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Definition of MySQL encode()

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The MySQL Encode() function implements the encryption of any plain string text provided as an argument during the execution of the function. Therefore, the function Encode() will give the result as a binary string that is of the identical size as the plain string text specified.

If the argument passed in the Encode() function delivered is a void string, it will also return the strings as an empty value. The MySQL Encode() function takes two parameters: the string that will be encrypted and the key string that is passed to encrypt the specified plain text string.


The syntax for the MySQL Encode() function is as follows:

ENCODE(Encrypt_String, Password_String);

The above syntax structure describes the two arguments of the Encode() function as:

Encrypt_String: The initial string that the MySQL Encode() function encrypts.

Password_String: The specific string passed alongside the initial one in the Encode() function serves as a key string for encrypting the first argument, Encrypt_String.

Return Value:

MySQL supports the Encode() function in versions like MySQL 4.1, 5.0, 5.1, 5.5, 5.6, and 5.7.

Generally, we can state that Encoding in MySQL database is a reversible data format conversion method implemented to preserve data usability. On the other hand, Encryption is typically a secure encoding process used to protect the confidentiality of server data records.

How does the Encode() function works in MySQL?

We have the basic syntax as Encode(Encrypt_String, Password_String) for the function in MySQL, where the encode function executes to encrypt the Encrypt-String value using the Password_String as the required password key string to perform the encryption.

The resultant will be present in binary string form having the identical length as str. Similarly, if we want to decode the encoded result, then, in MySQL, we will apply the Decode() function.

But if a user still needs to implement using MySQL Encode(), we can apply a salt value mainly with it, reducing the risk of causing no error or notice. Suppose we can view the following instance to illustrate the concept:

ENCODE('plain_text', CONCAT('demo_random_salt','demo_secret_pass_string'));

Hence, do remember a new value of random salt should be implemented whenever a password value is modified.

In MySQL, the MySQL Enterprise Encryption permits your enterprise to:

Protect the server data by grouping private, public, and symmetric keys to encode and decode data.

Encoding the information stored in the MySQL server using DSA, RSA, or even DH encryption algorithms.

The MySQL Encode() function involves a change of data into a fresh format with the help of schema. In this case, the encoding process can be reversed, as it allows data to be encoded and secured in a new arrangement and subsequently decoded back into its original format.

The encoding classically comprises a publicly existing scheme that can be reversed. This encoding ensures the usability and integrity of data and is a common choice when data cannot be directly transmitted in its current format between applications or systems. Typically, we do not employ encoding to protect or secure data since MySQL can easily reverse it.

While Encryption encodes the data securely so that only approved users with a key or a password can decrypt it back to disclose the original one. Like in the Encode() function, we need to take two arguments to be encrypted and the other encryption key string used to encrypt the original plain text into a binary string as a return value when the MySQL function is executed.

We may encounter two types of encryption methods: symmetric key encryption, where we use the same key for both encrypting and decrypting data using a password and asymmetric key encryption, where we use one key to encrypt a string as input and a different key to decrypt the encrypted data. Thus, encryption is useful when the information requires protection so that in the absence of the decryption keys, one cannot access the main data. For example, when a website receives data over HTTPS, it encrypts it using public key encryption.

Encryption consists of encoding data, so both cannot be said interchangeably because encryption is always related to data encoded firmly. We utilize data encoding when we refer to data records that lack secure encoding. Its example can be AES 256.


Let us discuss some of the examples showing the MySQL Encode() function and its working:

1. Executing Encode() function using a string:

We will implement the Encode() function using the SELECT statement as follows:

SELECT ENCODE('mysqlcommand', 'Password_String');

2. Executing Encode() function using a string having the grouping of integers and characters:

SELECT ENCODE('mysql123command','Password_String');

It gives the below output:

3. Executing the MySQL Encode() function using a NULL string:

SELECT ENCODE (' ','Password_String');

In the first argument above, we have provided the value as NULL or empty, then the output will result as follows:


MySQL Encode() function helps secure any data records by encrypting the values with certain key values and changing the originals to binary string form.

This encoding function helps protect the plain text from any unauthorized process, which secures our database information and integrity in MySQL.

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How Lambda Function Works In Pandas?

Introduction to Pandas Lambda

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Syntax and Parameters:

Lambda x:x


Lambda represents the keyword of the function.

