Preparing for coding interviews is no easy task. You need the skills to break down the problem and to deploy the right tools. Trying to memorize 1500+ Leetcode problems is not the best way to succeed.

That’s why we want to approach interview prep a bit differently today by tackling some **real world problems** faced by tech companies. Learning how to build real world features *(like “how to merge recommendations on Amazon”)* is more fun, and it's much easier to remember what you learned. If you can understand a problem’s underlying pattern, you can apply it to just about any question.

We will dive into the solutions for a few common real world coding problems and build 5 features. *We will offer our solutions in Java. If you'd like to see them in Python, check out my original post.*

**This tutorial at a glance:**

- Netflix Feature: Group Similar Titles (hash maps)
- Facebook Feature: Friend Circles (DFS)
- Google Calendar Feature: Find Meeting Rooms (heaps)
- Amazon Feature: Products in Price Range (BST)
- Twitter Feature: Add Likes (strings)
- Where to go from here

## Netflix Feature: Group Similar Titles (hash maps)

Netflix is one of the biggest video streaming platforms out there. The Netflix engineering team is always looking for new ways to display content. For this first problem, imagine you’re a developer on these teams.

Task:Our task here is toimprove search resultsby enabling users to see relevant search results without being hindered by typos, which we are calling the"Group Similar Titles"feature.

First, we need to determine how to individually group any character combination for a given title. Let’s imagine that our library has the following titles: `"duel", "dule", "speed", "spede", "deul", "cars"`

. You are tasked to design a functionality so that if a user misspells a word (for example `speed`

as `spede`

), they will still be shown the correct title.

First, we need to split our titles into sets of words so that the words in a set are anagrams. We have three sets: `{"duel", "dule", "deul"}`

,`{"speed", "spede"}`

, and `{"cars"}`

. The search results should include all members of the set that the string is found in.

Note:It’s best to pre-compute our sets rather than forming them when a user searches.

The members of each set have the same frequency of each alphabet, so the frequency of each alphabet in words in the same group is equal. For example, in our set `{{"speed", "spede"}}`

, the frequency of the characters are the same in each word: `s`

, `p`

, `e`

, and `d`

.

So, how do we design and implement this functionality? Let’s break it down.

For each title, we need to

**compute a 26-element vector**. Each vector element represents the frequency of an English letter in a title. We can represent frequency using a string that is fixed with`#`

characters. This mapping process generates identical vectors for the strings that are anagrams. For example, we represent`abbccc`

as`#1#2#3#0#0#0...#0`

.We then use this vector

**as a key**for inserting titles into a Hash Map. Our anagrams will all be mapped to the same entry. When a user searches a title or word, it should compute the 26-element English letter frequency vector based on input. It will then search the Hash Map and return all the map entries using the vector.We then store a list of calculated character counts as a

**key in a Hash Map**and assign a string as its value.Each value is an individual set, so we return the values of the Hash Map.

```
import java.util.*;
class Solution {
public static List<List<String>> groupTitles(String[] strs){
if (strs.length == 0)
return new ArrayList<List<String>>();
Map<String, List<String>> res = new HashMap<String, List<String>>();
int[] count = new int[26];
for (String s : strs) {
Arrays.fill(count, 0);
for (char c : s.toCharArray()){
int index = c - 'a';
count[index]++;
}
StringBuilder delimStr = new StringBuilder("");
for (int i = 0; i < 26; i++) {
delimStr.append('#');
delimStr.append(count[i]);
}
String key = delimStr.toString();
if (!res.containsKey(key))
res.put(key, new ArrayList<String>());
res.get(key).add(s);
}
return new ArrayList<List<String>>(res.values());
}
public static void main(String[] args) {
// Driver code
String titles[] = {"duel","dule","speed","spede","deul","cars"};
List<List<String>> gt = groupTitles(titles);
String query = "spede";
// Searching for all titles
for (List<String> g : gt){
if (g.contains(query))
System.out.println(g);
}
}
}
```

### Complexity measures

*n* is the size of the list of strings, and *k* is the maximum length of a single string.

