Ticketing API

• API served by the Ticketing System.
• It includes several endpoints: Reference.

1. Users
2. Tickets
3. Merchandise
4. Members
5. Users Permissions

API Usage Best Practices for Paginated Requests

To ensure optimal performance and avoid overloading the server, we strongly recommend that developers:

1) Avoid firing all paginated requests simultaneously. Doing so can lead to rate limiting, timeouts, or even temporary bans depending on server constraints.

2) Implement synchronous requests with controlled delays. This ensures a smooth data retrieval process while maintaining server stability.

3) Consider using a timeout or retry mechanism. This is particularly useful in case of transient network issues or server delays.

4) Schedule API Calls Outside Business Hours For optimal server performance and minimal disruption, execute the data collection process during non-peak hours, such as late nights or early mornings.

Example Python Implementation

Below is an example of how to fetch paginated results synchronously using Python:

import requests
import time

# Base URL of the API
base_url = "https://example.com/api/resource"

# Authorization token
headers = {
    "Authorization": "Bearer your_api_key"
}

# Pagination variables
page = 1
results = []
delay_seconds = 1  # Delay between requests

try:
    while True:
        # Make a GET request to fetch the current page
        response = requests.get(f"{base_url}?page={page}", headers=headers, timeout=10)
        response.raise_for_status()  # Raise an exception for HTTP errors
        
        # Parse JSON response
        data = response.json()
        if not data.get("results"):
            break  # Exit loop if no more results

        # Add current page results to the list
        results.extend(data["results"])
        print(f"Page {page} fetched successfully.")

        # Increment the page counter
        page += 1

        # Delay before the next request
        time.sleep(delay_seconds)

except requests.exceptions.RequestException as e:
    print(f"Error during API call: {e}")

print(f"Total results fetched: {len(results)}")

Using Node.js to Collect Paginated API Data

Below is an example of collecting paginated API data using Node.js. The script makes paginated requests synchronously, respecting a delay between requests to avoid overwhelming the server. It also recommends running the script outside business hours.

const axios = require('axios');

// Base URL of the API
const baseURL = "https://example.com/api/resource";

// Authorization token
const headers = {
    Authorization: "Bearer your_api_key"
};

// Pagination variables
let page = 1;
const results = [];
const delayMilliseconds = 1000; // 1-second delay between requests

(async () => {
    console.log("Starting API data collection. Consider executing this script outside business hours.");

    try {
        while (true) {
            console.log(`Fetching page ${page}...`);
            
            // Fetch the current page
            const response = await axios.get(`${baseURL}?page=${page}`, {
                headers,
                timeout: 10000 // 10-second timeout for the request
            });

            const data = response.data;

            // Exit loop if no more results
            if (!data.results || data.results.length === 0) {
                console.log("No more results to fetch.");
                break;
            }

            // Append current page results to the list
            results.push(...data.results);
            console.log(`Page ${page} fetched successfully with ${data.results.length} items.`);

            // Increment page counter
            page++;

            // Delay before the next request
            await new Promise(resolve => setTimeout(resolve, delayMilliseconds));
        }
    } catch (error) {
        console.error(`Error during API call: ${error.message}`);
    }

    console.log(`Total results fetched: ${results.length}`);
})();

Key Benefits of This Approach

1) Server Stability: By fetching pages sequentially with delays, you minimize server stress and avoid potential rate limits.

2) Error Handling: Implementing retries ensures that temporary issues don’t disrupt the entire data retrieval process.

3) Scalability: This approach can be adjusted with configurable delays or adaptive strategies based on server feedback.

Feel free to adapt this example to your preferred programming language or framework. If you encounter issues or have specific requirements, please contact us for guidance.