BigDataAnalytics

1.Downloading and installing Hadoop framework and implement Start-up scripts and Configuration files.

Sure, I can provide you with a basic overview of downloading, installing, and configuring Hadoop. Keep in mind that the steps might vary slightly based on the version of Hadoop you are using.

Prerequisites:

- Java Development Kit (JDK): Hadoop is a Java-based framework, so you need to have Java installed on your machine.

- SSH: Hadoop requires SSH to manage its nodes.

Step 1: Download Hadoop:

1. Visit the Apache Hadoop website: https://hadoop.apache.org/releases.html

2. Choose the version you want to download. For example, you might choose the latest stable release.

3. Download the binary distribution (not the source).

Step 2: Install Hadoop:

1. Extract the downloaded tarball to a directory of your choice:

Bash:

tar -xzvf hadoop-<version>.tar.gz

2. Move the extracted folder to the desired location:

Bash:

mv hadoop-<version> /path/to/hadoop

Step 3: Configure Environment Variables:

Add the following lines to your shell profile file (e.g., `.bashrc` or `.bash_profile`):

Bash:

export HADOOP_HOME=/path/to/hadoop

export PATH=$PATH:$HADOOP_HOME/bin

export PATH=$PATH:$HADOOP_HOME/sbin

Then, source the profile or restart your terminal:

Bash:

source ~/.bashrc

Step 4: Configure Hadoop:

1. Edit `hadoop-env.sh` to set the Java home:

Bash:

nano $HADOOP_HOME/etc/hadoop/hadoop-env.sh

Set `export JAVA_HOME=/path/to/java`.

2. Configure `core-site.xml`:

Bash:

nano $HADOOP_HOME/etc/hadoop/core-site.xml

Add the following:

xml

<configuration>

<property>

<name>fs.defaultFS</name>

<value>hdfs://localhost:9000</value>

</property>

</configuration>

3. Configure `hdfs-site.xml`:

Bash:

nano $HADOOP_HOME/etc/hadoop/hdfs-site.xml

Add the following:

xml

<configuration>

<property>

<name>dfs.replication</name>

<value>1</value>

</property>

</configuration>

Step 5: Format HDFS:

Run the following command to initialize the HDFS filesystem:

Bash:

hdfs namenode -format

Step 6: Start Hadoop:

Bash:

$HADOOP_HOME/sbin/start-all.sh

This will start the Hadoop daemons.

Step 7: Access Hadoop Web Interface:

You can access the Hadoop web interface at `http://localhost:50070` for the NameNode and `http://localhost:8088` for the ResourceManager.

That's a basic setup. Depending on your use case and configuration, you might need to adjust other configuration files. Additionally, in a production environment, you'd likely need a more robust configuration and possibly a multi-node setup.

2.Write a JAVA program for Matrix Multiplication with Hadoop Map Reduce

java:

import java.io.IOException;

import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.Mapper;

import org.apache.hadoop.mapreduce.Reducer;

public class MatrixMultiplication {

public static class TokenizerMapper extends Mapper<Object, Text, Text, Text> {

private Text outputKey = new Text();

private Text outputValue = new Text();

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

// Assuming input format: A|i|j|Aij

// OR B|i|j|Bij

String[] tokens = value.toString().split("\\|");

String matrixName = tokens[0];

int i = Integer.parseInt(tokens[1]);

int j = Integer.parseInt(tokens[2]);

int elementValue = Integer.parseInt(tokens[3]);

if (matrixName.equals("A")) {

for (int k = 0; k < MatrixDimension; k++) {

outputKey.set(i + "|" + k);

outputValue.set("A|" + j + "|" + elementValue);

context.write(outputKey, outputValue);

}

} else if (matrixName.equals("B")) {

for (int k = 0; k < MatrixDimension; k++) {

outputKey.set(k + "|" + j);

outputValue.set("B|" + i + "|" + elementValue);

context.write(outputKey, outputValue);

}

}

}

}

public static class MultiplicationReducer extends Reducer<Text, Text, Text, IntWritable> {

private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable<Text> values, Context context)

throws IOException, InterruptedException {

int[] a = new int[MatrixDimension];

int[] b = new int[MatrixDimension];

for (Text val : values) {

String[] tokens = val.toString().split("\\|");

String matrixName = tokens[0];

int index = Integer.parseInt(tokens[1]);

int value = Integer.parseInt(tokens[2]);

if (matrixName.equals("A")) {

a[index] = value;

} else if (matrixName.equals("B")) {

b[index] = value;

}

}

int sum = 0;

for (int i = 0; i < MatrixDimension; i++) {

sum += a[i] * b[i];

}

result.set(sum);

context.write(key, result);

}

}

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

Configuration conf = new Configuration();

Job job = Job.getInstance(conf, "MatrixMultiplication");

job.setJarByClass(MatrixMultiplication.class);

job.setMapperClass(TokenizerMapper.class);

job.setReducerClass(MultiplicationReducer.class);

