Apache Hadoop is built around a robust architecture designed to handle Big Data efficiently and reliably. This chapter delves into its core components—HDFS, MapReduce, and YARN—and introduces the broader ecosystem of tools that enhance Hadoop's functionality.
Hadoop Architecture
Hadoop's architecture is designed as a distributed system that ensures scalability, reliability, and high performance. The three foundational components of Hadoop are:
HDFS (Hadoop Distributed File System): A storage system for distributed data.
MapReduce: A processing framework for distributed data computation.
YARN (Yet Another Resource Negotiator): A resource management layer.
These components work together to process massive datasets across clusters of machines.
HDFS (Hadoop Distributed File System)
HDFS is the primary storage system in Hadoop, built to store and manage large files across multiple machines.
How HDFS Stores Data in Blocks
HDFS divides large files into fixed-size blocks, typically 128 MB or 256 MB, and distributes these blocks across the nodes in the cluster.
This chunking approach enables efficient storage and parallel processing.
For example, a 1 GB file is divided into eight 128 MB blocks stored on different nodes.
Components of HDFS
NameNode
Acts as the master node and manages the file system's metadata (file names, block locations, permissions, etc.).
It does not store the actual data but knows where the data blocks are located.
DataNodes
Serve as worker nodes and are responsible for storing the actual data blocks.
Each DataNode periodically reports its status and the blocks it holds to the NameNode.
Fault Tolerance and Replication Mechanism
HDFS ensures fault tolerance through data replication.
Each data block is replicated across multiple DataNodes (default replication factor: 3).
If a DataNode fails, the system retrieves the data from another replica and creates new replicas to maintain redundancy.
For instance, if a block is stored on nodes A, B, and C, and node A fails, the block can still be accessed from nodes B and C.
MapReduce
MapReduce is the distributed processing engine of Hadoop. It is based on the divide-and-conquer paradigm and is responsible for processing large-scale data efficiently.
The Programming Model: Mapper and Reducer
Mapper
Processes input data and transforms it into key-value pairs.
For example, in a word count program, the input “Hello world” is mapped to ("Hello", 1) and ("world", 1).
Reducer
Aggregates the output of the mappers to produce the final result.
In the word count example, the reducer consolidates all key-value pairs with the same key: ("Hello", 1) and ("world", 1).
How MapReduce Processes Data in Parallel
Input Splitting
The input data is split into chunks (input splits) corresponding to the HDFS blocks. Each split is processed independently by a mapper.
Mapping Phase
Mappers process the input splits and produce intermediate key-value pairs.
Shuffling and Sorting
The intermediate data is grouped by key and sorted for the reducer.
Reducing Phase
Reducers aggregate the sorted data and generate the final output.
By distributing tasks across multiple machines, MapReduce achieves high performance and scalability.
YARN (Yet Another Resource Negotiator)
YARN is the resource management layer of Hadoop, enabling multiple applications to run concurrently on the same cluster.
Role of YARN in Resource Management
YARN decouples resource management from data processing.
It dynamically allocates resources (CPU and memory) to applications based on their requirements.
This flexibility allows Hadoop to support a variety of workloads beyond MapReduce.
Components of YARN
ResourceManager
Central authority responsible for allocating resources across the cluster.
Tracks resource usage and schedules jobs.
NodeManager
Runs on each worker node and monitors the resource usage (CPU, memory, disk) on that node.
Reports the node's status to the ResourceManager.
ApplicationMaster
Manages the lifecycle of a single application (e.g., a MapReduce job).
Requests resources from the ResourceManager and coordinates task execution on NodeManagers.
Hadoop Ecosystem
Beyond its core components, Hadoop includes a rich ecosystem of tools designed for specific use cases in Big Data analytics.
Overview of Additional Tools
Hive
A data warehousing tool that enables SQL-like querying on Hadoop datasets.
It translates SQL queries into MapReduce jobs, making Hadoop accessible to non-programmers.
Pig
A high-level scripting language for data transformation.
Simplifies complex data processing tasks by abstracting the underlying MapReduce jobs.
Sqoop
A tool for transferring data between Hadoop and relational databases (e.g., MySQL, Oracle).
Useful for data import/export workflows.
Flume
Designed for ingesting streaming data into Hadoop.
Often used to collect log data from web servers and send it to HDFS.
HBase
A NoSQL database built on top of HDFS.
Provides real-time read/write access to large datasets.
Commonly used for applications requiring low-latency access.
Oozie
A workflow scheduler for managing Hadoop jobs.
Automates job execution and dependencies, allowing for complex workflows.
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