39 Hadoop Interview Questions and Answers for 2023
Post a Hadoop Job
You need to first create an effective job posting so that you can interview the right candidates. Include the following:
Describe your company. Provide facts but ensure that you make your description exciting. Talk about the growth opportunities you offer. Describe the organizational culture, work environment, and professional development opportunities in your company. Talk about the compensation and benefits policies.
Job descriptions for Hadoop developers
Describe the big data Hadoop project you want to undertake, and explain what you want from your Hadoop developers. Elaborate on how their contribution will enable you to deliver value to your customers. Talk about your team and how the Hadoop developer role fits into the larger picture.
Roles and responsibilities of an Apache Hadoop developer
You want an Apache Hadoop developer to fulfill the following responsibilities:
- Understanding the big data analytics project requirements from business analysts;
- Studying the technical solutions;
- Providing inputs to architects to create technical solutions for big data projects;
- Creating, operating, monitoring, and troubleshooting Hadoop infrastructure;
- Writing software to interact with HDFS (Hadoop Distributed File System) and MapReduce;
- Developing tools and libraries as required in the project;
- Maintaining tools, libraries, and processes so that other developers can access them;
- Creating documentation to operate Hadoop infrastructure;
- Evaluating hosted solutions like AWS, Azure, etc., in case the project requires a hosted solution;
- Writing performant, scalable, and maintainable ETL programs if the project requires ETL;
- Understanding and implementing Hadoop security solutions;
- Writing programs to ingest data into Hadoop;
- Collaborating effectively with the larger team;
- Providing the status of work;
- Participating in continuous improvement initiatives.
Skills and competencies that you need in a Hadoop developer
You need a Hadoop developer with a bachelor’s degree in computer science, information technology, or related fields. Look for the following skills:
- Knowledge of Hadoop and its rich ecosystem;
- Experience in working with HDFS, Hadoop MapReduce, etc.;
- Sound knowledge of HBase, Kafka, ZooKeeper, and other Apache software;
- Good knowledge of Java, JVM (Java Virtual Machine), and the Java ecosystem;
- Experience in writing algorithms of different complexity levels;
- Sound knowledge of distributed computing systems;
- Robust knowledge of software engineering processes, methods, and tools;
- Experience working with a Hadoop distribution;
- Knowledge of computation frameworks like Spark;
- Good knowledge of Linux, networking, and security;
- Excellent knowledge of software architecture;
- Knowledge of popular RDBMSs (Relational Database Management Systems);
- Good knowledge of SQL;
- Sufficient knowledge of popular NoSQL databases;
- Code review expertise;
- Sufficient familiarity with popular software development methodologies;
- The experience of moving large data around.
You need Hadoop developers with the following competencies:
- Problem-solving skills;
- Communication skills;
- Collaboration and teamwork;
- Passion for excellence;
- The ability to see the big picture.
Basic Hadoop interview questions
Use the following Hadoop basic interview questions:
Question 1: What are the “Four Vs of Big Data”?
Answer: The four Vs of big data are as follows:
- Volume: This denotes the processing scale of data stored;
- Velocity: This involves the analysis of streaming data;
- Variety: This refers to the different input formats of data;
- Veracity: This refers to the uncertainty of data.
Question 2: Explain the key differences between Hadoop and a Relational Database Management System (RDBMS).
Answer: The differences between Hadoop and an RDBMS are as follows:
- The type of data: Hadoop can handle semi-structured and unstructured data. However, an RDBMS can handle only structured data.
- Schema: Hadoop uses a schema for “read” operations, however, an RDBMS uses a schema for “Write” operations.
- The type of applications: Hadoop works best for applications that involve data discovery. It’s a good choice for applications involving massive storage and processing of unstructured data. An RDBMS is suited for OLTP (Online Transaction Processing) applications. It works well for applications requiring ACID (“Atomicity”, “Consistency”, “Isolation”, and “Durability”) compliance.
