Apache Hadoop Framework

What Is Apache Hadoop?

Apache Hadoop is an open-source framework designed to handle large-scale data processing and storage. It enables the processing of massive amounts of data across clusters of computers using simple programming models. Built to scale from a single server to thousands of machines, Apache Hadoop provides a reliable, distributed computing environment. Its architecture includes the Hadoop Distributed File System (HDFS) for storing data and the MapReduce programming model for processing it. With its ability to manage vast datasets efficiently and its resilience to hardware failures, Apache Hadoop is widely used in data-intensive industries such as finance, healthcare, and e-commerce. This framework has become a cornerstone for big data analytics, allowing organizations to derive valuable insights from their data and drive informed decision-making.

History of Hadoop

Hadoop is an Apache top-level project being built and used by a global community of contributors and users. It is licensed under the Apache License 2.0. Hadoop was created by Doug Cutting and Mike Cafarella in 2005.

It was originally developed to support distribution for the Nutch search engine project. Dough who is now the chief architect of Cloudera, was working on Yahoo at that time and it is popularly known that he named this project after his son’s toy elephant.

It is said that the 2-year-old toddler who had just begun to talk was playing with his favorite toy and like every other kid they would name their favorite toy with names. Cutting’s son named his yellow stuffed elephant ‘Hadoop’. Now that the name has gained so much popularity there are rumors that the dad and son who is now 12 years old share royalties for this popularity.

The Apache Hadoop Framework

Apache Hadoop is a robust and versatile framework designed for processing and storing large datasets across distributed systems. It is composed of several key modules that work together to handle big data efficiently:

1. Hadoop Common

Also known as Hadoop Core, this module contains essential libraries and utilities required by other Hadoop components. It serves as the backbone, providing necessary support to ensure seamless integration and functionality across the Hadoop ecosystem.

2. Hadoop Distributed File System (HDFS)

HDFS is a distributed file system designed to store data across multiple machines, offering high aggregate bandwidth and reliability. It allows Hadoop applications to store vast amounts of data while handling hardware failures gracefully. Data is replicated across different nodes, with a default replication factor of three to ensure fault tolerance and availability.

3. Hadoop MapReduce 

If there is a data processing of large scale then this programming model can be made use of. All these modules in Hadoop are designed with a fundamental assumption. If there a hardware failures (of individual machines or racks of machines) which is treated as a common issue such situations should be automatically handled in software by the framework. Apache Hadoop’s MapReduce and HDFS components are originally derived respectively from Google’s MapReduce and Google File System (GFS) papers.

MapReduce Java code is commonly used and well-known for the end-users. With Hadoop Streaming any programming language can be used to implement the map and reduce the parts of the user’s program. The projects that get exposed to higher levels of user interfaces like Pig Latin and a SQL variant are Apache Pig and Apache Hive. The most written framework in the Java programming language is the Hadoop framework which is combined with some native code in C and command line utilities written as shell scripts.

4. HDFS and MapReduce

HDFS (Hadoop Distributed File System) and MapReduce are core components of Apache Hadoop, inspired by Google’s technologies. They form the foundation of the Hadoop framework, enabling efficient processing and storage of large datasets.

HDFS is a scalable, distributed file system written in Java for the Hadoop framework. It stores large files across multiple machines in a fault-tolerant manner. The architecture consists of a single NameNode and a cluster of DataNodes. Each DataNode stores data blocks and communicates over the network using a protocol specific to HDFS. Data is replicated across different nodes to ensure reliability, with the default replication factor set to three: two copies on the same rack and one on a different rack. While HDFS is not POSIX-compliant, which can limit certain file system operations, it provides increased performance for data throughput and supports non-POSIX operations like appends.

MapReduce is a parallel processing framework that works on the data stored in HDFS. It divides tasks into map and reduce functions, processing data in parallel to improve performance. This model is fault-tolerant, handling hardware failures gracefully and processing large-scale data efficiently.

5. Hadoop YARN (Yet Another Resource Negotiator)

YARN is a resource management platform that handles cluster resources and schedules user applications. It separates the resource management and processing components, enabling a more flexible and scalable architecture compared to its predecessor. YARN supports a variety of processing models beyond MapReduce, allowing for diverse data processing needs.

6. Apache Hadoop NextGen MapReduce (YARN)

MapReduce 2.0 (MRv2) or YARN is obtained after MapReduce has undergone the complete overhaul in Hadoop-0.23. Apache™ Hadoop® YARN is a sub-project of Hadoop at the Apache Software Foundation introduced in Hadoop 2.0 that separates the resource management and processing components. YARN was a boon for the need that enables a broader array of interaction patterns for data stored in HDFS beyond Map Reduce. The lovely interfaced YARN-based architecture of Hadoop 2.0 provides a more general processing platform that is not constrained to Map Reduce.

