# Trending November 2023 # How Set Works In Haskell # Suggested December 2023 # Top 17 Popular

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In Haskell as the name suggest set is used to store the elements, set are immutable in nature and comes under the data structure category, which provide us a way to store elements inside it, which should be in orders and unique as well. The set in Haskell provide us effective and efficient way of deletion, insertion and other operations. As we said they are immutable in nature that means if we try to modify the existing set it will always return us the new set containing the new modified elements.

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The set is used to store the elements, and also we have to use some package to use it inside our program.

import qualified chúng tôi as Set:

name_of_set = Set.empty name_of_set = Set.singleton 'your single value' name_of_set = Set.fromList 'your list'

As you can see in the above line of syntax, we have three different approaches by the use of it we can create a set in Haskell, one issuing empty, another one using singleton and third is form the existing list we have

Let’s take an practice syntax:

demoset = Set.empty

As you can see in the above code of syntax we are trying to create an empty set by the use of ’empty’ method of set.

The set is used to store the elements, set store the unique element, also in order in which they inserted into the set. We have one simplest way to create the set in Haskell by the use of existing list we have, also we can create it by using the empty and singleton method available in Haskell set.

Let’s take a closer look at the import packages which is required to import in the program:

Packs to use for set:

1. import qualified chúng tôi as Set

This mentions package should be import in the program otherwise we will get error while creating the set in Haskell, because all the method of creating set present inside the package only. In order to access them it should be in the place.

2. Create set by using the existing list object

We can create set by the use of existing list variable. For this we can use the ‘fromList’ from the set package available. But to use this we have to have set package imported into the program.

Let’s have a look at the syntax for the ‘fromList’ to start using it.

As you can see in the above line of code, it is very easy to use a handle

Let’s take a sample piece of code:

demoset = Set.fromList [10, 20, 30]

This will create a set which contain the elements from the list, and it will return us the new set by the existing list object in Haskell.

3. Create an empty set

We can also create an empty set by the use of ’empty’ method available inside the set package, for this also set package is to be present in the program at the top.

Let’s take a look at the syntax for the ’empty’ to start using it while programming:

name_of_set = Set.empty

As you can see in the above line of code, it is very easy to use a handle.

Let’s take a sample piece of code:

Code:

demoset = Set.empty

This will create a set which is empty.

4. Create set using the single value

In Haskell we can also create a singleton set which will contain the single value inside it, for this we have ‘singleton’ method available in the set library of Haskell.

Let’s take an look at the syntax for the ‘singleton’ to start using it while programming:

name_of_set = Set.singleton 'your single value'

let’s take an sample piece of code understand:

demoset = Set.singleton 'A'

In the following ways we can create set in Haskell, but the set package is important to include it, is also an in built function of Haskell, so we do not require to install any dependency for this to use in our program.

Given below is the example mentioned:

In this example we are trying to create the set in Haskell using the set library and fromList function of the set package.

Code:

import qualified chúng tôi as Set set1 = Set.fromList ['a'..'z'] set2 = Set.fromList [10, 20, 30, 40, 50, 60] set3 = Set.fromList ["Hello", "world", "bye", "enjoy", "moment"] set4 = Set.fromList [1, 2, 3, 4, 5, 6, 7] set5 = Set.fromList [400, 200, 100, 500, 900, 800] set6 = Set.fromList ["value 1", "value 2", "value 3", "value 3", "value 4"] set7 = Set.fromList [1.1, 2.2, 3.3, 4.4, 5.5, 6.6] main = do print("Demo to show set in Haskell !!") print("Printing the result !!") print("first set is :::", set1) print("second set is :::", set2) print("third is :::", set3) print("fourth set is :::", set4) print("fifth set is :::", set5) print("sixth set is :::", set6) print("seventh set is :::", set7)

Output:

Conclusion

By the use of set we can store our elements inside the set, it also main the orders of the insertion of the elements. One important thing about set that we only contained the unique elements, so in this way we can avoid duplicate data as well while preforming business logic for the application.

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## How Bcrypt Works In Flask

Flask bcrypt is defined as a flask extension that enables users with utilities related to bcrypt chúng tôi bcrypt is a hashing function for password that is based on the Blowfish cipher and incorporates salt for protecting the application against any rainbow table attacks. We know, too many new terminologies. Let us decipher that! Salt is an additional input of random data that helps safeguard passwords when stored whereas rainbow table is a precomputed table that enables caching of cryptographic hash functions quite often used for cracking password hashes. The bcrypt is an adaptive function which can be deliberately made slower so that application is resistant to brute force attacks.

