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1. What is HDFS?

The Hadoop Distributed File System (HDFS) is a distributed file system designed to store and manage large volumes of data across multiple machines in a Hadoop cluster. It is a core component of the Apache Hadoop ecosystem and is optimized for high-throughput access to data, making it ideal for big data applications. HDFS is highly scalable, fault-tolerant, and cost-effective, as it can run on commodity hardware.

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2. Key Features

  • Distributed Storage: Data is split into blocks and stored across multiple nodes in a cluster.
  • Fault Tolerance: Replicates data across nodes to ensure availability even if a node fails.
  • Scalability: Can scale horizontally by adding more nodes to the cluster.
  • High Throughput: Optimized for batch processing and large data sets rather than low-latency access.
  • Cost-Effective: Runs on inexpensive commodity hardware.
  • Data Locality: Moves computation closer to the data to minimize network traffic.

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3. Architecture

HDFS follows a master-slave architecture with two main components:

  1. NameNode (Master):
    • Manages the file system namespace (metadata).
    • Tracks the location of data blocks across the cluster.
    • Handles client requests for file operations (e.g., read, write).
  2. DataNode (Slave):
    • Stores the actual data blocks.
    • Performs read/write operations as instructed by the NameNode.
    • Sends periodic heartbeats and block reports to the NameNode.

Additional components:

  • Secondary NameNode: Assists the NameNode by performing periodic checkpoints of the file system metadata.
  • Client: Interacts with the NameNode and DataNodes to access or modify files.

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4. How HDFS Works

  1. File Splitting: Files are divided into fixed-size blocks (default size: 128 MB or 256 MB).
  2. Replication: Each block is replicated across multiple DataNodes (default replication factor: 3).
  3. Storage: Blocks are distributed across the cluster for fault tolerance and load balancing.
  4. Metadata Management: The NameNode maintains metadata about file locations and block mappings.
  5. Data Access: Clients interact with the NameNode to locate data blocks and directly access them from DataNodes.

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5. Key Concepts in HDFS

  • Blocks: The smallest unit of data storage in HDFS (default size: 128 MB or 256 MB).
  • Replication: Copies of data blocks stored on multiple nodes for fault tolerance.
  • Rack Awareness: Ensures data replicas are stored on different racks to prevent data loss during rack failures.
  • Heartbeat: A signal sent by DataNodes to the NameNode to confirm they are operational.
  • Checkpointing: The process of saving the file system metadata to prevent data loss.

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6. Advantages of HDFS

  • Scalability: Can handle petabytes of data by adding more nodes.
  • Fault Tolerance: Data replication ensures high availability even during hardware failures.
  • Cost-Effective: Uses commodity hardware, reducing infrastructure costs.
  • High Throughput: Optimized for large-scale batch processing.
  • Data Locality: Minimizes data movement by processing data where it is stored.

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7. Limitations of HDFS

  • Not Suitable for Small Files: Designed for large files; storing many small files can overwhelm the NameNode.
  • High Latency: Not optimized for real-time or low-latency access.
  • Single Point of Failure: The NameNode is a critical component; its failure can disrupt the entire system (mitigated by HDFS High Availability features).
  • Complexity: Requires expertise to set up, configure, and manage.

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8. Use Cases of HDFS

  • Big Data Processing: Used as the storage layer for Hadoop-based big data applications.
  • Data Warehousing: Stores large volumes of structured and unstructured data for analytics.
  • Log Processing: Collects and processes log data from servers and applications.
  • Machine Learning: Stores training data for machine learning models.
  • Backup and Archiving: Provides a cost-effective solution for storing large backups.

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9. HDFS Commands

HDFS provides a command-line interface (CLI) for file operations. Some common commands include:

  • List Files: hdfs dfs -ls <path>
  • Create Directory: hdfs dfs -mkdir <path>
  • Copy from Local to HDFS: hdfs dfs -put <local_path> <hdfs_path>
  • Copy from HDFS to Local: hdfs dfs -get <hdfs_path> <local_path>
  • Delete File: hdfs dfs -rm <path>
  • View File Content: hdfs dfs -cat <path>

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10. Key Takeaways

  • HDFS: A distributed file system for storing and managing large datasets in a Hadoop cluster.
  • Architecture: Master-slave model with NameNode (metadata) and DataNodes (data storage).
  • Features: Distributed storage, fault tolerance, scalability, high throughput, and cost-effectiveness.
  • How It Works: Files are split into blocks, replicated, and distributed across nodes.
  • Advantages: Scalability, fault tolerance, cost-effectiveness, and high throughput.
  • Limitations: Not suitable for small files, high latency, and single point of failure.
  • Use Cases: Big data processing, data warehousing, log processing, machine learning, and backups.
  • Commands: CLI commands for file operations like listing, copying, and deleting files.