**Time Complexity:** *O(n*k)*, since we are counting each letter for each string in a list

**Space Complexity:** *O(n*k)*, since every string is stored as a value in the dictionary, and the size of the string can be *k*.

## Facebook Feature: Friend Circles (DFS)

Facebook is the biggest social media company in the world. They also own and operate Instagram. Pretend you are a Facebook engineer team, and you are tasked to improve integration among their sister platforms.

Task:Our task here is to find all the people on Facebook that are in a user’s friend circle, which we are calling the “Friend Circles” feature.

We need to first identify the people that are in each user’s friends circle, which includes users that are directly or indirectly friends with another user. Let’s assume there are *n* users on Facebook. The friendship connection is **transitive**.

Example:If Nick is a direct friend of Amy, and Amy is a direct friend of Matt, then Nick is an indirect friend of Matt.

We will use an *n*n* **square matrix.** For example, cell `[i,j]`

will hold value `1`

if these users are friends. If not, the cell will hold the value `0`

. In the illustration below, there are two friend circles in the above example. `Nick`

is only friends with `Amy`

, but `Amy`

is friends with `Nick`

and `Matt`

. This forms a friend circle. `Mario`

makes another friend circle on his own.

Think of our symmetric input matrix as an **undirected graph**. Both the indirect and direct friends who are in one friend circle also exist in one connected component in our graph. This means that the number of connected graph components will give us how many friend circles we have.

So, our task is to find the number of connected components. We treat the input matrix as an **adjacency matrix**. So, how do we design and implement this? Let’s break it down.

- First we initialize an array, named
`visited`

. This will track the visited vertices of size`n`

with`0`

as the value for each index. - Then, we traverse the graph using DFS if
`visited[v]`

is`0`

. If not, we move to the next`v`

. - Then, set
`visited[v]`

to`1`

for every`v`

that our DFS traversal runs into. - When the DFS traversal is done, we should increment the circles counter by
`1`

. This means that a connected component has been fully traversed.

```
class Solution {
public static void DFS (boolean[][] friends, int n, boolean[] visited, int v) {
for (int i = 0; i < n; ++i) {
// A user is in the friend circle if he/she is friends with the user represented by
// user index and if he/she is not already in a friend circle
if (friends[v][i] == true && !visited[i] && i != v) {
visited[i] = true;
DFS(friends, n, visited, i);
}
}
}
public static int friendCircles(boolean[][] friends, int n) {
if (n == 0) {
return 0;
}
int numCircles = 0; //Number of friend circles
//Keep track of whether a user is already in a friend circle
boolean visited[] = new boolean[n];
for (int i=0;i < n; i++){
visited[i] = false;
}
//Start with the first user and recursively find all other users in his/her
//friend circle. Then, do the same thing for the next user that is not already
//in a friend circle. Repeat until all users are in a friend circle.
for (int i = 0; i < n; ++i) {
if (!visited[i]) {
visited[i] = true;
DFS(friends, n, visited, i); //Recursive step to find all friends
numCircles = numCircles + 1;
}
}
return numCircles;
}
public static void main(String args[])
{
int n = 4;
boolean[][] friends = {
{true,true,false,false},
{true,true,true,false},
{false,true,true,false},
{false,false,false,true}
};
System.out.println("Number of friends circles: " + friendCircles(friends, n));
}
}
```

### Complexity measures

**Time Complexity:** *O(n^2)* because we traverse the complete matrix of size *n^2*

**Space Complexity:** *O(n)* because the `visited`

array that stores our visited users is of size *n*.

## Google Calendar Feature: Find Meeting Rooms (heaps)

The Google Calendar tool is part of the GSuite used to manage events and reminders. Imagine you are a developer on the Google Calendar application team, and you’re tasked with implementing some productivity enhancing features.

Task:Your goal is to create a functionality for scheduling meetings. You need to determine and block the minimum number of meeting rooms for these meetings.

To do this, we are given a some meeting times. We need to find a way to identify the number of meeting rooms needed to schedule them all. Each meeting will contain positive integers for a `startTime`

and an `endTime`

.