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

// Set input and output paths

Path inputPath = new Path(args[0]);

Path outputPath = new Path(args[1]);

MatrixDimension = Integer.parseInt(args[2]);

org.apache.hadoop.mapreduce.lib.input.FileInputFormat.addInputPath(job, inputPath);

org.apache.hadoop.mapreduce.lib.output.FileOutputFormat.setOutputPath(job, outputPath);

System.exit(job.waitForCompletion(true) ? 0 : 1);

}

}

run of jar file:

$ javac -classpath $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-client-core-<version>.jar -d . MatrixMultiplication.java

$ jar cf MatrixMultiplication.jar MatrixMultiplication*.class

$ hadoop jar MatrixMultiplication.jar MatrixMultiplication input_path output_path matrix_dimension

3.Write a command to implement file management tasks, such as Adding files and directories to HDFS.

Certainly! To manage files and directories in Hadoop Distributed File System (HDFS), you can use the `hadoop fs` command. Here are some basic commands to perform file management tasks in HDFS:

1. Copy files/directories from the local file system to HDFS:

Bash:

hadoop fs -copyFromLocal /path/to/local/file hdfs://localhost:9000/path/in/hdfs

This command copies a file or directory from the local file system to the specified path in HDFS.

2. Copy files/directories from HDFS to the local file system:

Bash:

hadoop fs -copyToLocal hdfs://localhost:9000/path/in/hdfs /path/to/local/destination

This command copies a file or directory from HDFS to the specified path in the local file system.

3. Create a directory in HDFS:

Bash:

hadoop fs -mkdir hdfs://localhost:9000/path/in/hdfs

This command creates a new directory in HDFS at the specified path.

4. List files/directories in a HDFS directory:

Bash:

hadoop fs -ls hdfs://localhost:9000/path/in/hdfs

This command lists the contents of the specified directory in HDFS.

5. Move files/directories within HDFS:

Bash:

hadoop fs -mv hdfs://localhost:9000/source/path hdfs://localhost:9000/destination/path

This command moves a file or directory from the source path to the destination path within HDFS.

6. Remove files/directories from HDFS:

Bash:

hadoop fs -rm hdfs://localhost:9000/path/in/hdfs/file_or_directory

This command removes the specified file or directory from HDFS.

7. Remove files/directories recursively from HDFS:

Bash:

hadoop fs -rm -r hdfs://localhost:9000/path/in/hdfs/directory

This command removes the specified directory and its contents recursively from HDFS.

Remember to replace `hdfs://localhost:9000` with the appropriate HDFS URI for your Hadoop cluster. Additionally, adjust the paths and filenames as needed for your specific use case.

4.Write a JAVA program for count the number of character in a word with Hadoop Map Reduce

Java:

import java.io.IOException;

import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.Mapper;

import org.apache.hadoop.mapreduce.Reducer;

public class WordCharacterCount {

public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {

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

private Text word = new Text();

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

StringTokenizer itr = new StringTokenizer(value.toString());

while (itr.hasMoreTokens()) {

word.set(itr.nextToken());

context.write(word, new IntWritable(word.getLength()));

}

}

}

public static class CharacterCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable<IntWritable> values, Context context)

throws IOException, InterruptedException {

int sum = 0;

for (IntWritable val : values) {

sum += val.get();

}

result.set(sum);

context.write(key, result);

}

}

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

Configuration conf = new Configuration();

Job job = Job.getInstance(conf, "WordCharacterCount");

job.setJarByClass(WordCharacterCount.class);

job.setMapperClass(TokenizerMapper.class);

job.setCombinerClass(CharacterCountReducer.class);

job.setReducerClass(CharacterCountReducer.class);

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

// Set input and output paths

Path inputPath = new Path(args[0]);

Path outputPath = new Path(args[1]);

org.apache.hadoop.mapreduce.lib.input.FileInputFormat.addInputPath(job, inputPath);

org.apache.hadoop.mapreduce.lib.output.FileOutputFormat.setOutputPath(job, outputPath);

System.exit(job.waitForCompletion(true) ? 0 : 1);

}

}

bash:

$ javac -classpath $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-client-core-<version>.jar -d . WordCharacterCount.java

$ jar cf WordCharacterCount.jar WordCharacterCount*.class

$ hadoop jar WordCharacterCount.jar WordCharacterCount input_path output_path

5.Write a command to implement file management tasks, such as Adding files to HDFS.

To add files to Hadoop Distributed File System (HDFS), you can use the `hadoop fs -copyFromLocal` command. Here's the basic syntax:

Bash:

hadoop fs -copyFromLocal /path/to/local/file hdfs://localhost:9000/path/in/hdfs

Explanation of the command:

- `hadoop fs`: This is the Hadoop File System shell command.

