24 Interview Questions & Answers for Hadoop MapReduce developers

Interview Questions & Answers for Hadoop
A good understanding of Hadoop Architecture is required to understand and leverage the power of Hadoop. Here are few important practical questions which can be asked to a Senior Experienced Hadoop Developer in an interview. This list primarily includes questions related to Hadoop Architecture, MapReduce, Hadoop API and Hadoop Distributed File System (HDFS).
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What is a JobTracker in Hadoop? How many instances of JobTracker run on a Hadoop Cluster?

JobTracker is the daemon service for submitting and tracking MapReduce jobs in Hadoop. There is only One Job Tracker process run on any hadoop cluster. Job Tracker runs on its own JVM process. In a typical production cluster its run on a separate machine. 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(from Hadoop Wiki:)

  • 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.

How JobTracker schedules a task?

The TaskTrackers send out heartbeat messages to the JobTracker, usually every few minutes, to reassure the JobTracker that it is still alive. These message 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.

What is a Task Tracker in Hadoop? How many instances of TaskTracker run on a Hadoop Cluster

A TaskTracker is a slave node daemon in the cluster that accepts tasks (Map, Reduce and Shuffle operations) from a JobTracker. There is only One Task Tracker process run on any hadoop slave node. Task Tracker runs on 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. 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 message 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.

What is a Task instance in Hadoop? Where does it run?

Task instances are the actual MapReduce jobs which are run on each slave node. 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. Each Task Instance runs on its own JVM process. There can be multiple processes of task instance running on a slave node. This is based on the number of slots configured on task tracker. By default a new task instance JVM process is spawned for a 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.

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.

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 seperately from the computations.
  • HDFS runs on a cluster of machines and provides redundancy usinga replication protocal. Whereas NAS is provided by a single machine therefore does not provide data redundancy.

How NameNode Handles data node failures?

NameNode periodically receives a Heartbeat and a Blockreport 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 recieved a hearbeat 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 transfer happens directly between datanodes and the data never passes through the namenode.

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 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.]

Where is the Mapper Output (intermediate kay-value data) stored ?

The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes. This is typically a temporary directory location which can be setup in config by the hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes.

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 transferred 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 is Writable & WritableComparable interface?

  • org.apache.hadoop.io.Writable is a Java interface. Any key or value type in the Hadoop Map-Reduce framework implements this interface. Implementations typically implement a static read(DataInput) method which constructs a new instance, calls readFields(DataInput) and returns the instance.
  • org.apache.hadoop.io.WritableComparable is a Java interface. Any type which is to be used as a key in the Hadoop Map-Reduce framework should implement this interface. WritableComparable objects can be compared to each other using Comparators.

What is the Hadoop MapReduce API contract for a key and value Class?

  • The Key must implement the org.apache.hadoop.io.WritableComparable interface.
  • The value must implement the org.apache.hadoop.io.Writable interface.

What is a IdentityMapper and IdentityReducer in MapReduce ?

  • org.apache.hadoop.mapred.lib.IdentityMapper Implements the identity function, mapping inputs directly to outputs. If MapReduce programmer do not set the Mapper Class using JobConf.setMapperClass then IdentityMapper.class is used as a default value.
  • org.apache.hadoop.mapred.lib.IdentityReducer Performs no reduction, writing all input values directly to the output. If MapReduce programmer do not set the Reducer Class using JobConf.setReducerClass then IdentityReducer.class is used as a default value.

What is the meaning of speculative execution in Hadoop? Why is it important?

Speculative execution is a way of coping with individual Machine performance. In large clusters where hundreds or thousands of machines are involved there may be machines which are not performing as fast as others. This may result in delays in a full job due to only one machine not performaing well. To avoid this, speculative execution in hadoop can run multiple copies of same map or reduce task on different slave nodes. The results from first node to finish are used.

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.

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 transfer 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 transferred 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 HDFS ? How it is different from traditional file systems?

HDFS, the Hadoop Distributed File System, is responsible for storing huge data on the cluster. This 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.

  • HDFS is highly fault-tolerant and is designed to be deployed on low-cost hardware.
  • HDFS provides high throughput access to application data and is suitable for applications that have large data sets.
  • HDFS is designed to support very large files. Applications that are compatible with HDFS are those that deal with large data sets. These applications write their data only once but they read it one or more times and require these reads to be satisfied at streaming speeds. HDFS supports write-once-read-many semantics on files.

What is HDFS Block size? How is it different from traditional file system block size?

