CCA Spark and Hadoop Developer test Dumps

CCA175 test Format | Course Contents | Course Outline | test Syllabus | test Objectives

Exam Detail:
The CCA175 (CCA Spark and Hadoop Developer) is a certification test that validates the skills and knowledge of individuals in developing and deploying Spark and Hadoop applications. Here are the test details for CCA175:

- Number of Questions: The test typically consists of multiple-choice and hands-on coding questions. The exact number of questions may vary, but typically, the test includes around 8 to 12 tasks that require coding and data manipulation.

- Time Limit: The time allocated to complete the test is 120 minutes (2 hours).

Course Outline:
The CCA175 course covers various courses related to Apache Spark, Hadoop, and data processing. The course outline typically includes the following topics:

1. Introduction to Big Data and Hadoop:
- Overview of Big Data concepts and challenges.
- Introduction to Hadoop and its ecosystem components.

2. Hadoop File System (HDFS):
- Understanding Hadoop Distributed File System (HDFS).
- Managing and manipulating data in HDFS.
- Performing file system operations using Hadoop commands.

3. Apache Spark Fundamentals:
- Introduction to Apache Spark and its features.
- Understanding Spark architecture and execution model.
- Writing and running Spark applications using Spark Shell.

4. Spark Data Processing:
- Transforming and manipulating data using Spark RDDs (Resilient Distributed Datasets).
- Applying transformations and actions to RDDs.
- Working with Spark DataFrames and Datasets.

5. Spark SQL and Data Analysis:
- Querying and analyzing data using Spark SQL.
- Performing data aggregation, filtering, and sorting operations.
- Working with structured and semi-structured data.

6. Spark Streaming and Data Integration:
- Processing real-time data using Spark Streaming.
- Integrating Spark with external data sources and systems.
- Handling data ingestion and data integration challenges.

Exam Objectives:
The objectives of the CCA175 test are as follows:

- Evaluating candidates' knowledge of Hadoop ecosystem components and their usage.
- Assessing candidates' proficiency in coding Spark applications using Scala or Python.
- Testing candidates' ability to manipulate and process data using Spark RDDs, DataFrames, and Spark SQL.
- Assessing candidates' understanding of data integration and streaming concepts in Spark.

Exam Syllabus:
The specific test syllabus for the CCA175 test covers the following areas:

1. Data Ingestion: Ingesting data into Hadoop using various techniques (e.g., Sqoop, Flume).

2. Transforming Data with Apache Spark: Transforming and manipulating data using Spark RDDs, DataFrames, and Spark SQL.

3. Loading Data into Hadoop: Loading data into Hadoop using various techniques (e.g., Sqoop, Flume).

4. Querying Data with Apache Hive: Querying data stored in Hadoop using Apache Hive.

5. Data Analysis with Apache Spark: Analyzing and processing data using Spark RDDs, DataFrames, and Spark SQL.

6. Writing Spark Applications: Writing and executing Spark applications using Scala or Python.

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Cloudera
CCA175
CCA Spark and Hadoop Developer
http://killexams.com/pass4sure/exam-detail/CCA175
Question: 94
Now import the data from following directory into departments_export table, /user/cloudera/departments new
Answer: Solution:
Step 1: Login to musql db
mysql �user=retail_dba -password=cloudera
show databases; use retail_db; show tables;
step 2: Create a table as given in problem statement.
CREATE table departments_export (departmentjd int(11), department_name varchar(45), created_date T1MESTAMP
DEFAULT NOW());
show tables;
Step 3: Export data from /user/cloudera/departmentsnew to new table departments_export
sqoop export -connect jdbc:mysql://quickstart:3306/retail_db
-username retaildba
�password cloudera
�table departments_export
-export-dir /user/cloudera/departments_new
-batch
Step 4: Now check the export is correctly done or not. mysql -user*retail_dba -password=cloudera
show databases;
use retail _db;
show tables;
select� from departments_export;
Question: 95
Data should be written as text to hdfs
Answer: Solution:
Step 1: Create directory mkdir /tmp/spooldir2
Step 2: Create flume configuration file, with below configuration for source, sink and channel and save it in
flume8.conf.
agent1 .sources = source1
agent1.sinks = sink1a sink1b agent1.channels = channel1a channel1b
agent1.sources.source1.channels = channel1a channel1b
agent1.sources.source1.selector.type = replicating
agent1.sources.source1.selector.optional = channel1b
agent1.sinks.sink1a.channel = channel1a
agent1 .sinks.sink1b.channel = channel1b
agent1.sources.source1.type = spooldir
agent1 .sources.sourcel.spoolDir = /tmp/spooldir2
agent1.sinks.sink1a.type = hdfs
agent1 .sinks, sink1a.hdfs. path = /tmp/flume/primary
agent1 .sinks.sink1a.hdfs.tilePrefix = events
agent1 .sinks.sink1a.hdfs.fileSuffix = .log
agent1 .sinks.sink1a.hdfs.fileType = Data Stream
agent1 . sinks.sink1b.type = hdfs
agent1 . sinks.sink1b.hdfs.path = /tmp/flume/secondary
agent1 .sinks.sink1b.hdfs.filePrefix = events
agent1.sinks.sink1b.hdfs.fileSuffix = .log
agent1 .sinks.sink1b.hdfs.fileType = Data Stream
agent1.channels.channel1a.type = file
agent1.channels.channel1b.type = memory
step 4: Run below command which will use this configuration file and append data in hdfs.
Start flume service:
flume-ng agent -conf /home/cloudera/flumeconf -conf-file /home/cloudera/flumeconf/flume8.conf �name age
Step 5: Open another terminal and create a file in /tmp/spooldir2/
echo "IBM, 100, 20160104" � /tmp/spooldir2/.bb.txt
echo "IBM, 103, 20160105" � /tmp/spooldir2/.bb.txt mv /tmp/spooldir2/.bb.txt /tmp/spooldir2/bb.txt
After few mins
echo "IBM.100.2, 20160104" �/tmp/spooldir2/.dr.txt
echo "IBM, 103.1, 20160105" � /tmp/spooldir2/.dr.txt mv /tmp/spooldir2/.dr.txt /tmp/spooldir2/dr.txt
Question: 96
Data should be written as text to hdfs
Answer: Solution:
Step 1: Create directory mkdir /tmp/spooldir/bb mkdir /tmp/spooldir/dr
Step 2: Create flume configuration file, with below configuration for
agent1.sources = source1 source2
agent1 .sinks = sink1
agent1.channels = channel1
agent1 .sources.source1.channels = channel1
agentl .sources.source2.channels = channell agent1 .sinks.sinkl.channel = channell
agent1 . sources.source1.type = spooldir
agent1 .sources.sourcel.spoolDir = /tmp/spooldir/bb
agent1 . sources.source2.type = spooldir
agent1 .sources.source2.spoolDir = /tmp/spooldir/dr
agent1 . sinks.sink1.type = hdfs
agent1 .sinks.sink1.hdfs.path = /tmp/flume/finance
agent1-sinks.sink1.hdfs.filePrefix = events
agent1.sinks.sink1.hdfs.fileSuffix = .log
agent1 .sinks.sink1.hdfs.inUsePrefix = _
agent1 .sinks.sink1.hdfs.fileType = Data Stream
agent1.channels.channel1.type = file
Step 4: Run below command which will use this configuration file and append data in hdfs.
