Hadoop 1 vs Hadoop 2

Introduction

Hadoop has grown through time, and two major versions, Hadoop 1 and Hadoop 2, have arisen, each with its own set of features and improvements.

The introduction of YARN (Yet Another Resource Negotiator) in Hadoop 2 allowed for better resource management and improved scalability. With YARN, multiple processing engines could run simultaneously on the same cluster, enabling better resource utilization. Hadoop 2 also supported alternative processing engines like Spark and Tez, which offered faster and more expressive data processing capabilities than MapReduce. High availability was addressed with failover mechanisms for the ResourceManager and NameNode components. Additionally, Hadoop 2 introduced federation and heterogeneous storage support, allowing for better cluster utilization and more flexible data storage options. Overall, Hadoop 2 provided superior performance, scalability, fault tolerance, and flexibility compared to Hadoop 1.

What is Hadoop?

Hadoop is a distributed computing platform that uses a cluster of computers to store, process, and analyze enormous amounts of data. It is based on the MapReduce programming architecture and was created by the Apache Software Foundation. Hadoop enables organizations to harness the power of parallel computing, allowing them to handle large datasets that would have been prohibitive or impossible to process using traditional approaches. Check out the Architecture of Hadoop.

Hadoop 1

Hadoop 1 is a robust and widely used framework for processing and storing massive datasets in a distributed computing environment. This section will look at its many components, daemons, working principles, restrictions, ecosystem, and support for Windows operating systems.

Components

Hadoop is made up of several critical components that work together to provide outstanding performance:

• Hadoop Distributed File System (HDFS): HDFS is a distributed file system allowing reliable and scalable data storage across numerous machines. Large datasets are divided into smaller blocks and distributed across the cluster for effective storage and retrieval.
• MapReduce: A programming approach that allows for the distributed processing of huge datasets. It breaks the data into smaller chunks, processes them in parallel across the cluster, and then aggregates the results to produce the final output.
• YARN (Yet Another Resource Negotiator): YARN is Hadoop's resource management layer. It is in charge of controlling and allocating resources to the many Hadoop cluster applications. YARN guarantees that resources are used efficiently and enables concurrent processing of multiple workloads.

Daemons

Hadoop 1 is made up of multiple daemons that work together to ensure the framework's smooth operation:

• NameNode: The NameNode is the primary node in HDFS which manages the file system namespace and metadata, along with tracking the data block positions and data access organization actions.
• DataNode: DataNodes are HDFS worker nodes in charge of storing and retrieving data blocks. They communicate with the NameNode to perform data read and write operations.
• ResourceManager: The ResourceManager is the primary authority in YARN for resource allocation and scheduling. It controls and assigns available cluster resources to applications.
• NodeManager: NodeManagers are responsible for managing resources and carrying out tasks assigned by the ResourceManager on each worker node in the cluster.

Working

When a job is submitted to Hadoop 1, it goes through the following stages:

• Data Input: The input data is broken into chunks and disseminated across the Hadoop cluster.
• Map Phase*: In this phase, the MapReduce tasks process the input data in parallel throughout the cluster. The map tasks convert the supplied data into key-value pairs.
• Shuffle and Sort: The output of the map jobs are sorted and grouped depending on keys to prepare it for the reduction phase.
• Reduce Phase: The reduced tasks process the shuffled and sorted data to produce the final result.

Limitations

Certain shortcomings of Hadoop 1 have been addressed in recent versions:

• Scalability: Hadoop 1 has a limit on the number of nodes it can efficiently handle. The centralized nature of the cluster's components becomes a bottleneck as the cluster grows.
• Single Point of Failure: The NameNode in HDFS is a single point of failure, putting the complete system's reliability at risk.
• Inefficient Resource Utilization: Hadoop 1 employs a static resource allocation paradigm, which may result in inefficient resource utilization.

Ecosystem

Hadoop 1 is surrounded by a thriving ecosystem of supporting tools and technologies that help to expand its capabilities. Apache Hive, Apache Pig, Apache HBase, Apache Sqoop, Apache Flume, and Apache Oozie are popular Hadoop ecosystem components.

Windows Support

Hadoop 1 now supports Windows operating systems, making it more accessible to various users. Users can use Windows-compatible distributions and installation procedures to set up Hadoop clusters on Windows machines.

Hadoop 2

Hadoop has developed as a solid and scalable platform in the vast area of large data processing. The capabilities of this open-source program have been greatly expanded with the release of Hadoop 2. We will go into the world of Hadoop 2 in this section, discussing its components, daemons, working principles, restrictions, ecosystem, and even compatibility with Windows.

Components

Hadoop 2 is based on a distributed file system called Hadoop Distributed File System (HDFS) and the MapReduce computational paradigm. Furthermore, Hadoop 2 has YARN (Yet Another Resource Negotiator) as a resource management layer. With YARN, Hadoop 2 is no longer limited to MapReduce jobs and can now execute various data processing frameworks, allowing for greater ecosystem flexibility.

Daemons

To ensure smooth operation, Hadoop 2 employs several daemons. NameNode, DataNode, ResourceManager, and NodeManager are the most important daemons.

• The NameNode manages the HDFS namespace and maintains metadata, whereas the DataNode is in charge of storing and retrieving data blocks.
• The ResourceManager oversees resource allocation and scheduling, whereas the NodeManager oversees individual compute nodes.
• These daemons collaborate to achieve fault tolerance, data reliability, and resource management efficiency.

Working

The operating process in Hadoop 2 is to divide the data into smaller pieces and distribute them throughout the cluster's nodes. The MapReduce approach allows for parallel processing by dividing data into maps and reducing tasks.

• The map tasks process the input data and generate intermediate key-value pairs.
• These pairs are then shuffled, sorted, and handed to the reduction tasks to be processed further.

The YARN architecture of Hadoop 2 ensures that resources are dynamically allocated, allowing several frameworks to run concurrently and improving cluster utilization.

Limitations

• One of the obstacles is its complexity, which necessitates using expert administrators and developers to set up and run the cluster efficiently.
• Hadoop 2's dependency on disk-based storage can result in slower processing performance for some workloads.
• HDFS's high replication factor adds storage expense.

Ecosystem

Hadoop 2 is supported by a robust ecosystem of supplementary technologies and frameworks that extend its functionality. On top of Hadoop, Apache Hive, Apache Pig, and Apache Spark are frequently used for data processing and analytics. HBase enables real-time read/write access to massive databases, whereas Apache Kafka delivers high-throughput, fault-tolerant messaging. Apache ZooKeeper also enables distributed coordination, and Apache Mahout provides scalable machine-learning techniques.

Windows Support

In response to increased demand, Hadoop 2 now has enhanced support for Windows operating systems. Native Windows binaries were introduced with Hadoop 2.2, making setting up and running Hadoop clusters on Windows workstations easier. This improvement has enabled Windows-based organizations to use Hadoop's capabilities for large data processing, boosting cross-platform compatibility and expanding acceptance.

Hadoop 1 vs Hadoop 2