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There are many different aspects to database organization, function, and optimization that make each database unique.

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Write a reply of at least 200 words for each thread below. Each reply must incorporate at least 2 citations.
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Algorithms Introduction
There  are many different aspects to database organization, function, and  optimization that make each database unique. Algorithm complexity and  style can defer depending on a few factors. Complexity, quantity, and  requirements all can play a pivotal role and what algorithm, or system  could be used. Understanding algorithms at an introductory level is an  essential aspect of understanding and executing database functions.  “Techniques for performing an external merge sort are often used to sort  data managed by a distributed processing device that processes  large-scale data” (External, 2015).  This method is also most when the  data is constantly increasing, but the system only has a certain  capacity of memory to process that large amount of data. Furthermore, a  merge sort algorithm is one of the most common. The merge sort function  is just a general sorting algorithm that arranges certain elements of  the database in a predetermined manner. Furthermore, merge sort breaks  down aspects of the information into subcategories and characteristics.  According to an article published by Runestone Academy, “Merge sort is a  recursive algorithm that continually splits a list in half…. If the  list has more than one item, we split the list and recursively invoke a  merge sort on both halves” (Merge). The merge sort method is also simply  known as the “divide and conquer” method. The algorithm “divides by  finding the midpoint and conquers by recursively sorting subarrays”  (Khan). Also, the worst case of the merge sort is O(n*log(n)), whereas  the best case is O(n) (Sorting). The merge sort algorithm function is to  split the data into smaller groups by halving the groups each time.  Then the groups will continue this process until each group only has one  element or no elements, and the result is uniquely sorted groups  (Sorting). Lastly, the groups are merged back together in an order that  places their elements in proper arrangements. The merge sort algorithm  works efficiently and effectively for most users needs.
Thread Reply 2
Query Optimizer Overview
According to oracle.com query  optimization is the overall process of choosing the most efficient means  of executing a SQL statement. In the article it also gave us an example  on when one would be using this optimizer. They gave us an instance  when looking for information regarding employees in a database. If there  was a query that would ask for employees that are managers, and the  statistics say that 80 percent of the employees are managers, then a  full table scan would be most effective in that scenario. However, when  the statistics are saying that very few employees are managers, then  reading an index with a table access by the row ID would be much more  efficient than a full scan of the entire table. The query optimizer does  these things to make it more efficient. With this, there are multiple  algorithm categories for a query optimizer. According to emory.edu there  are four steps in query optimization. The first step is to convert the  SQL query into a query parse tree. This is essentially an outline of  what the user’s query would be, statements like SELECT, FROM, and WHERE.  The second step is to make that parse tree into an initial logical plan  to see what query the user would like to initiate. The third step is to  transform that initial query plan into an optimal query plan which  makes what the user has searched to be more specific towards what the  user is trying to find. Finally, the last step is to select the  algorithm for each operator in the query plan that will output what the  user has asked for. There are two main algorithm categories according to  emory.edu. The two categories are the logical query plan and the  physical query plan. The logical query plan is optimal for relational  algebra operations, while the physical query plan is optimal for  relational algebra algorithms. The difference between these two are that  logical operators are used to describe the relational operation to  process a statement while physical operators implement the operation  given by the logical operators. And by definition as stated earlier an  algorithm is a set of instructions and an operation is more of a  particular method. This is just a very basic explanation of what a query  optimizer is and what are it’s functions.