Latest Hadoop Interview Questions -Part 13

131. Explain the Reducer’s reduce phase?

Ans: In this phase the reduce(MapOutKeyType, Iterable, Context) method is called for

each pair in the grouped inputs. The output of the reduce task is typically written to the

FileSystem via Context.write(ReduceOutKeyType, ReduceOutValType). Applications

can use the Context to report progress, set application-level status messages and update

Counters, or just indicate that they are alive. The output of the Reducer is not sorted.

132. How many Reducers should be configured?

Ans: The right number of reduces seems to be 0.95 or 1.75 multiplied by (<no. of

nodes> * mapreduce.tasktracker.reduce.tasks.maximum).

With 0.95 all of the reduces can launch immediately and start transfering map outputs as

the maps finish. With 1.75 the faster nodes will finish their first round of reduces and

launch a second wave of reduces doing a much better job of load balancing. Increasing

the number of reduces increases the framework overhead, but increases load balancing

and lowers the cost of failures.

133. It can be possible that a Job has 0 reducers?

Ans: It is legal to set the number of reduce-tasks to zero if no reduction is desired.

134. What happens if number of reducers are 0?

Ans: In this case the outputs of the map-tasks go directly to the FileSystem, into the

output path set by setOutputPath(Path). The framework does not sort the map-outputs

before writing them out to the FileSystem.

135. How many instances of JobTracker can run on a Hadoop Cluser?

Ans: Only one

136. What is the JobTracker and what it performs in a Hadoop Cluster?

Ans: JobTracker is a daemon service which submits and tracks the MapReduce

tasks to the Hadoop cluster. It runs its own JVM process. And usually it run on a

separate machine, and each slave node is configured with job tracker node

location.

The JobTracker is single point of failure for the Hadoop MapReduce service. If it

goes down, all running jobs are halted.

JobTracker in Hadoop performs following actions

 Client applications submit jobs to the Job tracker.

 The JobTracker talks to the NameNode to determine the location of the data

 The JobTracker locates TaskTracker nodes with available slots at or near the

data

 The JobTracker submits the work to the chosen TaskTracker nodes

 The TaskTracker nodes are monitored. If they do not submit heartbeat signals

often enough, they are deemed to have failed and the work is scheduled on a

different TaskTracker.

 A TaskTracker will notify the JobTracker when a task fails. The JobTracker

decides what to do then: it may resubmit the job elsewhere, it may mark that

specific record as something to avoid, and it may may even blacklist the

TaskTracker as unreliable.

 When the work is completed, the JobTracker updates its status.

 Client applications can poll the JobTracker for information.

137. How a task is scheduled by a JobTracker?

Ans: The TaskTrackers send out heartbeat messages to the JobTracker, usually

every few minutes, to reassure the JobTracker that it is still alive. These

messages also inform the JobTracker of the number of available slots, so the

JobTracker can stay up to date with where in the cluster work can be delegated.

When the JobTracker tries to find somewhere to schedule a task within the

MapReduce operations, it first looks for an empty slot on the same server that

hosts the DataNode containing the data, and if not, it looks for an empty slot on a

machine in the same rack.

138. How many instances of Tasktracker run on a Hadoop cluster?

Ans: There is one Daemon Tasktracker process for each slave node in the

Hadoop cluster.

139. What are the two main parts of the Hadoop framework?

Ans: Hadoop consists of two main parts

 Hadoop distributed file system, a distributed file system with high throughput,

 Hadoop MapReduce, a software framework for processing large data sets.

140. Explain the use of TaskTracker in the Hadoop cluster?

Ans: A Tasktracker is a slave node in the cluster which that accepts the tasks

from JobTracker like Map, Reduce or shuffle operation. Tasktracker also runs in

its own JVM Process.

Every TaskTracker is configured with a set of slots; these indicate the number of

tasks that it can accept. The TaskTracker starts a separate JVM processes to do

the actual work (called as Task Instance) this is to ensure that process failure

does not take down the task tracker.©Hadoop Learning Resources (Note: PappuPass.com is changed to HadoopExam.com) 13

The Tasktracker monitors these task instances, capturing the output and exit

codes. When the Task instances finish, successfully or not, the task tracker

notifies the JobTracker.