Start flume service:
flume-ng agent -conf /home/cloudera/flumeconf -conf-file /home/cloudera/fIumeconf/fIume7.conf �name agent1
Step 5: Open another terminal and create a file in /tmp/spooldir/
echo "IBM, 100, 20160104" � /tmp/spooldir/bb/.bb.txt
echo "IBM, 103, 20160105" � /tmp/spooldir/bb/.bb.txt mv /tmp/spooldir/bb/.bb.txt /tmp/spooldir/bb/bb.txt
After few mins
echo "IBM, 100.2, 20160104" � /tmp/spooldir/dr/.dr.txt
echo "IBM, 103.1, 20160105" �/tmp/spooldir/dr/.dr.txt mv /tmp/spooldir/dr/.dr.txt /tmp/spooldir/dr/dr.txt
Question: 97
Data should be written as text to hdfs
Answer: Solution:
Step 1: Create directory mkdir /tmp/spooldir2
Step 2: Create flume configuration file, with below configuration for source, sink and channel and save it in
flume8.conf.
agent1 .sources = source1
agent1.sinks = sink1a sink1b agent1.channels = channel1a channel1b
agent1.sources.source1.channels = channel1a channel1b
agent1.sources.source1.selector.type = replicating
agent1.sources.source1.selector.optional = channel1b
agent1.sinks.sink1a.channel = channel1a
agent1 .sinks.sink1b.channel = channel1b
agent1.sources.source1.type = spooldir
agent1 .sources.sourcel.spoolDir = /tmp/spooldir2
agent1.sinks.sink1a.type = hdfs
agent1 .sinks, sink1a.hdfs. path = /tmp/flume/primary
agent1 .sinks.sink1a.hdfs.tilePrefix = events
agent1 .sinks.sink1a.hdfs.fileSuffix = .log
agent1 .sinks.sink1a.hdfs.fileType = Data Stream
agent1 . sinks.sink1b.type = hdfs
agent1 . sinks.sink1b.hdfs.path = /tmp/flume/secondary
agent1 .sinks.sink1b.hdfs.filePrefix = events
agent1.sinks.sink1b.hdfs.fileSuffix = .log
agent1 .sinks.sink1b.hdfs.fileType = Data Stream
agent1.channels.channel1a.type = file
agent1.channels.channel1b.type = memory
step 4: Run below command which will use this configuration file and append data in hdfs.
Start flume service:
flume-ng agent -conf /home/cloudera/flumeconf -conf-file /home/cloudera/flumeconf/flume8.conf �name age
Step 5: Open another terminal and create a file in /tmp/spooldir2/
echo "IBM, 100, 20160104" � /tmp/spooldir2/.bb.txt
echo "IBM, 103, 20160105" � /tmp/spooldir2/.bb.txt mv /tmp/spooldir2/.bb.txt /tmp/spooldir2/bb.txt
After few mins
echo "IBM.100.2, 20160104" �/tmp/spooldir2/.dr.txt
echo "IBM, 103.1, 20160105" � /tmp/spooldir2/.dr.txt mv /tmp/spooldir2/.dr.txt /tmp/spooldir2/dr.txt
Question: 98
Data should be written as text to hdfs
Answer: Solution:
Step 1: Create directory mkdir /tmp/nrtcontent
Step 2: Create flume configuration file, with below configuration for source, sink and channel and save it in
flume6.conf.
agent1 .sources = source1
agent1 .sinks = sink1
agent1.channels = channel1
agent1 .sources.source1.channels = channel1
agent1 .sinks.sink1.channel = channel1
agent1 . sources.source1.type = spooldir
agent1 .sources.source1.spoolDir = /tmp/nrtcontent
agent1 .sinks.sink1 .type = hdfs
agent1 . sinks.sink1.hdfs .path = /tmp/flume
agent1.sinks.sink1.hdfs.filePrefix = events
agent1.sinks.sink1.hdfs.fileSuffix = .log
agent1 .sinks.sink1.hdfs.inUsePrefix = _
agent1 .sinks.sink1.hdfs.fileType = Data Stream
Step 4: Run below command which will use this configuration file and append data in hdfs.
Start flume service:
flume-ng agent -conf /home/cloudera/flumeconf -conf-file /home/cloudera/fIumeconf/fIume6.conf �name agent1
Step 5: Open another terminal and create a file in /tmp/nrtcontent
echo "I am preparing for CCA175 from ABCTech m.com " > /tmp/nrtcontent/.he1.txt
mv /tmp/nrtcontent/.he1.txt /tmp/nrtcontent/he1.txt
After few mins
echo "I am preparing for CCA175 from TopTech .com " > /tmp/nrtcontent/.qt1.txt
mv /tmp/nrtcontent/.qt1.txt /tmp/nrtcontent/qt1.txt
Question: 99
Problem Scenario 4: You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.categories
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Please accomplish following activities.
Import Single table categories (Subset data} to hive managed table, where category_id between 1 and 22
Answer: Solution:
Step 1: Import Single table (Subset data)
sqoop import �connect jdbc:mysql://quickstart:3306/retail_db -username=retail_dba -password=cloudera -
table=categories -where " � category_id � between 1 and 22" �hive-import �m 1
Note: Here the � is the same you find on ~ key
This command will create a managed table and content will be created in the following directory.
/user/hive/warehouse/categories
Step 2: Check whether table is created or not (In Hive)