In HDFS data is split into blocks and distributed across multiple nodes in the cluster. Each block is typically 64Mb or 128Mb in size. Each block is replicated multiple times. Default is to replicate each block three times. Replicas are stored on different nodes. HDFS utilizes the local file system to store each HDFS block as a separate file. HDFS Block size can not be compared with the traditional file system block size.

What is a NameNode? How many instances of NameNode run on a Hadoop Cluster?

The NameNode is the centerpiece of an HDFS file system. It keeps the directory tree of all files in the file system, and tracks where across the cluster the file data is kept. It does not store the data of these files itself. There is only One NameNode process run on any hadoop cluster. NameNode runs on its own JVM process. In a typical production cluster its run on a separate machine. The NameNode is a Single Point of Failure for the HDFS Cluster. When the NameNode goes down, the file system goes offline. Client applications talk to the NameNode whenever they wish to locate a file, or when they want to add/copy/move/delete a file. The NameNode responds the successful requests by returning a list of relevant DataNode servers where the data lives.

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.

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.

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 you think of a questions which is not part of this post? Please don't forget to share it with me in comments section & I will try to include it in the list.

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Post a Comment Default Comments

  1. Yeah i agree with you.Why is Obama snubbing the US media and giving his first TV interview to Arab TV?Is he paying his contributors.Thanks

    ReplyDelete
  2. Its always been good to find such a good interview questions. It is very useful. Thanks for this post.

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  3. The NameNode is a Single Point of Failure for the HDFS Cluster. When the NameNode goes down, the file system goes offline.

    I'm wondering what is the SencondaryNameNode for?

    ReplyDelete
    Replies
    1. secondary namenode is for creating checkpoint(which is merging Fsimage and edit log file) its not a backup node for namenode.

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    2. secondary name node is the not he one which can take control of the system automatically when name node fail.but the system admin can make that secondary name node as the name node and other machine as the secondary name node but it takes some time till this happens the system is in the stop mode and the processes running on the cluster will stop,that i why the name node is called as the single point failure and it has been overcome by cloudera in the yarn(mapreducev2).

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    3. @Unknow: Are you sure what have you written " name node is called as the single point failure and it has been overcome by cloudera in the yarn(mapreducev2)"?
      How mapreduce version is related to Namenode. MRV2 overcome the SPOF of jobtracker not of Namenode. Namenode SPOF handles by hdfs high availablity.

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    4. @rvi: High availability which removes the namenode as the single point of failure is a feature of 2.x release of Hadoop. So yes it is tied to a release. Because 1.x and 0.22 Hadoop releases does not support HDFS federation and high-availability. Although, I agree YARN and high availability are not really related because YARN is MapReduce2 runtime feature. Thanks, Rashid Khan

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  4. By default a new task instance JVM process is spawned for a task.

    ReplyDelete
  5. a good interview questions. It is very useful. Thanks for this post.

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  6. Hi,

    Nice post. I wanted to share some interview questions (map/reduce)

    1. How to implement sort for 1 trillion files using map/reduce?

    2. How to join 2 datasets A and B (Need to move single entry even if it s present in both datasets) using Map/Reduce framework?

    ReplyDelete
    Replies
    1. Can some experts answer this please

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    2. 1. Use Secondary Sorting in Reduce job.

      2. Store Dataset-A in Distributed Cache at the Mapper side using 'config' method. Then perform joining operation in 'map' method.

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    3. Hi Socky,

      I am a newbie to Hadoop,

      For the answer 2, it is a performance overhead when we use distributed cache especially when the input file runs in GB. We have mapper side joins and reducer side joins to implement this.

      Please correct me if I am wrong.

      Delete
  7. @Maheshwaran - Thanks for your comment. I would like to cover these questions in Map/Reduce categories. Stay tuned.

    ReplyDelete
  8. @Anonymous

    SencondaryNameNode: Is the worst name ever given to the module in the history of naming conventions. It is only a check point server which actually gets a back up of the fsimage+edits files from the namenode.

    It basically serves as a checkpoint server.

    But it does not come up online automatically when the namenode goes down!

    Although the secondary namenode can be used to bring up the namenode in the worst case scenario (manually) with some data loss.

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  9. It is bad practice to run the NameNode and JobTracker on the same node, except for small (<100 machines) clusters.

    The locations of the input data is determined by the client that is submitting the mapreduce application, not the JobTracker.

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  10. NameNode is a light weight process and chances of going down is fairly negligible.

    http://www.techiesinfo.com/

    ReplyDelete
  11. Hi,
    just few additional details for question 20, namely "How is it different from traditional file system block size?"
    - HDFS was design to store very large amount of data, default block size is 64 MB => fewer metadata information per file => quickly basic operations for files.
    - HDFS allows for fast streaming reads of data, by keeping large amounts of data sequentially laid out on the disk. This one is very important for a fast execution of a MapReduce job. Anyway, there are solutions also for work with many little files, ex. MultiFileInputFormat.

