CORRECT TEXTProblem Scenario 28 : You need to implement near real time solutions for collecting information when submitted in file with belowDataecho "IBM, 100, 20160104" >> /tmp/spooldir2/.bb.txtecho "IBM, 103, 20160105" >> /tmp/spooldir2/.bb.txtmv /tmp/spooldir2/.bb.txt /tmp/spooldir2/bb.txtAfter few minsecho "IBM, 100.2, 20160104" >> /tmp/spooldir2/.dr.txtecho "IBM, 103.1, 20160105" >> /tmp/spooldir2/.dr.txtmv /tmp/spooldir2/.dr.txt /tmp/spooldir2/dr.txtYou have been given below directory location (if not available than create it) /tmp/spooldir2 .As soon as file committed in this directory that needs to be available in hdfs in /tmp/flume/primary as well as /tmp/flume/secondary location.However, note that/tmp/flume/secondary is optional, if transaction failed which writes in this directory need not to be rollback.Write a flume configuration file named flumeS.conf and use it to load data in hdfs with following additional properties .1. Spool /tmp/spooldir2 directory2. File prefix in hdfs sholuld be events3. File suffix should be .log4. If file is not committed and in use than it should have _ as prefix.5. Data should be written as text to hdfs
Answer(s): A
Solution :Step 1: Create directory mkdir /tmp/spooldir2Step 2: Create flume configuration file, with below configuration for source, sink and channel and save it in flume8.conf.agent1 .sources = source1agent1.sinks = sink1a sink1bagent1.channels = channel1a channel1bagent1.sources.source1.channels = channel1a channel1b agent1.sources.source1.selector.type = replicatingagent1.sources.source1.selector.optional = channel1b agent1.sinks.sink1a.channel = channel1aagent1 .sinks.sink1b.channel = channel1bagent1.sources.source1.type = spooldiragent1 .sources.sourcel.spoolDir = /tmp/spooldir2agent1.sinks.sink1a.type = hdfsagent1 .sinks, sink1a.hdfs. path = /tmp/flume/primary agent1 .sinks.sink1a.hdfs.tilePrefix = eventsagent1 .sinks.sink1a.hdfs.fileSuffix = .logagent1 .sinks.sink1a.hdfs.fileType = Data Streamagent1 .sinks.sink1b.type = hdfsagent1 .sinks.sink1b.hdfs.path = /tmp/flume/secondary agent1 .sinks.sink1b.hdfs.filePrefix = eventsagent1.sinks.sink1b.hdfs.fileSuffix = .logagent1 .sinks.sink1b.hdfs.fileType = Data Streamagent1.channels.channel1a.type = fileagent1.channels.channel1b.type = memorystep 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 ageStep 5: Open another terminal and create a file in /tmp/spooldir2/echo "IBM, 100, 20160104" » /tmp/spooldir2/.bb.txtecho "IBM, 103, 20160105" » /tmp/spooldir2/.bb.txt mv /tmp/spooldir2/.bb.txt /tmp/spooldir2/bb.txtAfter few minsecho "IBM.100.2, 20160104" »/tmp/spooldir2/.dr.txtecho "IBM, 103.1, 20160105" » /tmp/spooldir2/.dr.txt mv /tmp/spooldir2/.dr.txt /tmp/spooldir2/dr.txt
Problem Scenario 83 : In Continuation of previous question, please accomplish following activities.1. Select all the records with quantity >= 5000 and name starts with 'Pen'2. Select all the records with quantity >= 5000, price is less than 1.24 and name starts with'Pen'3. Select all the records witch does not have quantity >= 5000 and name does not startswith 'Pen'4. Select all the products which name is 'Pen Red', 'Pen Black'5. Select all the products which has price BETWEEN 1.0 AND 2.0 AND quantityBETWEEN 1000 AND 2000.