First, x represents the bound variable.

The second x represents the body of the function that has to be implemented.

The watchword is compulsory, and it must be a lambda, while the contentions and body can change dependent on the necessities.

How does Lambda Function work in Pandas?

Given below shows examples of how lambda functions are implemented in Pandas.

Example #1

Utilizing Lambda function to a single column of the dataframe.


import pandas as pd info= [['Span',415],['Vetts',375],['Suchu',480], ['Appu',395],['Deepthi',260],['Madh',345]] dataframe = pd.DataFrame(info,columns=['Info','Result']) dataframe = dataframe.assign(Final_Percent = lambda y: (y['Result'] /700 * 100)) print(dataframe.assign(Final_Percent = lambda y: (y['Result'] /700 * 100)))


Example #2

Utilizing Lambda function to multiple columns of the Pandas dataframe.


import pandas as pd info = [[10,11,12,13], [14,15,16,17], [18,19,20,21], [22,23,24,25], [26,27,28,29], [30,31,32,33], [34,35,36,37]] dataframe = pd.DataFrame(info, columns=['First', 'Second', 'Third', 'Fourth']) dataframe = dataframe.assign(End_Result=lambda y: (y['First'] * y['Second'] * y['Third'] * y['Fourth'])) print(dataframe.assign(End_Result=lambda y: (y['First'] * y['Second'] * y['Third'] * y['Fourth'])))


We can utilize the apply() capacity to apply the lambda capacity to the two lines and segments of a dataframe. On the off chance that the hub contention in the apply() work is 0, at that point, the lambda work gets applied to every segment, and in the event that 1, at that point, the capacity gets applied to each column.

The channel() work takes a lambda work and a Pandas arrangement and applies for the lambda work on the arrangement and channels the information. This profits a grouping of True and False, which we use for sifting the information. Hence, the info size of the guide() work is consistently more noteworthy than the yield size. This guide() work maps the arrangement as per input correspondence. It is useful when we need to substitute an arrangement with different qualities. In map() works, the size of the info is equivalent to the size of the yield.

Lambda works likewise uphold restrictive proclamations, for example, if. Else. This makes lambda works amazing. Lambda capacities are very helpful when you are working with a great deal of iterative code. Reduce () work applies the lambda capacity to the initial two components of the arrangement and returns the outcome. At that point, it stores that outcome and again applies a similar lambda capacity to the outcome and the following component in the arrangement. Consequently, it diminishes the arrangement to a solitary worth. Lambda works in reduce() cannot take multiple contentions.

In Pandas, we have the opportunity to add various capacities at whatever point required, like lambda work, sort work, and so on. We can apply a lambda capacity to both the sections and lines of the Pandas information outline.


Hence we would like to conclude by stating that lambda functions are characterized utilizing the watchword lambda. They can have quite a few contentions yet just a single articulation. A lambda work cannot contain any assertions, and it restores a capacity object which can be appointed to any factor. They are commonly utilized for one-line articulations. Ordinary capacities are made utilizing the def watchword. They can have quite a few contentions and quite a few articulations. They can contain any assertions and are commonly utilized for huge squares of code.

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How Takeif Works In Kotlin?

Introduction to Kotlin takeIf

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In kotlin language has many default classes, methods, and keywords used to implement the mobile-based application. Like that takeif() is one of the default methods and it is mainly used to filterate the data. It has its own syntax below.

fun functionName(parameters) { val varaiableName:datatype? = kotlinNullable.takeIf(predicate) -----some logic codes--- }

The above codes are the basic syntax for using the teakeIf() method in the kotlin codes. It can be called upon the parameter values and their methods based on the predicate conditions. If the condition is satisfied it continues else it returns the null value.

How takeIf work in Kotlin?

In kotlin language, it has many defaults and standard libraries contain the functions like takeIf, and these functions are let you combined and the value is embedded with checks of the object state and its status. The object state infrequent call chains the called objects with the predicate condition provided and takeIf it returns the object if it is matched with the specified conditions. Else it will return the null values so that the takeIf() method as the filtering function for every single object. If returns the object and if it does not match the predicate condition the null value if it is does also the object is to be called and available as the lambda argument(it). While chaining other functions like after takeIf and other default functions like takeUnless is also to be performed with the null checks conditions and the safe call like ‘?.’ Operator because the return value is null. The takeIf() function is specially used together with the scope functions so that the chaining link is checked with them for running the code blocks on the specified object that can be matched with the given predicate conditions. We can do this and call the takeIf() method it returns the null and other local variable types like ‘var’ and ‘let’ are not be invoked.

