Relational Databases

Relational databases are, well... a collection of data items that have relations between them. These relations are made by associating a one table's primary key with another table's foreign key. It is a great advancement from the old long table that was used to store data which was inefficient in terms of search, memory and space. And as for normalization; it means a process in which tables are structured to eliminate redundancy and repetition among data and the CRUD operations side-effects. And as a direct result we improve the performance of our queries. An example of a relational database would be two tables; one for student and the other for school. Both of these tables have a column for the school id, and so we make a connection between by assigning the first one as a primary key and the other as foreign key.

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How Does While Loop Work?

I think that everybody knows what while means, right? Imagine you want to do something for a 'while' :) You're gonna need to repeat that thing for a number of times; limited or unlimited. For example, if you wanted to do something from your daily life like drinking water 7 times each day you're going to do a task (i.e process) for seven times and you're going to decrease that number each time you drink water. Before you start drinking, the number of times would be seven. So, a condition to check to stop drinking would be great. Like when the number of times is less than seven -> continue doing this process (drinking water) and increasing the number of times you have drank by one. Once the number of times reaches seven -> stop this process. Hope that helped clear the picture even if it was a little bit.

Big O Notation

Big O Notation is a mathematical expression that describes how much time an algorithm takes to run according to the size of it's inputs; mostly concerned about the worst case scenario. Types: 1- Constant Time O(1): On this order, regardless of the number of items, the iterations(time) are constant. Example: const getFirstItem = items => items[0]; getFirstITem([1, 2, 3, 4]); // 1 (one iteration) getFirstItem(['b', 'd', 'g']); // 'b' (one iteration) 2- Linear Time O(n): On this order, the worst case grows with the number of items. Example: Javascript's built in function indexOf, it loops over an array to find the correct index of the passed element. The worst case is looping over the whole array. [1, 2, 4, 9, 23, 12].indexOf(12); 3- Quadratic Time O(n ^ 2): For this order, the worst case time is the square of the number of inputs. It grows exponentially according to the number of inputs. Example: Using nested loo...

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