Solution :Step 1: Select all the records with quantity >= 5000 and name starts with 'Pen' val results = sqlContext.sql(......SELECT * FROM products WHERE quantity >= 5000 AND name LIKE 'Pen %.......)results.show()Step 2: Select all the records with quantity >= 5000 , price is less than 1.24 and name starts with 'Pen'val results = sqlContext.sql(......SELECT * FROM products WHERE quantity >= 5000 AND price < 1.24 AND name LIKE 'Pen %.......)results. showQStep 3: Select all the records witch does not have quantity >= 5000 and name does not starts with 'Pen'val results = sqlContext.sql('.....SELECT * FROM products WHERE NOT (quantity >= 5000 AND name LIKE 'Pen %')......)results. showQStep 4: Select all the products wchich name is 'Pen Red', 'Pen Black' val results = sqlContext.sql('.....SELECT' FROM products WHERE name IN ('Pen Red', 'Pen Black')......)results. showQStep 5: Select all the products which has price BETWEEN 1.0 AND 2.0 AND quantity BETWEEN 1000 AND 2000.val results = sqlContext.sql(......SELECT * FROM products WHERE (price BETWEEN 1.0 AND 2.0) AND (quantity BETWEEN 1000 AND 2000)......) results. show()
Problem Scenario 82 : You have been given table in Hive with following structure (Which you have created in previous exercise).productid int code string name string quantity int price floatUsing SparkSQL accomplish following activities.1. Select all the products name and quantity having quantity <= 20002. Select name and price of the product having code as 'PEN'3. Select all the products, which name starts with PENCIL4. Select all products which "name" begins with 'P\ followed by any two characters,followed by space, followed by zero or more characters
Solution :Step 1: Copy following tile (Mandatory Step in Cloudera QuickVM) if you have not done it.sudo su rootcp /usr/lib/hive/conf/hive-site.xml /usr/lib/sparkVconf/Step 2: Now start spark-shellStep 3 ; Select all the products name and quantity having quantity <= 2000 val results = sqlContext.sql(......SELECT name, quantity FROM products WHERE quantity <= 2000......)results.showQStep 4: Select name and price of the product having code as 'PEN' val results = sqlContext.sql(......SELECT name, price FROM products WHERE code = 'PEN.......)results. showQStep 5: Select all the products , which name starts with PENCIL val results = sqlContext.sql(......SELECT name, price FROM products WHERE upper(name) LIKE 'PENCIL%.......}results. showQStep 6: select all products which "name" begins with 'P', followed by any two characters, followed by space, followed byzero or more characters -- "name" begins with 'P', followed by any two characters, - followed by space, followed by zero or more characters val results = sqlContext.sql(......SELECT name, price FROM products WHERE name LIKE 'P_ %.......)results. show()
Problem Scenario 20 : You have been given MySQL DB with following details.user=retail_dbapassword=clouderadatabase=retail_dbtable=retail_db.categoriesjdbc URL = jdbc:mysql://quickstart:3306/retail_dbPlease accomplish following activities.1. Write a Sqoop Job which will import "retaildb.categories" table to hdfs, in a directoryname "categories_targetJob".
Solution :Step 1: Connecting to existing MySQL Database mysql -user=retail_dba -- password=cloudera retail_dbStep 2: Show all the available tables show tables;Step 3: Below is the command to create Sqoop Job (Please note that - import space is mandatory)sqoop job -create sqoopjob \ -- import \-connect "jdbc:mysql://quickstart:3306/retail_db" \-username=retail_dba \-password=cloudera \-table categories \-target-dir categories_targetJob \-fields-terminated-by '|' \-lines-terminated-by '\n'Step 4: List all the Sqoop Jobs sqoop job --listStep 5: Show details of the Sqoop Job sqoop job --show sqoopjobStep 6: Execute the sqoopjob sqoopjob --exec sqoopjobStep 7: Check the output of import jobhdfs dfs -Is categories_target_jobhdfs dfs -cat categories_target_job/part*
Problem Scenario 59 : You have been given below code snippet.val x = sc.parallelize(1 to 20)val y = sc.parallelize(10 to 30) operationlz.collectWrite a correct code snippet for operationl which will produce desired output, shown below. Array[lnt] = Array(16, 12, 20, 13, 17, 14, 18, 10, 19, 15, 11)
Solution :val z = x.intersection(y)intersection : Returns the elements in the two RDDs which are the same.
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