    You can find much more difference between HDFS and traditional file systems here : http://developer.yahoo.com/hadoop/tutorial/module2.html#basics

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  12. Does the replication of Data on other slave nodes take place only after a Data node failure or even before that?

    ReplyDelete
    Replies
    1. @Sushma - The data is stored on slave nodes redundantly to use them as fail over nodes. Data can not be copied once a node is failed. Each data block is stored redundantly on HDFS to reduce node failure scenario impact. The redundancy can be configured based on the need.

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    2. It happens as soon as the data is copied on to the HDFS. As I understand the client copies the HDFS file to Data Node 1 which copies to Data Node 2 which copies to Data Node 3

      -- Subu

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  13. First I want to thank you for your time to help others
    This is very useful information and explanation simplified.
    SENARIO:
    BLOCK size is "128 MB"
    Number of DATA Nodes in cluster are "15".
    How a file size size of "3 GB" will be stored, which will be segmented to 24 blocks? Will there araise any exception as number of blocks are greater than number of data nodes?

    ReplyDelete
    Replies
    1. No, Each data node can have multiple number of blocks depending on size of the data node. If a data node is 64gb with block size 64MB, it can have 1000 blocks.

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    2. Hi ..

      Nope, I do not see any reason for exceptions.

      3 GB file when expressed in terms of MB will be 3072 MB file. So dividing it by the block size i.e 24 gives the # of required blocks. In this case 3072 / 128 = 24. So the 3 GB file will be splitted into 24 blocks and based on replication factor the number of copies will be placed over 15 nodes.

      Delete
  14. We are satisfied pleased to you that you provide interview question-answers which is really helpful to fresher.I read all your post and most you try to show all the standard language interview solution.

    ReplyDelete
  15. Awesome collection dude.Thanks a lot for sharing this quality content

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  16. It is very useful stuff when we face interview and great stuff for understanding internals..Thank you

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  17. still lot of questions regarding partitions,mapreduce2,hdfs..then it is very very useful.Thank you

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  18. sir,,these question are very useful to us,,,kindly publish more questions sir....

    ReplyDelete
  19. You can find more than 130 question and answers here:

    http://www.slideshare.net/rohitkapa/hadoop-interview-questions

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  20. I always see that companies look for 6-8 experience in Database architect or data warehousing. Is there any position for freshers with good knowledge of hadoop and distributed operation.

    ReplyDelete
  21. Thanks for the posts. Good Job and it is very useful.

    ReplyDelete
  22. I am a beginner in hadoop mapreduce, applying for job in this field. This post helped a lot..thanks..

    ReplyDelete
  23. Hadoop Certification Question I gathered from difference sources and I'm able to certification. I've prepared a pdf file for the same. If someone need pl let me know.

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  24.  Hadoop Certification Question I gathered from difference sources and I'm able to certification. I've prepared a pdf file for the same. If someone need pl let me know.

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    17. Hi Dharma,

      Please send me the certification pdf file and other material for Hadoop on amit_rajdhani@rediffmail.com.

      Thanks..:)

      Delete
    18. Hi Dharma -

      Kindly share the certification pdf file with me as well - my id sivakumar121@gmail.com

      Thanks
      Siva

      Delete
    19. Hi
      can u pls send me that pdf to harinath.reddy@socgen.com

      thanks in advance,
      mhnr.

      Delete
    20. I want to do certification, could you please send me the certification PDFs and other materials related to Hadoop.
      My email id is "trinitron.p@gmail.com"

      Thanks
      Raj

      Delete
    21. Please send to sethramey65@gmail.com

      Delete
    22. Can you please share the document to my Id sales.lv21@gmail.com

      Appreciating your help
      Thanks

      Delete
    23. can u please send to manoj.horizonv@gmail.com

      Delete
    24. Can you send me this to albertpi0714@gmail.com

      Thx,:) :-)
      Albert

      Delete
    25. Please send it to pandu1631@gmail.com

      Delete
    26. Dharmraj, can you please send me the pdf at vlaxmi125@gmail.com?

      Delete
    27. Hi Dharma
      Plz send the pdf at ravi0204@gmail.com.

      Thanks

      Delete
    28. HI dharma raj,
      plz send your file ,i also need to do certification on hadoop.. my mail id is v.srinu.cse@gmail.com


      Thanks

      Delete
    29. Please share me the Certification pdf to praveen8678@gmail.com
      Thanks in advance..