The TaskTrackers also send out heartbeat messages to the JobTracker, usually

every few minutes, to reassure the JobTracker that it is still alive. These

messages also inform the JobTracker of the number of available slots, so the

JobTracker can stay up to date with where in the cluster work can be delegated.

Latest Hadoop Interview Questions -Part 12

121. Which object can be used to get the progress of a particular job ?

Ans: Context

122. What is next step after Mapper or MapTask?

Ans : The output of the Mapper are sorted and Partitions will be created for the

output. Number of partition depends on the number of reducer.

123. How can we control particular key should go in a specific reducer?

Ans: Users can control which keys (and hence records) go to which Reducer by

implementing a custom Partitioner.

124. What is the use of Combiner?

Ans: It is an optional component or class, and can be specify via

Job.setCombinerClass(ClassName), to perform local aggregation of the

intermediate outputs, which helps to cut down the amount of data transferred

from the Mapper to the Reducer.

125. How many maps are there in a particular Job?

Ans: The number of maps is usually driven by the total size of the inputs, that is,

the total number of blocks of the input files.

Generally it is around 10-100 maps per-node. Task setup takes awhile, so it is

best if the maps take at least a minute to execute.

Suppose, if you expect 10TB of input data and have a blocksize of 128MB, you'll

end up with 82,000 maps, to control the number of block you can use the

mapreduce.job.maps parameter (which only provides a hint to the framework).

Ultimately, the number of tasks is controlled by the number of splits returned by

the InputFormat.getSplits() method (which you can override).

126. What is the Reducer used for?

Ans: Reducer reduces a set of intermediate values which share a key to a (usually smaller) set of values.

The number of reduces for the job is set by the user via Job.setNumReduceTasks(int).

127. Explain the core methods of the Reducer?

Ans: The API of Reducer is very similar to that of Mapper, there's a run() method that receives a Context containing the job's configuration as well as interfacing methods that return data from the reducer itself back to the framework. The run() method calls setup() once, reduce() once for each key associated with the

reduce task, and cleanup() once at the end. Each of these methods can access the job's configuration data by using Context.getConfiguration(). As in Mapper, any or all of these methods can be overridden with custom implementations. If none of these methods are overridden, the default reducer operation is the identity function; values are passed through without further processing.

The heart of Reducer is its reduce() method. This is called once per key; the second argument is an Iterable which returns all the values associated with that key.

128. What are the primary phases of the Reducer?

Ans: Shuffle, Sort and Reduce

129. Explain the shuffle?

Ans: Input to the Reducer is the sorted output of the mappers. In this phase the

framework fetches the relevant partition of the output of all the mappers, via HTTP.

30. Explain the Reducer’s Sort phase 

Ans: The framework groups Reducer inputs by keys (since different mappers may

have output the same key) in this stage. The shuffle and sort phases occur simultaneously;

while map-outputs are being fetched they are merged (It is similar to merge-sort).

Latest Hadoop Interview Questions -Part 11

101. What Mapper does?

Ans: Maps are the individual tasks that transform i

nput records into intermediate records. The transformed intermediate records do not need

to be of the same type as the input records. A given input pair may map to zero or many

output pairs.

102. What is the InputSplit in map reduce software  

Ans: An InputSplit is a logical representation of a unit (A chunk) of input work for a

map task; e.g., a filename and a byte range within that file to process or a row set in a text

file.

103. What is the InputFormat ?

Ans: The InputFormat is responsible for enumerate (itemise) the InputSplits, and

producing a RecordReader which will turn those logical work units into actual physical

input records.

104. Where do you specify the Mapper Implementation?

Ans: Generally mapper implementation is specified in the Job itself.

105. How Mapper is instantiated in a running job  9

Ans: The Mapper itself is instantiated in the running job, and will be passed a

MapContext object which it can use to configure itself.

106. Which are the methods in the Mapper interface?

Ans : The Mapper contains the run() method, which call its own setup() method

only once, it also call a map() method for each input and finally calls it cleanup()

method. All above methods you can override in your code.

