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In this Hadoop interview questions blog, we will be covering all the frequently asked questions that will help you ace the interview with their best solutions. But before that, let me tell you how the demand is continuously increasing for Big Data and Hadoop experts.
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Following are a few stats that reflect the growth in the demand for Big Data & Hadoop certification quite accurately:
I would like to draw your attention towards the Big Data revolution. Earlier, organizations were only concerned about operational data, which was less than 20% of the whole data. Later, they realized that analyzing the whole data will give them better business insights & decision-making capability. That was the time when big giants like Yahoo, Facebook, Google, etc. started adopting Hadoop & Big Data related technologies. In fact, nowadays one of every fifth company is moving to Big Data analytics. Hence, the demand for jobs in Big Data Hadoop is rising like anything. Therefore, if you want to boost your career, Hadoop and Spark are just the technology you need. This would always give you a good start either as a fresher or experienced.
Prepare with these top Hadoop interview questions to get an edge in the burgeoning Big Data market where global and local enterprises, big or small, are looking for the quality Big Data and Hadoop experts. This definitive list of top Hadoop interview questions will take you through the questions and answers around Hadoop Cluster, HDFS, MapReduce, Pig, Hive, HBase. This blog is the gateway to your next Hadoop job.
In case you have come across a few difficult questions in a Hadoop interview and are still confused about the best answer, kindly put those questions in the comment section below. We will be happy to answer them.
In the meantime, you can maximize the Big Data Analytics career opportunities that are sure to come your way by taking Hadoop online training with Edureka. Click below to know more.
Here are the key differences between HDFS and relational database:
|Data Types||RDBMS relies on the structured data and the schema of the data is always known.||Any kind of data can be stored into Hadoop i.e. Be it structured, unstructured or semi-structured.|
|Processing||RDBMS provides limited or no processing capabilities.||Hadoop allows us to process the data which is distributed across the cluster in a parallel fashion.|
|Schema on Read Vs. Write||RDBMS is based on ‘schema on write’ where schema validation is done before loading the data.||On the contrary, Hadoop follows the schema on read policy.|
|Read/Write Speed||In RDBMS, reads are fast because the schema of the data is already known.||The writes are fast in HDFS because no schema validation happens during HDFS write.|
|Cost||Licensed software, therefore, I have to pay for the software.||Hadoop is an open source framework. So, I don’t need to pay for the software.|
|Best Fit Use Case||RDBMS is used for OLTP (Online Trasanctional Processing) system.||Hadoop is used for Data discovery, data analytics or OLAP system.|
“Big data” is the term for a collection of large and complex data sets, that makes it difficult to process using relational database management tools or traditional data processing applications. It is difficult to capture, curate, store, search, share, transfer, analyze, and visualize Big data. Big Data has emerged as an opportunity for companies. Now they can successfully derive value from their data and will have a distinct advantage over their competitors with enhanced business decisions making capabilities.
♣ Tip: It will be a good idea to talk about the 5Vs in such questions, whether it is asked specifically or not!
As we know Big Data is growing at an accelerating rate, so the factors associated with it are also evolving. To go through them and understand it in detail, I recommend you to go through Big Data Tutorial blog.
When “Big Data” emerged as a problem, Apache Hadoop evolved as a solution to it. Apache Hadoop is a framework which provides us various services or tools to store and process Big Data. It helps in analyzing Big Data and making business decisions out of it, which can’t be done efficiently and effectively using traditional systems.
♣ Tip: Now, while explaining Hadoop, you should also explain the main components of Hadoop, i.e.:
HDFS (Hadoop Distributed File System) is the storage unit of Hadoop. It is responsible for storing different kinds of data as blocks in a distributed environment. It follows master and slave topology.
♣ Tip: It is recommended to explain the HDFS components too i.e.
YARN (Yet Another Resource Negotiator) is the processing framework in Hadoop, which manages resources and provides an execution environment to the processes.
♣ Tip: Similarly, as we did in HDFS, we should also explain the two components of YARN:
If you want to learn in detail about HDFS & YARN go through Hadoop Tutorial blog.
Generally approach this question by first explaining the HDFS daemons i.e. NameNode, DataNode and Secondary NameNode, and then moving on to the YARN daemons i.e. ResorceManager and NodeManager, and lastly explaining the JobHistoryServer.
In this question, first explain NAS and HDFS, and then compare their features as follows:
This is an important question and while answering this question, we have to mainly focus on two points i.e. Passive NameNode and YARN architecture.
|Hadoop 1.x||Hadoop 2.x|
|Passive NameNode||NameNode is a Single Point of Failure||Active & Passive NameNode|
|Processing||MRV1 (Job Tracker & Task Tracker)||MRV2/YARN (ResourceManager & NodeManager)|
In HA (High Availability) architecture, we have two NameNodes – Active “NameNode” and Passive “NameNode”.
When the active “NameNode” fails, the passive “NameNode” replaces the active “NameNode” in the cluster. Hence, the cluster is never without a “NameNode” and so it never fails.