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1. Installing Flask bcrypt module in python.

2. Instantiate bcrypt object in python.

3. Hash a password using defined method through the bcrypt object created.

Python 2:

Python 3:

4. Check a hashed password through the bcrypt object created.

In order to understand about hashing and in particular about bcrypt, it is important for us to understand a few terminologies which might be either new are so confusingly used that there is a need to clear the air of confusion. The first interchangeable terms that are used are Authentication and Authorization. In the process of authentication, we make sure on who the user is as they claim them to be, whereas in authorization, it is made sure that user is allowed to access a route or resource. Hence, the password about which we will talk about in hashing using Bcrypt, needs to undergo authentication and once authenticated, the user will be authorized to proceed with the next tasks.

At first, we would need to install the flask-bcrypt module in the environment that is used for development of the flask application. We would need to use pip command while installing the module. As a next step, we would need to start building our flask application where we would need to first import the installed module. Once the import is complete, we would need to instantiate the object of bcrypt. With this we can now use the object created throughout the code. At this point we have an object of bcrypt created that will have all the API calls necessary for the utilities of hashing in a flask application.

There are various hash methods present. With any one of the hashing methods, we would try to generate a password hash by available hashlib ones and keep the hashed password same. With this hashed password, when a user tries to input another password, we would use the API of check_password_hash. This API will take in 2 values, one being the hash value and the other being the input of the user. Now the API will try to hash the value of the input by user and then look if the hash value of the user input matches to the hash value of the stored password and in this way it will authenticate the user.

Incase one tries to look at the hashed password, what they will see is a byte literal which might not make any sense, but instead contains information of the hashed value and also about the information on how the password was hashed. We also talked about a utility of bcrypt where in we can increase the time it takes to hash the password. By doing this we can delay the quick successive brute force attacks and thus saving from any malicious attacks. With this we now know the working of bcrypt in flask and what environment are they used in.

Given below are the examples of Flask bcrypt:

Example #1

Installing flask bcrypt module in python.

Syntax:

Output:

Example #2

Hash a password using defined method through the bcrypt object created.

Syntax:

Output:

Example #3

Check a hashed password through the bcrypt object created.

Syntax:

Password matches the none of the groups:

Conclusion

In this article we have got to know about the full details of what bcrypt is and an hands-on training on how hashing looks like and how hashed values are checked even without decrypting it back. Now it lies in hands of reader to utilize the knowledge in their flask application password hashing.

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## How Exists Works In Mongodb?

Definition of MongoDB exists

MongoDB provides different types of functionality to the user, the MongoDB exists as one of the functionalities provided by MongoDB. Basically, MongoDB provides a different comparison operator to the user, \$exists is one of the comparison operators, when we set the \$exists operator in the field of collection that means the \$exists operator is true. After that \$exists operator compares all the documents from the collection including the null field. That means as per our requirement we can set the \$exists operator in the collection and get the desired result. When the set \$exists operator is false then it returns the documents that do not match with the set value.

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Syntax:

Explanation

After that, we set the comparison operator that is \$exists is true as shown.

How exists works in MongoDB?

Now let’s see how the \$exists operator works in MongoDB as follows.

First, try to understand what the operator is. MongoDB provides different kinds of operators such as comparison, logical, element array and bitwise, etc. But we commonly used comparison operators among the different operators. Again we have different types of comparison operators such as eq, gt, lt, and gte, etc, and this operator we can use as per our requirement.

The working of the “not equal” operator is simple; we just need to mention the specified field name with the specified value that we want. The specified field name and specified value depend on the user. At the time of execution, it compares all specified values with all documents from the collection and if the value of the document is not equal to the specified value then it displays specified records.

Basically, the \$exists operator is used to compare the field values that we need which means as per user requirement we can display the document from the collection. If we set \$exists is true then it displays the matched field value that we already set and if we set \$exist is false then it shows all fields that do not contain the field value that we already set from the collection. In the event that we make an association between collection and assortment, we see likenesses between lines – reports and sections – fields. The greatest contrast is that each archive from the same assortment can contain altogether different arrangements of fields. So the field can contain invalid, yet it might likewise not be there. Now and then, we need to separate between those. The method of doing checks additionally relies upon the utilization and what would you like to accomplish.

Examples

Now let’s see different examples of the \$exists comparison operator in the MongoDB for better understanding as follows.

First, we need to create the new collection but before that, we need to create the new database by using the following statement as follows.

First, we created a sample database by using the following statement as follows.

use sample

Explanation

In the above statement, we use the command to create the new database, here we successfully created a sample database and we use it. The end result or we can say the output of the above statement we illustrated by using the following screenshot as follows.

After successful creation of the database, we need to create the collection by using the following statement as follows.

Explanation

In the above statement, we use create collection command to create the new collection; here we created a new collection name as a student_sample as shown. The end result or we can say the output of the above statement we illustrated by using the following screenshot as follows.

Now we have a new collection, so now we need to insert the different document into the newly created collection that is student_sample with a null field so we can get the result. For insertion of the document, we can use the following statement as follows.

db.student_sample.insert({ name: “Sachin”, dept: “IT”, marks: 98, dbsm:null})

Explanation

By using the above statement we insert a document into the student_sample collection, in which we insert names of students, department of the student, and dbsm marks with the null field as shown. Now we can see all documents from the collection by using the following statement as follows.

db.student_sample.find()

Explanation

Now we have a collection with documents now implementing the \$exists as follows.

db.student_sample.find({dbsm:{\$exists: true}})

Explanation

In the above example, we use a comparison operator that is \$exists as shown, here we set the field value that dbsm with true. The dbsm contains a null value. The end result or we can say the output of the above statement we illustrated by using the following screenshot as follows.

So similarly we can implement \$exists with false as per user requirement as follow here we just need to write the false instead of true.

db.student_sample.find({dbsm:{\$exists: false}})

Conclusion

We hope from this article you learn more about MongoDB exists. From the above article, we have learned the basic syntax of \$exists and we also see different examples of exists. From this article, we learned how and when we use MongoDB exists.

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## How Rollup Works In Postgresql

Introduction to PostgreSQL ROLLUP

The PostgreSQL ROLLUP is an extension of the GROUP BY clause. Generally, the PostgreSQL GROUP BY ROLLUP is used for data analytics operations. For performing data analytics related operations, so many tools or software are available in the market. PostgreSQL is not made for Data Analytics purpose; with the help of the operations like ROLLUP, we can support data analytics operations on real-time data.

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Syntax:

SELECT c1, c2, c3, aggregate_function(c4) FROM table GROUP BY ROLLUP (c1, c2, c3);

(c1, c2, c3)

(c1,c2)

(c1)

()

We can also perform a partial roll up to reduce the count of sub-totals created.

SELECT c1, c2, c3, aggregate(c4) FROM table_name GROUP BY c1, ROLLUP (c2, c3); How ROLLUP works in PostgreSQL?

We can use a single statement to generate multiple grouping sets with the help of the PostgreSQL ROLLUP option.

The PostgreSQL ROLLUP option adds extra rows in the result set, allowing us to get total and super-aggregate rows.

In order to analyze the hierarchical data like creating grand-total or sub-total, we use the PostgreSQL ROLLUP option.

Examples of ROLLUP in PostgreSQL

Let’s create a table named Furniture.

Code:

CREATE table furniture ( furniture_id SERIAL PRIMARY KEY, furniture_name VARCHAR (256) NOT null, furniture_type VARCHAR (256) NOT null, furniture_price int NULL );

Now, insert some data in the furniture table to execute SQL statements.

INSERT INTO furniture (furniture_name,furniture_type,furniture_price) VALUES ('Chair','Wood',2500), ('Chair','Plastic',2000), ('Table','Wood',5000), ('Table','Plastic',4000), ('Sofa','Wood',10000), ('Sofa','Plastic',8000), ('Bed','Wood',15000), ('Bed','Plastic',13000);

Illustrate the result of the above statement with the help of the following snapshot and the SELECT statement.

SELECT * FROM furniture; Example #1 – ROLLUP with one column

The following SQL statement uses the GROUP BY clause and the SUM() function to find the total furniture price from furniture_name.

Code:

SELECT furniture_name, SUM(furniture_price) FROM furniture GROUP BY furniture_name;

Output:

Illustrate the result of the above statement with the help of the following snapshot.

To fetch the total furniture price of all Furniture, we can use the PostgreSQL ROLLUP to the GROUP BY clause as follows:

SELECT furniture_name, SUM(furniture_price) FROM furniture GROUP BY ROLLUP (furniture_name);

Output:

Illustrate the result of the above statement with the help of the following snapshot.

You can see the NULL value in the furniture_name column, which shows the grand total super-aggregate result.

In this above example, the PostgreSQL ROLLUP option allows the statement to add an extra row showing the total furniture price.

As we have seen, the output shows a NULL value in a newly produced row, which we can make more readable using the COALESCE() function.

Code:

SELECT COALESCE(furniture_name, 'Total furniture price') AS furniture_name, SUM(furniture_price) FROM furniture GROUP BY ROLLUP (furniture_name);

Output:

Illustrate the result of the above statement with the help of the following snapshot.

Example #2 – ROLLUP with multiple columns

The following SQL statement generates the furniture result by furniture_name and furniture_type:

Code:

SELECT furniture_name, furniture_type, SUM(furniture_price) FROM furniture GROUP BY furniture_name, furniture_type;

Output:

Illustrate the result of the above statement with the help of the following snapshot.

Now add the ROLLUP to the GROUP BY clause as follows:

SELECT furniture_name, furniture_type, SUM(furniture_price) FROM furniture GROUP BY ROLLUP (furniture_name , furniture_type);

Output:

Illustrate the result of the above statement with the help of the following snapshot.

Explanation:

The set of furniture_type rows for a specified furniture_name, an additional summary row, generates the total furniture price. The values in the furniture_type column are set to NULL in the newly added row.

Following all rows, an additional summary row generates the total furniture price of all furniture names and furniture types. The values in the furniture_name and furniture_type columns are set to NULL in the newly added rows.

Example #3 – ROLLUP with a partial rollup

We can use it to do a partial rollup which reduces the count of sub-totals generated, as shown in the following example:

Code:

SELECT furniture_name, furniture_type, SUM(furniture_price) FROM furniture GROUP BY furniture_name, ROLLUP (furniture_type);

Output:

Illustrate the result of the above statement with the help of the following snapshot.

The above example generates an aggregate summary for the furniture_type column, not the furniture_name column.

Conclusion

From the above article, you have seen how to use it. Also, we have added some examples of PostgreSQL ROLLUP to understand it in depth.

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## How Debugging Works In Tensorflow?

Introduction to TensorFlow Debugging

In this article, we will try and understand what the different ways of debugging can be done in TensorFlow. Generally debugging is very useful for finding out the values getting flown down in the code and where exactly the code is breaking. All the languages present in the market provide inbuilt functionality for debugging. Similarly, in TensorFlow also provides different classes and packages with which we can identify the flow of the data in the algorithms and optimize the algorithm’s performance.

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How Debugging Works in TensorFlow?

Now let’s see how the debugging works in TensorFlow.

The core program part where the debugging can be enabled in TensorFlow are:

graph(though the use of this function we can build a computation graph)

session(though the use of this function we can execute the graph)

there are in total 4 ways as shown below through which we can perform debugging in TensorFlow

1. Fetching and Printing Values for a Particular Tensor

This is the easiest to use step where we can add breakpoints and print out the values to get the required information

It is very easy and quick to implement.

And information can be fetched from anywhere we want.

If we print any information at any point, then that will create a reference to that particular tensor which is not a good practice to keep

2. The tf.print function

This method can come handy while checking some output in runtime. It will just create a log for the particular line in the with the use of the session.run() method.

This method is handy as it helps us to monitor the development of the values during the run time.

Since this creates a log of the terminal data during the execution of the algorithm, it might fill up the screen with the logs that are not a good practice Afterall.

just want to discuss the tool which TensorFlow provides called Tensor Board. It’s a web UI for TensorFlow visualization developed by Google and runs locally in the system. Below is the screenshot for the website. It is generally used to visualize the performance of the TensorFlow algorithm and monitor its performance. This Dashboard also comes with a plugin for debugging.

3. TensorBoard visualization

With this visualization, we can use to monitor various things about the out model, such as:

We can summarize the model.

View the performance.

Serialize the data in the model.

Clean the graph and give proper nomenclature.

This is basically more or less a monitoring tool used to monitor the performance of our model.

Now moving on to TensorBoard Debugger.

