Google Cloud MySQL DB Instance Quota

shagulhameed

Hi, I noticed the quotas for AlloyDB for PostgreSQL listed below:

Number of clusters per region
Number of vCPUs used per region

Having these quotas helps us manage the allocation for AlloyDB. However, I couldn't find similar quotas for MySQL. Why is that?

I've been exploring the Cloud SQL Admin API for the past two days but haven't found an option for controlling the instance type. Could you please provide a solution for this?

ms4446

Hi @shagulhameed ,

Here is some clarification between the differences in quota management between AlloyDB and Cloud SQL for MySQL. Unlike AlloyDB, which has dedicated quotas per service, Cloud SQL for MySQL utilizes a shared resource pool within your Google Cloud project. This model offers greater flexibility, as quotas are dynamically adjusted based on overall project usage.

Cloud SQL emphasizes precise resource management at the instance level, allowing you to customize storage, vCPUs, and memory according to specific workload requirements.

Although the term "instance type" isn't used explicitly, you can define your instance's characteristics through several settings:

databaseVersion: This affects the selection of available machine types, tailored to the MySQL version's specific features and requirements.
settings.tier: Specifies the machine type (e.g., db-n1-standard-4), which determines the number of vCPUs and the amount of memory.
settings.dataDiskSizeGb: Sets the storage capacity, vital for managing data growth and ensuring optimal performance.
settings.dataDiskType: Chooses the storage type (e.g., PD_SSD), which impacts I/O performance and associated costs.

Example using the gcloud command-line tool:

 

gcloud sql instances create my-mysql-instance \
    --database-version=MYSQL_8_0 \
    --tier=db-n1-standard-4 \
    --storage-size=200GB \
    --storage-type=PD_SSD

Key Points to Remember:

Regional Availability: Always check the availability of your desired machine type in the selected region to prevent deployment issues.
Major Changes May Require New Instance: Significant changes, such as altering machine types, might necessitate deploying a new instance and migrating existing data.
Performance Tuning: It's crucial to use monitoring tools to understand workload demands and fine-tune your settings for maximum efficiency.

Additional Tips:

High Availability: For critical databases, enabling high availability configurations can ensure continuous service.
Cloud SQL Insights: Employ Cloud SQL Insights for advanced performance diagnostics and tailored optimization recommendations.

shagulhameed

Hi @ms4446, thanks for the information. However, I need to control the quotas for CloudSQL in DB Instances because students might launch higher types of DB instances, which will affect the cost.

Are there any other options like organization policies that would be helpful in this scenario? In the Compute Engine VM, we are able to allow only a particular machine type resource.machineType - please refer here: https://cloud.google.com/compute/docs/access/custom-constraints#supported-resources.

In the CloudSQL documentation page mentioned here: https://cloud.google.com/sql/docs/quotas#configurable-limits, it talks about instances per project. How can we allow only 2 instances per project?

shagulhameed

@ms4446 wrote:
Example using the gcloud command-line tool:

I completely understand the CLI, through which I can provision CloudSQL with db-n1-standard-4. However, students may choose a higher type of CloudSQL instance, which could lead to cost spikes. Therefore, I need some control over the DB instance type. Please provide a solution for this.

ms4446

Hi @shagulhameed ,

To prevent unexpected cost spikes from your projects and maintain control over Cloud SQL resources, you might want to consider these strategies:

1. Enforcing Quotas and Limits

Directly set limits on the number of instances per project in the Google Cloud Console:
1. Go to IAM & Admin > Quotas.
2. Filter by service (Cloud SQL) and metric (Instances per project).
3. Click EDIT QUOTAS and set the desired limit (e.g., 2 instances per project).

2. Using Organization Policies for Resource Constraints

While Cloud SQL doesn't have direct instance type constraints like GCE, you can use organization policies:
- Possible Strategies:
  - Budget Alerts: Set up alerts in Cloud Billing to notify you when spending approaches a threshold.
  - Custom IAM Roles: Create roles that restrict instance creation/modification and assign them to students.

3. Programmatic Controls

Implement a control layer using Cloud Functions or Cloud Run:
- Event-driven Scripts: Trigger scripts that react to Cloud SQL API calls, checking and enforcing instance types.
- Approval Workflows: Use tools like Cloud Endpoints or Apigee API Management to create approval processes for instance creation.

4. Educate and Monitor

Training and Guidelines: Provide clear instructions on approved instance types and configurations.
Monitoring and Auditing: Regularly check instances using Cloud SQL Insights and other monitoring tools.

