Questions about GraphQL API Resource Limits

[cttestid41]

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Hi GitHub team and community,

I’ve been reviewing the recent update on GraphQL API resource limits and had a few questions I’d love clarity on:
https://github.blog/changelog/2025-09-01-graphql-api-resource-limits/

1. How can we detect if these new limits are impacting our existing workflows or integrations?
2. Is there any endpoint or method to monitor or check our current resource usage under these limits?
3. What exactly qualifies as a “heavy” query under this new model?
4. How will users know in advance if their query might hit these limits—are there any predictive tools or best practices?
   Additionally, I’d like to ask:
5. Will GitHub provide more granular error codes or logs to help developers debug resource limit issues?
6. Are there any query profiling tools or metrics available to help optimize GraphQL queries proactively?

Thanks in advance for your insights and support!

[antorroybabu]

query {
rateLimit {
limit
remaining
used
resetAt
cost
}
}

[KaloudasDev]

Hi @cttestid41 👋

Great questions regarding the new GraphQL API resource limits. Here’s some guidance to help you manage and monitor your usage effectively:

• Detecting Impact on Workflows: Use the built-in rateLimit query to monitor your current API consumption and remaining quota. This is the best way to see if you’re approaching limits. Example query:

  query {
    rateLimit {
      limit
      remaining
      used
      resetAt
      cost
    }
  }

• Heavy Queries: Queries are considered “heavy” based on their complexity and the resources they consume, measured by the cost field returned by GitHub. Complex nested queries or those fetching large datasets have higher costs.

• Predictive Tools & Best Practices:

  • Before running queries, estimate costs using GitHub’s query cost calculators or by testing in development environments.
  • Break down complex queries into smaller ones when possible.
  • Use pagination to limit data returned per request.

• Error Codes & Logs: GitHub aims to provide more granular error messages when limits are hit, including details on which parts of the query caused the issue, helping with debugging.

• Profiling & Metrics: Currently, using the rateLimit API is the primary method to track usage. For proactive optimization, use GitHub’s documentation and community tools to analyze and simplify your queries.

Keep an eye on GitHub’s official docs and changelogs for updates on monitoring and tooling as these features evolve.

Hope this clarifies things — happy querying!

[cttestid41]

Hi @KaloudasDev,thanks for sharing the query for rate limit. Are there any new query parameters defined for checking the new introduced resource limits or we need to analyze it by checking the cost?

[Rupesh09876]

Great questions regarding the new GraphQL API resource limits! Here’s some guidance to help you manage and monitor your usage effectively:

1. Detecting Impact on Workflows
   You can use the built-in rateLimit query to monitor your current API consumption and remaining quota. This helps you detect if your integrations are close to hitting the new limits.
   Example query:

query {
rateLimit {
limit
remaining
used
resetAt
cost
}
}

2. Heavy Queries
   A query is considered “heavy” when its cost is high. GitHub calculates this based on query complexity and the amount of data being fetched. Deeply nested queries or large result sets typically incur higher costs.

3. Predictive Tools & Best Practices

Estimate costs by running queries in a development environment and checking the cost field.

Break down complex queries into smaller, more efficient ones.

Use pagination (first, last, before, after) to avoid fetching large datasets at once.

Cache results where possible to reduce redundant queries.

4. Error Codes & Logs
   GitHub will return errors if you exceed limits, and improvements are being made to provide more granular error messages, including details on which parts of the query caused the issue.

5. Profiling & Metrics
   At this time, the rateLimit API is the primary tool available for monitoring usage. For proactive optimization:

Regularly analyze your queries with the cost field.

Review GitHub’s GraphQL best practices documentation.

Keep an eye on GitHub’s changelogs for updates on query profiling tools as they evolve.

📌 In short: monitor with rateLimit, optimize queries by reducing complexity, and stay updated via GitHub’s documentation for future improvements.

[44erxyz]

Hi there 👋

Thanks for raising these questions about the new GraphQL API resource limits.

If a query is impacted, the response will include an error with details indicating the resource limit has been exceeded.

“Heavy” queries are those that traverse deeply nested connections or return a very large set of nodes. Using pagination and limiting fields helps reduce query weight.

At this time, we don’t provide a dedicated endpoint for monitoring usage, but we are exploring ways to improve visibility and tooling.

