Cloudera Vm Download For Mac
CLOUDERA QUICKSTART VM INSTALLATION By HadoopExam Learning Resources in Association with www.QuickTechie.com 1. System Requirements for this 64 bit VM x Windows Host Operating System must be 64 bit x VM Player 4.x and higher, we are using 7.x Here x VM Needs 4GB RAM at least, hence Host OS should have 8GB Memory (for Average Performance) 2. Download CDH 5.10.0 Get the most out of your data with CDH. The world's most popular Hadoop platform, CDH is Cloudera’s 100% open source platform that includes the Hadoop ecosystem. Built entirely on open standards, CDH features all the leading components to store, process, discover, model, and serve unlimited data.
Intro to Apache Spark Workshop Setup and Exercises
##General Notes
The Intro to Apache Spark workshop uses a modified verison of the CDH5.0Quick Start VM (Cloudera’s Distribution, including Apache Hadoop 5.X)installed in Psuedo-Distributed mode. The VM comes pre-installed withApache Spark running on Apache Hadoop as well as all the workshop codeand data loaded onto it.
The original VM can be found at:http://www.cloudera.com/content/cloudera/en/documentation/core/latest/topics/cloudera_quickstart_vm.html
##Instructions to Install the VM
Download VirtualBox from https://www.virtualbox.org/wiki/Downloads
a. Pick the appropriate binary for your operating system.
b. Then follow the prompts for installing VirtualBox.
Once the installation is complete, extract the VM provided in the course workshop documents named: cloudera-quickstart-vm-5.4.2-0-.clairvoyant-spark-workshop.ova.zip
a. You may need to download 7-zip from http://www.7-zip.org to extract the Cloudera VM. After uncompressing, you will get a file called cloudera-quickstart-vm-5.X.X-0-virtualbox.ovf. Move it to location of your choice in File System.
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Once you have extracted the VM, we will load the VM into VirtualBox.
a. Open Virtual Box and click on File -> Import Appliance..
b. From the file dialog open cloudera-quickstart-vm-5.4.2-0-.clairvoyant-spark-workshop.ova located in the decompressed (or unzipped) Cloudera VM download.
Setup 'Network Adapter 2' in Network Settings in the Virtual Box as'Host-only Adapter.' Another option is bridged but it has a bug inMac when using a wireless connection.
a. If when setting up the 'Host-only Adapter,' the 'Name' drop downis showing only 'Not selected', cancel and go back to VirtualBox preferences ('Virtual Box -> preferences -> network).
b. Select Host-Only Networks then Add and a new entry willbe created (something like 'vboxnet0').
c. Click OK. Now go back to Network Settings on the VM. Thistime Adapter 2 should show vboxnet0 in the 'Name' drop down box.Select 'vboxnet0.'
Update Virtual Box to the latest version if you are not able to addas described in step 4. The menu might be present in an olderversion, but may throw an exception/error message when you attemptto add.
Start the VM.
Once the VM starts up, you should see the Desktop within VirtualBox.This is your sandbox to play with Hadoop.
Test to ensure the VM was setup successfully. Open Terminal and runthe following commands:
a. Hadoop Verification:
b. If the setup is successful, it will print the current hadoop version.
c. Spark Shell Verificaiton:
d. In the shell that opens run command:
e. If the setup is successful, it will print a SparkContext object
##VM Notes
Workshop code is available at
Workshop data is available at 2 locations:
On the local file system of the VM at:
/home/cloudera/spark-workshop/spark-workshop-code
On HDFS of the VM at:
/user/cloudera/spark-workshop-code
How to install adobe cc 2018. Credentials for the VM
Username: cloudera
Password: cloudera
You can SSH to the VM by running the following command:
You can copy files to the VM by running the following command:
##Exercise 1 – Running Spark Jobs
In this exercise you will practice submitting spark jobs using themethods mentioned in the slides. The job you will submit will take in alist of strings and return the strings that start with “w”.
####Spark-Shell
Open Spark Shell
Wait for the shell to come up with the following prompt
Type in the following Scala code
After running the above code you should get the following result
Congratulations you just ran a Spark job using Scala!
####Pyspark
Open Pyspark
Wait for the shell to come up with the following prompt
Type in the following Python code
After running the above code you should get the result:
Congratulations you just ran a Spark job using Python!
####Spark Submit
Go to the spark_workshop code base provided (on VM at/home/cloudera/spark-workshop/spark_workshop_codebase) and go tothe exercise1 module. Run maven install to build the needed jarfile:
a. Note 1: The maven build has not been configured to set the mainclass. So when you submit the job you will need to define themain class to run as a command line argument.
Verify the required jar was built
Submit Java Code
Submit Scala Code
Submit Python Code
Congratulations you just ran a Spark job as a pre-packaged/built file!
##Exercise 2 – Access Logs
In this exercise you will analyze the access.log file using spark bycalculate the following:
Count how many times the “/health” URL was hit.
Map each line into the following tuple format (ip_address, full_line) and save the contents to HDFS.
Access log file can be found in two locations:
In the spark-workshop-data.zip file provided, in the “logs” subdirectory.
In HDFS (on the VM provided) at:
##Exercise 3 – Joining Datasets
Using the README.md and CHANGES.txt files, find out how many times the word “Spark” shows up in both of the files by joining the data together. Follow the bellow steps:
Create RDD’s for each file and filter each file to only keep all the instances of the work “Spark”
Perform a word count on each of the resulting datasets so the results are (K, V) pairs of type (word, count)
Join the two RDDs
Files can be found in two locations:
In the spark-workshop-data.zip file provided, in the “spark” subdirectory
In HDFS (on the VM provided) at
##Exercise 4 – Shared Variables
In this exercise you will take a file with mock bank transaction data and process it using Shared Variables.
File can be found in two locations:
In the spark-workshop-data.zip file provided, in the “transactions” subdirectory
In HDFS (on the VM provided) at
File is a tab-separated value file without a header. The file had the scheme:
####Steps:
Create a map with the following key value pairs (where the key isthe TransactionCode and the value is a translated TransactionCode)and Broadcast it to the nodes:
Use an Accumulator to count how many transactions from Bank “A”were of type “OTHER”.