What about Serialization?
This entry was posted in Hadoop, Hadoop Interview questions, Popular and tagged Hadoop on May 12, 2014 by Tarun.
What is map side join and reduce side join?
Two different large data can be joined in map reduce programming also. Joins in Map phase refers as Map side join, while join at reduce side called as reduce side join. Lets go in detail, Why we would require to join the data in map reduce. If one Dataset A has master data and B has sort of transactional data(A & B are just for reference). Continue reading “What is map side join and reduce side join?” »
This entry was posted in Hadoop, Hadoop Interview questions on January 31, 2014 by admin.
What are the Side Data Distribution Techniques
Side data refers to extra static small data required by map reduce to perform job. Main challenge is the availability of side data on the node where the map would be executed. Hadoop provides two side data distribution techniques.
Using Job Configuration
An arbitrary Key value pair can be set in job configuration. Continue reading “What are the Side Data Distribution Techniques” »
This entry was posted in Hadoop Interview questions, MapReduce, MapReduce Interview questions, Popular on January 29, 2014 by admin.
What is shuffleing in mapreduce?
Once map tasks started to complete, A communication from reducers is started. where map output sents to reducer, which is looking for the output data to process. at same time data nodes are still process multiple other tasks. The data transfer of mappers output to reducer known as shuffling.
This entry was posted in Hadoop Interview questions, MapReduce, MapReduce Interview questions on December 2, 2013 by admin.
What is partitioning?
Partitioning is a process to identify the reducer instance which would be used to supply the mappers output. Before mapper emits the data (Key Value) pair to reducer, mapper identify the reducer as an recipient of mapper output. All the key, no matter which mapper has generated this, must lie with same reducer.
This entry was posted in Hadoop, Hadoop Interview questions, MapReduce, MapReduce Interview questions on December 2, 2013 by admin.
Can we change the file cached by DistributedCache
No, DistributedCache tracks the caching with timestamp. cached file should not be changed during the job execution.
This entry was posted in Hadoop Interview questions on November 28, 2013 by admin.
What is Distributed Cache in mapreduce framework?
Distributed cache is an important feature provide by map reduce framework. Distributed cache can cache text, archive, jars which could be used by application to improve performance. Application provide details of file to jobconf object to cache. Mapreduce framework would copy the Continue reading “What is Distributed Cache in mapreduce framework?” »
This entry was posted in Hadoop, Hadoop Interview questions, MapReduce, MapReduce Interview questions, Popular on November 28, 2013 by admin.
What is speculative execution in Hadoop?
It becomes very important when dealing with a very large cluster. Lets assume if you have thousands of machine in your cluster, and one of your data node is not performing well in comparison to others. It would degrade the over all performance of a job executed by whole cluster. Speculative execution is technique, hadoop runs multiple copies of MR task on other data nodes. Which machine would finish the execution of task, will be consider for result.
This entry was posted in Hadoop, Hadoop Interview questions on November 28, 2013 by admin.
What if job tracker machine is down?
Single point failure from execution point of view.
This entry was posted in Hadoop Interview questions on November 15, 2013 by admin.
Can we deploye job tracker other than name node?
Yes, in production it is highly recommended. For self development and learning you may setup according to your need.
