Hadoop 1

Hadoop 1, also known as Hadoop version 1 or Hadoop 1.x, represents the initial release of the Apache Hadoop framework. It was the first major version of Hadoop and laid the foundation for the big data ecosystem. Here are the key features and components of Hadoop 1:

1. Hadoop Distributed File System (HDFS):

   • HDFS is a distributed file system designed to store and manage large volumes of data across a cluster of commodity hardware. It provides fault tolerance, scalability, and data redundancy through data replication.

2. MapReduce:

   • Hadoop 1 introduced the MapReduce programming model, which allows developers to process and analyze large datasets in parallel across a distributed cluster. MapReduce divides tasks into two phases: the Map phase for data processing and the Reduce phase for aggregation and summarization.

3. Hadoop Common:

   • Hadoop Common includes libraries, utilities, and APIs shared by all Hadoop components. It provides the core functionality for Hadoop, including distributed computing, configuration management, and cluster coordination.

4. JobTracker and TaskTracker:

   • In Hadoop 1, the JobTracker was responsible for managing and coordinating MapReduce jobs, while TaskTrackers were responsible for executing individual map and reduce tasks on cluster nodes.

5. Single Point of Failure:

   • One of the limitations of Hadoop 1 was its single point of failure, primarily in the JobTracker. If the JobTracker failed, it could lead to job failures and downtime. High Availability (HA) for Hadoop components was not a built-in feature in Hadoop 1.

6. Job Scheduling:

   • Hadoop 1 used the FIFO (First-In-First-Out) scheduler by default, which lacked fine-grained control over job priorities and resources. Later versions of Hadoop introduced improved schedulers like the Fair Scheduler and Capacity Scheduler.

7. ZooKeeper Integration:

   • To address some of the single point of failure issues, Hadoop 1 could be integrated with Apache ZooKeeper for coordination and high availability of certain components.

8. Hive and Pig:

   • Apache Hive and Apache Pig, high-level data processing languages and frameworks, were developed to simplify data processing tasks on Hadoop 1. They provided SQL-like and scripting interfaces, respectively, to work with Hadoop data.

9. Limited Ecosystem:

   • The Hadoop ecosystem was relatively limited in Hadoop 1, with fewer components and tools compared to later versions. Many additional ecosystem projects and tools were developed in subsequent Hadoop releases.

10. Community Growth:

    • During the Hadoop 1 era, the Hadoop community began to grow rapidly, with contributions from various organizations and individuals. This growth laid the foundation for further advancements in the Hadoop ecosystem.