Praduman Kushwaha

I'm Pratuman Praswaha from Gorakpur. I have completed my BTech in computer science from Dev Technology. Overall, I have a three-year success in full-stack development, and I have developed for multiple CRMs, so multiple client websites. My major fields of work include developing multiple projects in the fintech industry and ecommerce. Currently, I'm working with our skin care technology. Here, I have developed multiple CRMs, multiple client websites. I have also included many APIs, such as a payment gateway with Razorpay, Joho, LeadSquared, and other many more. Previously, I worked at Kabil Finance private limited, where I developed some dashboards and systems, like a ledger management system for tracking overall payment history, and a gold management system for tracking overall gold history. Technically, my skills include React JS, Next JS, JavaScript, SQL, NoSQL, and I'm also familiar with AWS deployment and CICD. It's all about me.

So, basically, in this, we are finding intersection. Oh, let's suppose we have two arrays. So, basically, we are finding the city. So, basically, our time complexity analysis is an outer loop that runs like big O of n. And, the inner loop also runs like big O of m. Like, we go off. I can say, yam. And the intersection includes like its run, big O of k. That's okay. Where k will be the size of the intersection. So in the worst case, basically the time complexity will be like we go up to big O of n. So let's suppose we talk about improving the time complexity, then we can use a set to reduce the lookup from big O of n to big O of 1. So, basically, I can improve the time complexity in this. Like, we can just create a set, and in the set, we can add elements from array a and array b. And, we will check if the set has a value from array of two. Then we will add it to the set. We will also create a new set, and in that set, we can add elements from array of two. Yeah. By this, we can improve the time complexity. So building a set takes big O of n and looping through array of one and checking basically takes big O of n. So in that case, the time complexity will be big O of n plus m. Yeah.

This query. For the query optimization. So, basically, for audiences, if there is no proper indexes, then MongoDB will perform a collating scan, or that will be a scan overall. So I can say, let's suppose we have no proper indexes. So first of all, we are querying, then in that case, that query will be scanned over all our table. That's what we will create indexes. So, basically, we can create further improving query performance. We can create indexes on the customer ID, and we can also create an index for the status and the total amount. So, basically, by using indexes, we can improve the query performance. And we sort of avoid a large sort in the memory. If we sort without any index, then MongoDB will basically load much into the memory. Then after that, there will be a sort.

The following component. Yes, I can identify the issue. So, basically, there are some issues. Basically, use the effect or missing dependency area. So we should use dependency area in the use effect. So, basically, that will run on every render without urgency. So, basically, we sort of use dependency area for and we can pass the pound. And then there's also the fetch option is wrong. Data is not validating. And, also, in the face, content type is missing. And we are also experiencing some issues in the increment. We are incrementing twice. These are the issues in this company.

So, basically, in this time complexity, we'll be like Vivo of n square, and it's phase complexity. Basically, we go up one. In the outer loop runs like n times and the inner loop runs like n minus one time. So, overall, time complexity will be like Vivo of n square. And the further improving this, we can use like. So we can improve, we can reduce time complexity to Vivo of n by sorting visited. So in that case, time complexity will be also Vivo of n and space complexity will be of Vivo of n square. The phase complexity will be also like big O in that case. And using two pointers, we can also do or if an array is sorted in that case. So in that case, time complexity will be like we go up n and phase complexity will be like we go up one.

So, basically, boxing this function, everything like a global variable is misused. And this global count is particularly outside. And the global count is particularly outside too. For systems with multiple function calls, so each call will accumulate results incorrectly. We are doing unnecessary checks, like if there is a loss of equality in this. And so, the best practice is to follow no global variables, no global state in this function. We should create pure functions. And we should do a strict equality, not a loose equality. And we should be doing local variables, not global forms, where.

So, basically, our line one and line two, we need to check if there is an overlap. So, basically, two line segments do not overlap. Only if one ends before the other starts. Like, if they integrate, then start two or in the two, like, if one is less than the other starts. Otherwise, this will overlap.

I want no privacy. In that case, several services, one and service two. Each time, counting affects. So, key accept. So, the only key accept from the code. Basically, service one is the fast, tender premium service, and, obviously, service two is non-premium. And this, like, something which service to use, depends on two parameters, is premium, which is light consuming, and preferred service. So, it will be checked like a service is down. So, basically, it will be checked if service is down, then return service two. If service exists, return it. So, in this, the possible test cases, basically, test when the service is down. So, basically, if service is down, then we should return service two. And, secondly, when our preferred service is provided, preferred service will be set to service one or two. So, in that case, the accepted result will be preferred service. I let you know the summary of this.