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NEW QUESTION: 1
You have an Azure website that runs on several instances. You have a WebJob that provides additional functionality to the website.
The WebJob must run on all instances of the website.
You need to ensure that the WebJob runs even when the website is idle for long periods of time.
How should you create and configure the WebJob object? To answer, select the appropriate options in the answer area.
* You can run programs or scripts in WebJobs in your App Service web app in three ways: on demand, continuously, or on a schedule.
* For continuous WebJobs there is an important feature called "always on" which is only available for a Standard Website, this will make sure your Website and WebJob are always up.
NEW QUESTION: 2
What are two requirements for the IPN network when implementing a Multi-Pod ACI fabric? (Choose two.)
A. BGP routing
B. PIM ASM multicast routing
C. VLAN ID 4
D. EIGRP routing
E. OSPF routing
NEW QUESTION: 3
A NetApp customer has been concerned about the fragmentation of its system. The customer is considering moving to an IBM Storwize solution.
What does the IBM Storwize Family use as the foundational file system for file sharing?
Shares and exports can be managed through the GUI or CLI by Storwize V7000 Unified administrative users that have a user role definition that is authorized to perform share and export management functions.
A share or export results from making a disk space accessible through the protocols specified during its creation. HTTP, SCP, FTP, CIFS and NFS shares and exports can be created, provided that the corresponding protocol is enabled for the system. Shares and exports can only be created for data that is stored in the GPFS file system of the Storwize V7000 Unified system. Non-GPFS file system content cannot be shared or exported.
NEW QUESTION: 4
Problem Scenario 44 : You have been given 4 files , with the content as given below:
Apache Hadoop is an open-source software framework written in Java for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common and should be automatically handled by the framework spark11/file2.txt
The core of Apache Hadoop consists of a storage part known as Hadoop Distributed File
System (HDFS) and a processing part called MapReduce. Hadoop splits files into large blocks and distributes them across nodes in a cluster. To process data, Hadoop transfers packaged code for nodes to process in parallel based on the data that needs to be processed.
his approach takes advantage of data locality nodes manipulating the data they have access to to allow the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking spark11/file4.txt
Apache Storm is focused on stream processing or what some call complex event processing. Storm implements a fault tolerant method for performing a computation or pipelining multiple computations on an event as it flows into a system. One might use
Storm to transform unstructured data as it flows into a system into a desired format
Write a Spark program, which will give you the highest occurring words in each file. With their file name and highest occurring words.
See the explanation for Step by Step Solution and configuration.
Step 1 : Create all 4 file first using Hue in hdfs.
Step 2 : Load all file as an RDD
val file1 = sc.textFile("sparkl1/filel.txt")
val file2 = sc.textFile("spark11/file2.txt")
val file3 = sc.textFile("spark11/file3.txt")
val file4 = sc.textFile("spark11/file4.txt")
Step 3 : Now do the word count for each file and sort in reverse order of count.
val contentl = filel.flatMap( line => line.split(" ")).map(word => (word,1)).reduceByKey(_ +
_).map(item => item.swap).sortByKey(false).map(e=>e.swap)
val content.2 = file2.flatMap( line => line.splitf ")).map(word => (word,1)).reduceByKey(_
+ _).map(item => item.swap).sortByKey(false).map(e=>e.swap)
val content3 = file3.flatMap( line > line.split)" ")).map(word => (word,1)).reduceByKey(_
+ _).map(item => item.swap).sortByKey(false).map(e=>e.swap)
val content4 = file4.flatMap( line => line.split(" ")).map(word => (word,1)).reduceByKey(_ +
_ ).map(item => item.swap).sortByKey(false).map(e=>e.swap)
Step 4 : Split the data and create RDD of all Employee objects.
val filelword = sc.makeRDD(Array(file1.name+"->"+content1(0)._1+"-"+content1(0)._2)) val file2word = sc.makeRDD(Array(file2.name+"->"+content2(0)._1+"-"+content2(0)._2)) val file3word = sc.makeRDD(Array(file3.name+"->"+content3(0)._1+"-"+content3(0)._2)) val file4word = sc.makeRDD(Array(file4.name+M->"+content4(0)._1+"-"+content4(0)._2))
Step 5: Union all the RDDS
val unionRDDs = filelword.union(file2word).union(file3word).union(file4word)
Step 6 : Save the results in a text file as below.