Question : You have two tables in Hive that are populated with data: Employee emp_id int salary string
Employee_Detail; emp_id int name string
You now create a new table de-normalized one and populate it with the results of joining the two tables as follows: CREATE TABLE EMPLOYEE_FULL AS SELECT Employee_Detail.*,Employee.salary AS s FROM Employee JOIN Employee_Detail ON (Employee.emp_id== Employee_Detail.emp_id);
You then export the table and download the file: EXPORT TABLE EMPLOYEE_FULL TO '/hadoopexam/employee/Employee_Detail.data';
You have downloaded the file and read the file as a CSV in R. How many columns will the resulting variable in R have?
Ans : 1 Exp : : When exporting a table from Hive, the data file will use the delimiters form the table. Because table3 wasn't created with specific delimiters, it will use the default Hive delimiter, which is \001 or Control-A. When the file is imported into R as a CSV, there will be only 1 column because the file isn't actually comma delimited.
Question : You use Sqoop to import a table from your RDBMS into HDFS. You know that Sqoop typically instantiates four Mappers. However, after the table import, you notice that five Mappers have run, there are five output files in HDFS, and one of the output files is empty. Why? 1. The administrator has set the sqoop.num.maps property on the slave nodes to 7 2. Some Map tasks failed and had to be rerun 3. Access Mostly Uused Products by 50000+ Subscribers 4. The HDFS block size was set to a very small value, resulting in more Mappers than usual running 5. The table was modified by a user of the RDBMS as Sqoop was running
Ans : 3 Exp : If some Map task attempts failed, they would be rerun but no data from the failed task attempts would be stored on disk. There is no sqoop.num.maps property. Sqoop typically reads the table in a single transaction, so modifying the data would have no effect; and the HDFS block size is irrelevant to the number of files created. The correct answer is that by default, Sqoop uses the table's primary key to determine how to split the data. If there is no numeric primary key, Sqoop will make a best-guess attempt at how the data is distributed, and may run more than its default four Mappers, although some may end up not actually reading any data.
Question : Using Apache Sqoop you can import the data to
Ans : 5 Exp : : Apache Sqoop can be used to import data from any relational DB into HDFS, Hive or HBase. To import data into HDFS, use the sqoop import command and specify the relational DB table and connection parameters:
sqoop import --connect "JDBC connection string" --table "tablename" --username "username" --password "password" This will import the data and store it as a CSV file in a directory in HDFS. To import data into Hive, use the sqoop import command and specify the option 'hive-import'.
sqoop import --connect "JDBC connection string" --table "tablename" --username "username" --password "password" --hive-importThis will import the data into a Hive table with the approproate data types for each column.
Question : You decide to use Hive to process data in HDFS. You have not created any Hive tables until now. Hive is configured with its default settings. You run the following commands from the Hive shell:
Ans : 2 Exp : : When you create a database named HADOOPEXAM in Hive, that creates a subdirectory of Hive's warehouse directory named HADOOPEXAM.db. All tables are placed in subdirectories of HADOOPEXAM.db; those subdirectory names are the names of the tables
Question :. For HadoopExam.com user profiles you need to analyze roughly ,, JPEG files of all the. Each file is no more than 3kB.Because your Hadoop cluster isn't optimized for storing and processing many small files, you decide to group the files into a single archive. The toolkit that will be used to process the files is written in Ruby and requires that it be run with administrator privileges. Which of the following file formats should you select to build your archive?
Exp :The two formats that are best suited to merging small files into larger archives for processing in Hadoop are Avro and SequenceFiles. Avro has Ruby bindings; SequenceFiles are only supported in Java.
JSON, TIFF, and MPEG are not appropriate formats for archives. JSON is also not an appropriate format for image data.
Question : SequenceFiles are flat files consisting of binary key/value pairs. SequenceFile provides Writer, Reader and SequenceFile.Sorter classes for writing, reading and sorting respectively. There are three SequenceFile Writers based on the SequenceFile.CompressionType used to compress key/value pairs: You have created a SequenceFile (MAIN.PROFILE.log) with custom key and value types. What command displays the contents of a SequenceFile named MAIN.PROFILE.log in your terminal in human-readable format?
