Merge K Sorted Lists IRL: Querying Databases

Data structures and algorithms are the basic building blocks of moving forward in your software development journey. As much as the thought of performance optimization and delivering the best results excites you, the tiresome and monotonous journey of mugging up codes and algorithms becomes extremely tiresome. What if there is a way to spice things up a bit? What if we start learning about algorithms and data structures the way we’re supposed to – “with their real-world applications”? 

In the first article of this series, we will be diving into how a popular leetcode question Merge K Sorted Lists can be used in real-life to query databases using Firebase and JavaScript. Let’s get to work! 

Introduction

Imagine this: You are a chef who has recently opened a new restaurant in Milan. Your restaurant is famous because of the quality of the ingredients you order. The secret is that you have a separate vendor for everything! One who supplies you with fruits, one who supplies you sauces, and one who supplies you with sugar and baking powder, etc. One of the major tasks is juggling between the data specific to each vendor which can be let’s say the receipts or the inventory and if you need to make this information known to someone, sharing tens of different lists would be extremely tedious. But now go back to your leetcode problem – do things sound familiar? Merge all the data from each vendor and sort it into one list, the merge K sorted list algorithm IRL. 

Merge K Sorted Lists Algorithm

Merge K Sorted Lists, labeled as a hard problem on leetcode is one of the most important questions students study when learning about data structures and algorithms. As the name suggests, this algorithm is focused on merging K-sorted arrays or linked lists into one single sorted list.

Let’s say we have three lists of apples, bananas, and strawberries:

The idea is to take out the smallest element from each list, put it inside a separate sorted list, and shift the pointer of the list from which an element is extracted out to the main list. Here is the diagrammatic explanation of the same:

JavaScript Implementation of Merging K Sorted Lists

We will be using @datastructures-js/priority-queue package to implement a priority queue. Utilising the same concept illustrated above, here is the implementation of the merge k sorted lists algorithm in JavaScript:

Time complexity: O(n log k)

Using Merge K-Sorted Lists Algorithm to Query Databases

The same concept that we saw in the previous code of merging K sorted lists containing numerical data in the previous section can be applied to various software development paradigms as well. Firebase is one of the most popular and student developer friendly backend as a service. Firestore is another popular choice that makes managing your data seem like a cakewalk. However, when you are working with large datasets, you need to ensure that you are querying the database efficiently to reduce the wait time for your users when they send in a request for the information. Using Merge K sorted lists algorithm for querying data makes your application more efficient, increases the performance and reduces the response time. We will be using the Firebase SDK for JavaScript to enable us to query our database.

Creating Collections in Firestore

Set up a new application in Firebase and navigate to Firestore. Create three collections – bakery, fruits and sauces. In each collection, add a certain number of elements which have only two fields – the name and the quantity.

For your reference, here is some sample data:

{
  "bakery":{
    "itemOne":{
      "name":"bagel",
      "quantity":3
    },
    "itemTwo":{
      "name":"croissant",
      "quantity":2
    },
    "itemThree":{
      "name":"donut",
      "quantity":4
    }
  },
  "fruits":{
    "itemOne":{
      "name":"banana",
      "quantity":5
    },
    "itemTwo":{
      "name":"orange",
      "quantity":7
    },
    "itemThree":{
      "name":"apple",
      "quantity":10
    }
  },
  "sauces":{
    "itemOne":{
      "name":"hot sauce",
      "quantity":6
    },
    "itemTwo":{
      "name":"barbeque",
      "quantity":1
    },
    "itemThree":{
      "name":"ketchup",
      "quantity":2
    }
  }
}Code language: JSON / JSON with Comments (json)

Using Firebase and JavaScript to Merge K Sorted Lists

Given below is an interactive playground that lets you understand how you can apply the merge k sorted lists algorithm to retrieve data from your firebase collections and display them to your web application. Please note that you will have to install the packages before you begin tinkering with the code. Do not forget to replace lines 14 to 21 with your own Firebase configuration.

The merge K sorted lists algorithm is a versatile algorithm that finds its application far and wide. Imagine you are a detective investigating a complex case with too many clues and strings of thoughts, and like Sherlock, you’re looking to make sense of them by stringing them together and establishing connections. Just like Sherlock sorts the cases out with his expertise, this algorithm sorts collections be it linked lists or arrays, or anything else.

The best part? Once you understand the Merge K Sorted Lists algorithm, you can apply it to various real-life applications, from sorting large datasets to optimizing search algorithms. It’s a versatile tool that can help you improve your programming skills and make you a more efficient and productive software developer.

Conclusion

Merge K Sorted Lists algorithm is a real-life hack that every developer needs to have in their toolbox. It can help you optimize your software, retrieve data more efficiently, and improve your programming skills. And the best part? You don’t need to be a DSA expert to use it. With the right resources and practice, anyone can learn to apply this algorithm to their projects.

So, whether you’re a chef, a detective, or a software developer, the Merge K Sorted Lists algorithm has got your back. And speaking of resources, have you heard of codedamn’s playgrounds? It’s the perfect environment for practicing your coding skills and trying out new algorithms like this one. Don’t wait, sign up for codedamn pro subscription today and start optimizing your software like a pro!

Frequently Asked Questions

Are there any alternative algorithms for querying databases? 

Yes, there are several alternative algorithms for querying databases – Binary Search Trees and Hash Tables are two of them. Binary Search Trees are used when a sorted collection of data is of primary importance and Hash Tables are used when data retrieval needs to be as fast as possible. Merge K sorted lists algorithm is suited for complex sorting requirements when working with large datasets with efficiency. 

What are the limitations of merge k-sorted arrays in database systems?

The primary limitation of the merge k sorted arrays algorithm in database systems is that it requires a large amount of memory space for its functioning.

What are the other real-life applications of merge k-sorted arrays?

Merge k-sorted arrays can be used in stock market analysis to combine the stock prices into a single sorted list, and can be used in NLP techniques to merge multiple text documents into a single, sorted document. Wherever there is a requirement for an efficient sorting algorithm, merge k-sorted arrays find its place.