Examples of Kotlin takeIf

Below are the different examples of Kotlin takeIf:

Example #1


abstract class Fans(fans:String){ abstract var fansname: String abstract fun fandetails() init { println("Fans fansname is: $fansname") } fun demo(){ println("Thank you the Fans fansname is") } } interface Crompton{ var vars: String fun demo1():String fun details2() { println("Have a Nice day users") } } class Havelles : Crompton { override var vars: String = "Welcome To My Domain its the first example taht related to the kotlin takeIf() method" override fun demo1() = "Thank you for choosing the Havelles fan" } class Bajaj(fans:String): Fans(fans) { override var fansname: String = "Thank you for choosing the Bajaj fan" override fun fandetails() { println("Thank you users your Fan name is $fansname") } } sealed class Usha { class demo : Usha() { fun show() { println("Welcome To My Domain its a Usha example regarding kotlin sealed class combined with the takeIf() method") } } class demo1 : Usha() { fun show() { println("Its a child sealed class so it cant change and its a fixed thing") } } } fun main() { fun firstExample(strinp1: String, strinp2: String) { println("The substring $strinp2 is found in $strinp1.") println("Its start position is $it.") } } firstExample("010000011", "1") firstExample("110000011", "2") firstExample("011000011", "3") firstExample("110000011", "4") val eg = Usha.demo1() val eg1 = Usha.demo() val m2 = Bajaj("June Month") println("Your curent Fans is : ${m2.fansname}") m2.fandetails() val j = Havelles() println("Your latest Fans is = ${j.vars}") print("Keep on spent your valuable time time with our application: ") j.details2() print("Nice day users please try again ") println(j.demo1()) }


The above example is the basic example we used for the takeif() method in kotlin classes.

Example #2


import kotlin.random.* enum class TV(val exam: Boolean = true){ Samsung, Sony, Onida, Philips, Vu, Airtec, Airtel; companion object{ fun TVDetails(obj: TV): Boolean { } } } fun details(tv: TV) { when(tv) { } } fun main() { val num = Random.nextInt(5) val evn = num.takeIf { it % 2 == 0 } val odd = num.takeUnless { it % 2 == 0 } println("The given number is even: $evn") println("The given number is odd: $odd") val i=2 var flag=false while(i<=num/2){ val prm=num.takeIf{it % i == 0} flag=true break ++i } if(!flag) { println("The given number is prime number") } else { println("The given number is not prime") } println("Welcome to my domain its a second example regarding the kotlin takeif() function") val out = mapOf(123 to "Samsung", 345 to "Sony", 678 to "Onida",901 to "Philips",213 to "Vu",141 to "Airtec",516 to "Airtel") println(out) println("Thank you users have a nice day please keep and spent time with our application") }


Here, we used enum, and companion classes with the takeif() method for users to calculate the prime and odd, seven numbers.

Example #3 fun main() { fun ThirdExample(num1: Int, str2: String) { println("Welcome User Integer is the first input and string type is the second input.") println("$it.") } } colmap["Model Name"] = "Lenovo" colmap["Model Price"] = "8000" colmap["Model ID"] = "1456" for ((k, v) in colmap) { println("Thank you users your inputs are $k = $v") } val news = mapOf("Model Name" to "HP", "Model Price" to 8032, "Model ID" to 1678) println("Your input Keys are:" + news.keys) println("Your input Values are:" + news.values) println("Thank you users have a nice day please keep and spent time with our application") ThirdExample(13, "Siva") ThirdExample(14, "Raman") }


In the final example, we used the collection class additionally with the takeif() class.


In kotlin language has many default methods for implementing the application logic and its operations which is needed on the user requirement. The takeif() method is one of the built-in methods for validating the condition like if to execute the chain-link condition if the condition is true it returns the object else it returns a null value.

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How Stream Works In Scala?

Introduction to Scala Stream

Scala Stream is also a part of scala collection which store data. This is similar to list in scala only with one difference. In scala stream value will only be calculated when needed Scala Stream are lazy list which evaluates the values only when it is required, hence increases the performance of the program by not loading the value at once.

