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Introduction to PySpark foreachPySpark 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 foreachLet 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 foreachLet 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 #2Let 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.
ConclusionFrom 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 Internal Working Of Explode
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 ExampleLet 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.
ConclusionFrom 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.
Recommended ArticlesThis is a guide to PySpark explode. Here we discuss the introduction, syntax, and working of EXPLODE in PySpark Data Frame along with examples. You may also look at the following articles to learn more –
Working And Examples Of Pyspark Collect
Introduction to PySpark collect
PYSPARK COLLECT is an action in PySpark that is used to retrieve all the elements from the nodes of the Data Frame to the driver node. It is an operation that is used to fetch data from RDD/ Data Frame. The operation involves data that fetches the data and gets it back to the driver node.
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The collect operation returns the data as an Array of Row Types to the driver; the result is collected and further displayed for PySpark operation. The data, once is available on the node, can be used in the loops and displayed. The collect operation is widely used for smaller Data Set the data which can be fit upon memory or post that can cause some certain memory exception too. Let’s check the Collect operation in detail and try to understand the functionality for the same.
The syntax for the COLLECT function is:-
cd = spark.sparkContext.parallelize(data1) cd.collect()explanation:
Cd:- The RDD made from the Data
.collect () :- The function used for Collecting the RDD.
Screenshot:
Working of Collect in PysparkLet us see somehow the COLLECT operation works in PySpark:-
Collect is an action that returns all the elements of the dataset, RDD of a PySpark, to the driver program. It is basically used to collect the data from the various node to the driver program that is further returned to the user for analysis.
Retrieving the huge data set can sometimes cause an out-of-memory issue over data collection.
This is a network movement action call where all the elements from the different nodes are sent to the driver memory where the data is collected, so the data movement is much over the collect operation. Since it is an action call of PySpark so every time it is called, all the transformations are done prior to implementing its action.
It retrieves the element in the form of Array [Row] to the driver program.
Let’s check the creation and usage with some coding examples.
Example of PySpark collectLet us see some Example of how the PYSPARK COLLECT operation works:-
Let’s start by creating simple data in PySpark.
data1 = [{'Name':'Jhon','ID':2,'Add':'USA'},{'Name':'Joe','ID':3,'Add':'USA'},{'Name':'Tina','ID':2,'Add':'IND'},{'Name':'Jhon','ID':2, 'Add':'USA'},{'Name':'Joe','ID':5,'Add':'INA'}]A sample data is created with Name, ID, and ADD as the field.
a = sc.parallelize(data1)RDD is created using sc. parallelize.
b = spark.createDataFrame(a) b.show()Screenshot:
Now let us try to collect the elements from the RDD.
a=sc.parallelize(data1) a.collect()This collects all the data back to the driver node, and the result is then displayed as a result at the console.
Screenshot:
a.collect()[0] a.collect()[1] a.collect()[2]The above code shows that we can also select a selected number of the column from an RDD/Data Frame using collect with index. The index is used to retrieve elements from it.
Screenshot:
Let’s try to understand this with more Example:-
data3 = sc.parallelize(data2) data2 = [1,2,3,4,5,6,7,8,9,10] data3 = sc.parallelize(data2) data3.collect()This is a very simple way to understand more about collect where we have made a simple RDD of type Int. Post collecting, we can get the data back to driver memory as a result. All the data Frames are called back to the driver, and the result is displayed back. Once the data is available, we can use the data back for our purpose, data analysis and data modeling.
Screenshot:-
These are some of the Examples of PYSPARK ROW Function in PySpark.
Note:-
COLLECT is an action in PySpark.
COLLECT collects the data back to the driver node.
PySpark COLLECT returns the type as Array[Row].
COLLECT can return data back to memory so that excess data collection can cause Memory issues.
PySpark COLLECT causes the movement of data over the network and brings it back to the driver memory.
COLLECTASLIST() is used to collect the same but the result as List.
ConclusionFrom the above article, we saw the use of collect Operation in PySpark. We tried to understand how the COLLECT method works in PySpark and what is used at the programming level from various examples and classification.
Recommended ArticlesThis is a guide to the PySpark collect. Here we discuss the use of collect Operation in PySpark with various examples and classification. You may also have a look at the following articles to learn more –
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 ModuleNow 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.
ConclusionFrom 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.
Recommended ArticlesThis is a guide to Drupal Views. Here we discuss the introduction and steps to create drupal views along with its working and modules. You may also have a look at the following articles to learn more –
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 parameterHere are the attributes of a parameter mentioned below
-RequiredThis 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.
-PositionPositional 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.
-TypeIt denotes the type of the parameter like string, int, switches, etc.
-Default Value -Accepts Multiple ValuesThis 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 InputThis 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 CharactersThis denotes whether the parameter can use the wildcard to match characters.
Validation of ParametersBelow are some ways of validating the value that is passed to a parameter.
1. Making a parameter Mandatory and allowing Null ValueThe 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=$true)] [AllowNull()] [String] )
In the above, UserName is a mandatory parameter, and it accepts null for a value.
2. AllowEmptyString validation attributeThis attribute is used to allow empty string as a value to the mandatory parameter. The Allow Empty collection attribute is used to allow empty collection as a value to a mandatory string parameter.
Example:
[Parameter(Mandatory=$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:
Conclusion – Powershell Parameter Recommended ArticlesThis is a guide to Powershell Parameter. Here we discuss the attributes of a parameter and some ways of validating the value that is passed to a parameter. You may also have a look at the following articles to learn more –
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 survivabilitySystem 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.
Recommended ArticlesThis is a guide to Security engineering. Here we have discussed the basic concepts of security Engineering and its various terms used for system protection. You may also have a look at the following articles to learn more –
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