Our meeting times can be listed as follows: `{{2, 8}, {3, 4}, {3, 9}, {5, 11}, {8, 20}, {11, 15}}`

. We could schedule each meeting in a separate room, but we want to use the minimum amount of rooms. We observe that three meetings overlap: `{2, 8}`

, `{3, 4}`

, and `{3, 9}`

. *Therefore, at least these three will require separate rooms.*

To solve this, we use either a **heap or priority queue** for storing meeting timings, using `end_time`

of each meeting as a key. The room at the top of our heap would become free earliest. If the meeting room from the top of the heap is not free, then no other room is free yet.

So, how do we build this functionality? Let's break it down.

- Sort the meetings by
`startTime`

. - Allocate the first meeting to a room. Add the
`endTime`

as an entry in the heap. - Traverse the other meetings and check if the meeting at the top has ended.
- If the room is free, extract this element, add it to the heap again with the ending time of the current meeting we want to process. If it is not free, allocate a new room and add it to our heap.
- After processing the lis of meetings, the size of the heap will tell us how many rooms are allocated. This should be the minimum number of rooms we need.

```
import java.util.Arrays;
import java.util.PriorityQueue;
class Solution {
public static int minMeetingRooms(int[][] meetingTimes){
if(meetingTimes.length == 0){
return 0;
}
Arrays.sort(meetingTimes, (a, b) -> Integer.compare(a[0], b[0]));
//min Heap keeps track of earliest ending meeting:
PriorityQueue<Integer> minHeap = new PriorityQueue<>((A, B) -> A - B);
minHeap.add(meetingTimes[0][1]);
for (int i = 1; i < meetingTimes.length; i++) {
int currStart = meetingTimes[i][0];
int currEnding = meetingTimes[i][1];
int earliestEnding = minHeap.peek();
if (earliestEnding <= currStart) {
minHeap.remove();
}
//update heap with curr ending
minHeap.add(currEnding);
}
return minHeap.size();
}
public static void main( String args[] ) {
// Driver code
int[][] meetingTimes = {{2, 8}, {3, 4}, {3, 9}, {5, 11}, {8, 20}, {11, 15}};
System.out.print(minMeetingRooms(meetingTimes));
}
}
```

### Complexity Measures

**Time complexity:** *O(n*log(n))*

**Space complexity:** *O(n)*, where *n* is the number of meetings

## Amazon Feature: Products in Price Range (BST)

Amazon is the largest online store around the world, and they are always on the lookout for better ways to recommend products to their customers. Imagine you are a developer for Amazon's store.

Task:Implement a search filter to find products in a given price range. The product data is in the form of a binary search tree. The values are the prices of products.

The parameters we are working with are `low`

and `high`

, which represent a user's price range. The example list of products below are mapped to their prices.

They are then stored in a binary search tree:

We can assume that the selected price range is `low = 7`

and `high = 20`

, so our function solution should only return the prices `{9, 8, 14, 20, 17}`

. So, how do we implement this? Let's break it down.

We will solve this problem using a variation of a preorder traversal on a binary tree, but other binary tree traversals would work as well.

- Implement a recursive helper function for a preorder traversal.
- Check heck if the current node value is in our given range. If so, add it to the
`output`

array. - Recursively call the preorder function on the left child of the node if the current node value greater than or equal to
`low`

. - If the current node value is less than or equal to
`high`

, recursively traverse the right child of the node. - When traversal is completed, the
`output`

array will be returned.

`main.java`

```
class Solution {
public static void preOrder(Node node, int low, int high, List<Integer> output){
if (node != null) {
if (node.val <= high && low <= node.val)
output.add(node.val);
if (low <= node.val)
preOrder(node.leftChild, low, high, output);
if (node.val <= high)
preOrder(node.rightChild, low, high, output);
}
}
public static List<Integer> productsInRange(Node root, int low, int high){
List<Integer> output = new ArrayList<Integer>();
preOrder(root, low, high, output);
return output;
}
public static void main( String args[] ) {
// Driver code
BinarySearchTree bst = new BinarySearchTree();
bst.insert(9);
bst.insert(6);
bst.insert(14);
bst.insert(20);
bst.insert(1);
bst.insert(30);
bst.insert(8);
bst.insert(17);
bst.insert(5);
System.out.println(productsInRange(bst.root, 7, 20));
}
}
```

