khekrn

import kotlinx.coroutines.*

//Start
fun main() = runBlocking {
    repeat(100_00_00){ // launches one million coroutines
        launch{
            delay(10000) // Wait for 10 Seconds
            print(".")
        }
    }

khekrn

public class MagicSquare {

	// O(N + N*M) = O(N * M)
	public boolean isMagicSquare(int[][] arr){
		int diag1Sum = 0, diag2Sum = 0;
		var rowSize = arr.length;
		var colSize = arr[0].length;

		for(int i = 0; i < rowSize; i++){
			diag1Sum += arr[i][i];

khekrn

public class LongestSubStringWithKDistinctChar{

	//O(N + K)
	public int findLength(String str, int K) {
		int windowStart = 0, maxLen = 0;
		
		var dict = new HashMap<Character, Integer>();
		for(int i = 0; i < str.length(); i++){
			char rightChar = str.charAt(i);
			dict.put(rightChar, dict.getOrDefault(rightChar, 0) + 1);

khekrn

package com.firebase.samples;

import java.io.FileInputStream;
import java.util.concurrent.ExecutionException;

import com.google.auth.oauth2.GoogleCredentials;
import com.google.firebase.FirebaseApp;
import com.google.firebase.FirebaseOptions;
import com.google.firebase.auth.FirebaseAuth;
import com.google.firebase.auth.FirebaseAuthException;

khekrn

package filters

import (
	"bytes"
	"encoding/binary"
	"errors"
	"hash"
	"math"
	"math/rand"

khekrn

1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep