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Introduction to PySpark explode

PYSPARK EXPLODE is an Explode function that is used in the PySpark data model to explode an array or map-related columns to row in PySpark. It explodes the columns and separates them not a new row in PySpark.  It returns a new row for each element in an array or map.

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It takes the column as the parameter and explodes up the column that can be further used for data modeling and data operation. The exploding function can be the developer the access the internal schema and progressively work on data that is nested. This explodes function usage avoids the loops and complex data-related queries needed.

Let us try to see about EXPLODE in some more detail.

The syntax for PySpark explode

The syntax for the EXPLODE function is:-

from pyspark.sql.functions import explode df2 = data_frame.select(data_frame.name,explode(data_frame.subjectandID)) df2.printSchema()

Df_inner: The Final data frame formed

Screenshot:

Working of Explode in PySpark with Example

Let us see some Example of how EXPLODE operation works:-

Let’s start by creating simple data in PySpark.

data1  = [("Jhon",[["USA","MX","USW","UK"],["23","34","56"]]),("Joe",[["IND","AF","YR","QW"],["22","35","76"]]),("Juhi",[["USA","MX","USW","UK"],["13","64","59"]]),("Jhony",[["USSR","MXR","USA","UK"],["22","44","76"]])]

The data is created with Array as an input into it.

data_frame = spark.createDataFrame(data=data1, schema = ['name','subjectandID'])

Creation of Data Frame.

data_frame.printSchema() data_frame.show(truncate=False)

Output:

Here we can see that the column is of the type array which contains nested elements that can be further used for exploding.

from pyspark.sql.functions import explode

Let us import the function using the explode function.

df2 = data_frame.select(data_frame.name,explode(data_frame.subjectandID))

Let’s start by using the explode function that is to be used. The explode function uses the column name as the input and works on the columnar data.

df2.printSchema() root |-- name: string (nullable = true) |-- col: array (nullable = true) |    |-- element: string (containsNull = true)

The schema shows the col being exploded into rows and the analysis of output shows the column name to be changed into the row in PySpark. This makes the data access and processing easier and we can do data-related operations over there.

df2.show()

The output breaks the array column into rows by which we can analyze the output being exploded based on the column values in PySpark.

The new column that is created while exploding an Array is the default column name containing all the elements of an Array exploded there.

The explode function can be used with Array as well the Map function also,

Let us check this with some example:-

data1  = [("Jhon",["USA","MX","USW","UK"],{'23':'USA','34':'IND','56':'RSA'}),("Joe",["IND","AF","YR","QW"],{'23':'USA','34':'IND','56':'RSA'}),("Juhi",["USA","MX","USW","UK"],{'23':'USA','34':'IND','56':'RSA'}),("Jhony",["USSR","MXR","USA","UK"],{'23':'USA','34':'IND','56':'RSA'})] data_frame = spark.createDataFrame(data=data1, schema = ['name','subjectandID']) data_frame.printSchema() root |-- name: string (nullable = true) |-- subjectandID: array (nullable = true) |    |-- element: string (containsNull = true) |-- _3: map (nullable = true) |    |-- key: string |    |-- value: string (valueContainsNull = true)

The data frame is created and mapped the function using key-value pair, now we will try to use the explode function by using the import and see how the Map function operation is exploded using this Explode function.

from pyspark.sql.functions import explode df2 = data_frame.select(data_frame.name,explode(data_frame.subjectandID)) df2.printSchema() root |-- name: string (nullable = true) |-- col: string (nullable = true) df2.show()

The  Output Example shows how the MAP KEY VALUE PAIRS are exploded using the Explode function.

Screenshot:-

These are some of the Examples of EXPLODE in PySpark.

Note:-

EXPLODE is a PySpark function used to works over columns in PySpark.

EXPLODE is used for the analysis of nested column data.

PySpark EXPLODE converts the Array of Array Columns to row.

EXPLODE can be flattened up post analysis using the flatten method.

EXPLODE returns type is generally a new row for each element given.

Conclusion

From the above article, we saw the working of EXPLODE in PySpark. From various examples and classification, we tried to understand how this EXPLODE function works and what are is used at the programming level. The various methods used showed how it eases the pattern for data analysis and a cost-efficient model for the same.

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Learn The Internal Working Of Pyspark Foreach

Introduction to PySpark foreach

PySpark foreach is explained in this outline. PySpark foreach is an active operation in the spark that is available with DataFrame, RDD, and Datasets in pyspark to iterate over each and every element in the dataset. The For Each function loops in through each and every element of the data and persists the result regarding that. The PySpark ForEach Function returns only those elements which meet up the condition provided in the function of the For Each Loop. A simple function that applies to each and every element in a data frame is applied to every element in a For Each Loop. ForEach partition is also used to apply to each and every partition in RDD. We can create a function and pass it with for each loop in pyspark to apply it over all the functions in Spark. This is an action operation in Spark used for Data processing in Spark. In this topic, we are going to learn about PySpark foreach.

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Syntax for PySpark foreach

The syntax for the PYSPARK WHEN function is:-

def function(x): Dataframe.foreach(function) def f(x): print(x) b=a.foreach(f)

ScreenShot:

Working of PySpark foreach

Let us see somehow the ForEach function works in PySpark:-

The ForEach function in Pyspark works with each and every element in the Spark Application. We have a function that is applied to each and every element in a Spark Application.

The loop is iterated for each and every element in Spark. The function is executed on each and every element in an RDD and the result is evaluated.

Every Element in the loop is iterated and the given function is executed the result is then returned back to the driver and the action is performed.

The ForEach loop works on different stages for each stage performing a separate action in Spark. The loop in for Each iterate over items that is an iterable item, One Item is selected from the loop and the function is applied to it, if the functions satisfy the predicate for the loop it is returned back as the action.

The number of times the loop will iterate is equal to the length of the elements in the data.

If the data is not there or the list or data frame is empty the loop will not iterate.

The same can be applied with RDD, DataFrame, and Dataset in PySpark.

Example of PySpark foreach

Let us see some Example of how PYSPARK ForEach function works:

Create a DataFrame in PYSPARK:

Let’s first create a DataFrame in Python.

CreateDataFrame is used to create a DF in Python

a= spark.createDataFrame(["SAM","JOHN","AND","ROBIN","ANAND"], "string").toDF("Name") a.show()

Now let’s create a simple function first that will print all the elements in and will pass it in a For Each Loop.

def f(x) : print(x)

This is a simple Print function that prints all the data in a DataFrame.

def f(x): print(x)

Code SnapShot:

Let’s iterate over all the elements using for Each loop.

b = a.foreach(f)

This is simple for Each Statement that iterates and prints through all the elements of a Data Frame.

b = a.foreach(f)

Stages are defined and the action is performed.

Row(Name=’ROBIN’) Row(Name=’ANAND’) Row(Name=’AND’) Row(Name=’JOHN’) Row(Name=’SAM’)

Code Snapshot:

a= spark.createDataFrame(["SAM","JOHN","AND","ROBIN","ANAND"], "string").toDF("Name") b=a.foreach(print) Example #2

Let us check the type of element inside a Data Frame. For This, we will proceed with the same DataFrame as created above and will try to pass a function that defines the type of variable inside.

Create a DataFrame in PYSPARK:-

Let’s first create a DataFrame in Python.

CreateDataFrame is used to create a DF in Python

a= spark.createDataFrame(["SAM","JOHN","AND","ROBIN","ANAND"], "string").toDF("Name").show()

Code SnapShot:

This function defines the type of the variable inside.

def f(x): print(type(x))

Let’s use ForEach Statement and print the type in the DataFrame.

b = a.foreach(f)

Output:

This will print the Type of every element it iterates.

Code SnapShot:

We can also build complex UDF and pass it with For Each loop in PySpark.

From the above example, we saw the use of the ForEach function with PySpark

Note:

For Each is used to iterate each and every element in a PySpark

We can pass a UDF that operates on each and every element of a DataFrame.

ForEach is an Action in Spark.

It doesn’t have any return value.

Conclusion

From the above article, we saw the use of FOR Each in PySpark. From various examples and classification, we tried to understand how the FOREach method works in PySpark and what are is used at the programming level.

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Learn The Steps To Create Drupal Views With Working

Introduction to Drupal Views

We know that drupal provides different features to the developers, so views are one of the features which are provided by drupal. Basically, with the help of views administrators are able to do different tasks such as creating and managing the web content as well they are also able to display the list of content as per requirement. Normally views are nothing but the module which is used for display, or we can say that output.

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Key Takeaways

With the help of a view, we can create the default page with different sorting options.

Drupal view allows us to customize the view as per our requirements.

The customization of the view is easy.

It provides the different types of modules as well as functionality to the developer, so they can easily configure the website with minimum time.

What is Drupal in Views?

Lists of content can be made, managed, and displayed with the view’s module, which is also used by site designers and administrators. The output of a view is referred to as a “display,” and each list managed by the views module is referred to as a “view.” Block or page displays are available, and a single view can have multiple displays. A discretionary route helps, including a framework way and menu thing that can be set for each page-based show of a view. Views that list content (view user type), content revisions (Node view type), or users (view user type) can be created by default. On the views administration page, a view can be added, edited, or deleted, and members of specific user roles can only access it.

How to Create Drupal Views?

Now let’s see how we can create views in drupal as follows.

Title: It is used to set the title for a specific page of view.

Format: By using this option, we set the displayed option means we display data.

Fields: It is used to define which field we need to display.

Filter Criteria: We can apply the filter criteria as per our requirements.

Page setting: Here, we can set the permission such as view, menu, and many more.

Header: It is used to set the custom header to the view page.

Footer: It is used to set the custom footer to the view page.

How do Drupal Views Work? Drupal Views Module

Now let’s see what modules are available for views as follows.

Add contextual filter to view: This is one of the modules used to filter the content as well as it provides the dynamic filter.

Add display to view: By using a module, we can add the content which we want to display in different formats.

Add field to view: By using this module, we can add more fields on the screen, and it provides the two methods, such as all display and this page override.

Add relationship to view: By using relationship, we can join more than one table as per our requirement to display the content.

Simple block view: It allows us to create the list of our data that we need to place as a block on site.

Simple page view: It allows us to create a page with a view.

Drupal also provides many other modules which are available based on the version of drupal.

Conclusion

From this article, we are able to understand Drupal views. It provides the basic idea and implementation of Drupal views, and we also see the representation of the Drupal views. At the end we got an idea about uses of drupal views.

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Learn The Attributes Of A Parameter

Introduction to Powershell Parameter

A parameter is nothing but an input provided to a function or any cmdlet. Every parameter will have a name and a data type associated with it. It is always not necessary that parameters are mandatory. Some parameters may also have default values, and these values are used when a value for the parameter is not explicitly mentioned. For a function, the parameters are defined with the Param block. Mandatory parameters are defined using the [Parameter (Mandatory)] attribute. It is also possible to validate the value passed to each parameter using the ValidateSet property. Parameter names are always preceded by a hyphen (-), which denotes the PowerShell that the word after (-) is a parameter. This article will explain in detail about the parameters and their types in PowerShell, the various types of parameters, how to pass parameters to a function, etc., in detail.

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Syntax of Powershell Parameter

The following example shows how to pass parameters to a cmdlet

In the above example, the path is a parameter for the cmdlet, and the corresponding value of the parameter is enclosed within “”.

To define parameters for a function, the below format is used

[Parameter()] [Parameter()] [Parameter()] )

Identifying the various parameters associated with a cmdlet:

To identify the various parameters that are available for a cmdlet, the below cmdlet can be used.

Get-Help CmdletName -Parameter *

Example:

Get-Help out-file -Parameter *

Output:

The above shows the various parameters that are associated with the Out-File cmdlet. It also shows whether a parameter is a mandatory one, its position, aliases.

Attributes of a parameter

Here are the attributes of a parameter mentioned below

-Required

This denotes whether the parameter is a must for running this cmdlet. If this value is true for a parameter, then it means that this is a mandatory one. An error will be thrown if the appropriate value for that parameter is not passed.

-Position

Positional parameters are parameters that have its position set to a positive integer. When using this type of parameter, the parameter name is not required, but the parameter value must be mentioned in the appropriate position. If the position value is 0, then the parameter name is not required, but its value should be the first to appear after the cmdlets name. If the position setting is excluded, it can be defined anywhere in the cmdlet.

-Type

It denotes the type of the parameter like string, int, switches, etc.

-Default Value -Accepts Multiple Values

This denotes whether a parameter can accept multiple values. In case if multiple values are allowed, they are typed in a comma-separated and passed, or the values can be saved in a comma-separated way in a variable, and that variable can be passed as a value to the parameter.

-Accepts Pipeline Input

This denotes whether the pipeline can be passed as input to the parameter. If its value is false, it denotes the parameter doesn’t accept the pipeline for an input.

-Accepts Wildcard Characters

This denotes whether the parameter can use the wildcard to match characters.

Validation of Parameters

Below are some ways of validating the value that is passed to a parameter.

1. Making a parameter Mandatory and allowing Null Value

The mandatory parameter is used to denote whether a parameter compulsorily requires a value or not. AllowNull attribute is used to allow null values as a value.

Example:

[Parameter(Mandatory=$true)] [AllowNull()] [String] )

In the above, UserName is a mandatory parameter, and it accepts null for a value.

2. AllowEmptyString validation attribute

This attribute is used to allow empty string as a value to the mandatory parameter. The Allow Empty collection attribute is used to allow empty collection as a value to a mandatory string parameter.

Example:

[Parameter(Mandatory=$true)] [AllowNull()] [AllowEmptyCollection()] [String] )

ValidateLenght attribute is used to specify the minimum and maximum length of the value that is passed to a parameter.

Validatepattern is used to match a regular expression with the value that is passed to the parameter.

ValdiateRange specifies a range in which the value of the variable must be.

ValidateSet denotes a set of values from which one of the values must be passed for the parameter. Value outside this set can’t be set to the parameter.

ValidateDrive is used to validate the value of a path parameter to a certain drive

Example:

[Parameter(Mandatory=$true)] [AllowNull()] [AllowEmptyCollection()] [ValidateCount(5,50)] [ValidateLength(10,20)] [ValidatePattern(“[1-9][0-4][4-9][1-4]”)] [ValidateDrive(“C”, “Function”, “Drive”)] [String] [parameter(Mandatory=$true)] [ValidateLength(1,30)] [String] [parameter(Mandatory=$true)] [Int] [parameter(Mandatory=$true)] [ValidateSet(“Chennai”, “Mumbai”, “Delhi”)] [String] [parameter(position=1)] [ValidateLength(1,30)] [String] [parameter(position=2)] [Int] [parameter(position=3)] [ValidateSet(“Chennai”, “Mumbai”, “Delhi”)] [String] test3 “viki” 35 “Chennai”

Output:

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Learn The Basic Concepts Of Security Engineering

Introduction to Security engineering

Security Engineering focuses on the security aspects in the development of the systems so that they can deal robustly with losses caused by accidents ranging from natural disasters to malicious attacks. The main motto of security Engineering is to not only satisfy pre-defined functional and user requirements but also preventing the misuse of the system and malicious behavior. Security is one of the quality factors of a system that signifies the ability of the system to protect itself from accidental and malicious external attacks. It is an important issue as networking of the system has increased, and external attacks to the system through the internet can be possible. Security factor makes the system available, safe, and reliable. If a system is a networked System, then the reliability and its safety factors become more unreliable.

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Why do we need security Engineering? Security risk management

Vulnerability avoidance: The system is designed so that vulnerabilities do not occur. Say if there is no network, then the external attack is not possible.

Detection and removal of attacks: The System is designed so that attacks can be detected and removed before they result in any exposure of data programs s same as the virus checkers who detect and remove the viruses before they infect the system.

Damage caused due to insecurity.

Corruption of programs and data: The programs or data in the system may be modified by unauthorized users.

Unavailability bod service: The system is affected and out into a state where normal services are not available.

Leakage of confidential information: Information that is controlled by the system may be disclosed to the people who are not authorized to read or use that information.

System survivability

System survivability is nothing but an ability of a system to continue performing difficult functions on time even if a few portions of the system are infected by malicious attacks or accidents. System survivability includes elements such an s reliability, dependability, fault tolerance, verification, testing, and information system security. Let’s discuss some of these elements.

Adaptability: even if the system is attacked by a threat, the system should have the capability to adapt to the threat and continue providing service to the user. Also, the network performance should not be degraded by the end-user.

Availability: The degree to which software remains operable in the presence of system failures.

Time: Services should be provided to the user within the time expected by the user.

Connectivity: It is the degree to which a system performs when all nodes and links are available.

Correctness: It is the degree to which all Software functions are specified without any misunderstanding and misinterpretations.

Software dependence: The degree to which hardware does not depend upon the software environment.

Hardware dependence: The degree to which software does not depend upon hardware environments.

Fault tolerance: The degree to which the software will continue to work without a system failure that would cause damage to the user and the degree to which software includes recovery functions

Fairness: It is the ability of the network system to organize and route the information without any failure.

Interoperability: It is the degree to which software can be connected easily with other systems and operated.

Performance: It is concerned with the quality factors kike efficiency, integrity, reliability, and usability. Sub factors include speed and throughput.

Predictability: It is the degree to which a system can provide countermeasures to the system failures in the situation of threats.

Modifiability: It is the degree of effort required to make modifications to improve the efficiency of functions of the software.

Safety: It is the ability of the system to not cause any harm to the network system or personnel system.

Recoverability: It is the ability of the system to recover from an accident and provide normal service on time.

Verifiability: It is about the efforts required to verify the specified Software functions and corresponding performance.

Security: it is the degree to which the software can detect and prevent the information leak, loss of information, and malicious use, and then any type of destruction.

Testability: It is about the efforts required to test the software.

Reusability: It is the degree to which the software can be reused in other applications.

Restorability: It is the degree to which a system can restore its services on time.

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Learn The Various Classes Of Javamail Api

Introduction to JavaMail Api

JavaMail API is used to read, write and compose E-mails (Electronic messages). The JavaMail API provides a framework to create mail and messaging client applications using Java by platform-independent and protocol-independent. The JavaMail API provides the abstract class which contains the object defined in the mailing system and also performs an interface to E-mail Applications. JavaMail API is commonly used for reading and sending E-mails and needs service providers to interact with mailing servers that execute on the transport protocol.

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Overview of JavaMail API

JavaMail API provides a framework to create mail and messaging client applications by platform-independent and protocol-independent. The JavaMail API provides a set of abstract class which contains the object comprises the mailing system and also performs an interface to E-mail applications. It is generally used for composing, reading, and sending E-Mails (Electronic Messages). JavaMail API is a set of API’s that is used for a mailing system and it offers services for sending and reading E-mails. JavaMail offers an element that is used to construct an interface for a messaging system with interfaces and system components. JavaMail includes various classes which implement the MIME Internet Messaging Standards and RFC822; those classes are in the JavaMail class packages.

Protocols Used in JavaMail API

Several protocols are used in JavaMail API, they are as follows,

1. SMPT (Simple Mail Transfer Protocol)

It provides the mechanism for delivering the E-mails and it is an Internet Standard for receiving and sending E-mail across the IP Networks which uses the TCP with the most acceptable port 25 to send and receive emails. It has secure SSL connections. The JavaMail API has the core classes and packages in chúng tôi this will perform SMTP protocol provider to make a connection with any SMTP servers. We can make use of the Postcast Server, email Server, Apache James Server as on SMTP Server. Once purchasing the host-space by default SMTP Server provides us the hosting provider.

