On this page, we have collected the most frequently asked questions along with their solutions that will help you to excel in the interview. But, before starting, I would like to draw your attention to the Hadoop revolution in the market. According to Forbes, 90% of global organizations report their investments in Big Data analytics, which clearly shows that the career for Hadoop professionals is very promising right now and the upward trend will keep progressing with time.
As the opportunities for Hadoop are unlimited, the competition for aspirants preparing for the interviews is also high. So, it's essential for you to have strong knowledge of different areas of Hadoop under which the questions are asked. This definitive list of top Hadoop Interview Questions will cover the concepts including Hadoop HDFS, MapReduce, Pig, Hive, HBase, Spark, Flume, and Sqoop.
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I hope these questions will be helpful for your Hadoop job and in case if you come across any difficult question in an interview and unable to find the best answer please mention it in the comments section below.
|Types of Hadoop Interview Questions|
|Data volume||RDBMS cannot store and process a large amount of data||Hadoop works better for large amounts of data. It can easily store and process a large amount of data compared to RDBMS.|
|Throughput||RDBMS fails to achieve a high Throughput||Hadoop achieves high Throughput|
|Data variety||Schema of the data is known in RDBMS and it always depends on the structured data.||It stores any kind of data. Whether it could be structured, unstructured, or semi-structured.|
|Data processing||RDBMS supports OLTP(Online Transactional Processing)||Hadoop supports OLAP(Online Analytical Processing)|
|Read/Write Speed||Reads are fast in RDBMS because the schema of the data is already known.||Writes are fast in Hadoop because no schema validation happens during HDFS write.|
|Schema on reading Vs Write||RDBMS follows schema on write policy||Hadoop follows the schema on reading policy|
|Cost||RDBMS is a licensed software||Hadoop is a free and open-source framework|
Big Data refers to a large amount of data that exceeds the processing capacity of conventional database systems and requires a special parallel processing mechanism. This data can be either structured or unstructured data.
Characteristics of Big Data:
Hadoop is an open-source framework used for storing large data sets and runs applications across clusters of commodity hardware.
It offers extensive storage for any type of data and can handle endless parallel tasks.
Core components of Hadoop:
Yet Another Resource Negotiator (YARN) is one of the core components of Hadoop and is responsible for managing resources for the various applications operating in a Hadoop cluster, and also schedules tasks on different cluster nodes.
|Regular File Systems||HDFS|
|A small block size of data (like 512 bytes)||Large block size (orders of 64MB)|
|Multiple disks seek large files||Reads data sequentially after single seek|
Generally, the daemon is nothing but a process that runs in the background. Hadoop has five such daemons. They are:
Hadoop provides a feature called SkipBadRecords class for skipping bad records while processing mapping inputs.
Ex: replication factors, block location, etc.
By default, the HDFS block size is 128MB for Hadoop 2.x.
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The Various HDFS Commands are listed bellow
|It is a distributed file system used for storing data by commodity hardware.||It is a file-level computer data storage server connected to a computer network, provides network access to a heterogeneous group of clients.|
|It includes commodity hardware which will be cost-effective||NAS is a high-end storage device that includes a high cost.|
|It is designed to work for the MapReduce paradigm.||It is not suitable for MapReduce.|
|Name||Hadoop 1.x||Hadoop 2.x|
|1. NameNode||In Hadoop 1.x, NameNode is the single point of failure||In Hadoop 2.x, we have both Active and passive NameNodes.|
|2. Processing||MRV1 (Job Tracker & Task Tracker)||MRV2/YARN (ResourceManager & NodeManager)|
The following steps need to be executed to resolve the NameNode issue and make the Hadoop cluster up and running:
Checkpoint Node is the new implementation of secondary NameNode in Hadoop. It periodically creates the checkpoints of filesystem metadata by merging the edits log file with FsImage file.
However, it is not possible to limit a cluster from becoming unbalanced. In order to give a balance to a certain threshold among data nodes, use the Balancer tool. This tool tries to subsequently even out the block data distribution across the cluster.
HDFS High availability is introduced in Hadoop 2.0. It means providing support for multiple NameNodes to the Hadoop architecture.
Hadoop fsck command is used for checking the HDFS file system.
There are different arguments that can be passed with this command to emit different results.
.hdfs dfsadmin -point topology is used for printing the topology. It displays the tree of racks and DataNodes attached to the tracks.
