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Download DemoVendor: Cloudera
Certifications: Cloudera Certified Associate CCA
Exam Code: CCA175
Exam Name: CCA Spark and Hadoop Developer Exam
Updated: Apr 17, 2024
Q&As: 95
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Problem Scenario 24 : You have been given below comma separated employee information.
Data Set:
name,salary,sex,age alok,100000,male,29 jatin,105000,male,32 yogesh,134000,male,39 ragini,112000,female,35 jyotsana,129000,female,39 valmiki,123000,male,29
Requirements:
Use the netcat service on port 44444, and nc above data line by line. Please do the following activities.
1.
Create a flume conf file using fastest channel, which write data in hive warehouse directory, in a table called flumemaleemployee (Create hive table as well tor given data).
2.
While importing, make sure only male employee data is stored.
Correct Answer: See the explanation for Step by Step Solution and configuration.
Step 1 : Create hive table for flumeemployee.' CREATE TABLE flumemaleemployee (
name string,salary int, sex string, age int ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ','; step 2 : Create flume configuration file, with below configuration for source, sink and channel and save it in flume4.conf. #Define source , sink, channel and agent. agent1 .sources = source1 agent1 .sinks = sink1 agent1 .channels = channel1 # Describe/configure source1 agent1 .sources.source1.type = netcat agent1 .sources.source1.bind = 127.0.0.1 agent1.sources.sourcel.port = 44444 #Define interceptors agent1.sources.source1.interceptors=il agent1 .sources.source1.interceptors.i1.type=regex_filter agent1 .sources.source1.interceptors.i1.regex=female agent1 .sources.source1.interceptors.i1.excludeEvents=true ## Describe sink1 agent1 .sinks, sinkl.channel = memory-channel agent1.sinks.sink1.type = hdfs agent1 .sinks, sinkl. hdfs. path = /user/hive/warehouse/flumemaleemployee hdfs-agent.sinks.hdfs-write.hdfs.writeFormat=Text agentl .sinks.sink1.hdfs.fileType = Data Stream # Now we need to define channel1 property. agent1.channels.channel1.type = memory agent1.channels.channell.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 3 : 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/flume4.conf --name agentl Step 4 : Open another terminal and use the netcat service, nc localhost 44444 Step 5 : Enter data line by line. alok,100000,male,29 jatin,105000,male,32 yogesh,134000,male,39 ragini,112000,female,35 jyotsana,129000,female,39 valmiki.123000.male.29 Step 6 : Open hue and check the data is available in hive table or not. Step 7 : Stop flume service by pressing ctrl+c Step 8 : Calculate average salary on hive table using below query. You can use either hive command line tool or hue. select avg(salary) from flumeemployee;
Problem Scenario 60 : You have been given below code snippet.
val a = sc.parallelize(List("dog", "salmon", "salmon", "rat", "elephant"}, 3}
val b = a.keyBy(_.length)
val c = sc.parallelize(List("dog","cat","gnu","salmon","rabbit","turkey","woif","bear","bee"), 3)
val d = c.keyBy(_.length)
operation1
Write a correct code snippet for operationl which will produce desired output, shown below.
Array[(lnt, (String, String))] = Array((6,(salmon,salmon)), (6,(salmon,rabbit)),
(6,(salmon,turkey)), (6,(salmon,salmon)), (6,(salmon,rabbit)),
(6,(salmon,turkey)), (3,(dog,dog)), (3,(dog,cat)), (3,(dog,gnu)), (3,(dog,bee)), (3,(rat,dog)),
(3,(rat,cat)), (3,(rat,gnu)), (3,(rat,bee)))
Correct Answer: See the explanation for Step by Step Solution and configuration.
solution: b.join(d).collect join [Pair]: Performs an inner join using two key-value RDDs. Please note that the keys must be generally comparable to make this work. keyBy : Constructs two-component tuples (key-value pairs) by applying a function on each data item. The result of the function becomes the data item becomes the key and the original value of the newly created tuples.
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.
Correct Answer: See the explanation for Step by Step Solution and configuration.
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 we 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 –name agent1 Wait for few mins and than stop log service. stop logs
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