments of up to 5000 users. This
documentation provides prescriptive solutions and reference architectures to
support successful customer deployments.
This document provides prescriptive solutions and reference architectures to
support successful deployments of up to 2000 users and outlines at a high-level
the methodology currently used to scale-test Coder.

Let's dive into the core concepts and terminology essential for understanding
This section outlines core concepts and terminology essential for understanding
Coder's architecture and deployment strategies.

## General concepts
Expand All@@ -20,9 +18,9 @@ management, template definitions, insights, and deployment configuration.
### Coder

Coder, also known as _coderd_, is the main service recommended for deployment
withKubernetes replicas to ensure high availability. It provides an API for
withmultiple replicas to ensure high availability. It provides an API for
managing workspaces and templates. Each _coderd_ replica has the capability to
host multiple provisioners (provisionerd).
host multiple provisioners (`provisionerd`).

### User

Expand All@@ -43,7 +41,7 @@ Users can connect to workspaces using SSH or via workspace applications like
`code-server`, facilitating collaboration and remote access. Additionally,
workspaces can be parameterized, allowing users to customize settings and
configurations based on their unique needs. Workspaces are instantiated using
Coder templates and deployed onnodes by provisioners.
Coder templates and deployed onresources created by provisioners.

### Template

Expand DownExpand Up@@ -85,23 +83,20 @@ components.
The Registry is hosted service and it is not available for air-gapped
deployments.

### Kubernetes cluster for Coder

A dedicated cluster for Coder is Kubernetes cluster specifically configured to
host and manage Coder workloads. Kubernetes provides container orchestration
capabilities, allowing Coder to efficiently deploy, scale, and manage workspaces
across a distributed infrastructure. This ensures high availability, fault
tolerance, and scalability for Coder deployments.

The cluster can be deployed using the Helm chart.

## Scale tests methodology

Scaling Coder involves careful planning and testing to ensure it can handle more
users without slowing down. This process encompasses infrastructure setup,
traffic projections, and aggressive testing to identify and mitigate potential
bottlenecks.

A dedicated cluster for Coder is Kubernetes cluster specifically configured to
host and manage Coder workloads. Kubernetes provides container orchestration
capabilities, allowing Coder to efficiently deploy, scale, and manage workspaces
across a distributed infrastructure. This ensures high availability, fault
tolerance, and scalability for Coder deployments. The cluster can be deployed
using the Helm chart.

In our scale tests, we adopt an approach with various stages to thoroughly
evaluate the system's performance. These stages include:

Expand All@@ -118,7 +113,7 @@ evaluate the system's performance. These stages include:
specific workspace apps, confirming their capability to echo back received
content effectively.

5. Cleanup:clean used workspace resources.
5. Cleanup:delete workspaces and users created in step 1.

### Infrastructure and setup requirements

Expand All@@ -135,18 +130,19 @@ customers.

The basic setup of scale tests environment involves:

1. Scale tests runner: `c2d-standard-32` (32 vCPU, 128 GB RAM)
1. Scale tests runner (32 vCPU, 128 GB RAM)
2. Coder: 2 replicas (4 vCPU, 16 GB RAM)
3. Database: 1 replica (2 vCPU, 32 GB RAM)
4. Provisioner: 50 instances (0.5 vCPU, 512 MB RAM)

No pod restarts or internal errors were observed.
The test is deemed successful if no crashes or restarts of `coderd` or other
internal errors were observed.

### Traffic Projections

In our scale tests, we simulate activity from 2000 users, 2000 workspaces, and
2000 agents, withmetadata being sent 2 x every 10 s. Here are the resulting
metrics:
2000 agents, withtwo items of workspace agent metadata being sent every 10
seconds. Here are the resultingmetrics:

Coder:

Expand All@@ -165,4 +161,4 @@ Database:

- Median CPU utilization: 80%.
- Median memory utilization: 40%.
- `write_ops_count`per minutebetween400 and500 operations.
- `write_ops_count` between6.7 and8.4 operations per second.
[8]ページ先頭
©2009-2025 Movatter.jp