How do you handle API data that requires pagination?

Handling API data that requires pagination involves making successive requests to an API endpoint, each time retrieving a subset of the total data until all desired data has been obtained. Pagination is often used by APIs to limit the amount of data returned in a single request for performance reasons.

Here’s a step-by-step guide on how to handle paginated data from an API:

1. Understand the Pagination Scheme

First, you need to understand the pagination scheme used by the API. Common schemes include:

• Page number: You request a specific page of results.
• Offset/limit: You specify an offset (the starting point) and a limit (the number of items to retrieve).
• Cursor-based: Each request returns a cursor that you use in the subsequent request to get the next set of results.
• Token-based: Similar to cursors, but a token is used to fetch the next set of results.

2. Initial Request

Make an initial request to the API endpoint to fetch the first page or batch of data. This request will often include parameters to indicate pagination preferences.

3. Process the Data

Process the data as needed for your application. This could involve storing the data in a database, performing calculations, or simply printing the results.

4. Check for More Data

Check the response to determine if there is more data to fetch. This could be indicated by a link to the next page, a "has more" flag, or by comparing the number of items returned against the expected total.

5. Loop Through Subsequent Requests

If more data is available, make additional requests to retrieve the remaining data. This will typically involve modifying the pagination parameters for each request based on the information returned by the API (such as updating the page number or the offset).

Example in Python (using requests library)

import requests

BASE_URL = "https://api.example.com/data"
PARAMS = {
    'page': 1,  # Starting page
    'per_page': 100  # Number of items per page
}

def fetch_paginated_data(base_url, params):
    results = []

    while True:
        response = requests.get(base_url, params=params)
        data = response.json()

        # Process the data (e.g., append to results list)
        results.extend(data['items'])

        # Check if there's a next page
        if 'next_page' not in data or data['next_page'] is None:
            break

        # Update the page number for the next request
        params['page'] += 1

    return results

all_data = fetch_paginated_data(BASE_URL, PARAMS)

Example in JavaScript (using fetch API)

const BASE_URL = "https://api.example.com/data";
let params = {
  page: 1,  // Starting page
  perPage: 100  // Number of items per page
};

async function fetchPaginatedData(baseUrl, params) {
  let results = [];
  let hasMore = true;

  while (hasMore) {
    const queryParams = new URLSearchParams(params).toString();
    const response = await fetch(`${baseUrl}?${queryParams}`);
    const data = await response.json();

    // Process the data (e.g., concatenate to results array)
    results = results.concat(data.items);

    // Check if there's more data
    hasMore = data.hasNextPage;  // Assuming the API returns this flag

    // Update the page number for the next request
    params.page += 1;
  }

  return results;
}

fetchPaginatedData(BASE_URL, params)
  .then(allData => {
    // Handle the full dataset
  });

When implementing pagination, it's essential to handle potential issues such as rate limiting, network errors, and API changes. You should include error handling and possibly respect any rate limits by introducing delays between requests as needed.