In today’s blog post, we will explore one of the fundamental concepts in SQL Server – HDFS (Hadoop Distributed File System). HDFS is a primary storage system used by Hadoop, and it plays a crucial role in managing and accessing data across Hadoop clusters.

Before we dive into the details of HDFS, let’s understand its purpose. HDFS is designed to provide high-performance access to data while ensuring fault tolerance. It achieves this by distributing data across multiple nodes in a Hadoop cluster, making it resilient to server failures.

The architecture of HDFS follows a master/slave model. Each HDFS cluster consists of a single NameNode, which acts as the master server. The NameNode manages the file system and regulates access to various files. Additionally, there are multiple DataNodes, with each data server having one DataNode.

When a large file is stored in HDFS, it is split into smaller blocks, which are then distributed across the DataNodes. This distribution ensures that the data is replicated on multiple nodes, reducing the risk of data loss in case of a node failure.

Let’s visualize how HDFS works:

In the diagram above, we can see that the client application or HDFS client connects to the NameNode and the DataNodes. The NameNode regulates access to the DataNodes, allowing the client application to connect directly to a DataNode.

When the client application writes data to HDFS, it only needs to write to one of the DataNodes. The NameNode then decides which other DataNodes should replicate the data. This replication ensures data availability and fault tolerance.

In the event of a disaster or a failed DataNode, the other DataNodes containing the same data blocks take over the responsibility of serving that data. This high availability feature of HDFS ensures that the system continues to function even in the face of failures.

It’s important to note that the NameNode is a critical component of the Hadoop architecture. To ensure high availability, the NameNode is usually replicated on another cluster or data rack. This replication ensures that even if the primary NameNode fails, the replicated node can take over its role.

The Hadoop architecture, including HDFS, is designed to handle big data efficiently. By leveraging commodity hardware and distributing data across multiple nodes, it overcomes the limitations of individual hardware failures.

In tomorrow’s blog post, we will explore the importance of relational databases in the context of big data. Stay tuned!