HDFS - Hadoop Distributed File System

Definition

HDFS is a distributed file system designed to run on commodity hardware, providing high-throughput access to application data. It is a core component of the Hadoop ecosystem, which allows for the storage and processing of large data sets across clusters of computers, enabling efficient data processing and analysis through frameworks like Apache Spark.

5 Must Know Facts For Your Next Test

1. HDFS splits large files into blocks (default size is 128 MB) and distributes these blocks across multiple nodes in the cluster for redundancy and parallel processing.
2. It is designed to be fault-tolerant, automatically replicating data blocks across different nodes to ensure data availability even in case of hardware failures.
3. HDFS is optimized for high-throughput rather than low-latency access, making it ideal for batch processing tasks rather than real-time applications.
4. Data in HDFS is written once and read many times, which aligns with the needs of big data applications where massive datasets are processed rather than frequently updated.
5. Integration with Apache Spark allows users to efficiently process large datasets stored in HDFS using distributed computing techniques, enhancing performance and scalability.

Review Questions

• How does HDFS support fault tolerance and what mechanisms are in place to ensure data reliability?
  • HDFS supports fault tolerance by replicating data blocks across multiple Data Nodes in the cluster. By default, each block is replicated three times, allowing the system to continue functioning even if one or more nodes fail. If a Data Node goes down, HDFS automatically detects the failure and re-replicates the affected blocks on other healthy nodes to maintain data integrity and availability.
• Discuss how HDFS's architecture contributes to the performance of distributed data processing frameworks like Apache Spark.
  • HDFS's architecture is designed to facilitate high-throughput access to large datasets, which is essential for distributed data processing frameworks like Apache Spark. By breaking files into large blocks and distributing them across nodes, HDFS allows Spark to process data in parallel, significantly speeding up computations. Additionally, the close integration between HDFS and Spark means that Spark can efficiently read data directly from HDFS, minimizing latency and maximizing resource utilization during data processing tasks.
• Evaluate the impact of using HDFS on the design of modern big data applications compared to traditional database systems.
  • The use of HDFS fundamentally changes how modern big data applications are designed compared to traditional database systems. HDFS's ability to handle vast amounts of unstructured data and its focus on high throughput make it ideal for analytics workloads that process large volumes of data at once. In contrast, traditional databases often prioritize transactional integrity and low-latency queries. Consequently, applications built on HDFS leverage batch processing models, such as those provided by Spark, enabling developers to design scalable solutions that can efficiently analyze massive datasets while accepting some trade-offs in terms of real-time access.

Related terms

MapReduce:

A programming model for processing large data sets with a distributed algorithm on a cluster.

YARN: Yet Another Resource Negotiator, which manages resources and scheduling in the Hadoop ecosystem.

Data Node: A worker node in HDFS that stores the actual data and serves read and write requests from clients.