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Hadoop Architecture: 4 Key Components and How to Design Your Hadoop Cluster

Written by Omer Mesika

What Is Hadoop? 

Hadoop, an Apache open-source framework, revolutionized big data processing and analytics when it was introduced. The core idea behind Hadoop is to process massive amounts of data across a network of computers using simple programming models. Hadoop was one of the first systems that could scale up from a single server to thousands of machines, each offering local computation and storage.

To understand Hadoop better, it’s crucial to explore its architecture. Hadoop’s architecture is designed to handle data processing challenges such as system failures, data replication, and large data storage. The architecture is primarily composed of Hadoop Distributed File System (HDFS) and its resource management component, Yet Another Resource Negotiator (YARN), along with MapReduce programming model and Hadoop Common.

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The journey to mastering Hadoop architecture begins with understanding these core components. An in-depth understanding of these components will provide a solid foundation for leveraging Hadoop’s capabilities in data processing, storage, and analysis.

In this article:

Core Components of Hadoop Architecture 

1. Hadoop Distributed File System (HDFS)

One of the most critical components of Hadoop architecture is the Hadoop Distributed File System (HDFS). HDFS is the primary storage system used by Hadoop applications. It’s designed to scale to petabytes of data and runs on commodity hardware. What sets HDFS apart is its ability to maintain large data sets across multiple nodes in a distributed computing environment.

HDFS operates on the basic principle of storing large files across multiple machines. It achieves high throughput by dividing large data into smaller blocks, which are managed by different nodes in the network. This nature of HDFS makes it an ideal choice for applications with large data sets.

2. Yet Another Resource Negotiator (YARN)

Yet Another Resource Negotiator (YARN) is responsible for managing resources in the cluster and scheduling tasks for users. It is a key element in Hadoop architecture as it allows multiple data processing engines such as interactive processing, graph processing, and batch processing to handle data stored in HDFS.

YARN separates the functionalities of resource management and job scheduling into separate daemons. This design ensures a more scalable and flexible Hadoop architecture, accommodating a broader array of processing approaches and a wider array of applications.

3. MapReduce Programming Model

MapReduce is a programming model integral to Hadoop architecture. It is designed to process large volumes of data in parallel by dividing the work into a set of independent tasks. The MapReduce model simplifies the processing of vast data sets, making it an indispensable part of Hadoop.

MapReduce is characterized by two primary tasks, Map and Reduce. The Map task takes a set of data and converts it into another set of data, where individual elements are broken down into tuples. On the other hand, the Reduce task takes the output from the Map as input and combines those tuples into a smaller set of tuples.

4. Hadoop Common

Hadoop Common, often referred to as the ‘glue’ that holds Hadoop architecture together, contains libraries and utilities needed by other Hadoop modules. It provides the necessary Java files and scripts required to start Hadoop. This component plays a crucial role in ensuring that the hardware failures are managed by the Hadoop framework itself, offering a high degree of resilience and reliability.

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Design Principles of the Hadoop Distributed File System (HDFS)

HDFS can be considered the ‘secret sauce’ behind the flexibility and scalability of Hadoop. Let’s review the key principles underlying this innovative system.

Data Replication and Fault Tolerance

HDFS is designed with data replication in mind, to offer fault tolerance and high availability. Data is divided into blocks, and each block is replicated across multiple nodes in the cluster. This strategy ensures that even if a node fails, the data is not lost, as it can be accessed from the other nodes where the blocks are replicated.

Data Locality

Hadoop architecture is designed considering data locality to improve the efficiency of data processing. Data locality refers to the ability to move the computation close to where the data resides in the network, rather than moving large amounts of data to where the application is running. This approach minimizes network congestion and increases the overall throughput of the system.

Storage Formats

HDFS provides a choice of storage formats. These formats can significantly impact the system’s performance in terms of processing speed and storage space requirements. Hadoop supports file formats like Text files, Sequence Files, Avro files, and Parquet files. The best file format for your specific use case will depend on the data characteristics and the specific requirements of the application.