show tables;
select * from categories;
Question: 100
Data should be written as text to hdfs
Answer: Solution:
Step 1: Create directory mkdir /tmp/spooldir/bb mkdir /tmp/spooldir/dr
Step 2: Create flume configuration file, with below configuration for
agent1.sources = source1 source2
agent1 .sinks = sink1
agent1.channels = channel1
agent1 .sources.source1.channels = channel1
agentl .sources.source2.channels = channell agent1 .sinks.sinkl.channel = channell
agent1 . sources.source1.type = spooldir
agent1 .sources.sourcel.spoolDir = /tmp/spooldir/bb
agent1 . sources.source2.type = spooldir
agent1 .sources.source2.spoolDir = /tmp/spooldir/dr
agent1 . sinks.sink1.type = hdfs
agent1 .sinks.sink1.hdfs.path = /tmp/flume/finance
agent1-sinks.sink1.hdfs.filePrefix = events
agent1.sinks.sink1.hdfs.fileSuffix = .log
agent1 .sinks.sink1.hdfs.inUsePrefix = _
agent1 .sinks.sink1.hdfs.fileType = Data Stream
agent1.channels.channel1.type = file
Step 4: Run below command which will use this configuration file and append data in hdfs.
Start flume service:
flume-ng agent -conf /home/cloudera/flumeconf -conf-file /home/cloudera/fIumeconf/fIume7.conf �name agent1
Step 5: Open another terminal and create a file in /tmp/spooldir/
echo "IBM, 100, 20160104" � /tmp/spooldir/bb/.bb.txt
echo "IBM, 103, 20160105" � /tmp/spooldir/bb/.bb.txt mv /tmp/spooldir/bb/.bb.txt /tmp/spooldir/bb/bb.txt
After few mins
echo "IBM, 100.2, 20160104" � /tmp/spooldir/dr/.dr.txt
echo "IBM, 103.1, 20160105" �/tmp/spooldir/dr/.dr.txt mv /tmp/spooldir/dr/.dr.txt /tmp/spooldir/dr/dr.txt
Question: 101
Problem Scenario 21: You have been given log generating service as below.
startjogs (It will generate continuous logs)
tailjogs (You can check, what logs are being generated)
stopjogs (It will stop the log service)
Path where logs are generated using above service: /opt/gen_logs/logs/access.log
Now write a flume configuration file named flumel.conf, using that configuration file dumps logs in HDFS file system
in a directory called flumel. Flume channel should have following property as well. After every 100 message it should
be committed, use non-durable/faster channel and it should be able to hold maximum 1000 events
Answer: Solution:
Step 1: Create flume configuration file, with below configuration for source, sink and channel.
#Define source, sink, channel and agent,
agent1. sources = source1
agent1 .sinks = sink1
agent1.channels = channel1
# Describe/configure source1
agent1 . sources.source1.type = exec
agent1.sources.source1.command = tail -F /opt/gen logs/logs/access.log
## Describe sinkl
agentl .sinks.sinkl.channel = memory-channel
agentl .sinks.sinkl .type = hdfs
agentl . sinks.sink1.hdfs.path = flumel
agentl .sinks.sinkl.hdfs.fileType = Data Stream
# Now they need to define channell property.
agent1.channels.channel1.type = memory
agent1.channels.channell.capacity = 1000
agent1.channels.channell.transactionCapacity = 100
# Bind the source and sink to the channel
agent1.sources.source1.channels = channel1
agent1.sinks.sink1.channel = channel1
Step 2: Run below command which will use this configuration file and append data in hdfs.
Start log service using: startjogs
Start flume service:
flume-ng agent -conf /home/cloudera/flumeconf -conf-file /home/cloudera/flumeconf/flumel.conf-
Dflume.root.logger=DEBUG, INFO, console
Wait for few mins and than stop log service.
Stop_logs
Question: 102
Problem Scenario 23: You have been given log generating service as below.
Start_logs (It will generate continuous logs)
Tail_logs (You can check, what logs are being generated)
Stop_logs (It will stop the log service)
Path where logs are generated using above service: /opt/gen_logs/logs/access.log
Now write a flume configuration file named flume3.conf, using that configuration file dumps logs in HDFS file system
in a directory called flumeflume3/%Y/%m/%d/%H/%M
Means every minute new directory should be created). Please us the interceptors to provide timestamp information, if
message header does not have header info.
And also note that you have to preserve existing timestamp, if message contains it. Flume channel should have
following property as well. After every 100 message it should be committed, use non-durable/faster channel and it
should be able to hold maximum 1000 events.
Answer: Solution:
Step 1: Create flume configuration file, with below configuration for source, sink and channel.
