How to manage the load of huge number of 10000 API calls in UI angular code?

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 Managing a huge number of API calls (such as 10,000) in an Angular application can overwhelm the browser, cause performance degradation, and lead to network issues. Here are several strategies to control and optimize the load of these API calls in an Angular UI:

  • Batch the API calls into smaller groups.
  • Lazy load data only when needed.
  • Use caching to avoid redundant API calls.
  • Implement debouncing for user input-related calls.
  • Consider pagination and server-side filtering to reduce data size.
  • Use throttling and rate limiting to control API call frequency.
  • Implement error handling and retry logic for improved reliability.

1. Batching API Calls:

Instead of making 10,000 individual API calls, batch the calls into smaller groups. This reduces the strain on both the client and server. For example, you could group them into batches of 100 or 500 and make requests sequentially or concurrently, depending on your needs.

  • Sequential Batching: Send one batch at a time. This prevents overloading the server with too many concurrent requests.
  • Concurrent Batching: Send multiple batches concurrently but in smaller chunks. Use Promise.all() or forkJoin from RxJS to handle the concurrency.
typescript
import { forkJoin } from 'rxjs';

const batch1 = [apiCall1(), apiCall2(), apiCall3()]; // Example calls
const batch2 = [apiCall4(), apiCall5()];

forkJoin([batch1, batch2]).subscribe(response => {
  console.log(response);
});

2. Lazy Loading (Lazy Fetching):

Load API data only when it's needed, either on scroll or as the user interacts with the application. This is known as lazy loading or infinite scrolling. It helps reduce the number of API calls and only fetches data that is actually being viewed.

  • For example, fetch the data when the user scrolls to the bottom of the page.
  • Combine this with batching to make fewer calls as the user interacts with the page.

3. Caching:

If the data being requested is frequently used or doesn't change often, caching it in memory (client-side) can significantly reduce the number of API calls. You can implement this caching logic in a service and check if the data is available before making a call.

typescript
@Injectable({
  providedIn: 'root'
})
export class DataService {
  private cachedData: any = {};

  getData(key: string): Observable<any> {
    if (this.cachedData[key]) {
      return of(this.cachedData[key]); // Return cached data
    }
    return this.http.get(`api/data/${key}`).pipe(
      tap(data => this.cachedData[key] = data) // Cache data after fetching
    );
  }
}

4. Debouncing API Calls:

In scenarios like user input (e.g., search or filter), debounce the API calls. This ensures that the server is not bombarded with API requests for every keystroke. A typical debounce time is around 300-500ms.

typescript
import { debounceTime, switchMap } from 'rxjs/operators';

this.searchSubject.pipe(
  debounceTime(300),
  switchMap(query => this.apiService.search(query))
).subscribe(results => {
  // Handle search results
});

5. Pagination:

If the data is large, implement pagination on both the server and client side. This ensures that only a small subset of data is requested at a time, improving the UI performance. For example, load 100 records at a time and allow the user to navigate through the pages.

typescript
getData(page: number, pageSize: number): Observable<any> {
  return this.http.get(`/api/data?page=${page}&size=${pageSize}`);
}

6. Progressive Data Fetching (Progressive Enhancement):

Load essential data first, then progressively load additional data in the background. This can be combined with lazy loading to improve initial load performance, and then background API calls can continue fetching more data as required.

7. Server-side Filtering and Aggregation:

If possible, offload the logic of filtering, sorting, and pagination to the server side. Instead of fetching all 10,000 records, ask the server for only the required subset. This reduces the load on the client and optimizes network usage.

For example, when filtering data, the API should return only the filtered results:

typescript
getFilteredData(query: string): Observable<any> {
  return this.http.get(`/api/data/filter?query=${query}`);
}

8. Web Workers for Parallelism:

Web workers allow you to run JavaScript in the background thread, separate from the main UI thread. This can be used to handle large computations or data processing in parallel, preventing the UI from freezing while processing a large number of API responses.

typescript
const worker = new Worker('./path/to/worker.js');
worker.postMessage(data);

worker.onmessage = (event) => {
  console.log('Data from worker:', event.data);
};

9. Rate Limiting/Throttling API Calls:

If there are restrictions on how often you can make API calls (e.g., rate limits on the server), implement throttling or rate limiting to control the frequency of requests. You can use RxJS operators such as throttleTime or create a custom rate-limiting mechanism to control how frequently API calls are made.

typescript
import { throttleTime } from 'rxjs/operators';

this.apiService.getData().pipe(
  throttleTime(1000) // Limit calls to one per second
).subscribe(response => {
  console.log(response);
});

10. Error Handling and Retry Logic:

API calls can sometimes fail due to network issues or server overloads. Implement retry logic using RxJS operators like retry or retryWhen. This ensures that failed requests are retried, improving the reliability of your API calls.

typescript
import { retry, catchError } from 'rxjs/operators';

this.http.get('/api/data').pipe(
  retry(3), // Retry up to 3 times on failure
  catchError(error => {
    console.error('Error occurred', error);
    return of([]); // Return an empty array on error
  })
).subscribe(response => {
  console.log(response);
});

Summary:

  • Batch the API calls into smaller groups.
  • Lazy load data only when needed.
  • Use caching to avoid redundant API calls.
  • Implement debouncing for user input-related calls.
  • Consider pagination and server-side filtering to reduce data size.
  • Use throttling and rate limiting to control API call frequency.
  • Implement error handling and retry logic for improved reliability.

By combining these techniques, you can efficiently manage a large number of API calls in your Angular application, ensuring better performance and user experience.

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