4. TensorBoard Debugger

As explained earlier, TensorBoard is a visualizing tool so that visualization can be debugged using this plugin. It provides various cool debugging features such as:

We can select particular nodes in the Tensor and debug them.

Graphically we can control the execution of the model.

And finally, we can also visualize the tensors and their values.

Below is the screenshot of this TensorBoard Debugger in action:

The code TensorFlow packages which are used for the debugging are:

Here tf_debug is the debugger that needs to be imported from the TensorFlow.python package to run the debugging on TensorFlow.

And the below two lines are used to invoke the TenorBoard locally through the terminal.

We can identify we can output value and a particular stage through the use of debugging while the algorithm is getting trained.

Using the Tensor board application, we can identify and see the performance of our algorithm in a graphical format.

We can also run the execution of each and every step of our model using the GUI provided in the Tensor Board.

The TensorBoard application is very user friendly and easy to understand.

With the use of a debugger or rather Tensor Board, we can identify if we still need more data cleaning is required on our training data.

Conclusion

In this article, we learned about the debugging in TensorFlow, the packages present for TensorFlow’s debugging purpose, and how to implement them. We have also seen the use of tensor board applications, which is a useful tool to debug the algorithm while getting trained.

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## How Database Works In Redshift?

Definition of Redshift Database

Normally Redshift database is a cloud-based solution that is provided by Amazon, we can also call a big data warehouse. The Redshift database provides the storage system to the organization that means the organization can store the data over the cloud and we can easily access any time anywhere as per user requirement and users can access that data through SQL. In another word we can say that clusters and clusters may contain different nodes, the nodes can be accessed independently by the organization and application. Basically, Redshift is designed to be used for different types of tools such as existing SQL.

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Syntax:

Explanation:

In the above syntax, we used a create database command to create the database, here the specified database name means the actual database name that we need to create.

How database works in Redshift?

Now let’s see how the database works in Redshift as follows.

Suppose you need to assemble a data set for your internet business; first, you’ll need to interface with your underlying data set made when you dispatched your group.

Now let’s see how we can load data into a database as follows.

First, we need to create the database that we want by using the above-mentioned syntax. After that, we need to create the table inside the newly created table by using create table command. After successful creation of the table, we can perform the different operations such as select, insert and drop as per user requirement.

Now let’s see how we can manage the database as well as how we can maintain it as follows.

Database maintenance and management is basically not the most crucial part of the database but it is the most important part of the database process. In database management, we can consider the following points to maintain and manage as follows.

Always we need to take the backup and also we need to set the backup and recovery.

We just need to maintain the database table in a timely manner.

We need to manage workload inefficiently as per requirement.

Regularly we need to optimize the database queries.

Greatly equal preparing,

Columnar information stockpiling

Designated information pressure encoding plans.

Now let’s see what types of operation we can perform on the database as follows.

Alter database operation:

Suppose we need the attribute of an existing database at that time we can alter the database command as per user requirement as follows.

Suppose we need to change the database name at that time we can use the following syntax as follows.

alter database existing specified database name rename to new specified database name;

Suppose if we need to change the database owner at that time we can use the following syntax as follows.

Delete database operation:

Suppose we need to delete the existing database at that time we can use the following syntax as follows.

drop database specified database name;

By using the select clause we can list all existing databases of the Redshift cluster as follows.

select * from pg_database;

Examples

Before the creation of the database, we just need to specify the cluster that means we need to create the cluster as shown in the following screenshot.

First, let’s see how we can create the database as follows.

Suppose we need to create the database at that time we can use the following statement as follows.

Explanation:

In the above example, we use a create a database command to create a new database; here sample_red is the database name that we need to create as shown in the above statement. The final output or we can say that the result of the above statement we can illustrate by using the following screenshot as follows.

Suppose we need to change the database at that time we can use the following statement as follows.

alter database sample_red rename to red_sample;

Explanation:

In the above example, we use the alter command to rename the existing database name, here we need to change the sample_red database name to red_sample as shown in the above statement. The final output or we can say that the result of the above statement we can illustrate by using the following screenshot as follows.

Similarly, we can delete the database by using the drop database command.

Conclusion

We hope from this article you learn more about the Redshift database. From the above article, we have learned the basic concept as well as the syntax of the Redshift database and we also see the different examples of the Redshift database. From this article, we learned how and when we use the Redshift database.

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