Understanding HDFS in Data Engineering

Hadoop Distributed File System (HDFS) is a distributed file system designed to store and manage large amounts of data across multiple machines. It is a key component of the Apache Hadoop software framework and is commonly used in big data applications. In this post, we’ll dive into the fundamentals of HDFS, its architecture, and how it works.

What is HDFS?

HDFS is a file system designed for distributed storage and processing of large data sets. It is part of the Apache Hadoop project and is used in many big data applications for storage and retrieval of large files. The main features of HDFS are:

• Scalability - HDFS can store and manage petabytes of data
• Fault-tolerance - HDFS is designed to handle failures of individual machines in the cluster
• High throughput - HDFS is designed for the efficient reading and writing of large files
• Cost-effective - HDFS runs on commodity hardware, making it a cost-effective solution for storing large data sets

HDFS is designed to run on a cluster of commodity hardware, meaning that individual machines in the cluster may be unreliable and can fail without affecting the overall operation of the file system.

HDFS Architecture

HDFS has a master/slave architecture, with a central NameNode that manages the file system namespace and multiple DataNodes that store the actual data. The NameNode is the single point of failure in the system, meaning that if it fails, the entire system becomes unavailable. The DataNodes, on the other hand, can be added or removed from the system at any time without affecting the overall operation of the file system.

The NameNode stores the metadata for the file system, including the file and directory structure, file permissions, and access control lists. It also tracks the location of data blocks and which DataNodes they are stored on. When a client requests access to a file, the NameNode returns the location of the blocks that make up the file.

The DataNodes store the actual data blocks and serve them to clients on request. They also communicate with the NameNode to report on the status of the blocks they store and to receive instructions on how to replicate or move blocks as needed.

HDFS Operations

HDFS supports a range of operations for managing files and directories, including:

• Create/Delete directories
• Read/Write files
• Append to files
• Delete files

Files in HDFS are stored in blocks, with a default block size of 128MB. This means that large files are broken up into multiple blocks, each of which can be stored on a different DataNode. The blocks are replicated multiple times across the cluster for fault tolerance and high availability.

HDFS also supports a range of advanced features, including:

• File permissions and access control lists
• Snapshots for backup and recovery
• Federation for scaling the file system across multiple NameNodes
• High availability for the NameNode using backup and standby NameNodes

Conclusion

Hadoop Distributed File System (HDFS) is a distributed file system designed for storing and managing large amounts of data across multiple machines. It is a critical component of the Apache Hadoop software framework and is used in many big data applications.

In this post, we’ve covered the fundamentals of HDFS, including its architecture, features, and operations. We hope this has given you a good understanding of HDFS and its applications in big data processing.

Category: Distributed System