> Functional Dependency is when one attribute determines another attribute in a DBMS
### Data Normalisation
> the process of splitting relations into well-structured relations that allow users to insert, delete, and update tuples without introducing database inconsistencies
> A set of logically related operation. A transaction is an action or series of actions. It is performed by a single user to perform operations for accessing the contents of the database.
> ACID (atomicity, consistency, isolation, durability) is a set of properties of database transactions intended to guarantee data validity despite errors, power failures, and other mishaps
> An anomaly is something that is unusual or unexpected; an abnormality
A very common source for issues in the database is redundancy. Apart from storage issues, having the same value present in multiple rows can lead to inconsistent data. Have a look at the following `STUDENTS` table.
| 1 | John Watson | j@sherlock.ed | 1 | Sherlock Season 6 |
| 2 | Mary Watson | m@sherlock.ed | 1 | Sherlock Season 6 |
| 3 | Kilvish | kil@vi.sh | 2 | Shaktimaan |
In the above table, just `Kilvish` is associated with the batch `Shaktimaan`.
Now, if we delete `Kilvish` from the database, we lose the data associated with the batch.
This results in database inconsistencies and is an example of how combining information that does not really belong together into one table can cause problems
### Updation Anomalies
> An update anomaly occurs when data is only partially updated in a database
> An update anomaly is a data inconsistency that results from data redundancy and a partial update
| 1 | John Watson | j@sherlock.ed | 1 | Sherlock Season 6 |
| 2 | Mary Watson | m@sherlock.ed | 1 | Sherlock Season 6 |
| 3 | Kilvish | kil@vi.sh | 2 | Shaktimaan |
| 4 | Mycroft Holmes | brother@sherlock.ed | 1 | Sherlock Season 6 |
In the above table, we have three students associated with the batch `Sherlock Season 6`.
If we have to update the batch name, each row will have to be updated due to redundancy.
This adds an overhead and a likely source of data inconsistency. If our developer or query misses a record, the database will be in an inconsistent state.
## Functional Dependencies
> a dependency FD: X → Y means that the values of Y are determined by the values of X. Two tuples sharing the same values of X will necessarily have the same values of Y.
* A functional dependency is a constraint that specifies the relationship between two sets of attributes where one set can accurately determine the value of other sets.
* It is denoted as `X → Y`, where X is a set of attributes that is capable of determining the value of Y
* The attribute set on the left side of the arrow, X is called Determinant, while on the right side, Y is called the Dependent
> Suppose one is designing a system to track vehicles and the capacity of their engines. Each vehicle has a unique vehicle identification number (VIN).
> One would write **VIN → EngineCapacity** because it would be inappropriate for a vehicle's engine to have more than one capacity.
> On the other hand, EngineCapacity → VIN is incorrect because there could be many vehicles with the same engine capacity
> the process of structuring a relational database in accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity.
> Normalization entails organizing the columns (attributes) and tables (relations) of a database to ensure that their dependencies are properly enforced by database integrity constraints
The goal of normalisation is to produce a set of tables that
* Is a faithful model of the enterprise
* Is highly flexible
* Reduces redundancy-saves space and reduces inconsistency in data
* Is free of update, insertion and deletion anomalies
| 3 | John Watson | i.am@sherlock.ed | 987654321 |
| 3 | John Watson | i.am@sherlock.ed | 123456789 |
**What will be primary key in the above table?**
Cons -
* A lot of redundant rows which can lead to anomalies
* Primary key needs to be altered
#### Separate Mapping Table
The two solutions above are not ideal due to the large amount of redundant data. In order to properly convert the above table to 1NF, we need to create a separate table that maps the redundant data to the primary key.
Hence, a `PHONE_NUMBER` table is created with `student_id` and `phone_number` columns. There are multiple rows for each student and the ID is used to map the redundant data. This minimises the amount of redundant data.
```mermaid
erDiagram
STUDENT {
int id
string name
string email
}
PHONE_NUMBER {
int student_id
string phone_number
}
STUDENT ||--|{ PHONE_NUMBER : has
```
### 2NF
* In the 2NF, relational must be in 1NF.
