---
title: "Role of HDFS in Hadoop"
subject: "Big Data Technologies"
chapter: 6
key: "semester_8/big_data_technologies:hdfs-role-in-hadoop"
source: https://www.bctengineer.com/q/semester_8/big_data_technologies/hdfs-role-in-hadoop
site: BCT Engineering
---

# Role of HDFS in Hadoop

Big Data Technologies · Chapter 6: Case Study: Hadoop

- Asked in: 2074 Bhadra, 4 marks
- Video: [What Is HDFS And How It Works? | Hadoop Distributed File System (HDFS) Architecture | Simplilearn](https://www.youtube.com/watch?v=vEp8oNMiQb8) — Simplilearn
- Answer checked by Claude Opus 5.5 (high effort) on 2026-10-14
- Page: https://www.bctengineer.com/q/semester_8/big_data_technologies/hdfs-role-in-hadoop
- Whole chapter: https://www.bctengineer.com/md/semester_8/big_data_technologies/chapter-6

## Question

What is the role of Hadoop Distributed File System in Hadoop?

## Short answer

HDFS is Hadoop's storage layer: it splits files into 128 MB blocks, replicates them across DataNodes for fault tolerance, and provides data locality so MapReduce tasks run near the data. It supports high-throughput streaming access and runs on commodity hardware.

## Answer

**HDFS (Hadoop Distributed File System)** is the storage layer of Hadoop. It stores very large files across many commodity machines and provides the data to MapReduce and other processing engines. It is modelled on the Google File System.

#### Roles of HDFS in Hadoop

1. **Distributed storage of big data:** a large file is split into blocks (128 MB default) that are spread across many DataNodes, so storage can grow to petabytes by adding nodes.
2. **Fault tolerance:** each block is replicated (default 3 copies) on different nodes and racks. If a node fails, the NameNode re-replicates the lost blocks from the surviving copies, so no data is lost.
3. **Data locality for processing:** HDFS tells the scheduler where blocks are stored, so MapReduce tasks run on the node (or rack) that holds the data. This moves computation to data instead of moving data, and saves network bandwidth.
4. **High-throughput streaming access:** it is optimised for write-once, read-many access to large files, rather than low-latency random access.
5. **Stores job input and output:** MapReduce reads input from HDFS and writes final output back to it.
6. **Runs on commodity hardware:** cheap machines are enough, since reliability comes from software replication.

#### Architecture

```text
 Client --> NameNode (metadata)
   |            |
   +--data----> DataNode1 DataNode2 DataNode3
               (blocks replicated across nodes)
```

The NameNode (master) stores the namespace and block locations, and DataNodes (slaves) store the blocks.

## Check yourself

The answer's key points as multiple-choice questions, with the correct option and why.

1. If a DataNode fails, why is no data lost in HDFS?
   A. The Secondary NameNode restores it from backup
   B. The NameNode re-replicates the lost blocks from the surviving copies on other nodes
   C. MapReduce recomputes the file from scratch
   D. Blocks are stored only on the NameNode
   Answer: B. Each block has 3 replicas on different nodes and racks, and the NameNode re-replicates after a failure. (Key point: Fault tolerance)

2. How does HDFS help MapReduce save network bandwidth?
   A. By compressing all blocks
   B. By moving all data to the master before processing
   C. By storing job output only in memory
   D. By telling the scheduler where blocks are, so tasks run on the node or rack holding the data
   Answer: D. Moving computation to the data avoids moving data across the network. (Key point: Data locality)

3. What access pattern is HDFS optimised for?
   A. Low-latency random updates of small records
   B. Write-once, read-many, high-throughput streaming access to large files
   C. Frequent in-place edits of files
   D. Transactional row-level updates
   Answer: B. HDFS favours throughput on large files rather than low-latency random access. (Key point: Access pattern)

## Related in Big Data Technologies

- [Google File System architecture](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Agfs-architecture) — Big Data Technologies, chapter 2, asked 2×
- [Distributed system in big data example](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Adistributed-system-example) — Big Data Technologies, chapter 1
- [Data analytics process and distributed computing](https://www.bctengineer.com/question.md?key=semester_8%2Fbig_data_technologies%3Adata-analytics-distributed-role) — Big Data Technologies, chapter 1

## Related in other subjects

- [Distributed system and Hadoop](https://www.bctengineer.com/question.md?key=semester_8%2Finformation_system%3Adistributed-system-hadoop-voluminous-data) — Information System, chapter 8
- [File system layers; linked list vs index](https://www.bctengineer.com/question.md?key=semester_6%2Foperating_system%3Afs-layers-linked-list-vs-index) — Operating System, chapter 5
- [File system layout, i-node, backup](https://www.bctengineer.com/question.md?key=semester_6%2Foperating_system%3Afs-layout-inode-backup) — Operating System, chapter 5
- [File system layout; i-node vs linked list](https://www.bctengineer.com/question.md?key=semester_6%2Foperating_system%3Afs-layout-inode-vs-linked-list) — Operating System, chapter 5