What YARN Does

YARN amplifies the power of a Hadoop compute cluster in the following ways:

1. Scalability

YARN improves scalability by allowing the Resource Manager to focus solely on scheduling, making it easier to manage and expand data centers and clusters as processing power grows.

2. Affinity with Map Reduce

YARN supports existing MapReduce applications and users, allowing them to run seamlessly on top of YARN without disrupting current processes.

3. Improved Cluster Utilization

YARN optimizes cluster utilization through the Resource Manager, which employs criteria like capacity guarantees, fairness, and SLAs. This ensures that resources are allocated efficiently across applications.

4. Support for Diverse Workloads

YARN extends beyond MapReduce by supporting additional programming models such as graph processing and iterative modeling. These capabilities enable near real-time processing and improve ROI on Hadoop investments.

5. Agility

By making MapReduce a user-land library, YARN allows it to evolve independently from the resource manager layer. This enhances the flexibility and agility of the system.

Functionality of YARN

YARN was developed to split up the two major responsibilities of the Job Tracker/Task Tracker into separate entities:

1. Resource Manager

The Resource Manager is responsible for allocating resources across the cluster and managing resource requests from applications. It acts as the central authority in resource management.

2. Application Master

Each application has its own Application Master, which negotiates resource allocation from the Resource Manager and coordinates with Node Managers to execute and monitor tasks.

3. Node Manager

Node Managers are responsible for managing resources on individual nodes. They launch application containers, monitor their resource usage (CPU, memory, disk, and network), and report this information back to the Resource Manager.

4. Containers

Containers are the units of resource allocation within a Node Manager. Each Application Master requests and manages containers to execute the application’s tasks.

What Is Apache Hadoop Used For? Understanding Its Key Applications

Apache Hadoop is a powerful and versatile open-source framework designed for processing and storing large datasets across distributed systems. It leverages a range of technologies and features that make it ideal for various applications. Here’s a look at some of the primary uses of the Hadoop framework:

1. Big Data Processing

Apache Hadoop is widely used for processing large volumes of data. Its distributed processing capabilities allow it to handle massive datasets efficiently. The Hadoop framework’s ability to process data in parallel across a cluster of computers makes it a go-to solution for big data applications. Whether it’s structured, semi-structured, or unstructured data, Hadoop can manage and analyze it effectively.

2. Data Storage and Management

Hadoop Distributed File System (HDFS), a core component of the Apache Hadoop software, provides scalable and fault-tolerant storage. It enables organizations to store vast amounts of data across multiple machines, ensuring high availability and reliability. This makes HDFS ideal for storing large datasets that traditional storage solutions cannot handle.

3. Data Warehousing

Apache Hadoop’s ecosystem includes tools like Apache Hive and Apache Pig, which facilitate data warehousing. These tools allow users to perform SQL-like queries and data manipulations on Hadoop clusters, making it easier to manage and analyze large datasets. This capability is crucial for businesses that need to derive insights from extensive data.

4. Real-Time Analytics

While Hadoop is traditionally known for batch processing, its ecosystem supports real-time analytics as well. Tools like Apache Flink and Apache Storm integrate with Hadoop to provide real-time data processing and analytics. This is beneficial for applications requiring instant data insights, such as fraud detection and live monitoring.

5. Machine Learning and Data Science

Hadoop frameworks are increasingly being used in machine learning and data science. Tools such as Apache Mahout and Apache Spark provide machine-learning libraries that integrate with Hadoop, enabling data scientists to build and deploy predictive models. This is particularly useful for analyzing large datasets to identify patterns and make data-driven decisions.

6. Log and Event Data Analysis

Apache Hadoop is ideal for analyzing log and event data generated by applications, websites, and other systems. By processing log files in a distributed manner, Hadoop can identify trends, detect anomalies, and generate reports, which are crucial for operational monitoring and troubleshooting.

7. Internet of Things (IoT) Data Processing

With the growth of IoT devices, managing and analyzing the data they generate is becoming increasingly important. Apache Hadoop is well-suited for this task due to its ability to process large volumes of streaming data from IoT devices. Its scalability and distributed architecture make it an effective solution for IoT data management.

8. Business Intelligence

Hadoop frameworks provide a solid foundation for business intelligence (BI) applications. By integrating with BI tools, Hadoop enables organizations to perform complex data analyses and generate insightful reports. This helps businesses to make informed decisions based on comprehensive data analysis.

5 FAQs on Apache Hadoop Framework