- Speed: Hadoop offers high speed for “write” operations whereas RDBMSs offer high speed for “read” operations.
Question 3: What are the advantages of using Hadoop in big data projects?
Answer: Hadoop offers the following advantages to big data projects:
- Scalability: As a storage platform for large data sets, Hadoop offers high scalability.
- Cost savings: Hadoop offers significant cost advantages when you need to deal with a large amount of data.
- Flexibility: Hadoop enables organizations to access new data sources. It allows organizations to store and process different types of data, e.g., structured, semi-structured, and unstructured.
- Speed: The distributed file system of Hadoop offers good speed.
- Resiliency: Hadoop offers fault tolerance. When Hadoop sends data to a single data node, it also replicates the data to the other nodes in the cluster. This fault-tolerant design prevents a “Single Point of Failure” (SPoF). In the case of a failure in one node, the organization can access the data in the other nodes.
- Tooling support: Hadoop offers a rich ecosystem of tools that help in big data projects. E.g., every Apache Hadoop distribution has a CLI (Command Line Interface) utility to load HDFS from a local file system. Such tools make life easier for developers.
Question 4: Explain the differences between structured and unstructured data.
Answer: Structured data is the data that you can store in a traditional database in the form of rows and columns. OLTP systems typically deal with structured data.
If you can only partially store a form of data in a traditional database system, then that’s semi-structured data. Examples are JSON objects, JSON arrays, and the data in XML records.
Data that can’t be categorized as structured or semi-structured is called unstructured data. A few examples are audio files, video files, Tweets on Twitter, log files, and Facebook updates.
Question 5: What are the main components of the Hadoop framework?
Answer: There are two main components of the Hadoop framework, which are as follows:
- HDFS (Hadoop Distributed File System): This is a Java-based file system known for its reliability. This helps Hadoop to store large data sets, and HDFS provides scalability. Hadoop stores data in HDFS in the form of data blocks. HDFS file system utilizes the master-slave architecture.
- Hadoop MapReduce: It’s a programming paradigm, and it’s based on Java. MapReduce provides scalability across different Hadoop clusters. It distributes the workload so that tasks can run in parallel. Hadoop MapReduce jobs perform two tasks. One is the “map” job, which breaks data sets into key-value pairs or tuples. The “reduce” takes the output of the “map” job and combines data tuples into small sets of tuples. The reduce function jar file contains the mapper, reducer, and driver classes.
Question 6: Explain the different Hadoop Daemons.
Answer: Hadoop Daemons are Hadoop processes. These processes run on Hadoop. Since Hadoop is written in Java, Hadoop Daemons are Java processes. Hadoop Daemons are as follows:
- NameNode: A NameNode works on the “Master” system and manages all the metadata.
- DataNode: A DataNode works on the Slave node system. It serves the read/write requests from the client. All DataNodes send a “heartbeat” and “block report” to the NameNode. This enables the NameNode in a Hadoop cluster to monitor whether DataNodes are alive.
- Secondary NameNode: This is used for taking hourly backups of the data.
- Resource Manager: This works on the “Master” system. Its purpose is resource management for an application that runs in a Hadoop cluster.
- Node Manager: This works on the “Slave” system. It manages the memory resource within the Node and Memory disk.
Question 7: What is a Hadoop cluster?
Answer: Apache Hadoop is an open-source software framework based on Java, and it’s a parallel data processing engine. A Hadoop cluster is one of its key building blocks.
A Hadoop cluster is a collection of computers. These computers are called “nodes”, and they are networked together. The Hadoop framework involves parallel computation on large data sets. The nodes on a Hadoop cluster perform these computational tasks.
Hadoop clusters are designed specifically to store and analyze large amounts of data, which can be structured or unstructured. A Hadoop cluster has a network of the master node and slave nodes. These nodes use high-availability commodity hardware that has low costs.
Question 8: Among Hadoop Daemons, what does a “DataNode” do?
