If you’re interviewing for a position that’ll require you to process and manipulate large volumes of data — from gigabytes to petabytes — it’s very likely that you’ll use Hadoop in some capacity. Professions like Data Engineer, Data Scientist, Big Data Analyst, Big Data Software Engineer, Business Intelligence Specialist, and more all use Hadoop to help companies make data-informed business decisions.
One of the best ways you can prepare for Hadoop interview questions is to set up a mock interview and practice answering as many Hadoop-related questions as you can before your real interview. You could ask a friend or family member to help out and play the role of the interview, or you can simply practice saying your answers out loud in a mirror.
Here are 15 popular Hadoop interview questions to help you get ready for the big day.
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1. What is Hadoop, and what are its primary components?
For this question, you can say that Hadoop is an infrastructure that includes tools and services for processing and storing big data. It helps companies analyze their data and make more informed decisions.
The primary components of Hadoop include:
- Hadoop Distributed File System (HDFS)
- Hadoop MapReduce
- Hadoop Common
- PIG and HIVE — components of data access
- HBase — for storage
- Ambari, Oozie, and ZooKeeper — for managing and monitoring data
- Thrift and Avro — for serializing data
- Apache Flume, Sqoop, Chukwa — for integrating data
- Apache Mahout and Drill — for data intelligence
2. What are the core concepts of the Hadoop framework?
Hadoop is based on two concepts: HDFS and MapReduce. HDFS is a file system for storing data across a distributed network that enables parallel processing and redundancy.
MapReduce is a programming scheme for the processing of large datasets. It consists of two functions or processes: Map segregates datasets into tuples, and Reduce further refines this data to yield a final, culled result.
3. What are the most common input formats in Hadoop?
Hadoop uses three common input formats. The default format is the Text Input Format, which is the base class for all file-based input formats. It specifies the input directory where the data files are located. The Sequence File Input Format is dedicated to storing sequences of binary key-value pairs. And the Key Value Text Input Format treats each input line as a separate record and reads plain text files.
4. What is YARN?
YARN stands for Yet Another Resource Negotiator and is the interface in Hadoop for leveraging the various processing systems (MapReduce, Spark, and others ) on the available data resources.
5. What is Rack Awareness?
Rack Awareness is an algorithm NameNode uses to determine the pattern for blocking: the most efficient way to leverage storage and bandwidth resources based on the topology of the network..
6. What are active and passive NameNodes?
NameNodes are objects that manage the filesystem tree and the file metadata. A Hadoop system with high availability contains both Active and Passive NameNodes to provide redundancy. The Hadoop cluster is run by the Active NameNode, and the standby, or Passive NameNode, stores the data of the Active NameNode.
If the Active NameNode ever crashes, the Passive NameNode takes over. This means that the failure of a NameNode won’t cause the system to fail.
7. What are the schedulers in the Hadoop framework?
The Hadoop framework contains three schedulers: the Capacity, Fair, and FIFO systems. The FIFO scheduler simply orders jobs in a queue based on their arrival time and processes them one at a time. The Capacity scheduler has a secondary queue that will run smaller jobs as they arrive. Fair Sharing dynamically allocates resources to jobs as needed.
8. What is Speculative Execution?
It’s a frequent occurrence for some nodes to run slower than others in the Hadoop framework, and this constrains the entire application. Hadoop overcomes this by detecting or speculating when a task is running slower than usual and launching an equivalent backup. The task that completes first is accepted, while the other is killed. This is known as Speculative Execution.
9. What are the main components of Apache HBase?
Three components make up Apache HBase. They are:
- Region Server, which forwards clusters of regions to the client using the Region Server. This occurs after a table divides into multiple regions.
- HMaster, which is a tool that helps manage and coordinate the Region Server.
- ZooKeeper, which is a coordinator in the HBase distributed environment that provides fault tolerance by monitoring the transaction state of servers.
10. What is Checkpointing?
Checkpointing is a procedure of producing intermediate backups to guard against data loss and maintain efficiency. In Hadoop, the fsimage file contains the entire filesystem metadata. In the checkpointing process, a secondary NameNode creates a new merged fsimage file based on the existing fsimage file in memory and edits received from transactions on the primary NameNode.
11. What are some best practices for debugging Hadoop code?
The effort of isolating a problem can often be streamlined by implementing several practices to make data and processes of the system more transparent. These can include:
- Capturing logs specific to input and output processes
- Carefully consider instances in which exceptions are raised or not and how they might be useful in adding context to a situation
- Use counters to monitor task execution and other status and summary information to provide direction in error finding
12. What does RecordReader do?
A RecordReader is simply an iterator that provides a Map function with the records it needs for creating key-value pairs that then get passed to the Reduce phase of a MapReduce job.
13. In what modes can Hadoop run?
- Standalone mode, a default mode for the purpose of debugging and development
- Pseudo-distributed mode, a mode for simulating a cluster on a local machine at a smaller scale
- Fully-distributed mode, Hadoop’s production stage where data is distributed across different nodes on a Hadoop cluster
14. What are some practical applications of Hadoop?
Companies use Hadoop for a variety of tasks where big data is used. Some real-life examples of this include detecting and preventing fraud, managing street traffic, analyzing customer data in real-time to improve business processes, and accessing unstructured medical data in hospitals and doctor offices.
15. Which Hadoop tools enhance big data performance?
Several Hadoop tools significantly boost the performance of big data. You could mention any of these tools in your answer to this question: Hive, HDFS, HBase, Oozie, Avro, Flume, and ZooKeeper.
More interview support
Looking for more interview prep? Check out our guide to acing the technical interview, tips for answering behavioral interview questions, and our advice for the whiteboard interview. We also have a guide to interviewing on Zoom.
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