#Define source, sink, channel and agent,
agent1 .sources = source1
agent1 .sinks = sink1
agent1.channels = channel1
# Describe/configure source1
agent1 . sources.source1.type = exec
agentl.sources.source1.command = tail -F /opt/gen logs/logs/access.log
#Define interceptors
agent1 .sources.source1.interceptors=i1
agent1 .sources.source1.interceptors.i1.type=timestamp
agent1 .sources.source1.interceptors.i1.preserveExisting=true
## Describe sink1
agent1 .sinks.sink1.channel = memory-channel
agent1 . sinks.sink1.type = hdfs
agent1 . sinks.sink1.hdfs.path = flume3/%Y/%m/%d/%H/%M
agent1 .sinks.sjnkl.hdfs.fileType = Data Stream
# Now they need to define channel1 property.
agent1.channels.channel1.type = memory
agent1.channels.channel1.capacity = 1000
agent1.channels.channel1.transactionCapacity = 100
# Bind the source and sink to the channel
Agent1.sources.source1.channels = channel1
agent1.sinks.sink1.channel = channel1
Step 2: Run below command which will use this configuration file and append data in hdfs.
Start log service using: start_logs
Start flume service:
flume-ng agent -conf /home/cloudera/flumeconf -conf-file /home/cloudera/flumeconf/flume3.conf -
DfIume.root.logger=DEBUG, INFO, console Cname agent1
Wait for few mins and than stop log service.
stop logs
Question: 103
Problem Scenario 21: You have been given log generating service as below.
startjogs (It will generate continuous logs)
tailjogs (You can check, what logs are being generated)
stopjogs (It will stop the log service)
Path where logs are generated using above service: /opt/gen_logs/logs/access.log
Now write a flume configuration file named flumel.conf, using that configuration file dumps logs in HDFS file system
in a directory called flumel. Flume channel should have following property as well. After every 100 message it should
be committed, use non-durable/faster channel and it should be able to hold maximum 1000 events
Answer: Solution:
Step 1: Create flume configuration file, with below configuration for source, sink and channel.
#Define source, sink, channel and agent,
agent1. sources = source1
agent1 .sinks = sink1
agent1.channels = channel1
# Describe/configure source1
agent1 . sources.source1.type = exec
agent1.sources.source1.command = tail -F /opt/gen logs/logs/access.log
## Describe sinkl
agentl .sinks.sinkl.channel = memory-channel
agentl .sinks.sinkl .type = hdfs
agentl . sinks.sink1.hdfs.path = flumel
agentl .sinks.sinkl.hdfs.fileType = Data Stream
# Now they need to define channell property.
agent1.channels.channel1.type = memory
agent1.channels.channell.capacity = 1000
agent1.channels.channell.transactionCapacity = 100
# Bind the source and sink to the channel
agent1.sources.source1.channels = channel1
agent1.sinks.sink1.channel = channel1
Step 2: Run below command which will use this configuration file and append data in hdfs.
Start log service using: startjogs
Start flume service:
flume-ng agent -conf /home/cloudera/flumeconf -conf-file /home/cloudera/flumeconf/flumel.conf-
Dflume.root.logger=DEBUG, INFO, console
Wait for few mins and than stop log service.
Stop_logs
Question: 104
Now import data from mysql table departments to this hive table. Please make sure that data should be visible using
below hive command, select" from departments_hive
Answer: Solution:
Step 1: Create hive table as said.
hive
show tables;
create table departments_hive(department_id int, department_name string);
Step 2: The important here is, when they create a table without delimiter fields. Then default delimiter for hive is ^A
(01). Hence, while importing data they have to provide proper delimiter.
sqoop import
-connect jdbc:mysql://quickstart:3306/retail_db
~username=retail_dba
-password=cloudera
�table departments
�hive-home /user/hive/warehouse
-hive-import
-hive-overwrite
�hive-table departments_hive
�fields-terminated-by �01�
Step 3: Check-the data in directory.
hdfs dfs -Is /user/hive/warehouse/departments_hive
hdfs dfs -cat/user/hive/warehouse/departmentshive/part�
Check data in hive table.
Select * from departments_hive;
Question: 105
Import departments table as a text file in /user/cloudera/departments.
Answer: Solution:
Step 1: List tables using sqoop
sqoop list-tables �connect jdbc:mysql://quickstart:330G/retail_db �username retail dba -password cloudera
Step 2: Eval command, just run a count query on one of the table.