Answer: A network running Hadoop is a distributed network. A Hadoop cluster resides on such a network, and it stores a large volume of data. The computers on it are called “Nodes”. A “DataNode” is one type of node.
DataNodes store data that resides in a Hadoop cluster. The term “DataNode” is also the name of a Daemon or process in Hadoop that manages the data. Hadoop replicates the data on multiple DataNodes. This improves the reliability of Hadoop. DataNodes on a Hadoop cluster should be uniform, e.g., they should have the same memory.
Question 9: What does the “jps” command in Hadoop do?
Answer: You can use the “jps” command to check whether the Hadoop Daemons are running. The command shows the NameNode, DataNode, resourcemanager, nodemanager, etc., that run on the computer.
Question 10: What is the “pseudo-distributed mode” in Hadoop?
Answer: The “pseudo-distributed mode” is one of the various ways to install Hadoop. the other two modes are standalone mode and fully distributed mode. In this mode, the NameNode and DataNodes reside on the same computer. This mode is also known as the “single-node cluster”.
Question 11: How you can debug the Hadoop code?
Answer: Developers can debug Hadoop code by using counters, a web interface that the Hadoop framework provides, etc.
Hadoop HDFS interview questions
Use the following Hadoop HDFS interview questions:
Question 12: What is a NameNode in HDFS?
Answer: A NameNode is an important component in HDFS. It manages the metadata. The NameNode doesn’t store the data on the files, however, it has a directory tree of all the files in the HDFS system on a Hadoop cluster.
The NameNode uses two files for the namespace, which are as follows:
- “fsimage” (file system metadata replica) file: This file tracks the latest checkpoint of the namespace.
- “edits” file: This is a log of changes made to a namespace since the last checkpoint.
Question 13: What is “rack awareness”?
Answer: In HDFS, there is a concept of a “rack”. It refers to all the data nodes that form a storage area. Therefore, a “rack” in HDFS is the physical location of the data nodes. The NameNode has the rack information, which is the rack ID of each data node. “Rack awareness” refers to the process of selecting closer data nodes depending on the rack information.
Question 14: Explain how to overwrite the replication factor in HDFS.
Answer: You can overwrite the replication factor in HDFS in the following ways:
The 1st is to use the Hadoop FS shell to change the replication factor for a file. The below code snippet shows an example:
$hadoop fs –setrep –w 2 /my/abc_file
The sets the replication factor of the “abc_file” to 2.
The 2nd option is to use the Hadoop FS shell to change the replication factor for all files under a directory. The below code snippet provides an example:
$hadoop fs –setrep –w 5 /my/abc_dir
All files in the “abc_dir” directory will have their replication factor set to 5.
Question 15: What happens if a replication factor of 1 is assigned to an HDFS block during a PUT operation instead of the default replication factor of 3?
Answer: The replication factor is a property of HDFS. It also causes data redundancy. It determines the number of times blocks are replicated to ensure high data availability. For every HDFS block, a Hadoop cluster has (n-1) duplicated HDFS data blocks.
If the replication factor changes to 1 from the default value of 3 during the PUT operation, then the cluster will have only 1 copy of the data. If the DataNode crashes, then this single copy of data will be lost.
Question 16: How does Hadoop HDFS ensure high availability?
Answer: Hadoop 2.x has several features that ensure high availability. Older versions of Hadoop had a SPoF (Single Point of Failure) problem. HDFS follows the master-slave architecture, and the NameNode is the master node.
HDFS can’t be used without the NameNode, which caused a SPoF scenario. HDFS achieves high availability by ensuring the following:
- Availability if the DataNode fails;
- Availability if the NameNode fails.
The NameNode availability architecture introduced in Hadoop 2.0 addresses the SPoF with respect to the NameNode. This architecture allows for 2 NameNodes in a cluster. One of them is active, whereas, the other one is passive. The passive node is on standby mode. It contains the same data as the active node. In the case of a failure of the active NameNode, the passive one takes over.
Question 17: Provide examples of a few HDFS user commands.