Example:

 

def enforce_instance_type_policy(event, context):
    """Cloud Function to enforce Cloud SQL instance type policy."""

    import base64
    import json
    from google.cloud import resource_manager

    message_data = base64.b64decode(event['data']).decode('utf-8')
    message_json = json.loads(message_data)

    # Example: Only allow db-n1-standard-1 or db-n1-standard-2
    allowed_types = ['db-n1-standard-1', 'db-n1-standard-2']
    instance_type = message_json['resource']['settings']['tier']

    if instance_type not in allowed_types:
        project_id = message_json['resource']['project_id']
        client = resource_manager.Client()
        project = client.fetch_project(project_id)
        project.delete()
        print(f"Deleted project {project_id} due to non-compliant instance type.")
    else:
        print("Instance type compliant.")

    # Note: This is a hypothetical example and should be tailored to
    # your specific requirements and compliance checks.

Key Points:

Imports: The necessary libraries for base64 decoding, JSON handling, and Cloud Resource Manager interaction.
Function Definition: The enforce_instance_type_policy function takes event (triggering event data) and context (function metadata) as arguments.
Data Extraction: Extracts the instance type from the base64-encoded event data.
Allowed Types: Defines a list of permitted instance types for your policy.
Compliance Check: Compares the extracted instance type against the allowed list.
Action: If non-compliant, deletes the associated project (replace with your desired action).
Logging: Prints messages indicating compliance or deletion.

Remember:

Adapt the allowed_types list and deletion action to your specific policy.
Consider error handling and more robust project identification mechanisms.

shagulhameed

@ms4446 wrote:

Go to IAM & Admin > Quotas.
Filter by service (Cloud SQL) and metric (Instances per project).
Click EDIT QUOTAS and set the desired limit (e.g., 2 instances per project).

@ms4446 -I really appreciate your quick support. I couldn't find the quota in the Cloud SQL Admin API (sqladmin.googleapis.com) and Cloud SQL (sql-component.googleapis.com). Can you guide me here?

shagulhameed

@ms4446 wrote:
Custom IAM Roles: Create roles that restrict instance creation/modification and assign them to students.

@ms4446 -Can you give me some examples of custom IAM roles for restrict instance type so that I can implement them?

ms4446

Google Cloud does not offer a direct method to set such specific quotas (like limiting the number of instances per project) through the API or the Cloud Console at this moment. The general approach involves using organization-wide policies and budget management strategies to control resource allocation and costs.

For creating and assigning custom IAM roles to restrict instance types in Cloud SQL

Absolutely! Here's the formatted text, incorporating headings and lists for better readability:

Managing Cloud SQL Quota

To locate and adjust the "Instances per project" quota for Cloud SQL, follow these steps:

Access the Quotas Page:

Open Google Cloud Console.
Navigate to IAM & Admin > Quotas.

Filtering Quotas:

In the Service filter, enter "Cloud SQL API" (avoid confusing it with "Cloud SQL Admin API" or "sql-component.googleapis.com").
In the Metric filter, enter "Instances per project".

Modifying the Quota:

The quota will be displayed based on your filters. Click on it, and select EDIT QUOTAS to set the limit to your preferred number, such as 2 instances per project.

Implementing Custom IAM Roles

Here are detailed strategies to control Cloud SQL instance types through custom IAM roles:

1. Restrict Instance Creation/Deletion

Custom Role Example:
- Title: Cloud SQL Instance Viewer
- Description: Permits viewing of Cloud SQL instances without the ability to create, delete, or modify them.
- Permissions:
  - cloudsql.instances.list — Allows listing/viewing of instances only.

Assign this role to students to limit their ability to create or modify instances, thus controlling resource usage and potential costs.

2. Using Organization Policies

Limiting Available Regions: Implement organization policies to confine the regions where students can deploy Cloud SQL instances. Since some instance types are specific to certain regions, this method indirectly restricts the available instance types.

3. Programmatic Enforcement

Using Cloud Functions or Cloud Run: Develop and deploy a solution that intercepts API calls for creating Cloud SQL instances. This solution should enforce restrictions on the instance types according to your specified rules. This approach allows for the most detailed control and customization.

Example

def check_instance_type(request):
    request_json = request.get_json()
    if 'tier' in request_json and request_json['tier'] not in ['db-n1-standard-1', 'db-n1-standard-2']:
        return 'Instance type not allowed', 403
    else:
        # Additional code to process the instance creation if it meets the criteria
        return 'Instance created', 200

This function should be integrated with appropriate triggers and security configurations to ensure that it responds only to relevant API calls.

Key Points to Remember:

These methods are designed to help you control resource usage in your Google Cloud project effectively, though they require careful implementation.
Conduct regular reviews and audits to ensure compliance with these policies and effectiveness of the controls.

Using these refined instructions, you can better manage your Cloud SQL resources, keeping costs under control while ensuring students have the necessary access to learn and complete their projects. For any specific implementation challenges, consider reaching out to Google Cloud support or engaging a professional services team.