Top 50 Hadoop Interview Questions You Must Prepare 26.Aug.2019

Top 50 Hadoop Interview Questions You Must Prepare 26.Aug.2019

Hadoop Interview Questions and Answers

Hadoop Interview Questions and Answers

Which Are The Two Types Of 'writes' In Hdfs?

There are two types of writes in HDFS: posted and non-posted write. Posted Write is when we write it and forget about it, without worrying about the acknowledgement. It is similar to our traditional Indian post. In a Non-posted Write, we wait for the acknowledgement. It is similar to the today’s courier services. Naturally, non-posted write is more expensive than the posted write. It is much more expensive, though both writes are asynchronous.

How Does Mapper's Run() Method Works?

The Mapper.run() method then calls map(KeyInType, ValInType, Context) for each key/value pair in the InputSplit for that task

How Many Daemon Processes Run On A Hadoop System?

Hadoop is comprised of five separate daemons. Each of these daemon run in its own JVM.Following 3 Daemons run on Master nodes

NameNode : This daemon stores and maintains the metadata for HDFS.

Secondary NameNode : Performs housekeeping functions for the NameNode.

JobTracker : Manages MapReduce jobs, distributes individual tasks to machines running the Task Tracker.

Following 2 Daemons run on each Slave nodes

DataNode : Stores actual HDFS data blocks.

TaskTracker : Responsible for instantiating and monitoring individual Map and Reduce tasks.

How The Client Communicates With Hdfs?

The Client communication to HDFS happens using Hadoop HDFS API. Client applications talk to the NameNode whenever they wish to locate a file, or when they want to add/copy/move/delete a file on HDFS. The NameNode responds the successful requests by returning a list of relevant DataNode servers where the data lives. Client applications can talk directly to a DataNode, once the NameNode has provided the location of the data.

Does Mapreduce Programming Model Provide A Way For Reducers To Communicate With Each Other? In A Mapreduce Job Can A Reducer Communicate With Another Reducer?

Nope, MapReduce programming model does not allow reducers to communicate with each other. Reducers run in isolation.

Can You Give A Detailed Overview About The Big Data Being Generated By Facebook?

As of December 31, 2012, there are 1.06 billion monthly active users on facebook and 680 million mobile users. On an average, 3.2 billion likes and comments are posted every day on Facebook. 72% of web audience is on Facebook. And why not! There are so many activities going on facebook from wall posts, sharing images, videos, writing comments and liking posts, etc. In fact, Facebook started using Hadoop in mid-2009 and was one of the initial users of Hadoop.

Since The Data Is Replicated Thrice In Hdfs, Does It Mean That Any Calculation Done On One Node Will Also Be Replicated On The Other Two?

Since there are 3 nodes, when we send the MapReduce programs, calculations will be done only on the original data. The master node will know which node exactly has that particular data. In case, if one of the nodes is not responding, it is assumed to be failed. Only then, the required calculation will be done on the second replica.

Who Is A 'user' In Hdfs?

A user is like you or me, who has some query or who needs some kind of data.

Replication Causes Data Redundancy Then Why Is Is Pursued In Hdfs?

HDFS works with commodity hardware (systems with average configurations) that has high chances of getting crashed any time. Thus, to make the entire system highly fault-tolerant, HDFS replicates and stores data in different places. Any data on HDFS gets stored at at least 3 different locations. So, even if one of them is corrupted and the other is unavailable for some time for any reason, then data can be accessed from the third one. Hence, there is no chance of losing the data. This replication factor helps us to attain the feature of Hadoop called Fault Tolerant.

What Is 'key Value Pair' In Hdfs?

Key value pair is the intermediate data generated by maps and sent to reduces for generating the final output.

How Can I Install Cloudera Vm In My System?

When you enrol for the hadoop course at Edureka, you can download the Hadoop Installation steps.pdf file from our dropbox.

Explain The Core Methods Of The Reducer?

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.

What Are Combiners? When Should I Use A Combiner In My Mapreduce Job?

Combiners are used to increase the efficiency of a MapReduce program. They are used to aggregate intermediate map output locally on individual mapper outputs. Combiners can help you reduce the amount of data that needs to be trferred across to the reducers. You can use your reducer code as a combiner if the operation performed is commutative and associative. The execution of combiner is not guaranteed, Hadoop may or may not execute a combiner. Also, if required it may execute it more then 1 times. Therefore your MapReduce jobs should not depend on the combiners execution.

What If Rack 2 And Datanode Fails?

If both rack2 and datanode present in rack 1 fails then there is no chance of getting data from it. In order to avoid such situations, we need to replicate that data more number of times instead of replicating only thrice. This can be done by changing the value in replication factor which is set to 3 by default.

Are Job Tracker And Task Trackers Present In Separate Machines?

Yes, job tracker and task tracker are present in different machines. The reason is job tracker is a single point of failure for the Hadoop MapReduce service. If it goes down, all running jobs are halted.