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valstream_name = value1 #:: value2 #:: value3 #:: Stream.empty valmyStream = 10 #:: 20 #:: 30 #:: Stream.empty

So in this way we can assign value to s stream, but it will only calculate the first value.

How Stream works in Scala?

As we have seen that scala stream is lazy list, they are like list on scala with only difference is that their element are computed lazily not at once. Only the element’s which we request are computed thus increases the performance of our program. But stream in scala is fast.


varmystm = 10 #:: 20 #:: 30 #:: Stream.empty

In this the head of the stream is 10 and 20, 30 are the tails of the stream. So in output also the head will be printed but not the tail because they have not been computed yet. In this we have 3 elements in the stream.

In scala we have Sequence, List and Stream.

1. Sequence 2. List

This is also a part of collection in scala. This is also used to store group of elements and this is the sub class of sequence in scala. List in scala are the default implementation of sequence. List process its all elements one by one. It is not strict to compute only one value.

3. Stream

This is also part of collection except one change it is based on lazy approach. That means only one element will be computed at a time. Otherwise it has all the characteristics like list in scala. Stream is also a sub class for sequence.

Performance Characteristics: As performance point of view we cannot decide which can be better but as we know stream is based on the lazy approach which computed elements when it is required so we can say it can provide us better performance compared to other collections.

Immutable Head Tail Apply Update Prepend Append Insert

Stream C C L L C L –

List C C L L C L –

We can have a look at it performance characteristics of stream and list collection in scala. In case of immutable.

L: This symbol defines that this operation depends upon the size of collection, we can say it is proportional to length of collection it is liner in nature.

C: This will take constant time.

-: This symbol means this operation is not supported in the mentioned collection.

Examples of Scala Stream

Given below are the examples mentioned:

Example #1

In this example we are creating different stream list.


object Main extends App{ val mystm1 = 001 #:: 002 #:: 003 #:: Stream.empty val mystm2 = "abc" #:: "aa" #:: "bb" #:: Stream.empty val mystm3 = 150 #:: 250 #:: 350 #:: Stream.empty val mystm4 = 401 #:: 509 #:: 687 #:: Stream.empty println("Values in stream first  ::" + mystm1) println("Values in stream second  ::" + mystm2) println("Values in stream third  ::" + mystm3) println("Values in stream four  ::" + mystm4) }


Example #2

In this example we are retrieving head of each stream list.


object Main extends App{ val mystm1 = 001 #:: 002 #:: 003 #:: Stream.empty val mystm2 = "abc" #:: "aa" #:: "bb" #:: Stream.empty val mystm3 = 150 #:: 250 #:: 350 #:: Stream.empty val mystm4 = 401 #:: 509 #:: 687 #:: Stream.empty println("Head of first stream  ::" + mystm1.head) println("Head of second stream  ::" + mystm2.head) println("Head of third stream  ::" + mystm3.head) println("Head of fourth stream  ::" + mystm4.head) }


Example #3

In this we are retrieving tail of the stream.


object Main extends App{ val mystm1 = 001 #:: 002 #:: 003 #:: Stream.empty println("Head of first stream  ::" + mystm1.tail) }


Example #4

Print all value of stream.


object Main extends App{ val mystm1 = 001 #:: 002 #:: 003 #:: Stream.empty val mystm2 = "abc" #:: "aa" #:: "bb" #:: Stream.empty val mystm3 = 150 #:: 250 #:: 350 #:: Stream.empty val mystm4 = 401 #:: 509 #:: 687 #:: Stream.empty println("all elements of the stream ::" + x) ) println("all elements of the stream ::" +x) ) println("all elements of the stream ::" + x) ) println("all elements of the stream ::" + x) ) }



In Scala Stream we computed the value of element one at a time not all at once. This can be used where performance is a concern. It follows the last approach while it comes to accessing the element of a stream.

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How Back_Inserter Method Works In C++?

Introduction to C++ Back_Inserter

The back_inserter method in C++ is used to construct an iterator, which holds the responsibility of inserting new elements to the end of the “x” or the container, with which the method is applied, and this method is defined within the header file of the program. In C++, this is a special type of output iterator designed to let the algorithms overwrite any elements and not make it mandatory to insert new elements.

