Hadoop Distributed File System (HDFS)

Definition

Hadoop Distributed File System (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 of large datasets across multiple machines while ensuring reliability and scalability. HDFS is optimized for large files and is built to handle failures gracefully by replicating data across different nodes in the cluster.

5 Must Know Facts For Your Next Test

1. HDFS divides large files into blocks and distributes them across multiple nodes in a cluster, with each block replicated for fault tolerance.
2. The default replication factor for HDFS is three, meaning each block of data is stored on three different machines to ensure data durability.
3. HDFS is designed to work with large-scale data processing frameworks like MapReduce, allowing seamless integration for processing data stored within HDFS.
4. Data in HDFS is stored in a write-once, read-many model, which optimizes performance for large files but makes it less suitable for frequent updates.
5. HDFS provides high availability through NameNode and DataNode architecture, where the NameNode manages the metadata and directory structure while DataNodes store the actual data.

Review Questions

• How does HDFS ensure data reliability and fault tolerance within a distributed computing environment?
  • HDFS ensures data reliability and fault tolerance by replicating each block of data across multiple nodes in the cluster. By default, each block is replicated three times, allowing for continued access to the data even if one or two nodes fail. This replication strategy minimizes the risk of data loss and ensures that there are always multiple copies available for processing and retrieval.
• Discuss how HDFS interacts with other components of the Hadoop ecosystem like MapReduce and YARN to process large datasets efficiently.
  • HDFS works closely with MapReduce and YARN to facilitate efficient processing of large datasets. When a MapReduce job is executed, it reads input data directly from HDFS, where files are stored across various nodes. YARN manages resources across the cluster, allocating computational power as needed for MapReduce tasks. This integration allows for parallel processing of data and optimizes resource utilization across the Hadoop ecosystem.
• Evaluate the advantages and limitations of using HDFS compared to traditional file systems for big data storage.
  • HDFS offers significant advantages over traditional file systems when it comes to big data storage. Its ability to handle very large files and its design for high throughput make it ideal for applications that require extensive data processing. However, HDFS also has limitations; it is not well-suited for applications that require frequent updates or real-time access since it follows a write-once model. Additionally, managing an HDFS cluster requires careful planning to address potential issues such as node failures and maintaining adequate replication levels.

Related terms

MapReduce: A programming model and processing engine in Hadoop that allows for the distributed processing of large data sets across clusters.

YARN: Yet Another Resource Negotiator, a resource management layer of Hadoop that manages computing resources in clusters and enables job scheduling.

Block: The smallest unit of data storage in HDFS, typically 128 MB or 256 MB, which allows HDFS to efficiently manage large files.