Introduction to HDFS
HDFS (Hadoop Distributed File System) is a distributed file system designed to run on commodity hardware. It is a core component of the Apache Hadoop ecosystem, providing reliable and scalable storage for large-scale data processing applications.
HDFS follows a master-slave architecture, where the NameNode serves as the master and the DataNodes act as the slaves. The NameNode manages the file system namespace, including file metadata, directory structure, and file-to-block mappings. The DataNodes are responsible for storing and retrieving data blocks on the local file system.
One of the key features of HDFS is its ability to handle large data sets. HDFS divides files into smaller blocks (typically 128MB) and distributes these blocks across multiple DataNodes. This data replication and distribution strategy ensures high availability and fault tolerance, as the system can continue to operate even if one or more DataNodes fail.
graph TD
NameNode -- Manages Metadata --> DataNodes
DataNodes -- Store Data Blocks --> HDFS
HDFS is designed to provide high throughput access to application data, making it well-suited for batch processing workloads, such as those found in big data analytics, machine learning, and scientific computing. It also supports a variety of data formats, including structured, semi-structured, and unstructured data, allowing for the processing of diverse data sources.
To interact with HDFS, users can use the command-line interface (CLI) or programming APIs, such as the Java, Python, or Scala APIs. These interfaces provide methods for creating, deleting, and managing files and directories within the HDFS file system.
from hdfs import InsecureClient
client = InsecureClient('http://namenode:50070')
client.upload('/input/data.txt', 'data.txt')
By understanding the basic concepts and architecture of HDFS, users can effectively leverage this distributed file system to store and process large-scale data within the Hadoop ecosystem.