Hadoop System Structure
The Hadoop ecosystem, an open-source framework for processing large datasets, is built upon four fundamental components: Hadoop Common, HDFS (Hadoop Distributed File System), MapReduce, and YARN (Yet Another Resource Negotiator). Each component plays a crucial role in the Hadoop architecture, providing essential functionalities for distributed storage, processing, and resource management.
Hadoop Common
Serving as the foundation for other Hadoop modules, Hadoop Common offers a suite of libraries, utilities, and tools that support the entire ecosystem. Key functionalities include file system and operating system abstractions, Java RPC (Remote Procedure Call) facilities, serialization frameworks, configuration management, and tools for managing Hadoop installations.
HDFS (Hadoop Distributed File System)
HDFS is a distributed, scalable, and fault-tolerant file system designed to run on commodity hardware. It acts as the primary storage layer in Hadoop, storing large datasets in a reliable, redundant fashion. The system is built around the concept of blocks, with each file split into fixed-size blocks and distributed across DataNodes for storage.
MapReduce
The original processing framework for large-scale data processing on Hadoop, MapReduce executes distributed data processing in parallel by dividing tasks into Map and Reduce phases. It was the primary processing engine in Hadoop until the introduction of YARN, handling job scheduling, resource allocation, and task tracking in traditional Hadoop versions.
YARN (Yet Another Resource Negotiator)
YARN, introduced from Hadoop 2.x, decouples resource management and job scheduling/monitoring from the MapReduce programming model. As Hadoop's cluster resource management and job scheduling framework, YARN enables better cluster utilization, scalability, and multi-tenancy by supporting multiple processing models beyond MapReduce.
Summary Table
| Component | Role | Key Functions | |-----------------|-------------------------------------------|-----------------------------------------------------| | Hadoop Common | Core libraries and utilities | File/OS abstractions, Java RPC, serialization, tools| | HDFS | Distributed storage system | Block storage & replication, metadata management, fault tolerance | | MapReduce | Distributed data processing framework | Map and Reduce phases, job scheduling (old paradigm)| | YARN | Resource management and job scheduling platform | Cluster resource management, container orchestration, multi-framework support |
These components interact seamlessly during a typical Hadoop job execution, with HDFS managing data storage and Hadoop Common providing shared libraries and utilities for all components. YARN manages cluster resources and job scheduling, while MapReduce executes the distributed data processing tasks.
With this foundational understanding of the Hadoop ecosystem, you're now better equipped to tackle large-scale data processing challenges and harness the power of distributed computing.
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