sqoop eval
�connect jdbc:mysql://quickstart:3306/retail_db
-username retail_dba
-password cloudera
�query "select count(1) from ordeMtems"
Step 3: Import all the tables as avro file.
sqoop import-all-tables
-connect jdbc:mysql://quickstart:3306/retail_db
-username=retail_dba
-password=cloudera
-as-avrodatafile
-warehouse-dir=/user/hive/warehouse/retail stage.db
-ml
Step 4: Import departments table as a text file in /user/cloudera/departments
sqoop import
-connect jdbc:mysql://quickstart:3306/retail_db
-username=retail_dba
-password=cloudera
-table departments
-as-textfile
-target-dir=/user/cloudera/departments
Step 5: Verify the imported data.
hdfs dfs -Is /user/cloudera/departments
hdfs dfs -Is /user/hive/warehouse/retailstage.db
hdfs dfs -Is /user/hive/warehouse/retail_stage.db/products
Question: 106
Problem Scenario 2:
There is a parent organization called "ABC Group Inc", which has two child companies named Tech Inc and MPTech.
Both companies employee information is given in two separate text file as below. Please do the following activity for
employee details.
Tech Inc.txt
Answer: Solution:
Step 1: Check All Available command hdfs dfs
Step 2: Get help on Individual command hdfs dfs -help get
Step 3: Create a directory in HDFS using named Employee and create a Dummy file in it called e.g. Techinc.txt hdfs
dfs -mkdir Employee
Now create an emplty file in Employee directory using Hue.
Step 4: Create a directory on Local file System and then Create two files, with the given data in problems.
Step 5: Now they have an existing directory with content in it, now using HDFS command line, overrid this existing
Employee directory. While copying these files from local file System to HDFS. cd /home/cloudera/Desktop/ hdfs dfs -
put -f Employee
Step 6: Check All files in directory copied successfully hdfs dfs -Is Employee
Step 7: Now merge all the files in Employee directory, hdfs dfs -getmerge -nl Employee MergedEmployee.txt
Step 8: Check the content of the file. cat MergedEmployee.txt
Step 9: Copy merged file in Employeed directory from local file ssytem to HDFS. hdfs dfs -put MergedEmployee.txt
Employee/
Step 10: Check file copied or not. hdfs dfs -Is Employee
Step 11: Change the permission of the merged file on HDFS hdfs dfs -chmpd 664 Employee/MergedEmployee.txt
Step 12: Get the file from HDFS to local file system, hdfs dfs -get Employee Employee_hdfs
Question: 107
Problem Scenario 30: You have been given three csv files in hdfs as below.
EmployeeName.csv with the field (id, name)
EmployeeManager.csv (id, manager Name)
EmployeeSalary.csv (id, Salary)
Using Spark and its API you have to generate a joined output as below and save as a text tile (Separated by comma)
for final distribution and output must be sorted by id.
ld, name, salary, managerName
EmployeeManager.csv
E01, Vishnu
E02, Satyam
E03, Shiv
E04, Sundar
E05, John
E06, Pallavi
E07, Tanvir
E08, Shekhar
E09, Vinod
E10, Jitendra
EmployeeName.csv
E01, Lokesh
E02, Bhupesh
E03, Amit
E04, Ratan
E05, Dinesh
E06, Pavan
E07, Tejas
E08, Sheela
E09, Kumar
E10, Venkat
EmployeeSalary.csv
E01, 50000
E02, 50000
E03, 45000
E04, 45000
E05, 50000
E06, 45000
E07, 50000
E08, 10000
E09, 10000
E10, 10000
Answer: Solution:
Step 1: Create all three files in hdfs in directory called sparkl (We will do using Hue}. However, you can first create in
local filesystem and then
Step 2: Load EmployeeManager.csv file from hdfs and create PairRDDs
val manager = sc.textFile("spark1/EmployeeManager.csv")
val managerPairRDD = manager.map(x=> (x.split(", ")(0), x.split(", ")(1)))
Step 3: Load EmployeeName.csv file from hdfs and create PairRDDs
val name = sc.textFile("spark1/EmployeeName.csv")
val namePairRDD = name.map(x=> (x.split(", ")(0), x.split(�")(1)))
Step 4: Load EmployeeSalary.csv file from hdfs and create PairRDDs
val salary = sc.textFile("spark1/EmployeeSalary.csv")
val salaryPairRDD = salary.map(x=> (x.split(", ")(0), x.split(", ")(1)))
Step 4: Join all pairRDDS
val joined = namePairRDD.join(salaryPairRDD}.join(managerPairRDD}
Step 5: Now sort the joined results, val joinedData = joined.sortByKey()
Step 6: Now generate comma separated data.
val finalData = joinedData.map(v=> (v._1, v._2._1._1, v._2._1._2, v._2._2))
Step 7: Save this output in hdfs as text file.
finalData.saveAsTextFile("spark1/result.txt")
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ROM dump