      Delete
  25. Some more questions here on Hadoop Fundamentals

    Hadoop Fundamentals Quiz

    ReplyDelete
  26. Dharmraj, can you please send me the certification pdf at balaji.hknv@gmail.com?

    ReplyDelete
  27. Dharmraj, can you please send me the certification pdf at rajurajuds@gmail.com?

    ReplyDelete
  28. Dharmraj, can you please send me the certification pdf at rajurajuds@gmail.com?

    ReplyDelete
  29. too good piece of information, I had come to know about your site from my friend sajid, bangalore,i have read atleast 11 posts of yours by now, and let me tell you, your web-page gives the best and the most interesting information. This is just the kind of information that i had been looking for, i'm already your rss reader now and i would regularly watch out for the new post, once again hats off to you! Thanks a lot once again, Regards, hadoop interview questions

    ReplyDelete
  30. Dharmraj, can you please send me the certification pdf at ***andrawaala@yahoo.com***

    ReplyDelete
  31. please send me the certification pdf at aruna_ranganath@yahoo.com

    ReplyDelete
  32. thanks for providing such a good information

    ReplyDelete
  33. Hi Sir,

    Can you please forward me the certification pdf to venkatasureshbabub@gmail.com

    Thanks & Regards,
    B.V.SURESH BABU.

    ReplyDelete
  34. Dharmraj, can you please send me the certification pdf at ramanareddy@hotmail.com?

    ReplyDelete
    Replies
    1. Hi, can you pls send the certification pdf sateesht56@gmail.com

      Delete
    2. Dharmraj, please send me the certification pdf at shashang13@gmail.com

      Delete
    3. Hi Dharmraj, Please send me the certification pdf at gopal.ford@gmail.com. Thanks in advance.

      Thanks
      Gopal

      Delete
    4. Hi Dharmaraj,

      Please do share with me too my mail id is sjvvijay@gmail.com. Thanks............

      Delete
  35. Hi Dharmraj, could you please send me the certification pdf at kmshussain@gmail.com, thank you.

    ReplyDelete
  36. Hi Dharmraj, Kindly share the certification pdf at dilip.is.85@gmail.com, thank you.

    ReplyDelete
  37. Hi Dharmraj,

    I am planning to give the certification, Kindly share the certification pdf at
    hai.sunilkumar@gmail.com,

    thank you.

    ReplyDelete
  38. hi Dharma raj,

    i am very new to hadoop.and I am trying to learn it as fast as I can.will u plz send the documentation and pdf for me to this email
    "venkat.ramana907@gmail.com".
    u r material may be helpful to me

    regards ramana
    thank you.

    ReplyDelete
  39. Hi,

    If any one got the interview questions, please send to my e-mailid:

    sgurram4114@gmail.com

    ReplyDelete
  40. Please share the PDF for Hadoop certification to me also.

    sgurram4114@gmail.com

    ReplyDelete
  41. Please share the PDF for Hadoop certification to me also.

    suresh.pathipati@gmail.com

    ReplyDelete
  42. It should necessary for us if you want to hire a guy for your companies there is need to take interview without interview you can't be estimate about him he will comfort for your company or not !! Thanks

    ReplyDelete
  43. Hi Guys,

    Nice details.
    I am newbie to Hadoop and planning to take cloudera hadoop developer certification, can you please send me the any pdf related to Hadoop (including Certification pdf)at konda1979@yahoo.com

    ReplyDelete
  44. Please share the PDF for Hadoop certification to me also.

    cityaddpost2089@gmail.com

    ReplyDelete
  45. These hadoop questions are very helpful to blush up my hadoop concepts. Thanks!

    ReplyDelete
  46. Please share the PDF for Hadoop certification to me also. kiran.mudradi@gmail.com

    ReplyDelete
  47. Hi Dharma -

    Kindly share the certification pdf file with me as well - my id aman.mail@gmail.com
    Thank You in advance.

    Thanks
    Aman

    ReplyDelete
  48. Hi
    Dharmraj Buwade

    Kindly share the certification pdf file with me as well - my id :- purnachandra.mahesh@gmail.com
    Thank You in advance

    ReplyDelete
    Replies
    1. Hi Dharmraj,

      Kindly share the certification pdf file with me as well - my id :- nagi.scjp@gmail.com
      Thank You in advance

      Delete
  49. Hi Dharma

    Kindly share the certification pdf file with me as well - my id:- purnachandra.mahesh@gmail.com
    Thank You in advance.

