Implement API Pagination and Caching

Oct 13, 2025 by recoveryshake.com 53 views

Improve GitHub API Rate Limit Handling for Large Repositories

As the popularity of GitHub continues to grow, so does the number of repositories with a large number of stars. However, the current script relies on a GitHub access token, which has a rate limit of 5000 API calls per hour. When fetching stargazers for repositories with a large number of stars, the script exceeds this limit, causing it to fail and requiring a reload after an hour. In this article, we will explore and implement solutions to handle API rate limits effectively, making the script more reliable and efficient for large repositories.

Currently, the script relies on a GitHub access token, which has a rate limit of 5000 API calls per hour. When fetching stargazers for repositories with a large number of stars, the script exceeds this limit, causing it to fail and requiring a reload after an hour. To improve efficiency and make the script more reliable for large repositories, we need to explore and implement solutions to handle API rate limits effectively.

The script should be able to handle large repositories without exhausting API limits quickly. It should gracefully handle rate limits and avoid abrupt failures. Users should be able to continue fetching stargazers without waiting for an hour.

The script stops working when the 5000 API call per hour limit is exceeded. It forces users to wait for an hour before they can resume fetching. No built-in handling for rate limits or alternative approaches exists.

1. Implement API Pagination and Caching

Optimize API calls by storing already fetched stargazers in cache/local storage. Fetch only new stargazers instead of repeatedly requesting the full list.

Benefits: Reduces the number of API calls, improves performance, and reduces the load on the GitHub API.
Implementation: Use a caching library like Redis or Memcached to store the fetched stargazers. Implement pagination by fetching only a limited number of stargazers at a time.

2. Use Multiple GitHub Access Tokens

Support multiple GitHub tokens to distribute API requests across different credentials. Implement token rotation to automatically switch when one token reaches the limit.

Benefits: Reduces the load on a single token, improves reliability, and allows for more API calls.
Implementation: Use a token rotation library to automatically switch between tokens when one reaches the limit.

3. Implement GraphQL API Instead of REST

The GitHub GraphQL API allows fetching more data in fewer requests, reducing API usage. Convert API calls to GraphQL queries for better efficiency.

Benefits: Reduces the number of API calls, improves performance, and allows for more data to be fetched in a single request.
Implementation: Use a GraphQL library to convert API calls to GraphQL queries.

4. Introduce Exponential Backoff & Retry Mechanism

When nearing the API limit, reduce request frequency dynamically. Implement a backoff strategy to retry after a delay instead of stopping completely.

Benefits: Reduces the load on the GitHub API, improves reliability, and allows for more API calls.
Implementation: Use an exponential backoff library to reduce request frequency and implement a retry mechanism.

5. Use Third-Party Proxy Services or Caching Mechanisms

Store API responses in a temporary database (e.g., Firebase, Redis) to minimize repeated calls. Explore GitHub Apps authentication, which has higher rate limits compared to personal access tokens.

Benefits: Reduces the load on the GitHub API, improves performance, and allows for more API calls.
Implementation: Use a third-party proxy service or caching mechanism to store API responses.

6. Innovation and Custom Solutions

Contributors are welcome to come up with their own creative solutions to optimize API requests. Any novel approach that improves efficiency and scalability is highly encouraged!

Implementing these optimizations will make the script more robust and scalable for repositories with a high number of stargazers. It will ensure continuous operation without abrupt failures due to API rate limits. The script will improve efficiency and reliability by optimizing API requests.

Handling API rate limits efficiently is crucial to ensure seamless functionality for large repositories. Implementing these optimizations will:

Prevent users from waiting an hour due to exceeded rate limits.
Improve performance by reducing unnecessary API calls.
Enhance scalability, allowing the script to support large repositories effortlessly.

By implementing these optimizations, we can make the script more reliable and efficient for large repositories, ensuring seamless functionality and improving user experience.
Improve GitHub API Rate Limit Handling for Large Repositories: Q&A

In our previous article, we explored and implemented solutions to handle API rate limits effectively, making the script more reliable and efficient for large repositories. In this article, we will address some frequently asked questions (FAQs) related to improving GitHub API rate limit handling.

A: The current rate limit for GitHub API calls is 5000 API calls per hour. This limit applies to all API calls, including those made using personal access tokens.

A: Handling API rate limits is crucial to ensure seamless functionality for large repositories. If the script exceeds the rate limit, it will fail and require a reload after an hour, causing users to wait for an hour before they can resume fetching.

A: Implementing API pagination and caching reduces the number of API calls, improves performance, and reduces the load on the GitHub API. This approach also allows for more efficient data retrieval and reduces the likelihood of exceeding the rate limit.

A: Yes, you can use multiple GitHub access tokens to distribute API requests across different credentials. Implementing token rotation will automatically switch between tokens when one reaches the limit, reducing the load on a single token and improving reliability.

A: The GitHub REST API and GraphQL API are two different APIs that provide access to GitHub data. The REST API is a traditional API that returns data in a JSON format, while the GraphQL API is a more modern API that allows for more efficient data retrieval using GraphQL queries.

A: You can implement exponential backoff and retry mechanisms using a library that provides this functionality. This approach reduces the load on the GitHub API, improves reliability, and allows for more API calls.

A: Third-party proxy services or caching mechanisms are services that store API responses in a temporary database, allowing for more efficient data retrieval and reducing the likelihood of exceeding the rate limit.

A: Yes, you can use GitHub Apps authentication to improve rate limits. GitHub Apps authentication has higher rate limits compared to personal access tokens, making it a more reliable option for large repositories.

A: You can contribute to improving GitHub API rate limit handling by suggesting new solutions or optimizing existing ones. Any novel approach that improves efficiency and scalability is highly encouraged!

Improving GitHub API rate limit handling is crucial to ensure seamless functionality for large repositories. By implementing the suggested solutions and addressing the FAQs, we can make the script more reliable and efficient for large repositories, ensuring seamless functionality and improving user experience.