Error messages for resource limits are specific, and we’re considering additional granularity in error codes to make debugging easier.

The GraphQL Explorer remains the best tool to iteratively test and optimize queries.

We appreciate your feedback and will continue to improve the developer experience around these limits.

[odanieldamasio]

Hi there 👋

Thanks for raising these questions about the new GraphQL API resource limits.

[kumarbasnet100]

Hi there,
Thank you for reaching out with your questions regarding GitHub's recent changes to GraphQL API resource limits. Here's a detailed breakdown to help you navigate these updates:

1. Detecting Impact on Existing Workflows
   To monitor if your workflows or integrations are approaching the new resource limits, you can utilize the built-in rateLimit query. This query provides insights into your current API consumption and remaining quota.
   Example Query:
   query {
   rateLimit {
   limit
   remaining
   used
   resetAt
   cost
   }
   }
   This will return data indicating how many points you've used, how many are remaining, and when the limit will reset.
   GitHub

2. Monitoring Current Resource Usage
   The rateLimit query mentioned above is your primary tool for monitoring current resource usage. It offers real-time data on your API consumption, helping you stay within the allocated limits.

3. Understanding "Heavy" Queries
   GitHub doesn't provide explicit definitions for what constitutes a "heavy" query. However, patterns that may cause queries to hit resource limits include:
   Deeply nested queries
   Requests for large datasets
   Multiple fields or objects being queried simultaneously
   To avoid hitting these limits, consider optimizing your queries by reducing depth, limiting the number of fields, and paginating large datasets.
   The GitHub Blog

4. Predicting Potential Limit Exceedance
   Currently, GitHub does not offer predictive tools to warn you before hitting resource limits. The best approach is proactive monitoring using the rateLimit query and optimizing your queries to stay within the allocated points.

5. Error Codes and Logs for Debugging
   While GitHub provides error messages when resource limits are exceeded, they don't offer granular error codes or detailed logs specifically for resource limit issues. Monitoring your usage through the rateLimit query and optimizing your queries are the primary methods for avoiding such errors.

6. Query Profiling and Optimization Tools
   GitHub does not currently provide built-in query profiling tools or metrics for the GraphQL API. However, you can implement your own profiling by logging query execution times and analyzing the performance of your queries. Additionally, consider using third-party tools and libraries designed for GraphQL query optimization.

Hope this finds you well.

[SaurabhM2410]

Hi 👋,

Great set of questions — here’s a consolidated response on the new GraphQL API resource limits and how to work with them effectively:

🔍 Detecting Impact on Existing Workflows
Use the built-in rateLimit query to check your quota and costs. Example:

query {
rateLimit {
limit
remaining
used
resetAt
cost
}
}
This tells you how much of your allowance is left and the “cost” of your query execution. If workflows start failing with limit errors, that’s a clear sign you’re hitting the new constraints.

📊 Monitoring Current Resource Usage
Currently, the rateLimit field is the primary monitoring tool. You’ll want to add checks/logging around it in your integrations to catch when you’re close to limits.

⚖️ What Counts as a “Heavy” Query?
Queries are considered “heavy” if they:
Traverse deeply nested connections.
Request large datasets without pagination.
Query too many fields/objects at once.
The cost field in the rateLimit query is your best indicator of how “expensive” a query is.

🔮 Predicting Limit Hits / Best Practices
Test queries in the GraphQL Explorer to see cost before deploying.
Break down large queries into smaller paginated requests.
Cache results where possible to avoid repeated heavy calls.
Monitor execution time + rateLimit together for early warning.

🛠 Error Codes & Debugging
Right now, GitHub sends an error if you exceed limits (e.g., Resource limit exceeded). There isn’t yet a set of fine-grained error codes, but they are improving messages to highlight which part of the query triggered the issue.

📌 Profiling & Optimization Tools
No official profiling tools yet, but you can:
Log the cost field regularly.
Benchmark query execution times in your app.
Use GraphQL linting/optimization tools to refactor queries.
GitHub changelogs are the best place to watch for new monitoring or debugging features.

✅ In short: use rateLimit as your main compass, reduce query complexity with pagination and caching, and monitor cost proactively to stay safe.

[Gitjm34]

nice