What is the Hadoop Architecture?

Hadoop follows a distributed computing architecture designed to handle and process large-scale data across a cluster of commodity hardware. The core components of Hadoop architecture include: Hadoop Distributed File System (HDFS): HDFS is the primary storage system of Hadoop. It divides large files into smaller blocks (typically 128 MB or 256 MB) and distributes them across multiple nodes in a cluster. HDFS provides fault tolerance by replicating these blocks across nodes. NameNode: The NameNode is a crucial component in the HDFS architecture. It manages metadata about files and directories, including their locations, permissions, and block details. The NameNode does not store the actual data but tracks its location in the cluster. DataNode: DataNodes are responsible for storing and managing the actual data blocks. They store data locally on their respective nodes and communicate with the NameNode to report the status of the stored blocks. DataNodes are distributed across the cluster. Resource Manager: The Resource Manager is part of the YARN (Yet Another Resource Negotiator) framework and manages the allocation of resources in the cluster. It receives resource requests from applications and coordinates with NodeManagers for resource assignment. NodeManager: NodeManagers run on individual nodes in the cluster and are responsible for managing resources (CPU, memory) on that node. They communicate with the Resource Manager to request and release resources and oversee the execution of application tasks. JobTracker (Deprecated): In older versions of Hadoop using the MapReduce 1 (MRv1) architecture, the JobTracker was responsible for coordinating MapReduce jobs. However, this architecture has been largely deprecated in favor of YARN. Newer versions use the ResourceManager for job coordination. TaskTracker (Deprecated): Similar to the JobTracker, the TaskTracker was part of the MapReduce 1 architecture and executed individual tasks of a MapReduce job. This component has been deprecated in YARN-based architectures, where NodeManagers handle task execution. YARN (Yet Another Resource Negotiator): YARN is a resource management layer that separates the resource management and job scheduling functions in Hadoop. It allows multiple applications to share resources on the same cluster. YARN includes ResourceManager and NodeManager components. MapReduce: MapReduce is a programming model and processing engine used for parallel processing of large datasets. It consists of two main phases: Map phase, where data is processed and transformed, and Reduce phase, where results are aggregated. Secondary NameNode: The Secondary NameNode is not a backup or failover NameNode. It periodically merges the edits log with the current snapshot of the file system to prevent the edits log from becoming too large. It assists the NameNode in checkpointing. Hadoop Ecosystem Components: Beyond the core components, the Hadoop ecosystem includes various projects and tools that extend its functionality. Examples include Apache Hive for data warehousing, Apache HBase for NoSQL storage, Apache Spark for in-memory processing, and many others. The architecture described here is based on a simplified, high-level view. Hadoop's architecture can vary based on the distribution (Cloudera, Hortonworks, etc.) and specific configurations within an organization. The shift to YARN has played a significant role in making Hadoop more versatile by allowing various data processing frameworks to run on the same cluster. read less