**DBMS - Sorted Files — Transcript & Summary | SozAI**
Source: https://sozai.app/transcript/dbms-sorted-files/

This video explains sorted files in DBMS, covering data storage, binary search, query types, advantages, disadvantages, and insertion challenges.

## Key Takeaways

- Sorted files enable efficient searching using binary search.
- Equality and range queries have different performance implications.
- Insertion in sorted files is costly due to the need to maintain order.
- Storing sorted records on the same disk track can reduce seek time.
- Partial solutions to insertion challenges have limitations.

## What the video covers

- Sorted files store rows sorted by specific attributes, such as product ID.
- Binary search is used for efficient searching with complexity O(log n).
- Two main query types are equality search and range search, each with different costs.
- Equality search retrieves a single tuple, while range search retrieves multiple tuples within a range.
- The complexity factor depends on the total number of pages (F) in the system.
- Seek time is minimized if sorted records are stored on the same disk track.
- Insertion in sorted files requires finding the correct position, causing costly page reads and writes.
- Partial solutions like leaving empty space (fill factor) in pages have drawbacks.
- Overflow chains are used to handle insertions when pages are full.
- Disadvantages include unpredictable seek times and high insertion costs.

## Chapters

1. 00:00 Introduction to Sorted Files
2. 00:35 Example of Sorted File with Product Table
3. 01:07 Attributes and Sorting Order
4. 01:38 Binary Search and Search Complexity
5. 02:10 Equality vs Range Queries
6. 03:22 Range Search Details and Complexity
7. 05:43 Seek Time and Disk Storage Optimization
8. 07:45 Insertion Challenges and Solutions
9. 08:15 Summary of Advantages and Disadvantages

Answers

## Questions about this video

What is a sorted file in DBMS?

A sorted file is a file where rows are stored sorted based on one or more attributes, enabling efficient searching and retrieval.

How does binary search improve searching in sorted files?

Binary search reduces the search complexity to O(log n) by repeatedly dividing the search space, making lookups faster in sorted files.

What are the challenges of inserting new tuples in sorted files?

Insertion requires finding the correct position to maintain order, which involves costly page reads and writes, and may require overflow chains or leaving empty space.

## Full Transcript — Download SRT & Markdown

00:00

Speaker A

We were discussing sorted file in this session. What is the sorted file? How will the data be stored in the sorted file? What are the advantages, disadvantages, and other features? We shall be discussing here. Sorted file rows are sorted based on some attributes.

00:17

Speaker A

on some attributes so depending upon the value of some attribute the rows will be shorted accordingly let us have one example at first this is a shorted file which is containing the product so product Abel kept in shorted file so

00:35

Speaker A

So, depending upon the value of some attribute, the rows will be sorted accordingly. Let us have one example at first. This is a sorted file which is containing the product. So, product table kept in sorted file.

00:50

Speaker A

tuples have got shorted access path is binary search we know that in case of shorted data if I want to perform any kind of searching then we can use binary search and binary search is having the searching complexity that is log of n to

01:07

Speaker A

So, this is my product ID, this is the product name, and this is the product price. So, here you see all these tuples and records have got sorted in ascending order on the product ID. So, this is the attribute on which the

01:24

Speaker A

if has been denoted as if is the total number of pages existing so here the complicity factor is love of F - the best - it is the complexity factor now we are having two kinds of queries one

01:38

Speaker A

tuples have got sorted. Access path is binary search. We know that in case of sorted data, if I want to perform any kind of searching, then we can use binary search, and binary search has the searching complexity that is log of n to

01:53

Speaker A

as the equality search and now what is the range search in case of range such what will happen the query might be like this select start from product where product ID greater than equal to 150 and product ID less than equal to 250 so in

02:10

Speaker A

the base 2, where n is the number of data. Equality or range query based on that attribute has cost log of F to the base 2 to retrieve page containing first row. Now, what is F here?