Answer: The following are a few HDFS user commands:
Question 18: What is network-attached storage?
Answer: NAS or network-attached storage is a file-level computer data storage server connected to a computer network that provides network access to a heterogeneous group of clients.
Hadoop MapReduce interview questions
Use the following Hadoop MapReduce interview questions:
Question 19: Explain the function of a combiner.
Answer: A combiner is an optional class used in the MapReduce framework. It accepts the inputs from the “Map” class. Subsequently, it passes the output key-value pairs to the “Reducer” class.
The combiner class summarizes the “map” output records with the same key. It then sends the output over the Hadoop network, and the reduced task receives that as input. The use of the combiner class in between the “Map” and “Reduce” classes reduces the volume of data transferred between “Map” and “Reduce”.
Question 20: What do the “Mapper” and “Reducer” do in Hadoop?
Answer: Hadoop MapReduce is a programming paradigm. It’s a key component of Hadoop. MapReduce plays an important role to provide massive scalability across hundreds of Hadoop clusters, which could be built on commodity hardware.
MapReduce also helps Hadoop to process large unstructured data sets. It uses distributed algorithms to do that on a Hadoop cluster.
The MapReduce framework has 2 components. One is the “Map” job or “Mapper”. The other is the “Reduce” job or “Reducer”. The “Map” job processes the input data sets and produces key-value pairs. The basic parameters of a Mapper are LongWritable, Text, Text and IntWritable.
The “Reduce” job takes the “map output”, i.e., the output of “Map” tasks as the input. It aggregates the key-value pairs to produce results. The Map and Reduce jobs store their inputs and outputs in HDFS.
Question 21: What does the Hadoop JobTracker do?
Answer: The Hadoop JobTracker is a service in the Hadoop framework. This service farms out the tasks in the MapReduce job to the specific nodes in the Hadoop cluster. It works as follows:
- The client application submits a job.
- The Job Tracker gets the location of the actual data from the NameNode.
- It locates the Task Tracker nodes with available slots.
- The JobTracker submits tasks to the selected TaskTracker nodes and it monitors these nodes.
- If the job fails, then TaskTracker monitors the Job Tracker. The JobTracker might submit the job elsewhere or take other actions.
- The JobTracker updates the status of the job when it completes.
Question 22: Explain Hadoop MapReduce “RecordReader” and “InputSplit”.
Answer: “RecordReader” is a class with important utility in Hadoop MapReduce. It takes the byte-oriented view of input, which is provided by the “Input Split”.
“InputSplit” is the logical representation of data. The Hadoop MapReduce “InputSplit” describes a unit of work. This unit of work contains one “Map” task in a MapReduce program. “InputSplit” represents data that’s processed by one “Mapper”. A Hadoop “InputSplit” is measured in bytes, and they have storage locations.
The MapReduce “RecordReader” presents a record-oriented view for the “Mapper”. For this, the “RecordReader” uses the data within the boundaries that were created by the “InputSplit”. The “RecordReader” then creates key-value pairs.
Question 23: What is a “distributed cache” in Hadoop?
Answer: The Hadoop MapReduce framework has a facility called the “distributed cache”. This can cache small-to-moderate read-only files, e.g., text files, zip files, jar files, etc.
The “distributed cache” feature can then broadcast these files to all the DataNodes that have MapReduce running. This enables you to access cache files as a local file in a “Mapper” or “Reducer” job.
Question 24: What is “speculative execution” in Hadoop?
Answer: “Speculative execution” is a key feature of Hadoop MapReduce. Hadoop doesn’t diagnose or troubleshoot tasks that run slowly. It detects when a task runs slower than expected. Instead of fixing it, Hadoop launches another task for the same purpose.
This second task works as a backup of the first task. It’s called a “speculative task”. Hadoop accepts the task that completes first, and it kills the other task. This process is called “speculative execution”.
