Top Answers to HDFS Interview Questions
|Data write process||Append method||Bulk incremental, random write|
|Data read process||Table scan||Table scan/random read/small range scan|
|Hive SQL querying||Excellent||Average|
Hadoop forms part of Apache project provided by Apache Software Foundation.
With Hadoop the user can run applications on the systems that have thousands of nodes spreading through innumerable terabytes. Rapid data processing and transfer among nodes helps uninterrupted operation even when a node fails preventing system failure.
Windows and Linux are the preferred operating system though Hadoop can work on OS x and BSD.
Big Data refers to assortment of huge amount of data which is difficult capturing, storing, processing or reprieving. Traditional database management tools cannot handle them but Hadoop can.
Facebook alone generates more than 500 terabytes of data daily whereas many other organizations like Jet Air and Stock Exchange Market generates 1+ terabytes of data every hour. These are Big Data.
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The three characteristics of Big Data are volume, velocity, and veracity. Earlier it was assessed in megabytes and gigabytes but now the assessment is made in terabytes.
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Analysis of Big Data identifies the problem and focus points in an enterprise. It can prevent big losses and make profits helping the entrepreneurs take informed decision.
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Data scientists analyze data and provide solutions for business problems. They are gradually replacing business and data analysts.
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Written in Java, Hadoop framework has the capability of solving issues involving Big Data analysis. Its programming model is based on Google MapReduce and infrastructure is based on Google’s Big Data and distributed file systems. Hadoop is scalable and more nodes can be added to it.
Get to know the history, timeline and architecture of Hadoop!
Introduce in 2002 by Doug Cutting, Hadoop was used in Google MapReduce and HDFS project in 2004 and 2006. Yahoo and Facebook adopted it in 2008 and 2009 respectively. Major commercial enterprises using Hadoop include EMC, Hortonworks, Cloudera, MaOR, Twitter, EBay, and Amazon among others.
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RDBMS can be useful for single files and short data whereas Hadoop is useful for handling Big Data in one shot.
Main components of Hadoop are HDFS used to store large databases and MapReduce used to analyze them.
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HDFS is filing system use to store large data files. It handles streaming data and running clusters on the commodity hardware.
Great fault tolerance, high throughput, suitability for handling large data sets, and streaming access to file system data are the main features of HDFS. It can be built with commodity hardware.
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Systems with average configuration are vulnerable to crash at any time. HDFS replicates and stores data at three different locations that makes the system highly fault tolerant. If data at one location becomes corrupt and is inaccessible it can be retrieved from another location.
This insightful Cloudera article shows the steps for running HDFS on a cluster.
No! The calculation would be made on the original node only. In case the node fails then only the master node would replicate the calculation on to a second node.
HDFS works on the principle of “write once, read many” and the focus is on fast and accurate data retrieval. Steaming access refers to reading the complete data instead of retrieving single record from the database.
Average and non-expensive systems are known as commodity hardware and Hadoop can be installed on any of them. Hadoop does not require high end hardware to function.
Name node is the master node in HDFS and job tracker runs on it. The node contains metadata and works as high availability machine and single pint of failure in HDFS. It cannot be commodity as the entire HDFS works on it.
Data node is the slave deployed in each of the systems and provides the actual storage locations and serves read and writer requests for clients.
Daemon is the process that runs in background in the UNIX environment. In Windows it is ‘services’ and in DOS it is ‘TSR’.
Job tracker is one of the daemons that runs on name node and submits and tracks the MapReduce tasks in Hadoop. There is only one job tracker who distributes the task to various task trackers. When it goes down all running jobs comes to a halt.
Data nodes and task trackers send heartbeat signals to Name node and Job tracker respectively to inform that they are alive. If the signal is not received it would indicate problems with the node or task tracker.
No! They can be on different hosts.
Block in HDFS refers to minimum quantum of data for reading or writing. Default block size is 64 MB in HDFS. If a file is 52 MB then HDFS would store it and leave 12 MB empty and ready to use.
Blocks in HDFS cannot be broken. Master node calculates the required space and how data would be transferred to a machine having lower space.
Once data is stored HDFS will depend on the last part to find out where the next part of data would be stored.
When a data node is full and has no space left the name node will identify it.
Hadoop processes the digital data only.
Name node contains metadata or information in respect of all the data nodes and it will decide which data node to be used for storing data.
Anyone who tries to retrieve data from database using HDFS is the user. Client is not end user but an application that uses job tracker and task tracker to retrieve data.
The communication mode for clients with name node and data node in HDFS is SSH.
Rack is the storage location where all the data nodes are put together. Thus it is a physical collection of data nodes stored in a single location.