107. What happens if you don’t override the Mapper methods and keep them as

it is?

Ans: If you do not override any methods (leaving even map as-is), it will act as

the identity function, emitting each input record as a separate output.

108. What is the use of Context object?

Ans: The Context object allows the mapper to interact with the rest of the Hadoop

system. It Includes configuration data for the job, as well as interfaces which allow it to emit

output.

109. How can you add the arbitrary key-value pairs in your mapper?

Ans: You can set arbitrary (key, value) pairs of configuration data in your Job,

e.g. with Job.getConfiguration().set("myKey", "myVal"), and then retrieve this data

in your mapper with Context.getConfiguration().get("myKey"). This kind of

functionality is typically done in the Mapper's setup() method.

110. How does Mapper’s run() method works?

Ans: The Mapper.run() method then calls map(KeyInType, ValInType, Context) for each

key/value pair in the InputSplit for that task

Latest Hadoop Interview Questions-Part 10

91. What is Hadoop framework?

Ans: Hadoop is a open source framework which is written in java by apche

software foundation. This framework is used to wirite software application which

requires to process vast amount of data (It could handle multi tera bytes of data).

It works in-paralle on large clusters which could have 1000 of computers (Nodes)

on the clusters. It also process data very reliably and fault-tolerant manner. See

the below image how does it looks.

92. On What concept the Hadoop framework works?

Ans : It works on MapReduce, and it is devised by the Google.

93. What is MapReduce ?

Ans: Map reduce is an algorithm or concept to process Huge amount of data in a

faster way. As per its name you can divide it Map and Reduce.

 The main MapReduce job usually splits the input data-set into independent chunks.

(Big data sets in the multiple small datasets)

 MapTask: will process these chunks in a completely parallel manner (One node can

process one or more chunks).©Hadoop Learning Resources (Note: PappuPass.com is changed to HadoopExam.com) 4

 The framework sorts the outputs of the maps.

 Reduce Task : And the above output will be the input for the reducetasks, produces

the final result.

Your business logic would be written in the MappedTask and ReducedTask.

Typically both the input and the output of the job are stored in a file-system (Not

database). The framework takes care of scheduling tasks, monitoring them and

re-executes the failed tasks.

94. What is compute and Storage nodes?

Compute Node: This is the computer or machine where your actual business

logic will be executed.

Storage Node: This is the computer or machine where your file system reside to

store the processing data.

In most of the cases compute node and storage node would be the same

machine.

95. How does master slave architecture in the Hadoop?

Ans: The MapReduce framework consists of a single master JobTracker and

multiple slaves, each cluster-node will have one TaskskTracker.

The master is responsible for scheduling the jobs' component tasks on the

slaves, monitoring them and re-executing the failed tasks. The slaves execute the

tasks as directed by the master.

96. How does an Hadoop application look like or their basic components?

Ans: Minimally an Hadoop application would have following components.

 Input location of data

 Output location of processed data.

 A map task.

 A reduced task.

 Job configuration

The Hadoop job client then submits the job (jar/executable etc.) and configuration

to the JobTracker which then assumes the responsibility of distributing the

software/configuration to the slaves, scheduling tasks and monitoring them,

providing status and diagnostic information to the job-client.

97. Explain how input and output data format of the Hadoop framework?

Ans: The MapReduce framework operates exclusively on pairs, that is, the

framework views the input to the job as a set of pairs and produces a set of pairs

as the output of the job, conceivably of different types. See the flow mentioned below

(input) -> map -> -> combine/sorting -> -> reduce -> (output)

98. What are the restriction to the key and value class ?

Ans: The key and value classes have to be serialized by the framework. To make them

serializable Hadoop provides a Writable interface. As you know from the java itself that

the key of the Map should be comparable, hence the key has to implement one more

interface WritableComparable. 

99. Which interface needs to be implemented to create Mapper and Reducer 

    for the Hadoop?

Ans:

org.apache.hadoop.mapreduce.Mapper

org.apache.hadoop.mapreduce.Reducer