Explanation: SequenceFiles are flat files consisting of binary key/value pairs.SequenceFile provides SequenceFile.Writer, SequenceFile.Reader and SequenceFile.Sorter classes for writing, reading and sorting respectively. There are three SequenceFile Writers based on the SequenceFile.CompressionType used to compress key/value pairs: Writer : Uncompressed records. RecordCompressWriter : Record-compressed files, only compress values. BlockCompressWriter : Block-compressed files, both keys & values are collected in 'blocks' separately and compressed. The size of the 'block' is configurable. The actual compression algorithm used to compress key and/or values can be specified by using the appropriate CompressionCodec. The recommended way is to use the static createWriter methods provided by the SequenceFile to chose the preferred format. The SequenceFile.Reader acts as the bridge and can read any of the above SequenceFile formats. SequenceFile Formats Essentially there are 3 different formats for SequenceFiles depending on the CompressionType specified. All of them share a common header described below. SequenceFile Header version - 3 bytes of magic header SEQ, followed by 1 byte of actual version number (e.g. SEQ4 or SEQ6) keyClassName -key class valueClassName - value class compression - A boolean which specifies if compression is turned on for keys/values in this file. blockCompression - A boolean which specifies if block-compression is turned on for keys/values in this file. compression codec - CompressionCodec class which is used for compression of keys and/or values (if compression is enabled). metadata - SequenceFile.Metadata for this file. sync - A sync marker to denote end of the header. Uncompressed SequenceFile Format Header, Record , Record length , Key length , Key, Value A sync-marker every few 100 bytes or so.A SequenceFile contains the name of the classes used for the key and value as part of its header. hadoop fs -text reads the records, and calls the toString() method of the relevant class to display human-readable output on the console. The hadoop fs -cat command would display the raw data from the file, which is not human-readable. hadoop fs -get retrieves the file from HDFS and places it on the local disk, which is not what was required. The other options are syntactically incorrect.
Question : Speculative execution is an optimization technique where a computer system performs some task that may not be actually needed. The main idea is to do work before it is known whether that work will be needed at all, so as to prevent a delay that would have to be incurred by doing the work after it is known whether it is needed. If it turns out the work was not needed after all, any changes made by the work are reverted and the results are ignored. In a ETL MapReduce job which will use Mappers to process data and then using DBMSOutputFormat with the Reducers you directly push to Oracle database. Select the correct statement which applies for speculative execution.
1. Disable speculative execution for the data insert job 2. Enable speculative execution for the data insert job 3. Access Mostly Uused Products by 50000+ Subscribers 4. Configure only single mapper for the data insert job
Explanation: I usually disable speculative execution for MapReduce task when I write to RDBMS in Hive user defined table function.
set mapred.map.tasks.speculative=false; set mapred.reduce.tasks.speculative.execution=false; set hive.mapred.reduce.tasks.speculative.execution=false;
And if you tune the mapred.reduce.tasks, you can control RDBMS session-running number. It is good also to use Batch mode and control the commit If we do not disable speculative execution, it is possible that multiple instances of a given Reducer could run, which would result in more data than was intended being inserted into the target RDBMS. None of the other options presented is required; although you need the database driver on the client machine if you plan to connect to the RDBMS from that client, it does not need to be present. It is certainly not necessary for yours to be the only job running on the cluster, and the values ofdfs.datanode.failed.volumes.tolerated and the block size of the input data are irrelevant. Finally, the RDBMS does not need to allow passwordless login.
Question : Apache MRUnit is a Java library that helps developers unit test Apache Hadoop map reduce jobs. MRUnit testing framework is based on JUnit and it can test Map Reduce programs written on 0.20 , 0.23.x , 1.0.x , 2.x version of Hadoop You have a Reducer which simply sums up the values for any given key. You write a unit test in MRUnit to test the Reducer, with this code: @Test public void testETLReducer() { List < IntWritable > values = new ArrayList < IntWritable > (); values.add(new IntWritable(1)); values.add(new IntWritable(1)); List < IntWritable > values2 = new ArrayList < IntWritable > (); values2.add(new IntWritable(1)); values2.add(new IntWritable(1)); reduceDriver.withInput(new LongWritable("5673"), values); reduceDriver.withInput(new LongWritable("109098"), values2); reduceDriver.withOutput(new LongWritable("109098"), new IntWritable(2)); reduceDriver.runTest(); } What is the result?
Correct Answer : Get Lastest Questions and Answer : Example : @Test public void testMapReduce() { mapReduceDriver.withInput(new LongWritable(), new Text( "655209;1;796764372490213;804422938115889;6")); List (IntWritable) values = new ArrayList(IntWritable)(); values.add(new IntWritable(1)); values.add(new IntWritable(1)); mapReduceDriver.withOutput(new Text("6"), new IntWritable(2)); mapReduceDriver.runTest(); } MRUnit supports two style of testings. First style is to tell the framework both input and output values and let the framework do the assertions, second is the more traditional approach where you do the assertion yourself. Lets write a test using the first approach.When testing a Reducer using MRUnit, you should only pass the Reducer a single keyand list of values. In this case, we use the withInput() method twice, but only the second call will actually be used -- the first will be overridden by the second. If you want to test the Reducer with two inputs, you would have to write two tests. Testing a Hadoop job requires a lot of effort not related to the job. You must configure it to run locally, create a sample input file, run the job on your sample input, and then compare to an expected output file. This not only takes time, but makes your tests run very slow due to all the file I/O. MRUnit is: a unit test library designed to facilitate easy integration between your MapReduce development process and standard development and testing tools such as JUnit With MRUnit, there are no test files to create, no configuration parameters to change, and generally less test code. You can cut the clutter and focus on the meat of your tests.
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1. It will create a new file called output1.txt in local file system, with the merged content from the all three files 2. It will create a new file called output1.txt in hdfs file system, with the merged content from the all three files 3. Access Mostly Uused Products by 50000+ Subscribers 4. This command will successful but will not merge the files because of, what to do with new line character is not defined.