Skip to main content

Why is this async/Promise method running synchronously?

I'm really a noob in JavaScript and I'm trying to write a small ElectronJS/NodeJS app. I think the purpose of the App is not relevant to the question, suffice it to say that the App will listen to TCP connections that will send ~100MB of XML content and then will do some further processing on the parsed XML. Since the XML content is "large" the parsing is taking some time to process, so I decided to put the parsing in an asynchronous function but the code is apparently running synchronously. I'm not sure if I'm doing something wrong or if it's just because of the single-thread (AFAIU) nature of JavaScript.

var net = require('net');
const fs = require('fs');
const { XMLParser, XMLBuilder, XMLValidator} = require("fast-xml-parser");

const parser = new XMLParser();
var file_counter = 0;

var server = net.createServer(function(connection) { 
  console.log('Client Connected');

  var content = "";
   
  connection.on('data', function(data) {
    content += data.toString();
  });

  connection.on('end', async function() {
    console.log("client " + file_counter + " disconnected");

    saveObj(content, file_counter);

    console.log("returned from " + file_counter);
    file_counter++;
   });
   
   connection.write('y');
});

async function saveObj(data, cnt) {
  return new Promise(res => setTimeout(() => {
    let jObj = parser.parse(data);
    console.log("Parsed content number " + cnt);
  }, 1000));
}

The output that I get consistently is Parsed content number 0, 1, 2...N. Even though the size of "data" is quite different from one connection to another.

Via Active questions tagged javascript - Stack Overflow https://ift.tt/0D5wbTj

Comments

Popular posts from this blog

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...

Sorting large arrays of big numeric stings

I was solving bigSorting() problem from hackerrank: Consider an array of numeric strings where each string is a positive number with anywhere from to digits. Sort the array's elements in non-decreasing, or ascending order of their integer values and return the sorted array. I know it works as follows: def bigSorting(unsorted): return sorted(unsorted, key=int) But I didnt guess this approach earlier. Initially I tried below: def bigSorting(unsorted): int_unsorted = [int(i) for i in unsorted] int_sorted = sorted(int_unsorted) return [str(i) for i in int_sorted] However, for some of the test cases, it was showing time limit exceeded. Why is it so? PS: I dont know exactly what those test cases were as hacker rank does not reveal all test cases. source https://stackoverflow.com/questions/73007397/sorting-large-arrays-of-big-numeric-stings

How to load Javascript with imported modules?

I am trying to import modules from tensorflowjs, and below is my code. test.html <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Document</title </head> <body> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script> <script type="module" src="./test.js"></script> </body> </html> test.js import * as tf from "./node_modules/@tensorflow/tfjs"; import {loadGraphModel} from "./node_modules/@tensorflow/tfjs-converter"; const MODEL_URL = './model.json'; const model = await loadGraphModel(MODEL_URL); const cat = document.getElementById('cat'); model.execute(tf.browser.fromPixels(cat)); Besides, I run the server using python -m http.server in my command prompt(Windows 10), and this is the error prompt in the console log of my browser: Failed to loa...