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Now that we have understood what this C++ back_inserter method is let us learn the syntax and understand it. The syntax for back_inserter is as follows:

std::back_inserter ( Container & x );

When we have to insert the new elements at the end of the container, we pass the container within the parameters, and that is the same container we see here in the syntax. So what this method returns is the list of elements that are inserted at the end of the container. Moving on, we will now have a look at how the method explained works.

How does Back_Inserter Method work in C++?

Understanding how the back_inserter method works is important, and the way it works is that it creates a back-insert iterator, which is responsible for adding or inserting new elements to the list. This back insert iterator is of a special kind which allows the algorithm to overwrite the elements. Moving on, we will now understand the examples and demonstrate the working of back_inserter.

Examples of C++ Back_Inserter

Different examples are mentioned below:

Example #1

Our first example is the simple working of back_inserter; here, we add elements to the end of the list. The code is as follows:


int main() { std::fill_n(std::back_inserter(v), 3, -1); std::cout << “n The output for the code is: “; for (int n : v) std::cout  <<  n  << ‘ ‘; }

Code Explanation:

Simply start with importing important system files and then into our main code. Then we have our std, which is our namespace for the program, followed by defining the scope; we have a vector with integer data type and values of 1 to 10. We then our statement of back_inserter, with container passed as n, just like we learned with syntax and followed by x parameter.

Then we have our first output print statement, which simply prints a string statement, and the back_inserter result will follow it. Finally, we have our for the statement, where we pass our vector holding the numbers as n and then the next line if our final output statement prints the numbers from vector in a negative form. Refer to the below-attached screenshot for a proper understanding of the output.


As expected, the output starts with the print statement and is then followed by the list of numbers. These numbers at the end include the result of back_inserter, which are the numbers in negative, and now, moving on to the next example.

Example #2


using namespace std; int main()  { std::copy(v1.begin(), v1.end(), std::back_inserter(v2)); cout << “n Elements of Container 1 are : “; int i; for (i = 0; i < 3; ++i) { cout << v1[i] << ” “; } cout << “n Elements of Container 2 are : “; for (i = 0; i < 5; ++i) { cout << v2[i] << ” “; } return 0; }

Code Explanation:

Started with all the system files needed, followed by initializing main. Then we have our first vector with an integer data type, and it holds 3 values, and the second vector of the same type, but with no specific size or values. Then begins our copy operation, where we are copying the begin and end part of vector 1 and implementing the back_inserter for vector 2.

Now we start printing the list of values that our vectors hold. First, a cout statement followed by the for a statement where we count and print each element of the vector. For our first vector, this for statement will only print 3 values, no more than that. Moving to our second vector, within for, we want it to print 5 values. Here we will have 2 new values, which will be zeros but added to the last part of the vector. Refer to the below-attached screenshot for a proper understanding of the output.


As expected, we have two print statements with values of 2 vectors.

Example #3


int main () { for (int i=1; i<=5; i++){ dab.push_back(i); bar.push_back(i*5); } std::copy (bar.begin(),bar.end(),back_inserter(dab)); std::cout << “n Our Container contains: “; std::cout << ‘ ‘ << *it; std::cout << ‘n’; return 0; }

Code Explanation:

Similar to our earlier example, we have system files followed by declaring two vectors of integer type. Then we have our for the statement, to add new value to the list and next statement we have push_back and us multiple our value from the list. These things happen within our first vector, from which we later copy all values into our second vector. Then comes our print statements and for statement for properly printing the values. Refer to the below attached screenshot.


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How To Declare & Store Boolean Values In Mysql?

Introduction to MySQL Boolean

There is none of the built-in datatype present in MySQL for boolean values. However, MySQL provides us with the TINYINT data type, which can store values of integers with small values. We can declare the column’s data type whose behavior is like boolean with TINYINT(1) data type. That will function in the same way as a boolean. The 0(zero) is the FALSE value, while all other non-zero values are 1 in MySQL. MySQL provides keywords such as BOOLEAN or BOOL, which are internally treated in the same manner as TINYINT(1). In this article, we will explore the available data types in MySQL that can be utilized to store boolean values. We will also demonstrate the usage of boolean values in tables through examples.

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How to Declare & Store Boolean Values in MySQL?