One of the most attractive features of the Hadoop framework is its utilization of commodity hardware. However, this leads to frequent “DataNode” crashes in a Hadoop cluster. Another striking feature of Hadoop Framework is the ease of scale in accordance with the rapid growth in data volume. Because of these two reasons, one of the most common task of a Hadoop administrator is to commission (Add) and decommission (Remove) “Data Nodes” in a Hadoop Cluster.
Read this blog to get a detailed understanding on commissioning and decommissioning nodes in a Hadoop cluster.
HDFS supports exclusive writes only.
When the first client contacts the “NameNode” to open the file for writing, the “NameNode” grants a lease to the client to create this file. When the second client tries to open the same file for writing, the “NameNode” will notice that the lease for the file is already granted to another client, and will reject the open request for the second client.
NameNode periodically receives a Heartbeat (signal) from each of the DataNode in the cluster, which implies DataNode is functioning properly.
A block report contains a list of all the blocks on a DataNode. If a DataNode fails to send a heartbeat message, after a specific period of time it is marked dead.
The NameNode replicates the blocks of dead node to another DataNode using the replicas created earlier.
The NameNode recovery process involves the following steps to make the Hadoop cluster up and running:
Whereas, on large Hadoop clusters this NameNode recovery process may consume a lot of time and this becomes even a greater challenge in the case of the routine maintenance. Therefore, we have HDFS High Availability Architecture which is covered in the HA architecture blog.
In brief, “Checkpointing” is a process that takes an FsImage, edit log and compacts them into a new FsImage. Thus, instead of replaying an edit log, the NameNode can load the final in-memory state directly from the FsImage. This is a far more efficient operation and reduces NameNode startup time. Checkpointing is performed by Secondary NameNode.
When data is stored over HDFS, NameNode replicates the data to several DataNode. The default replication factor is 3. You can change the configuration factor as per your need. If a DataNode goes down, the NameNode will automatically copy the data to another node from the replicas and make the data available. This provides fault tolerance in HDFS.
The smart answer to this question would be, DataNodes are commodity hardware like personal computers and laptops as it stores data and are required in a large number. But from your experience, you can tell that, NameNode is the master node and it stores metadata about all the blocks stored in HDFS. It requires high memory (RAM) space, so NameNode needs to be a high-end machine with good memory space.
HDFS is more suitable for large amounts of data sets in a single file as compared to small amount of data spread across multiple files. As you know, the NameNode stores the metadata information regarding the file system in the RAM. Therefore, the amount of memory produces a limit to the number of files in my HDFS file system. In other words, too many files will lead to the generation of too much metadata. And, storing these metadata in the RAM will become a challenge. As a thumb rule, metadata for a file, block or directory takes 150 bytes.
Blocks are the nothing but the smallest continuous location on your hard drive where data is stored. HDFS stores each as blocks, and distribute it across the Hadoop cluster. Files in HDFS are broken down into block-sized chunks, which are stored as independent units.
Yes, blocks can be configured. The dfs.block.size parameter can be used in the hdfs-site.xml file to set the size of a block in a Hadoop environment.
The ‘jps’ command helps us to check if the Hadoop daemons are running or not. It shows all the Hadoop daemons i.e namenode, datanode, resourcemanager, nodemanager etc. that are running on the machine.
Rack Awareness is the algorithm in which the “NameNode” decides how blocks and their replicas are placed, based on rack definitions to minimize network traffic between “DataNodes” within the same rack. Let’s say we consider replication factor 3 (default), the policy is that “for every block of data, two copies will exist in one rack, third copy in a different rack”. This rule is known as the “Replica Placement Policy”.
To know rack awareness in more detail, refer to the HDFS architecture blog.
If a node appears to be executing a task slower, the master node can redundantly execute another instance of the same task on another node. Then, the task which finishes first will be accepted and the other one is killed. This process is called “speculative execution”.
This question can have two answers, we will discuss both the answers. We can restart NameNode by following methods:
These script files reside in the sbin directory inside the Hadoop directory.
The “HDFS Block” is the physical division of the data while “Input Split” is the logical division of the data. HDFS divides data in blocks for storing the blocks together, whereas for processing, MapReduce divides the data into the input split and assign it to mapper function.
The three modes in which Hadoop can run are as follows:
It is a framework/a programming model that is used for processing large data sets over a cluster of computers using parallel programming. The syntax to run a MapReduce program is hadoop_jar_file.jar /input_path /output_path.
If you have any doubt in MapReduce or want to revise your concepts you can refer this MapReduce tutorial.
The main configuration parameters which users need to specify in “MapReduce” framework are:
This answer includes many points, so we will go through them sequentially.
The “InputSplit” defines a slice of work, but does not describe how to access it. The “RecordReader” class loads the data from its source and converts it into (key, value) pairs suitable for reading by the “Mapper” task. The “RecordReader” instance is defined by the “Input Format”.
Distributed Cache can be explained as, a facility provided by the MapReduce framework to cache files needed by applications. Once you have cached a file for your job, Hadoop framework will make it available on each and every data nodes where you map/reduce tasks are running. Then you can access the cache file as a local file in your Mapper or Reducer job.
This is a tricky question. The “MapReduce” programming model does not allow “reducers” to communicate with each other. “Reducers” run in isolation.
