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Resource Limits

Limits define the maximum consumption thresholds for specific resources allocated to your TagoIO project profiles. These constraints are essential for managing resource allocation, maintaining system stability, and ensuring fair usage across all tenants within your infrastructure. By enforcing limits, the platform protects against excessive usage that could otherwise degrade service quality for other users or overwhelm backend components.

It is important to recognize that increasing resource limits such as raising the number of requests your API can handle per minute, or expanding the maximum data volume per request directly impacts the operational requirements of your API service. Higher limits may necessitate additional computational resources, either through vertical scaling (increasing CPU and memory per instance) or horizontal scaling (adding more API instances). For detailed guidance on scaling strategies and their implications, refer to the API Documentation.

Before increasing Request limits, it is highly recommended to ensure that all Analysis scripts within the profile are thoroughly optimized. Optimization efforts should include refactoring Analysis code to eliminate unnecessary API calls and implementing parallel queue systems to reduce strain on the API. Addressing inefficiencies at the script level can significantly reduce peak resource consumption, reducing the need for additional computational resources.

Modifying Resource Limits​

To update resource limits for a specific profile, follow these steps:

  1. Select the relevant profile from your project dashboard.
  2. Choose the edit option associated with the resource limit you wish to modify.
  3. Enter the new desired limit value, taking into account the potential impact on system performance and infrastructure requirements.
  4. Confirm and apply the changes to enforce the updated limit.

Important: After increasing limits, closely monitor API performance and system metrics to ensure that your infrastructure continues to meet the demands of your workloads. Adjust scaling configurations as needed to maintain optimal availability and responsiveness.