Building Your Hadoop Cluster Architecture 

The design of a Hadoop cluster is the foundation on which the entire Hadoop ecosystem is built. Here are the basic steps to creating an effective cluster architecture.

1. Define Cluster Topology

Cluster topology refers to the arrangement and interconnection of nodes within the cluster. A well-defined topology can greatly enhance the performance and efficiency of your Hadoop cluster.

The key decision you need to make when defining your cluster topology is the number of nodes. This number should be based on the volume of data you anticipate processing. Larger datasets require larger clusters to ensure efficient processing. Another consideration is the geographical distribution of your nodes. Depending on your data processing needs, you may choose to distribute your nodes across multiple locations, or concentrate them in a single data center.

2. Selecting Node Types and Sizes

After defining your cluster topology, the next step is to select node types and sizes. There are three types of nodes in a Hadoop cluster: Master nodes, Data nodes, and Client nodes.

Master nodes are responsible for managing the cluster and distributing data to the data nodes. Data nodes store and process data, while client nodes are used by end-users to interact with the cluster. The size of each node should be chosen based on the amount of data you plan to process. Larger nodes can handle larger volumes of data, but they are also more expensive.

3. Network and Bandwidth Considerations

The network plays a significant role in data transfer between nodes in the cluster. As such, the choice of network technology can greatly impact the performance of your Hadoop cluster.

The bandwidth, or the maximum data transfer rate of your network, should be high enough to support the volume of data you plan to process. Additionally, you should ensure that your network has sufficient redundancy to prevent data loss in case of network failure.

4. Set Up High Availability

Hadoop 2.x and later provides a feature called High Availability Cluster. The HDFS master node can become a bottleneck and single point of failure in a Hadoop cluster. By configuring a High Availability Cluster, you can have two HDFS master nodes in an active/passive configuration. This means that if one master node goes offline, the other can immediately take over. Learn more in the official documentation.

Hadoop Resource Management with YARN 

Once you’ve designed your Hadoop Architecture, the next step is to manage your resources efficiently. This is where Yet Another Resource Negotiator (YARN), Hadoop’s resource management framework, comes into play.

Efficient Resource Allocation

Resource allocation in YARN is dynamic and based on the demand of applications. YARN’s Resource Manager is responsible for allocating resources to various applications running in the cluster. It does this based on the application’s resource requirements and the availability of resources in the cluster.

To ensure efficient resource allocation, you should regularly monitor the resource usage of your applications and adjust their resource requirements accordingly. You should also configure YARN’s scheduler to prioritize critical applications and balance resource usage across the cluster.

Containerization Strategy

Containerization is a key strategy in YARN for isolating application execution and managing resources. In YARN, a container represents a collection of physical resources such as RAM, CPU cores, and disk space. When an application is submitted to YARN, it is divided into a set of tasks, each of which is executed in its own container.

The containerization strategy in YARN allows for fine-grained resource management and isolation of application execution. This ensures that resources are efficiently utilized and that one application does not interfere with another.

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Job Scheduling Policies

YARN’s scheduler is responsible for assigning resources to applications and managing their execution. The scheduler uses policies to determine how resources are allocated and how tasks are scheduled.

There are various scheduling policies available in YARN, including the Fair Scheduler and the Capacity Scheduler. The Fair Scheduler ensures that all applications get, on average, an equal share of resources. The Capacity Scheduler, on the other hand, allows for multiple queues of applications, with each queue having a certain capacity of the cluster’s resources.

Monitoring and Managing Cluster Resources

YARN provides various tools for monitoring the status and performance of the cluster, including the ResourceManager UI and the ApplicationMaster UI.

These tools provide detailed information about the cluster, including the status of nodes, the utilization of resources, and the progress of applications. By regularly monitoring your cluster, you can identify and address any issues before they impact the performance of your cluster.

Learn more in our detailed guide to Hadoop monitoring (coming soon)

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