Hadoop HBase interview questions
Use the following Hadoop HBase interview questions:
Question 25: Why does HBase have a lower block size than HDFS?
Answer: Although HBase can use HDFS as the back-end distributed file system, their respective block sizes are different. The default block size of HBase is 64KB. On the other hand, the default block size is 64MB in HDFS.
A block in HDFS is the unit of storage on the disk. However, a block in HBase is a unit of storage for memory. HBase uses its block size to maximize efficiency from HDFS. Many HBase blocks fit in one HDFS block. The smaller block size of HBase enables random access.
Question 26: List the key components of Hbase.
Answer: The key components of HBase are as follows:
- Region: This contains the memory data store and the Hfile.
- Region server: This component monitors the region. Write-ahead log (WAL), Block Cache, MemStore, etc., are a few of the region server components.
- HBase master: It monitors the region server.
- ZooKeeper: ZooKeeper coordinates between the HBase master and the client.
- Catalog tables: HBase has two important catalog tables, namely, “ROOT”, and “META”. The first one tracks where the “META” table data is, and the second stores all the regions in the system.
Hadoop Sqoop Interview Questions
Use the following Hadoop Sqoop interview questions:
Question 27: What are the different file formats to import data using Sqoop?
Answer: Sqoop enables us to transfer data between Hadoop and relational databases. One can import data from RDBMSs like MySQL to Hadoop HDFS using Sqoop. Sqoop also allows exporting of data from a Hadoop file system to a relational database.
Users can import data using the following file formats:
- “Delimited text”: This is the default input format, and it’s appropriate for most of the non-binary data types.
- “Sequence Files”: This is a binary format. A sequence file format stores individual records in custom record-specific data types.
Question 28: Does Sqoop support incremental imports?
Answer: Sqoop supports incremental imports. There are the following two types of incremental imports:
- Append: Uses this if you only need to insert rows.
- “Last modified”: For both inserting and updating rows, you should use the “Last Modified” option.
Hadoop Flume Interview Questions
Use the following Hadoop Flume interview questions:
Question 29: What are the key advantages of Apache Flume?
Answer: The key advantages of Flume are as follows:
- Streaming: Flume helps to stream data efficiently. This helps to obtain near real-time analytics.
- Scalability: Flume makes it easy to scale horizontally when the streaming data grows.
- Reliability: Flume has built-in fault tolerance. Developers can tune its reliability too. These protect against data loss, furthermore, this ensures the delivery of streaming data even in the case of a failure.
Question 30: List the core components of Flume.
Answer: Flume has the following core components:
- Event: This is the log entry of a unit of data that is transported.
- Source: Data enters the Flue workflow through this component.
- Sink: This component transports data to the desired destination.
- Channel: This component is the duct between the sink and the source.
- Agent: This is the JVM that runs Flume.
- Client: This component transmits events to the source.
Hadoop ZooKeeper Interview Questions
Use the following Hadoop ZooKeeper interview questions:
Question 31: What are the minimum configuration parameters for configuring ZooKeeper?
Answer: The ZooKeeper configuration files govern the behavior of ZooKeeper. The design of this file makes it easy for all of the servers running ZooKeeper to use it. The main configuration parameters for configuring ZooKeeper are as follows:
- “ClientPort”: This is the port that the clients attempt to connect to.
- “dataDir”: This is the location where ZooKeeper stores the in-memory database snapshots.
- “tickTime”: This is the length of a single “tick”, the basic time unit used by ZooKeeper.
Question 32: Explain the role of ZooKeeper in the HBase architecture.
Answer: ZooKeeper acts as the monitoring server that provides different services for running HBase. A few of these services are tracking server failures, maintaining the configuration information, and establishing communication between the clients and region servers.
Hadoop Pig interview questions and answers
Use the following Hadoop Pig interview questions:
Question 33: List the different modes in which you can execute Apache Pig in the Hadoop environment.
Answer: You can execute Apache Pig in the following 2 modes:
- “Pig (Local Mode) Command Mode”: This mode requires access to only one computer where all files are installed.