30. What does a “MapReduce Partitioner” do?
A “MapReduce Partitioner” makes sure that all the values of a single key go to the same “reducer”, thus allowing even distribution of the map output over the “reducers”. It redirects the “mapper” output to the “reducer” by determining which “reducer” is responsible for the particular key.
Custom partitioner for a Hadoop job can be written easily by following the below steps:
A “Combiner” is a mini “reducer” that performs the local “reduce” task. It receives the input from the “mapper” on a particular “node” and sends the output to the “reducer”. “Combiners” help in enhancing the efficiency of “MapReduce” by reducing the quantum of data that is required to be sent to the “reducers”.
“SequenceFileInputFormat” is an input format for reading within sequence files. It is a specific compressed binary file format which is optimized for passing the data between the outputs of one “MapReduce” job to the input of some other “MapReduce” job.
Sequence files can be generated as the output of other MapReduce tasks and are an efficient intermediate representation for data that is passing from one MapReduce job to another.
Apache Pig is a platform, used to analyze large data sets representing them as data flows developed by Yahoo. It is designed to provide an abstraction over MapReduce, reducing the complexities of writing a MapReduce program.
Pig Latin can handle both atomic data types like int, float, long, double etc. and complex data types like tuple, bag and map.
Atomic data types: Atomic or scalar data types are the basic data types which are used in all the languages like string, int, float, long, double, char, byte.
Complex Data Types: Complex data types are Tuple, Map and Bag.
To know more about these data types, you can go through our Pig tutorial blog.
Different relational operators are:
If some functions are unavailable in built-in operators, we can programmatically create User Defined Functions (UDF) to bring those functionalities using other languages like Java, Python, Ruby, etc. and embed it in Script file.
Apache Hive is a data warehouse system built on top of Hadoop and is used for analyzing structured and semi-structured data developed by Facebook. Hive abstracts the complexity of Hadoop MapReduce.
The “SerDe” interface allows you to instruct “Hive” about how a record should be processed. A “SerDe” is a combination of a “Serializer” and a “Deserializer”. “Hive” uses “SerDe” (and “FileFormat”) to read and write the table’s row.
To know more about Apache Hive, you can go through this Hive tutorial blog.
“Derby database” is the default “Hive Metastore”. Multiple users (processes) cannot access it at the same time. It is mainly used to perform unit tests.
The default location where Hive stores table data is inside HDFS in /user/hive/warehouse.
HBase is an open source, multidimensional, distributed, scalable and a NoSQL database written in Java. HBase runs on top of HDFS (Hadoop Distributed File System) and provides BigTable (Google) like capabilities to Hadoop. It is designed to provide a fault-tolerant way of storing the large collection of sparse data sets. HBase achieves high throughput and low latency by providing faster Read/Write Access on huge datasets.
To know more about HBase you can go through our HBase tutorial blog.
HBase has three major components, i.e. HMaster Server, HBase RegionServer and Zookeeper.
To know more, you can go through this HBase architecture blog.
The components of a Region Server are:
Write Ahead Log (WAL) is a file attached to every Region Server inside the distributed environment. The WAL stores the new data that hasn’t been persisted or committed to the permanent storage. It is used in case of failure to recover the data sets.
HBase is an open source, multidimensional, distributed, scalable and a NoSQL database written in Java. HBase runs on top of HDFS and provides BigTable like capabilities to Hadoop. Let us see the differences between HBase and relational database.
|It is schema-less||It is schema-based database|
|It is column-oriented data store||It is row-oriented data store|
|It is used to store de-normalized data||It is used to store normalized data|
|It contains sparsely populated tables||It contains thin tables|
|Automated partitioning is done is HBase||There is no such provision or built-in support for partitioning|
The answer to this question is, Apache Spark is a framework for real-time data analytics in a distributed computing environment. It executes in-memory computations to increase the speed of data processing.
It is 100x faster than MapReduce for large-scale data processing by exploiting in-memory computations and other optimizations.
Yes, one can build “Spark” for a specific Hadoop version. Check out this blog to learn more about building YARN and HIVE on Spark.
RDD is the acronym for Resilient Distribution Datasets – a fault-tolerant collection of operational elements that run parallel. The partitioned data in RDD are immutable and distributed, which is a key component of Apache Spark.
Apache ZooKeeper coordinates with various services in a distributed environment. It saves a lot of time by performing synchronization, configuration maintenance, grouping and naming.
Apache Oozie is a scheduler which schedules Hadoop jobs and binds them together as one logical work. There are two kinds of Oozie jobs:
“Oozie” is integrated with the rest of the Hadoop stack supporting several types of Hadoop jobs such as “Java MapReduce”, “Streaming MapReduce”, “Pig”, “Hive” and “Sqoop”.
To understand “Oozie” in detail and learn how to configure an “Oozie” job, do check out this introduction to Apache Oozie blog.
Feeling overwhelmed with all the questions the interviewer might ask in your Hadoop interview? Now it is time to go through a series of Hadoop interview questions which covers different aspects of the Hadoop framework. It’s never too late to strengthen your basics. Learn Hadoop from industry experts while working with real-life use cases.
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