Apache Hue
2024-01-23 09:54:31

Apache Hue

Introduction

An online tool for exploring and visualizing data in a Hadoop cluster is called Apache Hue. For several Hadoop-related services, including HDFS, YARN, MapReduce, Pig, Hive, Impala, Oozie, and Sqoop, it offers an intuitive graphical user interface (GUI). We will provide a thorough introduction to Apache Hue in this post, covering its features, architecture, and application. By offering a user-friendly interface that does not need users to possess in-depth knowledge of Hadoop and its associated services, Apache Hue is intended to make the usage of Hadoop more straightforward. Users may quickly complete operations including file browsing, job submission, job scheduling, and data querying utilizing Pig, Hive, and Impala by using Apache Hue.

Built on top of the Hadoop environment, Apache Hue is intended to operate with Hadoop services like HDFS, YARN, and MapReduce without any issues. Python, Java, and SQL are just a few of the many programming languages it supports, making it simpler for developers to use Hadoop. To protect data and limit access to authorized users only, Apache Hue additionally offers security capabilities including authentication, authorization, and audit logging.

Overall, Apache Hue is a strong tool that makes it easier to utilize Hadoop by offering a simple user interface for several services linked to Hadoop. For users who wish to analyze and visualize data in a Hadoop cluster, its capabilities, architecture, and use make it an essential tool.

Features of Apache Hue

These are some characteristics that Apache Hue offers:

  • Web-Based Interface: Any web browser may access the web-based interface that Apache Hue offers. Users are no longer needed to install any software on their local PCs thanks to it.
  • Multiple Language Support: Python, Java, and SQL are just a few of the programming languages that Apache Hue supports.
  • Security: Security features offered by Apache Hue include audit logging, authentication, and authorisation.
  • Job Scheduling: Users of Apache Hue may program tasks to run at certain intervals or times.
  • Visualization: Charts, graphs, and tables are just a few of the visualization tools offered by Apache Hue for data analysis.

Architecture of Apache Hue

Three tiers make up the architecture of Apache Hue:

  • Client Tier: The web browser that users use to access Apache Hue is part of the client layer.
  • Server Tier: The Apache Hue server, which functions as part of a Hadoop cluster, is part of the server tier. In addition to communicating with the client tier, it offers the necessary services.
  • Hadoop Cluster: The Hadoop cluster consists of the Hadoop services that Apache Hue communicates with, including HDFS, YARN, MapReduce, and other services.

Usage of Apache Hue

On a Hadoop cluster, Apache Hue may be used to carry out several functions, such as:

  • File Browser: Users may explore files and directories in HDFS using the file browser provided by Apache Hue.
  • Job Submission: Users may submit MapReduce, Pig, and Hive tasks to a Hadoop cluster using Apache Hue.
  • Job Scheduling: Users of Apache Hue may program tasks to run at certain intervals or times.
  • Data Querying: Pig, Hive, and Impala may be used to query data via Apache Hue's web-based interface.
  • Visualization: Charts, graphs, and tables are just a few of the visualisation tools offered by Apache Hue for data analysis.

Example Programs

Here are a few examples of program that Apache Hue can run:

MapReduce: MapReduce jobs may be submitted by users via a graphical interface in Apache Hue. The following MapReduce program illustrates how to count the occurrences of each word in a text file:

// Java program

Import java.io.*;

Import java.util.*;

Class map

{

    public static class WCM extends Mapper<LongWritable, Text, Text, IntW> {

    private final static IntW one = new IntW(1);

    private Text word = new Text();

    public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

        String l = value.toString();

        StringTokenizer tk = new StringTk(l);

        while (tk.hasMoreTokens()) {

            word.set(tk.nextToken());

            context.write(word, one);

        }

    }

}

public static class WCR extends Reducer<Text, IntW, Text, IntW> {

    private IntW res = new IntW();

    public void reduce(Text key, Iterable<IntW> values, Context context) throws IOException, InterruptedException {

        int sum = 0;

        for (IntW val : values) {

            sum += val.get();

        }

        res.set(sum);

        context.write(key, result);

    }

}

public static void main(String[] args) throws Exception {

    Configuration conf = new Configuration();

    Job j = Job.getInstance(conf, "word count");

    j.setJarByClass(WordCount.class);

    j.setMapperClass(WCM)

}