- “Hadoop MapReduce (Java) Command Mode”: This mode requires access to the Hadoop cluster.
Question 34: What is the COGROUP operator in Apache Pig?
Answer: Developers use the COGROUP operator in Apache Pig to work with multiple tuples. They apply this operator to statements containing two or more relations. They can apply this operator on up to 127 relations at a time.
Hive Interview Questions
Use the following Hive interview questions:
Question 35: What is the difference between “InputFormat” and “OutputFormat” in Hive?
Answer: Apache Hive supports different file formats including “InputFormat” and “OutputFormat”. The “InputFormat” file format is used for the following:
- Creating a “HiveInputDescription” object;
- Filling the above-mentioned object with the information about the table to read;
- Initializing “HiveApiInputFormat” with the above-mentioned information;
- Using the “HiveApiInputFormat” with a Hadoop-compatible reading system.
The “OutputFormat” file format is used for the following:
- Creating a “HiveOutputDescription” object;
- Filling the above-mentioned with the information about the table to write;
- Initializing “HiveApiOutputFormat” with the above-mentioned information;
- Using the “HiveApiOutputFormat” with a Hadoop-compatible writing system.
Question 36: Mention the different ways to connect an application if you run Hive as a server.
Answer: If you run Hive as a server, then you can connect an application in the following 3 ways:
- ODBC driver: This supports the ODBC protocol.
- JDBC driver: This supports the JDVC protocol.
- Thrift client: You can use this client to make calls to all the Hive commands. You can use different programming languages like Java, Python, C++, Ruby, and PHP for this.
Apache Hadoop YARN interview questions and answers
Use the following Hadoop YARN questions:
Question 37: What are the differences between Hadoop 1.x and Hadoop 2.x?
Answer: The differences are as follows:
- MapReduce handles both big data processing and cluster management in Hadoop 1.x. However, YARN takes care of cluster management in Hadoop 2.x. Other processing models take care of processing data in Hadoop 2.x.
- Hadoop 2.x offers better scalability than Hadoop 1.x.
- In the case of Hadoop 1.x, there can be a SPoF (Single Point of Failure) if the NameNode fails. The recovery of the NameNode is a manual process. In Hadoop 2.x, StandBy NameNode solves this problem. It’s configured for automatic recovery.
- Hadoop 1.x works on the concept of slots, however, Hadoop 2.x works on the concept of containers.
Question 38: What are the different schedulers in YARN?
Answer: Apache YARN (Yet Another Resource Navigator) is a cluster resource management platform that’s often used with Hadoop. A scheduler in YARN manages the resource allocation of jobs submitted to YARN. YARN has the following 3 schedulers:
- FIFO (“First In, First Out”): This scheduler doesn’t need any configuration. It runs applications in the order of their submission.
- “Capacity” scheduler: This scheduler operates a separate queue for small jobs so that these jobs can start as soon as the request initiates. This might cause the larger jobs to take more time.
- “Fair” scheduler: This scheduler dynamically balances the resources into all the jobs that are accepted. The “Fair” scheduler helps to complete small jobs on time and maintains a high cluster utilization.
Question 39: What do the Hadoop YARN web services REST APIs do?
Answer: The Hadoop YARN web services REST APIs are a set of URI resources. They give access to clusters, nodes, applications, and application historical information. Some URI resources return collections, however, other URI resources return singletons.
You can certainly have a few freshers on your project team. However, you should have a good mix of experienced Hadoop developers and freshers. Projects involving Hadoop can be complex. You need experienced developers too and not only freshers.
Apache Spark is a useful unified analytics engine for large-scale data processing. Spark offers speed, furthermore, it’s easy to use. It supports SQL, streaming, and complex analytics. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or on the cloud. You can certainly use it.
Hadoop big data projects can be complex. You need to focus on functionality, furthermore, non-functional requirements (NFRs) are important too. Such projects require institutionalized code review processes.