    Thanks
    mahesh

    ReplyDelete
  50. Dear Dharmra

    Can you please send me the certification pdf at moustafa_fci @yahoo.com

    Thanks

    ReplyDelete
  51. Hi Dharma

    Kindly share the certification pdf file with me as well - my id:- pavankumar.neu@gmail.com
    Thank You in advance.

    Regards
    Pavan.

    ReplyDelete
  52. Hi,

    what if secondary name node down how it impacts..

    Regards,
    Sagar

    ReplyDelete
  53. Hi,

    What if secondary name down, how it impacts?

    Regards,
    Sagar

    ReplyDelete
  54. Awesome Info, Thanks for sharing this valuable info.

    ReplyDelete
  55. Hi Dharma -

    Kindly share the certification pdf file with me as well - my id sundeep.thani@gmail.com
    Thank You in advance.

    Thanks

    ReplyDelete
  56. Dear Dharmra

    Can you please send me the certification pdf at praveen0790@gmail.com

    Thanks

    ReplyDelete
  57. Does MapReduce programming model provide a way for mappers to communicate with each other? In a MapReduce job can a mapper communicate with another mapper?

    ReplyDelete
  58. Nice Blog, For More HADOOP Online training & Interview Questions and Answersvisit this page

    ReplyDelete
  59. i am new to linux and hadoo/hbase. could you please send me the pdf at ankit281992@gmail.com

    ReplyDelete
  60. Appreciate if you share these certification questions to my mail id inform.sgr@gmail.com
    Thanks,
    sgr

    ReplyDelete
  61. Hi,

    Thank you sharing interview questions and answers. It is useful to me in interviews...

    ReplyDelete
  62. Hi Dharmraj,

    Please share at narenerp450@gmail.com

    ReplyDelete
  63. thank u so much

    ReplyDelete
  64. Hi Dharmraj, Can you send the Hadoop certification pdf to sree0213@gmail.com

    Thanks
    nice blog

    ReplyDelete
  65. Hi Dharmraj, Can you send the Hadoop certification pdf to sree0213@gmail.com

    nice blog

    ReplyDelete
  66. Hi Dharma, Could you please send to pnreddy.svu@gmail.com?

    ReplyDelete
  67. Hi Dharmraj,

    Could you Please send Hadoop Certification pdf to prasadgs2@gmail.com
    Thanks

    ReplyDelete
  68. Dear Dharmaj, I am taking certification exam on Hadoop ,can you please send me the any pdf related to Hadoop (including Certification pdf)at yalagala.sudheer@gmail.com
    Thanks in advance

    ReplyDelete
    Replies
    1. Hi
      Dharmraj Buwade

      Kindly share the certification pdf file with me as well - my id :- nagi.scjp@gmail.com
      Thank You in advance

      Delete
  69. Hi Dharmraj Buwade,

    Can you please share the pdf file which you have created for your hadoop certification preparation to abhishekkorpe03@gmail.com

    Thanks in advance!!!!

    ReplyDelete
  70. Hi Dharmraj,

    Can you please share the certification prep pdf to my email ID: shansundar@yahoo.com

    Thanks in advance!!!!

    ReplyDelete
  71. Hi Dharmraj Buwade,

    Could you please share the certification prep pdf file to email ID: shansundar@yahoo.com, m.shansundar@gmail.com

    Thanks in advance!!!!

    ReplyDelete
  72. Hi
    Dharmraj Buwade

    Kindly share the certification pdf file with me as well - my id :- rc298579@hotmail.com
    Thank You in advance

    ReplyDelete
  73. Hi Dharmraj

    Can you please share the certification PDF file with me ....
    My id : pankaj_comps@yahoo.co.in

    Thank you in advance

    ReplyDelete
  74. Better if you can add partitioner and sorting techniques as well.

    ReplyDelete
  75. Hi Dharmraj

    Can you please share the certification PDF file with me .... rmk546@gmail.com

    ReplyDelete
    Replies
    1. Hi Guys,
      If you have received any interview questions from Dharmraj, please send it across to sudhartcs@gmail.com..

      Delete
    2. Hi Dharmraj/Guys, please share the interview questions to sudhartcs@gmail.com

      Delete
    3. Hello - Please share when you get to see this message at chiluka.anandk@gmail.com

      Delete
  76. Hi Dharam

    Can you please share your valuable materials on Hadoop cert to sanjaynk@gmail.com

    ReplyDelete
  77. Hey Dharmraj, thanks for these really good questions and answers, and also for this nice discussion.May you please forward the questions to rakesh.bandhu.gupta@gmail.com.

    Thanks a ton!
    Rakesh

    ReplyDelete

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