02:25

Speaker A

shall get a single tuple if the searching is successful so a by 2 log of F - the best - to retrieve page containing first row now what is the first row the first row is that we're having ten tuples selected against the

02:42

Speaker A

F has been denoted as F is the total number of pages existing. So, here the complexity factor is log of F to the base 2. It is the complexity factor. Now, we are having two kinds of queries: one

02:56

Speaker A

so to reach to the first of all of the range the searching complexity will be log of F to the base 2 but if is the number of total number of pages existing in the system successive rows are in the

03:10

Speaker A

is the equality search, another one is the range search. What is the equality search? Consider this particular product table. If the query is something like this: select \* from product where product ID is equal to 11, then that is known

03:24

Speaker A

the same page or in the successive pages if we can keep all these tuples records in the same order in which they are existing on the same track of a disk then the seek time can be minimized what is the seek 10 seconds to time

03:42

Speaker A

as the equality search. And now, what is the range search? In case of range search, what will happen? The query might be like this: select \* from product where product ID greater than or equal to 150 and product ID less than or equal to 250. So, this

03:57

Speaker A

then the Sikh temple in amazed during insertion correct position for an insult has been determined and inserting the row requires on the average a by two reads and a by two number of writes let us suppose we're having F number of

04:16

Speaker A

is a good sample of range search. Now, in case of range search, what will happen? We will be having bunch of tuples within that particular range. Let us suppose we are having ten tuples within that particular range, but in case of equality search, we

04:30

Speaker A

very attribute so I might be reading a by two number of pages to get that respective double and then to make it rule I may have to write rest a by two number of pages I'm talking everything on the reference so a by two number of

04:46

Speaker A

shall get a single tuple if the searching is successful. So, by 2 log of F to the base 2 to retrieve page containing first row. Now, what is the first row? The first row is that we are having ten tuples selected against the

05:00

Speaker A

tuples to be brought in to be written here so that is a very costly affairs because we are supposed to have a by two number of pages have to be read to get the desired position of the new tuples

05:13

Speaker A

range search. So, to reach to the first tuple of the range, so this is the range of tuples. There are bunch of tuples. Let us suppose there are ten tuples in between that product ID range, and this is the first tuple of the range.

05:26

Speaker A

solution number one leave empty space in each page also known as the fill factor that means I shall leave some space in each and every page for the future insertion of tuples but that is also not a good option for us because we don't

05:43

Speaker A

So, to reach to the first of all of the range, the searching complexity will be log of F to the base 2. But F is the number of total number of pages existing in the system. Successive rows are in the

05:59

Speaker A

also unpredictable living space for each and every page might not might not get used in the near future so this is my this is a virtual said disadvantages against the partial solution number one we are going for the partial solution

06:15

Speaker A

same or in the successive pages. By storing all pages on the same track on disk, seek time can be minimized. So, now you see it is a festival of the range, and other tuples might be existing in

06:32

Speaker A

should be inserted here they should be inserted here so how do you cert we can do in this way is our page number three so here I will be writing this pointer to the overflow chain pointer to overflow change so here

06:48

Speaker A

the same page or in the successive pages. If we can keep all these tuples records in the same order in which they are existing on the same track of a disk, then the seek time can be minimized. What is the seek time? It is the time

07:03

Speaker A

will be having two zero five they are to be considered in between but accordingly it has not been written it is written that after page number 0 I should not go to page number one that is the page

07:14

Speaker A

required to place the read/write head on the desired track on the disk. It is known as seek time. So, if we expect that if we can keep all our records, sorted records, on the same track of the disk,

07:28

Speaker A

cannot be applied there so the search searching complexity will not lower who can be log of F - the best - so so many disadvantages are coming into the system so disadvantages successive pages no longer stored in a quantity of your

07:45

Speaker A

then the seek time is minimized. During insertion, correct position for an insert has been determined, and inserting the row requires on the average a by two reads and a by two number of writes. Let us suppose we are having F number of

07:59

Speaker A

F by 2 number of page reads and then a by 2 number of page writes on the average and that is also very costly so that's why these are the different issues are there features advantages and disadvantages related to the shorted

08:15

Speaker A

pages, whatever you have considered earlier also. Let us suppose I want to insert one tuple here. I want to insert one tuple. So, where that tuple to be inserted so that ultimately the record should remain sorted on that

Topics: DBMS sorted files binary search equality search range search seek time insertion overflow chain fill factor database storage

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