Apache Hadoop Interview Questions Preparation Course

Explore the four v's of big data—volume, velocity, variety, and veracity—and how they shape data storage, speed, data forms, and accuracy.

Compare structured and unstructured data. Structured data fits rows and columns in RDBMS like Oracle or SQL Server, while unstructured data cannot, with examples such as logs and social content.

Identify the four main Hadoop components—hdfs, mapreduce, yarn, and common libraries—and explain how they enable a distributed file system, data processing, and resource management.

Discover the core concept behind Apache Hadoop with MapReduce, where mappers and reducers process data and enable scalability and fault tolerance for large-scale processing.

Hadoop streaming provides a bridge to run map jobs using executable scripts within the Hadoop distribution, passing them to streaming for execution and monitoring in production.

Describe the roles of name node, checkpoint node, and backup node in HDFS: name node stores namespace data, checkpoint node merges checkpoints, backup node maintains a synchronized in-memory namespace.

Design a Hadoop setup on commodity hardware by using dual-core machines with dual processors, adequate memory, and high-capacity SATA storage, while ensuring sufficient bandwidth for effective clustering.

Explore how HDFS uses blocks and the block scanner to store large files across multiple blocks, verify integrity, detect corruption, and control the scanner's active period through configuration.

Identify the default port numbers for the name node, job tracker, and task tracker, and review practical interview-ready guidance for Hadoop cluster configuration.

Discover why Hadoop uses commodity hardware for cost-effective, scalable data processing on inexpensive servers, and how distributed architecture enables fast, scalable execution with easy scale up or scale down.

Explore inter-cluster data copying in Hadoop using a high-performance copy utility with map-based transfers and post-copy verifications, and note NTFS cannot update a file at an arbitrary location.

Explain the replication factor in HDFS as the number of copies of a file, and show how to set it for a specific file or by default for a directory.

Compare NAS and DAS in a Hadoop cluster by defining network attached storage versus direct attached storage, and emphasize compute near data, local storage, and performance on commodity hardware.

The NameNode receives two key messages from each DataNode: a heartbeat indicating aliveness and a block report listing hosted blocks for metadata updates.

Compare file-based indexing with input-split indexing in Hadoop to improve performance. Understand how the NameNode stores metadata as a directory and why it can be a single point of failure.

If the NameNode crashes in an HDFS cluster, the system becomes a single point of failure; recover by using a secondary NameNode and its checkpoints to recreate metadata and restart.

Explain the main functions of the secondary name node, including maintaining a copy of the fsimage and edit logs, performing checkpoints, applying updates, and enabling quick restart after a crash.

Explain how HDFS replication factor affects data availability when a DataNode crashes, why maintaining at least two or three replicas matters, and how replication requests fetch the nearest copy.

Understand rack awareness in Hadoop and its impact on performance and fault tolerance by prioritizing reads and writes within the same rack to minimize cross-rack latency.

Learn how to check if a file exists in HDFS using the test command, with options for existence, non-zero size, and directory status, and see practical command examples.

Understand why we use the fsck command in HDFS and how it can delete files, move files, print block locations, and view block reports.

Learn how the reducer's core methods: setup, reduce, and cleanup configure parameters, process mapped key-value data, and clean up intermediate files to produce final output.

Identify the reducer’s three main phases in Hadoop: shuffle, sort, and reduce; map outputs are copied to reducers, then sorted and reduced into final results.

Explore how the Hadoop context object enables interaction with the system, exposes configuration for jobs, and passes environment data during map operations, while explaining the default replication factor of three.

Partitioning in Hadoop routes map outputs to reducers using a partitioner, with a partition count equal to reducers. It uses hash-based or custom criteria to group data for targeted reductions.

The lecture explains that a combiner is an optional step between map and reduce in Hadoop, summarizing map outputs by key to reduce data transfer and resemble a reducer.

We examine how HDFS stores files in fixed-size blocks. A 25 MB file occupies a single 64 MB block, so the allocated storage equals the block size.

Explain why HDFS stores data in blocks to enable replication across machines for fault tolerance and handling large files via block division with block-level checksums.

Create a custom partitioner by extending the Hadoop Partitioner class and overriding getPartition to map values to a reducer; then set the partitioner in the job to route data.

Compare the RDBMS and HBase data models by outlining schema, normalization, and partitioning. RDBMS uses a defined schema with validations, while HBase remains schema-less and does not require normalization.

Discover how the HDFS checkpoint node creates and uploads checkpoints to the NameNode, while a second node maintains checkpoints for recovery and faster restarts.

Maintains synchronization between the backup node in HDFS and the NameNode by reading the edit stream to store a local checkpoint, and does not upload checkpoints.

Data locality in Hadoop means moving computation to the data, not data across the network. Schedule tasks on a local node, then on the same rack, then on other racks.

Explore the differences between data science, big data, and Hadoop, and learn how Hadoop enables big data processing beyond traditional relational databases.

learn how the hdfs balancer analyzes block placement and spreads blocks uniformly to address uneven data distribution.

NameNode selects DataNodes by placing replicas on different nodes and racks, ensuring at least one replica on another node and spreading blocks evenly to minimize rack or node failures.

Describe safemode in hdfs as a read-only startup mode where the namenode collects block data and replication details before enabling writes, preventing redundant replication and maintaining data integrity.

Replace HDFS data volume before shutting down a DataNode by formatting the new drive, updating data directories, restarting the related process, and unmounting the old volume.

Explore the important Hadoop configuration files: the default read-only configs and site-specific custom files such as core-site.xml and hdfs-site.xml, loaded by the Hadoop Configuration class for jobs.

Learn how to monitor memory usage in a Hadoop cluster and set per-task memory limits for map and reduce tasks to prevent out-of-memory errors and crashes.

Serialization in Hadoop MapReduce converts objects to a standard binary format for network transfer between nodes. This enables safe cross-machine data exchange and improves reliability across heterogeneous hardware.

Discover how the distributed cache improves Hadoop job performance by caching frequently used files in memory, including jars, with configuration to specify files and read-only synchronization across nodes.