Hadoop is an open-source framework designed for distributed storage and processing of large data sets using a cluster of commodity hardware. It is part of the Apache Software Foundation and is widely used for big data analytics and processing. Here's an overview of how Hadoop works:
1. Hadoop Distributed File System (HDFS):
- HDFS is the storage component of Hadoop. It divides large files into smaller blocks (typically 128 MB or 256 MB) and distributes these blocks across multiple nodes in a Hadoop cluster.
- Each block is replicated to provide fault tolerance. By default, each block is replicated three times, but this can be configured.
2. Hadoop MapReduce:
- MapReduce is the processing component of Hadoop. It is a programming model and processing engine for distributed data processing.
- MapReduce operates in two phases: the Map phase and the Reduce phase.
- Map Phase: Input data is divided into smaller chunks, and a Map function is applied to each chunk to produce intermediate key-value pairs.
- Shuffle and Sort Phase: Intermediate data is shuffled and sorted to group data with the same key together.
- Reduce Phase: The reduced function is applied to the grouped and sorted intermediate data to produce the final output.
3. Hadoop Cluster:
- A Hadoop cluster consists of a master node (NameNode) and multiple worker nodes (DataNodes).
- The NameNode manages the metadata and keeps track of the location of each data block in HDFS.
- DataNodes store the actual data blocks and perform data read and write operations.
4. JobTracker and TaskTrackers:
- In Hadoop 1. x, MapReduce has a centralized JobTracker that manages job scheduling and coordination. However, in Hadoop 2. x and later versions, YARN (Yet Another Resource Negotiator) is introduced to manage resources and job scheduling.
- TaskTrackers (or NodeManagers in YARN) run on each worker node and execute the Map and Reduce tasks assigned by the JobTracker or ResourceManager.
5. Fault Tolerance:
- Hadoop provides fault tolerance by replicating data blocks across multiple nodes. If a node or a data block fails, the system can retrieve the data from its replicated copies.
- The MapReduce framework also monitors the progress of tasks and can restart failed tasks on other nodes.
6. Hadoop Ecosystem:
- Hadoop has a rich ecosystem of tools and frameworks that work together with HDFS and MapReduce. Examples include Apache Hive for SQL-like queries, Apache Pig for high-level scripting, Apache HBase for NoSQL storage, Apache Spark for in-memory processing, and more.
In summary, Hadoop works by distributing large datasets across a cluster of machines, storing data redundantly for fault tolerance, and using the MapReduce programming model to process and analyze the data in parallel. The modular design of Hadoop allows for scalability and flexibility, making it suitable for handling vast amounts of data in a distributed environment.
Dear Doctor
"Hadoop is an open source framework based on Java that manages the storage and processing of large amounts of data for applications. Hadoop uses distributed storage and parallel processing to handle big data and analytics jobs, breaking workloads down into smaller workloads that can be run at the same time."
- Badges
- Science topic
- Similar topics
- Mathematics