Hadoop 2.x vs Hadoop 3.x
Overview
Hadoop 2.x and Hadoop 3.x are the two major editions of Hadoop, the well-known open-source platform for distributed processing. While both versions share the essential ideas of scalability and fault tolerance, Hadoop 3.x adds some substantial improvements. The introduction of Erasure Coding, which optimizes storage capacity utilization, is one major advancement. Hadoop 3.x integrates containerization, facilitates seamless connectivity with container management solutions, incorporates the YARN Timeline Service for enhanced data management and resource utilization, and offers a superior, efficient solution for big data challenges.
Introduction
Hadoop 3.x has substantially improved its scalability, allowing organizations to handle large data volumes with ease and efficiency. It enables containerization using YARN, allowing varied workloads to coexist and improving resource management, improving total cluster utilization. Furthermore, Hadoop 3.x provides improved data storage capabilities with erasure coding, enabling efficient data replication and lowering storage costs. It allows for quicker data recovery if a failure, assuring business continuity.
Hadoop 2.x
Hadoop 2.x is one of the popular large data processing platform, and includes several notable enhancements. Let's look at its main components, operating system, prominent features, ecosystem, Windows support, and restrictions.
Components
Hadoop 2.x comprises of two primary components are the Hadoop Distributed File System (HDFS) and YARN (Yet Another Resource Negotiator). HDFS oversees data storage and retrieval, while YARN oversees resource allocation and job scheduling. This modular architecture improves scalability and flexibility.
Working
Hadoop 2.x employs a master-slave design. The NameNode, acts as the master or controller node, manages file system information and coordinates data storage across several DataNodes. In contrast, DataNodes acts as slave nodes that store and analyze data in parallel, offering immense processing capability for managing large datasets.
Key Features
Hadoop 2. x provides several significant features, making it a top choice for massive data processing. Its ability to efficiently process and analyze large volumes of structured and unstructured data is unrivaled. Hadoop 2. x offers parallel processing, allowing for faster data processing, and fault tolerance, allowing for continuous operation even in the event of hardware or software faults.
Ecosystem
The Hadoop ecosystem includes diverse tools and frameworks such as Apache Hive, Apache Pig, and Apache Spark provide higher-level data processing functionality. Solutions such as Apache HBase and Apache ZooKeeper provide distributed data storage and collaboration making it a wide ecosystem with a versatile platform.
Windows Support
Regarding Windows support, Hadoop 2.x is more compatible with the operating system.
Limitations
One noteworthy restriction is its complexity, which necessitates a certain amount of skill to set up and run the Hadoop cluster efficiently. Also, Hadoop's batch-processing nature may not be suited for real-time data processing scenarios where low latency is critical.
Hadoop 3.x
Hadoop 3.x, the most recent version of the renowned big data processing platform where its components operate in tandem to efficiently handle and analyze massive amounts of data.
Components
The Hadoop Distributed File System (HDFS), YARN (Yet Another Resource Negotiator), and MapReduce are major components of Hadoop 3.x. HDFS enables distributed data storage and retrieval over a cluster of computers, whereas YARN controls resources and tasks. MapReduce enables parallel data processing and analysis across numerous nodes.
Working
Hadoop 3.x operates in a step-by-step fashion. The data is first partitioned into blocks and distributed throughout the cluster using HDFS. YARN then assigns resources to diverse applications, ensuring that resources are used efficiently. The data is subsequently processed in parallel across the nodes by MapReduce, with each node performing computations on its allotted data blocks.
Key Features
Hadoop 3.x enables containerization, allowing applications to operate in Docker containers. The facilitation of deployment, application isolation, and improved storage efficiency through erasure coding lowers storage costs. Another major addition is GPU capability, which enables quicker processing for machine learning and data-intensive workloads.
Ecosystem
The Hadoop ecosystem adds many tools and frameworks to the essential components such as Hive and Pig. Apache Hive and Apache Pig provide SQL-like querying and data processing capabilities. Apache Spark delivers in-memory processing and real-time analytics, whereas Apache HBase is a NoSQL database. Other tools include Apache Kafka, Apache Storm, and Apache Flume facilitate data ingestion and streaming.
Windows Support
Hadoop 3.x strongly emphasizes Windows support, allowing users to run Hadoop clusters on Windows workstations.
Limitations
One constraint with Hadoop 3.x is knowledge of distributed computing and programming languages such as Java, which can be difficult for some users. Administering and maintaining large-scale Hadoop clusters necessitates substantial hardware resources and operational knowledge. Hadoop's speed may suffer when dealing with small data sets.
Hadoop 2.x vs Hadoop 3.x: Comparison Table
| Feature | Hadoop 2.x | Hadoop 3.x |
|---|---|---|
| Introduction | Second major release, designed to process and store big datasets across distributed clusters. | Third major release with significant upgrades and new features to increase the platform's performance, scalability, and adaptability. |
| YARN | YARN was introduced as a resource management layer. Separated the processing engine (MapReduce) from resource management. | Extends YARN to support many processing engines such as MapReduce, Spark, and Tez, making it more adaptable and capable of handling a variety of workloads quickly. |
| NameNode High Availability (HA) | Supports NameNode HA through a backup NameNode, but there is a single point of failure. | Features QuorumJournalManager, a more robust NameNode HA design that eliminates the single point of failure and improves fault tolerance for the NameNode. |
| Erasure Coding | It employs replication for data fault tolerance, resulting in significant storage costs. | Erasure coding enhances data durability and reduces storage overhead in Hadoop 3.x. |
| Containerization | It relies on traditional JVM-based isolation for running tasks, limiting scalability and resource utilization. | Hadoop 3.x uses Docker and Kubernetes for task execution, improving resource management and scalability. |
Conclusion
- In large data processing, Hadoop 3.x has outperformed its predecessors with improved its scalability, resource management, efficient data storage, faster data processing, and support for newer technologies.
- It provides containerization via YARN, allowing varied workloads to coexist and providing improved resource management, consequently improving overall cluster utilization.
- With erasure coding, Hadoop 3.x has increased data storage capabilities, allowing for efficient data replication and lowering storage costs.
- Hadoop 2.x is a distributed processing framework for big data analytics. It is designed for handling and analyzing large datasets across clusters of computers using a scalable and fault-tolerant approach.