We can specify the column’s datatype that might store the boolean value as BOOLEAN, BOOL, or TINYINT(1). All of these behave similarly and are synonyms of each other. The column declared with the data type of any of the mentioned databases evaluates the FALSE value as 0 and stores it as 0. On the other hand, the column considers and stores all other values, including TRUE or any non-zero value, as 1 for the respective record. Let us fire a straightforward command in MySQL.



The output of the above query statement is as follows

Hence, we can conclude that MySQL considers true as one and false as 0. Note that true and false are treated the same irrespective of the case in which they are used.

Example to Implement of MySQL Boolean

1. Let us create one table named marathon_players that will store the participants’ details in the marathon and have columns that will store boolean values in it, such as healthChecked and runCompleted. We will declare the data type of the healthChecked column as BOOLEAN and runCompleted as BOOL and check the results of the created table. For the table creation, we will use the following CREATE TABLE query.


CREATE TABLE marathon_players( player_id INT NOT NULL AUTO_INCREMENT, Name VARCHAR(100), Age INT, healthChecked VARCHAR(100), runCompleted VARCHAR(100), completionTime TIME, PRIMARY KEY (player_id) );

Now, let us describe the created table by using the following query statement:

DESC marathon_players;


Executing the above query gives the following output

2. We can conclude that MySQL automatically converts and treats the data type of the “healthChecked” column as TINYINT(1) and the data type of the “runCompleted” column as BOOL, even though we specified them as BOOLEAN.

Let us now insert some values in the marathon_players table using the following insert queries:


INSERT INTO marathon_players (player_id, Name, Age, healthChecked, runCompleted, completionTime) VALUES('1','Ramesh','25','true','false','02:50:56'); INSERT INTO marathon_players (player_id, Name, Age, healthChecked, runCompleted, completionTime) VALUES('2','Suresh','27','-12','25','01:30:21');

3. Now, let’s execute the following select query to observe the retrieved results


SELECT * FROM marathon_players;


4. In the first record, Ramesh’s name was inserted and we specified the healthChecked and runCompleted columns as true and false, respectively. The insertion stored them as 1 and 0, respectively. In the second record, we stored the values -12 and 25 in the healthChecked and runCompleted columns, respectively, using the same format that we declared. Even though we declared those columns as BOOLEAN and BOOL, the database internally treated them as TINYINT datatypes, expanding the column length to store the values.


To know whether a particular column contains the value that evaluates to true or false, MySQL provides us with four clauses: IS TRUE, IS FALSE, IS NOT TRUE, and IS NOT FALSE clauses. Out of them, IS TRUE and IS NOT FALSE behave in the same fashion, and IS NOT TRUE and similarly IS FALSE function and result in the same results. Let us try to find out the records in our table marathon_players whose healthChecked columns value is true or is equivalent to true. For this, I will first use the clause IS TRUE, and my query statement will be as follows –


SELECT * FROM marathon_players WHERE healthChecked IS TRUE;


6. From the results, it is evident that both the values 1 and 12 are considered true. Any non-zero value, whether positive or negative, will be considered true. Now, let us use IS NOT FALSE clause and see whether we retrieve the same results. Our query statement will be as follows –


SELECT * FROM marathon_players WHERE healthChecked IS NOT FALSE;


7. Now, let’s review the results for all individuals whose run was not completed by checking the value of the “runCompleted” column. This will allow us to verify the functionality of the remaining two clauses. Let us prepare the query using the IS FALSE clause in it. The query will be as follows:


SELECT * FROM marathon_players WHERE runCompleted IS FALSE;


8. Now, let us use the IS NOT TRUE clause in our query and retrieve the results. Our query statement is as follows –


SELECT * FROM marathon_players WHERE runCompleted IS NOT TRUE;


We can conclude that IS NOT TRUE and IS FALSE give the same output.


MySQL does not provide any specific datatype that will store the boolean values. However, we can use the keywords “BOOLEAN” and “BOOL” to declare the data type of the column, which will be internally treated and considered as TINYINT(1) data type. Hence, we can say that BOOLEAN and BOOL are synonyms of the TINYINT(1) data type. In MySQL, any truth value, regardless of its case, is considered and stored as 1. Similarly, any non-zero value and the value “1” are treated as TRUE when using clauses such as IS TRUE, IS FALSE, IS NOT TRUE, or IS NOT FALSE. The opposite is applicable for 0 and FALSE in MySQL.

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