What is the concept of HDFS?

Tomcat is a free-of-charge, lightweight, open source web application server developed by Apache Software Foundation, Sun and other companies, and individuals. Gaining support from Sun, Tomcat complies with the latest Servlet and JavaServer Page (JSP) specifications. It features advanced technology, stable performance, and good scalability, and occupies a small number of system resources during running. As a result, Tomcat applies to small- and medium-sized systems and scenarios with a few concurrent users. An HTTP server embedded in Tomcat enables Tomcat to work as a web server. It provides a configuration management tool and supports XML configuration files. Tomcat is different from the Apache web server, which is an HTTP server using C language.

Yarn is the resource management system of Hadoop 2.0. It is a general resource module that manages and schedules resources for applications. Yarn can be used in the MapReduce framework as well as other frameworks such as Tez, Spark, and Storm.

Flume is a distributed, highly reliable, and HA massive log aggregation system. It supports custom data transmitters for data collection. Flume also processes data roughly and writes data to customizable receivers.

Hue provides the GUI for FusionInsight HD applications. Currently, Hue supports the display of the HDFS, MapReduce, and Hive components. You can perform the following operations on the components on the Hue interface: 1. HDFS: Create files or directories, modify file or directory permissions; upload, download, view, and modify files. 2. MapReduce: Check status, start and end time, and run logs for ongoing and completed MapReduce tasks in a Hadoop cluster. 3. Hive: Edit and execute HQL statements; use MetaStore to add, delete, modify, and query databases, tables, and views.

Spark is a memory-based distributed computing framework. In iterative computing scenarios, data is stored in the memory during processing. This provides a computing capability that is 10 to 100 times greater than that provided by MapReduce. Spark can use HDFS as the underlying storage system, enabling users to quickly switch to Spark from MapReduce. In addition, Spark provides one-stop data analysis capabilities, including small-batch stream processing, off-line batch processing, SQL query, and data mining. Users can use all these capabilities seamlessly within an application.