Last updated 19/06/2020
Software, tools, programming languages- all of them are as much important as the technologies we use. Don’t you think?
So, what Apache Hadoop is and what is it used for? Also, why it is so important to prepare for Apache Hadoop during your cloud computing and Big Data interviews?
Apache Hadoop is a collection of open-source software that provides a software framework for distributed storage and processing of big data using the MapReduce programming model. It was originally designed for computer clusters but has been used in Yahoo’s search web map and by social media sites like Facebook as well. And what about the cloud you may ask? Well, the cloud allows organizations to deploy Hadoop without acquiring hardware or specific setup expertise.
Hadoop adoption has become pretty widespread since 2013. You’d be shocked to know,50000 organizations of the entire world are using Hadoop currently.
So you can understand, if you know Hadoop well, that will be an added advantage to your CV. Isn’t it?
But first, you need to know what are the Hadoop questions you might get asked during the interview. This is why we picked up the top 20 questions for you which are more likely to asked by interviewers in 2020. Have a look!
Some vendor-specific distributions of Hadoop are mentioned below:
A few Hadoop configuration files are mentioned below:
There are 3 modes where Hadoop can run:
In regular FileSystem, data is maintained in a single system. Hence, if the machine crashes, the data recovery becomes challenging due to low fault tolerance. Seek time stretches more and thus the total time of data processing increases.
On the other hand, in HDFS data can be distributed and maintained on multiple systems. If a DataNode crashes, the main data can still be recovered from other nodes in the cluster. Hence, the total required time to read data is comparatively more, as the coordination of data can be accessed from multiple systems.
HDFS is fault-tolerant because it replicates data on different DataNodes. When a block of data is replicated on three DataNodes, the data blocks can be stored in different DataNodes. If one node crashes, the data can still be retrieved from other DataNodes.
The two types of metadata that a NameNode server holds are:
Each block in HDFS is divided into 128 MB. Hence, The blocks sizes would be, 128 MB except for the last block. For an input file of 350 MB, there will be 3 input splits in total. The size of each split is 128 MB, 128MB, and 94 MB
To check the status of the blocks, you can use the command:
hdfs fsck <path> -files -blocks
This following command is used to check the health status of FileSystem:
hdfs fsck / -files –blocks –locations > dfs-fsck.log
Storing several small files on HDFS usually generates a lot of metadata files. To store these metadata in the RAM is a tough task as each file, block, or directory takes 150 bytes for metadata. Hence, the tentative size of all the metadata will be too large.
This command will help you to copy data from the local file system onto HDFS:
Hadoop fs –copyFromLocal [source] [destination]
dfsadmin -refreshNodes is commonly used to run the HDFS client and refresh node configuration for the NameNode.
rmadmin -refreshNodes is mainly used to perform administrative tasks for ResourceManager.
Following are ways to change the replication of files on HDFS:
$HADOOP_HOME/bin/Hadoop dfs –setrep –w4 /path of the file
A distributed cache is a mechanism where the data coming from the disk can be cached and made available for all worker nodes. When a MapReduce program is running, instead of reading the data from the disk every time, it would pick up the data from the distributed cache to benefit the MapReduce processing.
This communicates with the InputSplit and converts the data into key-value pairs suitable for the mapper to read.
This is an optional phase; it is like a mini reducer. The combiner receives data from the map tasks, works on it, and then passes its output to the reducer phase.
The partitioner decides how many reduced tasks would be used to summarize the data. It also confirms how outputs from combiners are sent to the reducer, and controls the partitioning of keys of the intermediate map outputs.
MapReduce is slower because:
Some Hadoop-specific data types that are used in a MapReduce program are:
The major parameters required in a MapReduce program are mentioned below:
MapReduce relies on the OutputCommitter for the following:
The number of mappers and reducers can be set in the command line using the command:
-D mapred.map.tasks=5 –D mapred.reduce.tasks=2
In the code, one can configure JobConf variables:
job.setNumMapTasks(5); // 5 mappers
job.setNumReduceTasks(2); // 2 reducers
So, that was all about Apache Hadoop’s interview questions. What do you think? Are you prepared for your next Hadoop interview? No? Well, you can join our Apache Hadoop training sessions to have a more clear vision along with QA sessions that will help you rock the interview. Want to give it a try?
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