`BinarySearchTree.java`

```
public class BinarySearchTree{
public Node root;
public BinarySearchTree(){
this.root = null;
}
public void insert(int val){
if (this.root == null)
this.root = new Node(val);
else
this.root.insert(val);
}
}
class Node{
public int val;
public Node leftChild;
public Node rightChild;
public Node(int val){
this.val = val;
this.leftChild = null;
this.rightChild = null;
}
public void insert(int val){
Node current = this;
Node parent = current;
while (current != null){
parent = current;
if (val < current.val)
current = current.leftChild;
else
current = current.rightChild;
}
if(val < parent.val)
parent.leftChild = new Node(val);
else
parent.rightChild = new Node(val);
}
}
```

### Time complexity

**Time complexity:** *O(n)*

**Space complexity:** *O(n)*

## Twitter Feature: Add Likes (strings)

Twitter is a popular social media platform. Imagine you're a Twitter developer, and your team must create an API that calculates the number of likes on a given person’s Tweets.

Task:Create an API that calculates the total number of likes on a person’s Tweets. Create a module that takes two numbers and returns the sum of the numbers.

The data is already extracted and stored in a simple text file for you. All of the values should remain strings and *cannot be converted into integers*. We must do digit-by-digit addition due these restrictions. So, how do we create this module? Let's break it down.

- Initialize an empty
`res`

variable and the`carry`

as`0`

. - Then, set two pointers,
`p1`

and`p2`

, that will point to the end of`like1`

and`like2`

. - Traverse the strings from the end using these pointers. Set it to stop when both strings are done.
- Set
`x1`

equal to a digit from string`like1`

at`index p1`

. If`p1`

has reached the beginning of`like1`

, set`x1`

to`0`

. Do the same for`x2`

with`like2`

at`index p2`

. - Compute the current value using
`value = (x1 + x2 + carry) % 10`

. Then update the`carry`

so that`carry = (x1 + x2 + carry) / 10`

. - Append the current value to the result.
- If both of the strings are traversed but the
`carry`

is still non-zero, append the`carry`

to`res`

. - Lastly, reverse the result and convert it to a string. Return the final string.

```
class Solution {
public static String addLikes(String like1, String like2){
StringBuilder res = new StringBuilder();
int carry = 0;
int p1 = like1.length() - 1;
int p2 = like2.length() - 1;
while (p1 >= 0 || p2 >= 0) {
int x1, x2;
if(p1 >= 0)
x1 = like1.charAt(p1) - '0';
else
x1 = 0;
if(p2 >= 0)
x2 = like2.charAt(p2) - '0';
else
x2 = 0;
int value = (x1 + x2 + carry) % 10;
carry = (x1 + x2 + carry) / 10;
res.append(value);
p1--;
p2--;
}
if (carry != 0)
res.append(carry);
return res.reverse().toString();
}
public static void main(String args[]) {
System.out.println(addLikes("1545", "67"));
}
}
```

### Complexity measures

**Time complexity:**

*O(max(n^1,n^2))*, where *n^1* and *n^2* are the lengths of the input.

**Space complexity:** *O(max(n^1,n^2))*

## Where to go from here

Congrats on making it to the end! You just build 5 real-world features, using skills like DFS, BST, heaps, and more. As you can see, this is a powerful way to prepare for coding interviews that tends to be more effective. If you can understand a problem’s underlying pattern, you can solve **just about any question**.

There are still dozens of other practice problems like this out there, like:

- Merge Tweets in Twitter Feed
- AT&T Determine Location
- Zoom: Display Lobby Meeting
- Search Engine: Fetch Words

If you want practice with problems like these, check out Educative's one-of-a-kind course **Decode the Coding Interview: Real-World Examples**. Available in multiple languages, this course takes you through 100+ real-world questions like these. After each project, you'll better understand what techniques you just applied.

This is **coding interview prep reimagined**, with your needs in mind!

*Happy learning!*

## Discussion (0)