What Is Structured And Unstructured Data?

Structured data is the data that is easily identifiable as it is organized in a structure. The most common form of structured data is a database where specific information is stored in tables, that is, rows and columns. Unstructured data refers to any data that cannot be identified easily. It could be in the form of images, videos, documents, email, logs and random text. It is not in the form of rows and columns.

If Datanodes Increase, Then Do We Need To Upgrade Namenode?

While installing the Hadoop system, Namenode is determined based on the size of the clusters. Most of the time, we do not need to upgrade the Namenode because it does not store the actual data, but just the metadata, so such a requirement rarely arise.

How Many Maximum Jvm Can Run On A Slave Node?

One or Multiple instances of Task Instance can run on each slave node. Each task instance is run as a separate JVM process. The number of Task instances can be controlled by configuration. Typically a high end machine is configured to run more task instances.

Are Namenode And Job Tracker On The Same Host?

No, in practical environment, Namenode is on a separate host and job tracker is on a separate host.

How Can You Add The Arbitrary Key-value Pairs In Your Mapper?

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.

If A Data Node Is Full How It's Identified?

When data is stored in datanode, then the metadata of that data will be stored in the Namenode. So Namenode will identify if the data node is full.

What Is The Jobtracker And What It Performs In A Hadoop Cluster?

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.
  • 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.
  • 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.

Explain The Shuffle?

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.

Why 'reading' Is Done In Parallel And 'writing' Is Not In Hdfs?

Reading is done in parallel because by doing so we can access the data fast. But we do not perform the write operation in parallel. The reason is that if we perform the write operation in parallel, then it might result in data inconsistency. For example, you have a file and two nodes are trying to write data into the file in parallel, then the first node does not know what the second node has written and vice-versa. So, this makes it confusing which data to be stored and accessed.

What Are Some Of The Characteristics Of Hadoop Framework?

Hadoop framework is written in Java. It is designed to solve problems that involve analyzing large data (e.g. petabytes). The programming model is based on Google’s MapReduce. The infrastructure is based on Google’s Big Data and Distributed File System. Hadoop handles large files/data throughput and supports data intensive distributed applications. Hadoop is scalable as more nodes can be easily added to it.

What Is A Secondary Namenode? Is It A Substitute To The Namenode?

The secondary Namenode constantly reads the data from the RAM of the Namenode and writes it into the hard disk or the file system. It is not a substitute to the Namenode, so if the Namenode fails, the entire Hadoop system goes down.

What Is Configuration Of A Typical Slave Node On Hadoop Cluster? How Many Jvms Run On A Slave Node?

  • Single instance of a Task Tracker is run on each Slave node. Task tracker is run as a separate JVM process.
  • Single instance of a DataNode daemon is run on each Slave node. DataNode daemon is run as a separate JVM process.
  • One or Multiple instances of Task Instance is run on each slave node. Each task instance is run as a separate JVM process. The number of Task instances can be controlled by configuration. Typically a high end machine is configured to run more task instances.

How Does A Namenode Handle The Failure Of The Data Nodes?

HDFS has master/slave architecture. An HDFS cluster consists of a single

NameNode, a master server that manages the file system namespace and regulates access to files by clients.

In addition, there are a number of DataNodes, usually one per node in the cluster, which manage storage attached to the nodes that they run on. The NameNode and DataNode are pieces of software designed to run on commodity machines.

NameNode periodically receives a Heartbeat and a Block report from each of the DataNodes in the cluster. Receipt of a Heartbeat implies that the DataNode is functioning properly. A Blockreport contains a list of all blocks on a DataNode. When NameNode notices that it has not received a heartbeat message from a data node after a certain amount of time, the data node is marked as dead. Since blocks will be under replicated the system begins replicating the blocks that were stored on the dead DataNode. The NameNode Orchestrates the replication of data blocks from one DataNode to another. The replication data trfer happens directly between DataNode and the data never passes through the NameNode.

What Is Hadoop Framework?

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.

Is A Job Split Into Maps?

No, a job is not split into maps. Spilt is created for the file. The file is placed on datanodes in blocks. For each split, a map is needed.

When The Reducers Are Are Started In A Mapreduce Job?

In a MapReduce job reducers do not start executing the reduce method until the all Map jobs have completed. Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The programmer defined reduce method is called only after all the mappers have finished.

If reducers do not start before all mappers finish then why does the progress on MapReduce job shows something like Map(50%) Reduce(10%)? Why reducers progress percentage is displayed when mapper is not finished yet?

Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The progress calculation also takes in account the processing of data trfer which is done by reduce process, therefore the reduce progress starts showing up as soon as any intermediate key-value pair for a mapper is available to be trferred to reducer.

Though the reducer progress is updated still the programmer defined reduce method is called only after all the mappers have finished.

What Is A Metadata?

Metadata is the information about the data stored in data nodes such as location of the file, size of the file and so on.

What Are The Two Main Parts Of The Hadoop Framework?

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.

What Is The Reducer Used For?

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).

According To Ibm, What Are The Three Characteristics Of Big Data?

According to IBM, the three characteristics of Big Data are:

Volume: Facebook generating 500+ terabytes of data per day.

Velocity: Analyzing 2 million records each day to identify the reason for losses.

Variety: images, audio, video, sensor data, log files, etc.

What Is Streaming Access?

As HDFS works on the principle of ‘Write Once, Read Many‘, the feature of streaming access is extremely important in HDFS. HDFS focuses not so much on storing the data but how to retrieve it at the fastest possible speed, especially while analyzing logs. In HDFS, reading the complete data is more important than the time taken to fetch a single record from the data.

How Mapper Is Instantiated In A Running Job?

The Mapper itself is instantiated in the running job, and will be passed a MapContext object which it can use to configure itself.

What Is The Difference Between Mapreduce Engine And Hdfs Cluster?

HDFS cluster is the name given to the whole configuration of master and slaves where data is stored. Map Reduce Engine is the programming module which is used to retrieve and analyze data.

What Is The Communication Channel Between Client And Namenode/datanode?

The mode of communication is SSH.

Where Do You Specify The Mapper Implementation?

Generally mapper implementation is specified in the Job itself.

Is Client The End User In Hdfs?

No, Client is an application which runs on your machine, which is used to interact with the Namenode (job tracker) or datanode (task tracker).

Explain The Wordcount Implementation Via Hadoop Framework ?

We will count the words in all the input file flow as below

  • input Assume there are two files each having a sentence Hello World Hello World (In file 1) Hello World Hello World (In file 2)
  • Mapper : There would be each mapper for the a file For the given sample input the first map output:

    < Hello, 1>
    < World, 1>
    < Hello, 1>
    < World, 1>

    The second map output:

    < Hello, 1>
    < World, 1>
    < Hello, 1>
    < World, 1>

  • Combiner/Sorting (This is done for each individual map) So output looks like this The output of the first map:

    < Hello, 2>
    < World, 2>

    The output of the second map:

    < Hello, 2>
    < World, 2>

  • Reducer : It sums up the above output and generates the output as below

    < Hello, 4>
    < World, 4>

Output

Final output would look like

Hello 4 times
World 4 times

What Is The Difference Between Hdfs And Nas ?

The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware. It has many similarities with existing distributed file systems. However, the differences from other distributed file systems are significant.

Following are differences between HDFS and NAS

  • In HDFS Data Blocks are distributed across local drives of all machines in a cluster. Whereas in NAS data is stored on dedicated hardware.
  • HDFS is designed to work with MapReduce System, since computation are moved to data. NAS is not suitable for MapReduce since data is stored separately from the computations.
  • HDFS runs on a cluster of machines and provides redundancy using a replication protocol. Whereas NAS is provided by a single machine therefore does not provide data redundancy.

What Is A Heartbeat In Hdfs?

A heartbeat is a signal indicating that it is alive. A datanode sends heartbeat to Namenode and task tracker will send its heart beat to job tracker. If the Namenode or job tracker does not receive heart beat then they will decide that there is some problem in datanode or task tracker is unable to perform the assigned task.

Can Reducer Talk With Each Other?

No, Reducer runs in isolation.

When Is The Reducers Are Started In A Mapreduce Job?

In a MapReduce job reducers do not start executing the reduce method until the all Map jobs have completed. Reducers start copying intermediate key-value pairs from the mappers as soon as they are available. The programmer defined reduce method is called only after all the mappers have finished.

Which Interface Needs To Be Implemented To Create Mapper And Reducer For The Hadoop?

org.apache.hadoop.mapreduce.Mapper
org.apache.hadoop.mapreduce.Reducer

How The Hdfs Blocks Are Replicated?

HDFS is designed to reliably store very large files across machines in a large cluster. It stores each file as a sequence of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once and have strictly one writer at any time.

The NameNode makes all decisions regarding replication of blocks. HDFS uses rack-aware replica placement policy. In default configuration there are total 3 copies of a datablock on HDFS, 2 copies are stored on datanodes on same rack and 3rd copy on a different rack.

Can I Set The Number Of Reducers To Zero?

Yes, Setting the number of reducers to zero is a valid configuration in Hadoop. When you set the reducers to zero no reducers will be executed, and the output of each mapper will be stored to a separate file on HDFS. [This is different from the condition when reducers are set to a number greater than zero and the Mappers output (intermediate data) is written to the Local file system(NOT HDFS) of each mappter slave node.]

What Is A Datanode? How Many Instances Of Datanode Run On A Hadoop Cluster?

A DataNode stores data in the Hadoop File System HDFS. There is only One DataNode process run on any hadoop slave node. DataNode runs on its own JVM process. On startup, a DataNode connects to the NameNode. DataNode instances can talk to each other, this is mostly during replicating data.