Methods to efficiently handle 1000 requests using Promise and JavaScript

1. Preparation

First, we need some preparation. Suppose we have an array of 1000 URLs and need to get data from these URLs. We also need an HTTP request library, here we useaxiosAs an example. Let's define these basic elements first:

const axios = require("axios");
const urls = [
  "/1",
  "/2",
  // ... Other URLs];

2. Concurrent processing of requests

To handle these requests efficiently, we need to divide them into small batches to execute concurrently. We will create two asynchronous functions, one for handling requests for each small batch and the other for managing the entire process.

2.1. Processing small batch requests

First, we define a function for concurrently processing a set of requestsprocessBatch：

async function processBatch(batch) {
  const requests = (url => (url));
  return (requests);
}

This function takes an array of URLs as input, usingMethod wrap each URL into a promise and passWait for them to be all finished. In this way, we can efficiently process a set of requests concurrently.

2.2. Process all requests in batches

Next, we define a function for dividing all requests into small batches of concurrent processing.processRequests：

async function processRequests(urls, batchSize) {
  const results = [];
  for (let i = 0; i < ; i += batchSize) {
    const batch = (i, i + batchSize);
    const batchResults = await processBatch(batch);
    (...batchResults);
  }
  return results;
}

This function first initializes a result arrayresults, and then iterate through all URLs using a loop, split them into small batches, and useprocessBatchFunctions to process requests for each small batch concurrently. Finally, merge the results for each small batch intoresultsin the array and return the final result.

3. Control the concurrency

To better control the degree of concurrency, we can use third-party libraries to limit the number of requests processed per time. In this case, we usep-limitLibrary:

const pLimit = require("p-limit");
const limit = pLimit(5); // Restrictions on simultaneous executionPromiseThe quantity is5

Then, inprocessRequestsIn the function, we willlimitFunction wrapping inprocessBatchto ensure only 5 requests are processed at a time:

async function processRequests(urls, batchSize) {
  const results = [];
  for (let i = 0; i < ; i += batchSize) {
    const batch = (i, i + batchSize);
    const batchResults = await limit(() => processBatch(batch));
    (...batchResults);
  }
  return results;
}

In this way, we can better control the use of resources under high concurrency and prevent resource exhaustion.

4. Error handling

Error handling of high concurrent requests is equally important. In the example, we cancatchThe situation where the request failed in the block:

processRequests(urls, batchSize)
  .then(results => {
    ("All requests are completed:", results);
  })
  .catch(error => {
    ("An error occurred:", error);
  });

If any request fails, the entire Promise chain will be immediately rejected, and then we cancatchCatch and handle errors in blocks. This ensures the stability of the application.

5. Summary

Using Promise and JavaScript can help us better manage concurrency and performance when handling large-scale high-concurrency requests. By dividing requests into small batches and using concurrency restriction tools, we can better control resource usage and avoid resource exhaustion. At the same time, proper error handling is the key to ensuring application stability. Hopefully, the methods and examples provided in this article can help you effectively manage high concurrent requests and improve the performance and maintainability of your application.

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