in case you purchase any sort of electronic gadget these days, likelihood is it’s powered by using a microcontroller with a program saved in its internal flash ROM. That application’s code is often jealously guarded with the aid of the company, who will are trying their premier to be sure you could’t simply study lower back the chip’s contents by using lock bits or some form of encryption. things were greater laid again within the Seventies and ’80s, when code was stored unencrypted in standard EPROM chips, or, for prime-volume functions, in mask ROMs built-in in microcontrollers. analyzing back the code of such micros changed into nonetheless very tricky as a result of chips without problems didn’t have a way of dumping their contents. [Andrew Menadue] ran into this problem when trying to fix an old HP calculator printer, and needed to follow a clever hack to dump the contents of its Mostek MK3870 chip.

The leading trick [Andrew] used turned into one found out with the aid of [Sean Riddle] and defined on his site. It makes use of the undeniable fact that the MK3870 has a verify pin that can be used to disable the masks ROM and load alternative software code directly into the micro’s processing core. with the aid of setting up a LOAD guide pointing at a ROM area and in short disabling check mode while that instruction is carried out, the ROM’s contents may also be study out via the externally loaded program.

elementary as this hack can also appear, really enforcing it changed into intricate adequate as a result of the strict timing requirements between alerts on the clock pins, the facts bus, and the check pin. [Andrew] obtained it to work on his Raspberry Pi Pico setup lots of the time, but in some way the micro nonetheless lower back a it seems that wrong cost each few hundred bytes. not willing to spend too a great deal time debugging this challenge, [Andrew] applied a quite crude hack to his code: in its place of analyzing each and every byte once, it runs the study cycle 200 times, and most effective returns a outcomes when all 200 runs return the same value. Dumping the entire 4 kB of ROM now takes a number of minutes, however this isn’t a lot of an argument given that [Andrew] only has one chip to read out.

if you do have a bucketload of MK3870 chips that you just need to dump, you might want to try and optimize the code on [Andrew]’s GitHub web page. It’s a fortunate accident that the ‘3870 has the exploitable look at various function; frequently, the handiest technique to get inner masks ROM code is by using decapping the chip and optically memorizing the bits one after the other. masks ROMs are extremely good for extremely long term records storage, although.

continue studying “A Nineteen Seventies mask ROM MCU Spills Its secrets” →

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