* There should be no partial dependencies.
* Every non candidate-key attribute must depend on the whole candidate key, not just part of it
In the first dependency, `ID` and `BATCH_ID` can determine a non-candiate key attribute. However, in the second dependency just `BATCH_ID` can determine a non-candidate key. This is an example of a partial dependency and hence violates 2NF.
The above table can be normalised by creating a separate table for batch information.
```mermaid
erDiagram
STUDENT {
int id
int batch_id
string name
float psp
}
BATCH {
int batch_id
string name
}
STUDENT ||--|{ BATCH : joins
```
### 3NF
* In the 3NF, relational must be in 2NF.
* It should also not contain any transitive dependencies.
| <u>ID</u> | NAME | BATCH_ID | BATCH_NAME |
| --------- | ---- | -------- | ---------- |
Listing out the dependencies for the above table:
*`ID → NAME`
*`ID → BATCH_ID`
*`BATCH_ID → BATCH_NAME`
*`ID → BATCH_NAME`
It can be observed in the last three dependencies that `ID` can determine `BATCH_ID` that can be used to determine `BATCH_NAME` i.e. `ID → BATCH_ID → BATCH_NAME`.
This is an example of a **transitive dependency** and hence violates 3NF.
A relation is in third normal form if it holds atleast one of the following conditions for every non-trivial function dependency X → Y.
* X is a super key.
* Y is a prime attribute, i.e., each element of Y is part of some candidate key.
> A database transaction symbolizes a unit of work performed against a database, and treated in a coherent and reliable way independent of other transactions.
Transactions in a database environment have two main purposes:
* To provide reliable units of work that allow correct recovery from failures and keep a database consistent even in cases of system failure.
* To provide isolation between programs accessing a database concurrently. If this isolation is not provided, the programs' outcomes are possibly erroneous.
Imagine a scenario where Alice has pay Bob 500 rupees.
Instead of paying each other directly, they use a bank.
Alice requests a payment to be made to Bob. The bank has to do two operations:
1. Deduce the amount to be paid to Bob from Alice's account.
2. Add the amount to be paid to Bob's account.
```mermaid
sequenceDiagram
actor Alice
participant Bank
actor Bob
Alice ->> Bank: Request transfer of 500 rupees
par Bank to Alice
Bank->>Alice: Debit 500 rupees
and Bank to Bob
Bank->>Bob: Credit 500 rupees
end
```
The above set of operations need to be performed in a single transaction else, in case of failure, the bank will be left in an inconsistent state.
For example, the debit operation succeeds but the credit operation fails.
Now Alice has 500 rupees less but Bob does not have 500 rupees more.
A sequence of database operations that satisfies the ACID properties (which can be perceived as a single logical operation on the data) is called a transaction.
> Atomicity is the guarantee that series of database operations in an atomic transaction will either all occur (a successful operation), or none will occur (an unsuccessful operation)
If the logical transaction consists of transferring funds from Alice to Bob, this may be composed of
- first removing the amount from account A,
- then depositing the same amount in account B.
We would not want to see the amount removed from Alice before we are sure it has also been transferred into Bob. Then, until both transactions have happened and the amount has been transferred to Bob, the logical transfer has not occurred.
#### Consistency
* The integrity constraints are maintained so that the database is consistent before and after the transaction.
* The execution of a transaction will leave a database in either its prior stable state or a new stable state.
In the banking example of Alice and Bob, the total value of the accounts is 2000, split equally. Now if Alice transfers 500 rupees to Bob, the total value of the accounts should still be the same.
But in case of credit failure, the total value will now be 1500 and hence the system will be left in an inconsistent state.
#### Isolation
> Ensures that concurrent execution of transactions leaves the database in the same state that would have been obtained if the transactions were executed sequentially
#### Durability
> Guarantees that once a transaction has been committed, it will remain committed even in the case of a system failure (e.g., power outage or crash).
> This usually means that completed transactions (or their effects) are recorded in non-volatile memory.
