ISRO 2024

Question

Which of the following scenario may not be a good fit for HDFS in Big data paradigm?

1. HDFS is not suitable for scenarios requiring multiple.simultaneous writes to the same file
2. HDFS is suitable for storing data related to applications requiring low latency data access
3. HDFS is suitable for storing data related to applications requiring high latency data access
4. None of the above

Answer 1

Option A )is correct  because

HDFS can be used for storing archive data since it is cheaper as HDFS allows storing the data on low cost commodity hardware while ensuring a high degree of fault-tolerance.Read more on Sarthaks.com - https://www.sarthaks.com/2451019/which-of-the-following-scenario-may-not-be-a-good-fit-for-hdfs

HDFS can be used for storing archive data since it is cheaper as HDFS allows storing the data on low cost commodity hardware while ensuring a high degree of fault-tolerance.Read more on Sarthaks.com - https://www.sarthaks.com/2451019/which-of-the-following-scenario-may-not-be-a-good-fit-for-hdfs

HDFs can be used for storing archive data since it is cheaper as HDFS allows storing the data on low-cost commodity hardware while ensuring a high degree of fault- tolerance.

 

Correct answer is (a) HDFS is not suitable for scenarios requiring multiple/simultaneous writes to the same file To elaborate: HDFS can be used for storing archive data since it is cheaper as HDFS allows storing the data on low cost commodity hardware while ensuring a high degree of fault-tolerance. Read more on Sarthaks.com - https://www.sarthaks.com/2451019/which-of-the-following-scenario-may-not-be-a-good-fit-for-hdfs

Correct answer is (a) HDFS is not suitable for scenarios requiring multiple/simultaneous writes to the same file To elaborate: HDFS can be used for storing archive data since it is cheaper as HDFS allows storing the data on low cost commodity hardware while ensuring a high degree of fault-tolerance. Read more on Sarthaks.com - https://www.sarthaks.com/2451019/which-of-the-following-scenario-may-not-be-a-good-fit-for-hdfs

answer is (a) HDFS is not suitable for scenarios requiring multiple/simultaneous writes to the same file To elaborate: HDFS can be used for storing archive data since it is cheaper as HDFS allows storing the data on low cost commodity hardware while ensuring a high degree of fault-tolerance. Read more on Sarthaks.com - https://www.sarthaks.com/2451019/which-of-the-following-scenario-may-not-be-a-good-fit-for-hdfs

Answer 2

The answer is HDFS is not suitable for scenarios requiring multiple/simultaneous writes to the same file.

Limitations of HDFS for Simultaneous Writes:

• Write-Once, Read-Many Access Model: HDFS is primarily designed for appending data to files, not for random updates or multiple writers. This makes it suboptimal for scenarios where multiple users or processes need to write to the same file concurrently.

 

• Single NameNode Bottleneck: All file system metadata is managed by a single NameNode, which can become a bottleneck for concurrent write operations. Multiple writers can create contention for metadata updates, potentially leading to performance slowdowns or even conflicts.

 

• Append-Only Operation: HDFS appends new data to the end of existing files, not allowing modification of existing data blocks. This constraint makes it unsuitable for applications that require random updates or in-place modifications.

When to Use HDFS:

• Large, Write-Once Data: HDFS excels in storing massive amounts of data that is written once and read many times, such as:
  • Large log files
  • Historical data archives
  • Data sets for batch processing
  • Large-scale machine learning training data

 

• High Scalability and Fault Tolerance: HDFS is designed for horizontal scaling, allowing you to store massive datasets across many commodity servers. It provides high fault tolerance through data replication, ensuring data availability even in the event of hardware failures.