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 key component of the Hadoop ecosystem, enabling the storage and management of large datasets across multiple machines while ensuring fault tolerance and scalability. HDFS is built to handle big data applications by breaking files into blocks and distributing them across a cluster, which facilitates efficient data processing with the MapReduce programming model.

5 Must Know Facts For Your Next Test

1. HDFS stores large files as blocks, typically 128 MB or 256 MB in size, allowing for easier management and retrieval of data across a distributed system.
2. Each block in HDFS is replicated across multiple nodes (usually three times) to ensure data redundancy and availability in case of hardware failure.
3. The architecture of HDFS allows for streaming access to data, which makes it well-suited for big data applications that require reading large volumes of data efficiently.
4. HDFS uses a master/slave architecture where the Namenode acts as the master server, while DataNodes serve as slave nodes that store actual data blocks.
5. Data locality is a significant feature of HDFS, allowing computations to be moved closer to where the data is stored, thus reducing network congestion and improving processing speed.

Review Questions

• How does HDFS enhance the performance of big data applications through its architecture?
  • HDFS enhances the performance of big data applications by utilizing a distributed architecture where files are broken into blocks and stored across multiple nodes. This not only allows for parallel processing but also facilitates data locality, enabling computations to be executed near the stored data. By replicating blocks for fault tolerance and enabling efficient streaming access to large files, HDFS optimizes resource usage and accelerates data processing within the Hadoop ecosystem.
• What role does replication play in ensuring fault tolerance within HDFS?
  • Replication in HDFS plays a crucial role in ensuring fault tolerance by creating multiple copies of each data block across different DataNodes. This means that if one DataNode fails or becomes unavailable, the system can still access the replicated blocks from other nodes without losing any data. By default, HDFS replicates each block three times, which provides a robust mechanism for maintaining data availability and reliability, crucial for handling large-scale data processing tasks.
• Evaluate how HDFS's design choices support scalability and high-throughput data access in the context of MapReduce jobs.
  • HDFS's design choices, such as block-based storage and its master/slave architecture, significantly support scalability and high-throughput data access needed for MapReduce jobs. By breaking files into manageable blocks that are distributed across a cluster, it allows multiple MapReduce tasks to run concurrently on different nodes. Additionally, the replication of blocks enhances availability and minimizes downtime during job execution. This design ensures that as datasets grow, HDFS can scale out easily by adding more nodes while maintaining efficient access speeds for large-scale data processing.

Related terms

MapReduce:

A programming model for processing large data sets in parallel across a distributed cluster, dividing tasks into 'Map' and 'Reduce' phases.

YARN: Yet Another Resource Negotiator, a resource management layer in Hadoop that manages computing resources and scheduling for applications.

Namenode: The master server in HDFS that manages metadata and the namespace of the filesystem, keeping track of where data blocks are stored in the cluster.