Gathering Bankruptcy Data from the PACER API - A Brief Approach

Gathering Bankruptcy Data from the PACER API - A Brief Approach


In this blog post, we will take a deeper look into the process of gathering bankruptcy data from the PACER API. We will examine the best practices for designing a cost-effective and efficient system that can handle API requests, data extraction, and storage.






1. Understand the PACER API Documentation

Before diving into the development process, it is crucial to thoroughly understand the PACER API's documentation. This will help you determine the appropriate query structure, learn about the API's limitations, and understand the cost structure associated with the number of pages of search results.


2. Identify Specific Data Requirements

To minimize the number of requests and reduce the cost, carefully identify the specific data points you need for your project. This will ensure that you only retrieve the necessary information and avoid incurring additional charges for extraneous data.


3. Optimize API Requests

To further minimize costs and improve efficiency, consider the following strategies for optimizing API requests:

  • Batch case numbers: If the PACER API allows for batching case numbers, group them together in a single request to minimize the number of API calls.

  • Pagination: If the PACER API returns paginated results, implement a mechanism to fetch only the required pages, avoiding unnecessary requests for irrelevant data.

  • Rate limiting: To respect the PACER API's rate limits and avoid request throttling, implement a rate-limiting mechanism that adheres to the specified request frequency.


4. Error Handling and Retries

Implement robust error handling and retries to ensure your script can handle API failures or interruptions gracefully. This includes:

  • Handling HTTP errors: If the API returns an HTTP error, your script should be able to identify the error, log it, and either retry the request or exit gracefully.

  • Handling timeouts: Set appropriate timeout values for your requests and handle timeouts accordingly. This might include logging the timeout and retrying the request after a specified waiting period.

  • Implementing a retry mechanism: If a request fails due to an error or timeout, implement a retry mechanism with exponential backoff to avoid overwhelming the API with repeated requests in a short period.


5. Data Extraction and Storage

After successfully retrieving the bankruptcy data from the PACER API, extract the relevant information and store it in a database. This involves:

  • Parsing the API response: Depending on the response format (e.g., XML, JSON), use an appropriate parsing library to extract the required data points.

  • Data validation: Perform data validation checks to ensure the extracted data is accurate and complete. This may include checking for missing fields, incorrect data types, or invalid values.

  • Database insertion: Insert the extracted data into a database, ensuring proper indexing and data normalization to optimize query performance.



Gathering bankruptcy data from the PACER API requires a thorough understanding of the API documentation, optimization of API requests, robust error handling, and efficient data extraction and storage. By employing best practices and leveraging Python's powerful libraries, you can build a cost-effective and reliable system that meets your project's requirements.

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