RAID (redundant array of independent disks) is a data storage virtualization technology used for improving performance and data redundancy by combining multiple disk drives into a single entity.
It is mainly responsible for:
$ hdfs namenode -format
MapReduce is a programming model used for processing and generating large datasets on the clusters with parallel and distributed algorithms.
The syntax for running the MapReduce program is
hadoop_jar_file.jar /input_path /output_path.
Ans. MapReduce framework is used to write applications for processing large data in parallel on large clusters of commodity hardware.
It consists of:
It allocates the resources (containers) to various running applications based on resource availability and configured shared policy.
It is mainly responsible for managing a collection of submitted applications
Hadoop Counters measures the progress or tracks the number of operations that occur within a MapReduce job. Counters are useful for collecting statistics about MapReduce jobs for application-level or quality control.
The job configuration requires the following:
Steps involved in Hadoop job submission:
It views the input data set as a set of pairs and processes the map tasks in a completely parallel manner.
The basic parameters of Mapper are listed below:
In Apache Hadoop, if nodes do not fix or diagnose the slow-running tasks, the master node can redundantly perform another instance of the same task on another node as a backup (the backup task is called a Speculative task). This process is called Speculative Execution in Hadoop.
The methods used for restarting the NameNodes are the following:
These script files are stored in the sbin directory inside the Hadoop directory store.
Reducers always run in isolation and the Hadoop Mapreduce programming paradigm never allows them to communicate with each other.
The MapReduce reducer has three phases:
The MapReduce Partitioner manages the partitioning of the key of the intermediate mapper output. It makes sure that all the values of a single key pass to same reducers by allowing the even distribution over the reducers.
A Combiner is a semi-reducer that executes the local reduce task. It receives inputs from the Map class and passes the output key-value pairs to the reducer class.
SequenceFileInputFormat is the input format used for reading in sequence files. It is a compressed binary file format optimized for passing the data between outputs of one MapReduce job to the input of some other MapReduce job.
Hadoop Pig runs both atomic data types and complex data types.
Apache Hive offers a database query interface to Apache Hadoop. It reads, writes, and manages large datasets that are residing in distributed storage and queries through SQL syntax.
/usr/hive/warehouse is the default location where Hive stores the table data in HDFS.
By default, Hive Metastore uses the Derby database. So, it is not possible for multiple users or processes to access it at the same time.
SerDe is a combination of Serializer and Deserializer. It interprets the results of how a record should be processed by allowing Hive to read and write from a table.
|Schema on Reading||Schema on write|
|Batch processing jobs||Real-time jobs|
|Data stored on HDFS||Data stored on the internal structure|
|Processed using MapReduce||Processed using database|
Apache HBase is multidimensional and a column-oriented key datastore runs on top of HDFS (Hadoop Distributed File System). It is designed to provide high table-update rates and a fault-tolerant way to store a large collection of sparse data sets.
|It is a row-oriented datastore||It is a column-oriented datastore|
|It’s a schema-based database||Its schema is more flexible and less restrictive|
|Suitable for structured data||Suitable for both structured and unstructured data|
|Supports referential integrity||Doesn’t supports referential integrity|
|It includes thin tables||It includes sparsely populated tables|
|Accesses records from tables using SQL queries.||Accesses data from HBase tables using APIs and MapReduce.|
Apache Spark is an open-source framework used for real-time data analytics in a distributed computing environment. It is a data processing engine that provides faster analytics than Hadoop MapReduce.
Yes, we can build “Spark” for any specific Hadoop version.
RDD(Resilient Distributed Datasets) is a fundamental data structure of Spark. It is a distributed collection of objects, and each dataset in RDD is further distributed into logical partitions and computed on several nodes of the cluster
Apache ZooKeeper is a centralized service used for managing various operations in a distributed environment. It maintains configuration data, performs synchronization, naming, and grouping.
Apache Oozie is a scheduler that controls the workflow of Hadoop jobs.
There are two kinds of Oozie jobs:
Integrate Oozie with the Hadoop stack, which supports several types of Hadoop jobs such as Streaming MapReduce, Java MapReduce, Sqoop, Hive, and Pig.
Apache Sqoop is a tool particularly used for transferring massive data between Apache Hadoop and external datastores such as relational database management, enterprise data warehouses, etc.
Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .