Hadoop Admin Interview Questions and Answers for 2025

The hdfs-site.xml file is used to configure HDFS. Changing the dfs.replication property to hdfs- site.xml changes the default replication for all files stored in HDFS. The replication factor on a per-file basis can also be changed as follows.  

Hadoop FS Shell:[training@localhost ~]$ hadoopfs –setrep –w 3 /my/fileConversely, 

The replication factor of all the files under a directory can also be changed. 

[training@localhost ~]$ hadoopfs –setrep –w 3 -R /my/dir 

There are two ways to include native libraries in YARN jobs: 

1. By specifying -Djava.library.path on the command line, but in this case, there is a possibility that the native libraries will not be loaded correctly, and errors may occur.  
2. The better option to include native libraries is to set the LD _LIBRARY_ PATH in the .bashrc file.

YARN is not a substitute for Hadoop, but a more powerful and efficient technology that supports MapReduce and is also referred to as Hadoop 2.0 or MapReduce 2. 

The file system check utility FSCK is used to check and display the state of the file system, files and blocks in it. When used with a path (bin/Hadoop fsck / -files -blocks -locations -racks), it recursively displays the state of all files under the path. When used with '/', the entire file system is checked. By default, FSCK ignores files that are still open for writing by a client. To list such files, run FSCK with the -openforwrite option. 

FSCK scans the file system, prints a dot for each file that is found to be healthy, and prints a message about the files that are not quite healthy, including those that have over-replicated blocks, under-replicated blocks, incorrectly replicated blocks, damaged blocks, and missing replicas.  

The configuration files that need to be updated to set up a fully distributed mode of Hadoop are: 

• Hadoop-env.sh 
• Core-site.xml 
• Hdfs-site.xml 
• Mapred-site.xml 
• Masters 
• Slaves 

These files can be found in your Hadoop > conf directory. If Hadoop daemons are started individually with "bin/Hadoop-daemon.sh start xxxxxx', where xxxx is the name of the daemon, then the master and slave files do not need to be updated and can be empty. When starting daemons in this way, commands must be issued on the appropriate node to start the appropriate daemons. When Hadoop daemons are started with "bin/start-dfs.sh' and 'bin/start-mapred.sh', the master and slave configuration files must be updated on the NameNode machine. 

• Masters - IP address/hostname of the node on which SecondaryNameNode is run.
• Slaves - IP address/hostname of the node on which the DataNodes and possibly the task trackers will run. 

DataNodes can store blocks in multiple directories, usually located on different local drives. To set up multiple directories, you must specify a comma-separated list of pathnames as values under the dfs.data.dir/dfs.datanode.data.dir configuration parameters. DataNodes will try to put the same amount of data in each of the directories. NameNode also supports multiple directories where the namespace image and processing logs are stored. To set up multiple directories, one must specify a comma-separated list of pathnames as values under the configuration parameters dfs.name.dir/dfs.namenode.data.dir. The namespace directories are used for namespace data replication so that image and log can be recovered from the remaining disks/volumes if one of the disks fails.  

The replication factor is a feature of HDFS that can be set appropriately for the entire cluster to adjust the number of times the blocks are replicated to ensure high data availability. For each block stored in HDFS, the cluster has n-1 duplicated blocks. Thus, if the replication factor is set to 1 instead of the default value of 3 during the PUT operation, there will be a single copy of the data. If the replication factor is set to 1, only a single copy of the data would be lost if the data node crashed under these circumstances.  

Reducers have 3 core methods, and they are: 

• Setup () – This method of the reducer is used for configuring various parameters like the input data size, distributed cache, heap size, etc. Function Definition- public void setup (context) 
• Reduce () it is the heart of the reducer which is called once per key with the associated reduce task. 
• Function Definition -public void reduce (Key,Value,context) 
• Cleanup () – This method is called only once at the end of reduce task for clearing all the temporary files. 

HBase should be used when the big data application has:

• A variable schema
• When data is stored in the form of collections.
• If the application demands key-based access to data while retrieving.

And the essential components of Hbase are:

• Region- This component contains a memory data store and Hfile.
• Region Server-This monitors the Region.
• HBase Master- It is responsible for monitoring the region server.
• Zookeeper- It takes care of the coordination between the HBase Master component and the client.
• Catalog Tables-The two important catalog tables are ROOT and META.ROOT table tracks where the META table is, and META table stores all the regions in the system.

The decision for a certain file format depends on the following factors

1. Schema development for adding, modifying, and renaming fields.
2. Pattern of use, e.g., access to 5 of 50 columns versus access to most columns.
3. Suitability for parallel processing.
4. Read/write/transfer performance vs. block compression to save storage space. 

File formats that can be used with Hadoop - CSV, JSON, Columnar, Sequence files, AVRO, and Parquet files. 

1. CSV files: CSV files are ideal for exchanging data between Hadoop and external systems. It is advisable not to use headers and footers when using CSV files. 
2. JSON files: Each JSON file has its own data set. JSON stores both data and schema together in one record, and also allows for full schema evolution and partitioning capabilities. However, JSON files do not support block-level compression. 
3. Avro files: This type of file format is best suited for long-term storage with schema. Avro files store metadata along with the data and allow you to specify an independent schema for reading the files. 
4. Parquet files: a columnar file format that supports block-level compression and is optimized for query performance, allowing you to select 10 or fewer columns from datasets with more than 50 columns. 
5. edits file: It is a log of changes made to the namespace since the checkpoint.
   Checkpoint Node- Checkpoint Node stores the last checkpoint in a directory that has the same structure as the namespace node directory. Checkpoint Node periodically creates checkpoints for the namespace by downloading the changes and the fsimage file from the NameNode and merging them locally. The new image is then written back to the active NameNode. 
6. Backup node: The backup node also provides checkpointing functions like the checkpoint node, but additionally maintains an up to date in-memory copy of the file system namespace that is synchronized with the active NameNode. 

By default, Hadoop 1x has a block size of 64MB and Hadoop 2x has a block size of 128MB, though let us take the block size to be 100MB, which means there will be 5 blocks replicated 3 times (the default replication factor).  

To illustrate how a block is stored in HDFS, let us use a scenario with a file containing 5 blocks (A/B/C/D/E), a client, a NameNode and a DataNode. Initially, the client will ask the NameNode for the locations of the DataNodes where it can store the first block (A) and the replicated copies.  

Once the client knows the location of the DataNodes, it will send block A to the DataNodes and the replication process will begin. After block A has been stored and replicated on the DataNodes, the client will be informed, and then it will initiate the same process for the next block (Block B).  

In this process, if the first block of 100MB is written to HDFS, and the next block has been started by the client, to store then 1st block will be visible to readers. Only the current block being written will not be visible to the readers. 

We are familiar with the steps to decommission a DataNode and there is a lot of information available on the internet to do so, however, what about a task tracker running a MapReduce job on a DataNode that is planned to be decommissioned? Unlike the DataNode, there is no easy way to decommission a task tracker.  

It is usually assumed that when we intend to move the same task to another node, we have to make the task process fail and let it be re-allocated elsewhere in the cluster. It is possible that a task on its last attempt is running on the task tracker and that a final failure may result in the whole job not succeeding. Unfortunately, it is not always possible to prevent this from happening. Consequently, the concept of decommissioning will stop the DataNode, but to move the present task to another node, we have to manually shut down the task tracker running on the decommissioned node. 

One of the most frequently posed Hadoop admin scenario based interview questions and answers, be ready for this conceptual question.

Hadoop and Spark can be integrated by using Hadoop's HDFS as the storage layer for Spark and using YARN as the resource manager for both Hadoop and Spark. This allows Spark to read data stored in HDFS and process it using its in-memory computing capabilities, while YARN manages the allocation of resources such as CPU and memory.  

Hive can also be integrated with Hadoop by using Hive's SQL-like query language, HiveQL, to query data stored in HDFS. This allows for more efficient querying and analysis of large data sets stored in Hadoop. Hive can also be used to create and manage tables, similar to a relational database, on top of data stored in HDFS. In addition, Hive can be integrated with Spark, by using Hive as the metadata store and Spark SQL as the execution engine. This allows for HiveQL queries to be executed using Spark's in-memory computing capabilities, resulting in faster query execution.  

Overall, the integration of Hadoop with other big data technologies such as Spark and Hive, allows for a powerful and flexible big data processing ecosystem, where different tools can be used for different purposes and can work together to process and analyze large data sets. 

There are several ways to handle a sudden increase in data volume on a Hadoop cluster:  

1. Scale Up: Add more resources (such as nodes) to the existing cluster to handle the increased data volume.  
2. Scale Out: Add more clusters to handle the increased data volume.  
3. Partitioning: Divide the data into smaller chunks and distribute them across multiple nodes.  
4. Compression: Compress the data to reduce its size and decrease the amount of storage required. 
5. Data Archiving: Move infrequently used data to a separate storage system to free up space on the main cluster.  
6. Data Deletion: Remove unnecessary or redundant data from the cluster.  

It also depends on the data's access pattern, if it is write-heavy then we can go for more storage or if it is read-heavy then we can go for more processing power. Overall, the approach to handling a sudden increase in data volume will depend on the specific use case and the resources available. 

There are several ways to implement a real-time streaming pipeline using Hadoop technologies, but one possible approach is to use Apache Kafka as the data stream source, Apache Nifi as the data flow manager, and Apache Hadoop HDFS or Apache Hadoop Hive as the data storage and processing layer.  

1. First, you would set up a Kafka cluster that can handle high-throughput data streams and configure it to receive data from various sources.  
2. Next, you would use Apache Nifi to pull data from Kafka, perform data transformation and enrichment, and route it to the appropriate destination.  
3. Then, you would use Apache Nifi processors like ExtractText, ReplaceText, and EvaluateJsonPath to extract, format, and enrich the data as needed. 
4. After that, you would use Apache Nifi to route data to HDFS or Hive for long-term storage and batch processing using tools like Apache Hive or Apache Pig.  
5. Finally, you would use Apache Nifi to route the data to a real-time processing engine like Apache Storm or Apache Spark Streaming for further analysis, and then send the results to a data visualization tool like Apache Zeppelin or Kibana for real-time monitoring and alerting.  
6. It's also important to make sure that the pipeline is secure, and data is encrypted as well as implement a good data governance strategy.  
7. Monitoring and management of the pipeline are crucial. You can use tools like Ambari, Ganglia, and Graphite to monitor the health and performance of the pipeline.  
8. You can also use Nifi's built-in monitoring features such as the Reporting Task and Provenance Repository to track data flow and troubleshoot any issues. 

A staple in Hadoop admin interview questions and answers, be prepared to answer this one using your hands-on experience.

Handling data replication and data integrity in a Hadoop cluster can be done using several different tools and techniques. Some possible methods include:  

1. HDFS Replication: HDFS is Hadoop's distributed file system, and it provides built-in data replication features. By default, HDFS replicates each block of data three times across different nodes in the cluster to ensure data availability and fault tolerance. This can be configured based on the requirement, and it is recommended to have at least 3 copies for data availability.  
2. Data Checksum: HDFS provides data checksum feature to ensure data integrity. It calculates checksum for each block of data and compares it with the original data while reading.  
3. Distributed File System Snapshots: HDFS also provides the ability to take snapshots of the file system, which can be used to recover from data loss or corruption.  
4. Third-Party Replication: You can also use third-party tools like Apache Nifi, Apache Flume, and Apache Kafka to replicate data across multiple clusters or systems.  
5. Data Backup: You should also consider implementing a backup strategy that includes regular backups of the entire cluster or specific data sets to ensure that you can recover from data loss or corruption.  
6. Data Governance: Implementing a data governance strategy that includes data quality checks, data lineage tracking, and access controls can help ensure data integrity and security.  
7. Monitoring: Regularly monitoring the cluster for errors or issues and troubleshooting them in a timely manner can help prevent data loss or corruption.  

This, along with other interview questions on Hadoop admin, is a regular feature in Hadoop admin interviews, be ready to tackle it with the approach mentioned below.

Upgrading a Hadoop cluster to a newer version can be a complex process and it depends on the current version and the target version, but some general steps that can be followed include:  

1. Planning: Before upgrading, it is important to understand the changes and new features in the target version, and plan accordingly. This includes identifying any compatibility issues or deprecated features and making necessary adjustments to your data and applications.  
2. Backup: Create a backup of your current Hadoop cluster, including all data, configurations, and metadata, to ensure that you can roll back if necessary.  
3. Test: Test the upgrade process on a small test cluster before applying it to the production cluster. This will help identify any issues and make any necessary adjustments. 4. 
4. Upgrade the cluster: Perform the upgrade by following the instructions provided by the vendor or the community. The process will depend on the current version and the target version, but it will typically involve upgrading the master nodes and then upgrading the worker nodes.  
5. Validate: Once the upgrade is complete, validate the cluster's functionality and performance to ensure that everything is working as expected.  
6. Monitor: Monitor the cluster for any issues and troubleshoot them in a timely manner.  
7. Update your Applications: update your applications to the latest version if they are compatible with the new version of Hadoop.  
8. Rollback: If the upgrade process failed or caused issues, you can roll back to the previous version using the backup.  
9. Repeat the process: Repeat the process for all the Hadoop components like Hive, Pig, Hbase, etc.  
10. Keep the documentation: Keep a detailed documentation of the upgrade process, including the version details, issues faced, and the resolution. This will be helpful for future reference and troubleshooting. 

A staple in Hadoop admin interview questions and answers, be prepared to answer this one using your hands-on experience.

Implementing a disaster recovery plan for a Hadoop cluster can be done using several different tools and techniques, some of which include: 

1. Data Backup: Regularly backup all data, configurations, and metadata in the Hadoop cluster to a remote location or a cloud storage, which can be used to restore the cluster in case of data loss or corruption.  
2. Cluster Replication: Replicate the Hadoop cluster to a secondary location, which can be used as a failover in case of a disaster.  
3. Data Mirroring: Use tools like HDFS mirroring to replicate the data between clusters, so that data is available in both the primary and secondary clusters.  
4. Automated Failover: Implement automated failover mechanisms that can detect a failure and automatically switch to the secondary cluster.  
5. Network Connectivity: Ensure that the secondary cluster is connected to the network and has access to the same data sources as the primary cluster.  
6. Resource allocation: Ensure that the secondary cluster has the same or similar resources as the primary cluster.  
7. Testing: Regularly test the disaster recovery plan to ensure that it is working as expected and that the failover process is smooth.  
8. Documentation: Create and maintain detailed documentation of the disaster recovery plan, including procedures and contact information for key personnel.

Handling missing or corrupt data in a Hadoop cluster can be done using several different tools and techniques, some of which include:

1. Data Validation: Use data validation techniques like data type validation, format validation, and range validation to detect and correct missing or corrupt data.
2. Data Profiling: Use data profiling tools like Apache Jalapeno or Talend to identify and fix data quality issues. Data Backup: Regularly backup all data, configurations, and metadata in the Hadoop cluster to a remote location or a cloud storage, which can be used to restore the cluster in case of data loss or corruption.
3. Data Replication: Use tools like HDFS replication to replicate the data across multiple nodes in the cluster, which can be used to recover from data loss or corruption.
4. Data Auditing: Regularly audit the cluster for data access and modification to detect any potential data breaches or unauthorized access.
5. Data Governance: Implement a data governance strategy that includes data quality checks, data lineage tracking, and access controls to ensure data integrity.

It is important to keep in mind that missing or corrupt data can have a significant impact on the performance of the cluster and the accuracy of the results, so it is crucial to have a data governance strategy in place, and always monitor the data in the cluster.