2. POP (Post Office Protocol)

It is also called POP3, the main thing to provides the mechanism to receive the E-mails. It defines and maintains the particular/single Mailbox for every single user. The POP3 protocol is defined in RFC 1939.  POP3 is a Standard Internet Protocol used by the Local E-mail Clients to right to use E-mail from Remote Server over TCP/IP protocols and it listens to the Port 110. The provider of POP3 Protocol in JavaMail API is com.sun.mail.pop3.

3. IMAP (Internet Message Access Protocol) 4. MIME (Multipurpose Internet Mail Extension)

It defines and tells the browser about what the content is being transferred it does not have a mailing transfer protocol it only tells that what is actually sent for example any attachment, format of messages being sent, and so on. For various documents they have different effects they are RFC 2047, RFC 2046, RFC 2045, AND RFC 822, those formats are exist and used in the mail program itself.

5. NNTP (News Transfer Protocol) and others

There are several protocols which are provided by the Third-Party Organisers and some are NNTP, S/MIME (Secure Multipurpose Internet Mail Extension), and so on.

Architecture

As said earlier, Java Application uses JavaMail API to read, write and compose E-mails (Electronic messages). The JavaMail API provides a framework to create mail and messaging client applications using Java by platform-independent and protocol-independent. JavaMail API makes use of the SPI (Service Provider Interface) which is an intermediate service to the application of Java deals with various protocols. Let’s see the following architecture which describes the flow of JavaMail,

The conceptual mechanism of JavaMail API is related to other J2EE API’s like JNDI, JDBC, and JMS, by seeing the above architectural diagram the JavaMail API is divided into two essential parts,

Application-Independent: This part is an Application Programming Interface (API) which is used by the application components for sending and receiving the mailing messages. It underlies the independent provider or protocol used.

Service-Dependent: this part is a Service Provider Interface (SPI) which tells about the protocol-specific languages like POP, IMAP, SMTP, and NNTP. It is based on the plug-in provider of E-mail service to the J2EE Platform.

Properties

In JavaMail API properties, there are only a few recognized properties referenced in javax. mail execution which comes up with J2EE reference implementation, those properties are set as Strings in that the type column defines that how the string is interpreted. Let’s see few properties as follows,

1. IMAP Configuration Properties

Name Type Description

Mail. imap.host String IMAP server to the connection – connect to

Mail. imap.user String The default user for IMAP – user name

2. SMTP Configuration Properties

Name Type Description

mail. smtp.port int It symbolizes the connection to the port. It sets the default to 25.

mail. smtp.timeout int The default timeout is infinite. The I/O timeout value defines in milliseconds.

3. POP3 Configuration Properties

Name Type Description

Mail.pop3.host String POP3 server to the connection – connect to

Mail.pop3.user String The default user for POP3 – user name

API provides classes that model a mail system

Classes Description

Javax.mail.Message It is an e-mail message which models the abstract class. The sub-class provides the implementations

Javax.mail.Session This is the main key class of API. It represents the connection factory and it is a multi-threaded object.

Javax.mail.Address Their sub-class provides the implementations. The abstract class describes the addresses from end-to-end in the message.

Javax.mail.Transport It is a message transport mechanism for sending/transferring E-mail messages.

Javax. mail.Authenticator This is used to secure the mailing resources on the mail server.

Javax.mail.internet.MimeMessage It is an E-mail messaging service that understands the MIME types (what is being used like formats etc.) and headers. It works on the sub-classes. The messages are abstract class.

Javax. mail.Store This purpose is to models a message store and accessing protocol for retrieving and storing the messages. The Stores is separated into folders.

Javax. mail.Folder It contains the sub-folders and the class represents a folder structure of mail messages.

Javax.mail.internet.InternetAddress

The InternetAddress class represents an internal address of e-mail that uses the syntax of RFC822. The address format will be [email protected] or the Personal Name [email protected]

Conclusion

In this article, I have explained the JavaMail API. Hope the article helps you to understand.

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