complexity | A command line tool to identify complex code | Code Quality library

I'm trying to create a multi-level dropdown using Bootstrap and some JSON data.

Ideally, I want the dropdown to have this kind of nested functionality:

I'm working with an array of objects with nested data, and it looks like:

I was checking "minimum number of jumps to reach the end" problem in GeekforGeeks https://www.geeksforgeeks.org/minimum-number-of-jumps-to-reach-end-of-a-given-array/ . I got confused about the time complexity mentioned there which is O(n^n).

I couldn't find this information on Microsoft's Docs for the Sort-Object cmdlet.

I am using Powershell's Sort-Object to sort objects based on a property, i.e.: $foo | Sort-Object -Property x in an Azure RunBook, what would be time complexity of this?

I have spent some hours playing with Electron and I have observed that it consistently takes more than 2.5 seconds to draw a trivial html file to the screen. The timeline is roughly as follows:

• 60 ms: app ready event is triggered; we create a window using new BrowserWindow()
• 170 ms: a blank window appears on the screen
• 2800 ms: the window shows the specified HTML

I have set up a repository with my code, which is derived from Electron's quick start docs.

Regarding my machine, I am running Windows 10 on a ThinkPad T460 from 2016 with a SSD and enough memory.

Shipping an application that shows a blank window for so long upon startup is a no-go for me. I assume most people developing Electron apps think similarly. Hence my first question: am I doing something wrong? Or is this the expected loading time for a trivial Electron app?

Assuming this is normal behavior, what is the common way to deal with this problem? Some ideas come to mind:

1. Asking Electron to show a splash screen: unless there is specific built-in functionality for this, it seems like a no-go, since the splash screen itself would be shown only after 2.5 seconds.
2. Hide the app's window until it is rendered (using the ready-to-show event), so no blank window is shown. This isn't ideal, since it means that the user doesn't get any feedback whatsoever that the application is actually loading.
3. Create a wrapper application (using native code) that displays a splash screen, launches electron and hides itself once the electron window is shown. Kind of defeats the purpose of using Electron in the first place, because you end up writing native code and adding accidental complexity.
4. Setting the background color of the window to something resembling your app, as suggested by the docs. This just doesn't look very well.

Given this must be a common problem, I hope standard solutions have been found by the community. I'd be glad if someone can point me in the right direction.

I'm kinda new at algorithms and I'm afraid that my solutions are not correct, help me fix them.

Hello all!

I recently learned that in newer versions of SQL Server, the query optimizer can "expand" a SQL view and utilize inline performance benefits. This could have some drastic effects going forward on what kinds of database objects I create and why and when I create them, depending upon when this enhanced performance is achieved and when it is not.

For instance, I would not bother creating a parameterized inline table-valued function with a start date parameter and an end date parameter for an extremely large transaction table (where performance matters greatly) when I can just make a view and slap a WHERE statement at the bottom of the calling query, something like

I am into programming from past 7-8 months and I generally use selection sort whenever I want to sort arrays or structures. So I got idea and implemented it. selection sort find max OR min value in each loop and place it at one of the border (depends on max or min) and make it out of scope. So I thought why not find max AND min in each loop and move them to borders (min-left and max-right) and reduce the scope from both side by value 1. It would have half of previous time complexity i guess. Here is the code:

I was writing a simple loop in C++ and was wondering what the time complexity would be.

My intuition tells me that it is O(n*log(n)) but I couldn't come up for a proof for the n*log(n)

Problem Statement: Given a binary tree and a number ‘sum’, find all paths from root-to-leaf such that the sum of all the node values of each path equals ‘sum’.

How do I calculate the time complexity of this algorithm?

What is its recurrence relation?