- `-copyFromLocal`: Copies files or directories from the local file system to HDFS.

- `/path/to/local/file`: The path to the file or directory in the local file system that you want to copy to HDFS.

- `hdfs://localhost:9000/path/in/hdfs`: The destination path in HDFS where you want to copy the file or directory.

Make sure to replace `/path/to/local/file` with the actual path of the file or directory in your local file system, and `hdfs://localhost:9000/path/in/hdfs` with the desired path in HDFS.

Here's an example:

Bash:

hadoop fs -copyFromLocal /local/path/myfile.txt hdfs://localhost:9000/user/yourusername/

This command copies the file `myfile.txt` from the local file system to the specified HDFS directory.

Note: Ensure that your Hadoop cluster is running, and the HDFS URI (`hdfs://localhost:9000`) is configured correctly according to your Hadoop setup. Adjust the paths as needed for your specific use case.

6.Write a JAVA program for count the number of words with Hadoop Map Reduce

Java:

import java.io.IOException;

import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.Mapper;

import org.apache.hadoop.mapreduce.Reducer;

public class WordCount {

public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> {

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

private Text word = new Text();

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

StringTokenizer itr = new StringTokenizer(value.toString());

while (itr.hasMoreTokens()) {

word.set(itr.nextToken());

context.write(word, one);

}

}

}

public static class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

private IntWritable result = new IntWritable();

public void reduce(Text key, Iterable<IntWritable> values, Context context)

throws IOException, InterruptedException {

int sum = 0;

for (IntWritable val : values) {

sum += val.get();

}

result.set(sum);

context.write(key, result);

}

}

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

Configuration conf = new Configuration();

Job job = Job.getInstance(conf, "WordCount");

job.setJarByClass(WordCount.class);

job.setMapperClass(TokenizerMapper.class);

job.setCombinerClass(WordCountReducer.class);

job.setReducerClass(WordCountReducer.class);

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

// Set input and output paths

Path inputPath = new Path(args[0]);

Path outputPath = new Path(args[1]);

org.apache.hadoop.mapreduce.lib.input.FileInputFormat.addInputPath(job, inputPath);

org.apache.hadoop.mapreduce.lib.output.FileOutputFormat.setOutputPath(job, outputPath);

System.exit(job.waitForCompletion(true) ? 0 : 1);

}

}

Bash:

$ javac -classpath $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-client-core-<version>.jar -d . WordCount.java

$ jar cf WordCount.jar WordCount*.class

$ hadoop jar WordCount.jar WordCount input_path output_path

7.Write a command to implement file management tasks, such as deleting files from HDFS.

To delete files from Hadoop Distributed File System (HDFS), you can use the `hadoop fs -rm` command. Here's the basic syntax:

Bash:

hadoop fs -rm hdfs://localhost:9000/path/in/hdfs/file_or_directory

Explanation of the command:

- `hadoop fs`: This is the Hadoop File System shell command.

- `-rm`: Removes files or directories from HDFS.

- `hdfs://localhost:9000/path/in/hdfs/file_or_directory`: The path to the file or directory in HDFS that you want to delete.

Make sure to replace `hdfs://localhost:9000/path/in/hdfs/file_or_directory` with the actual path of the file or directory in HDFS that you want to delete.

Here's an example:

Bash:

hadoop fs -rm hdfs://localhost:9000/user/yourusername/myfile.txt

This command deletes the file `myfile.txt` from the specified HDFS directory.

If you want to delete a directory and its contents recursively, you can use the `-r` option:

Bash:

hadoop fs -rm -r hdfs://localhost:9000/user/yourusername/mydirectory

This command deletes the directory `mydirectory` and its contents recursively from HDFS.

Note: Ensure that your Hadoop cluster is running, and the HDFS URI (`hdfs://localhost:9000`) is configured correctly according to your Hadoop setup. Adjust the paths as needed for your specific use case. Deleting files in HDFS is a permanent operation, so use it with caution.

8.Downloading and installing HIVE and implement Start-up scripts

Hive is a data warehousing and SQL-like query language tool for Hadoop. It enables users to query and manage large datasets stored in Hadoop's distributed file system (HDFS). Here are the steps to download, install, and configure Hive, along with start-up scripts:

Prerequisites:

1. Hadoop should be installed and running on your system.

2. Java should be installed.

Downloading and Installing Hive:

1. Visit the Apache Hive website: https://hive.apache.org/

2. Go to the "Downloads" section and select the version of Hive you want to install.

3. Download the binary version of Hive.

Installation Steps:

1. Extract the downloaded Hive archive to a directory of your choice:

Bash:

tar -xzvf apache-hive-x.x.x-bin.tar.gz

2. Move the extracted directory to a location of your choice:

Bash:

mv apache-hive-x.x.x /path/to/hive

3. Configure the environment variables by adding the following lines to your shell profile file (e.g., `.bashrc` or `.bash_profile`):

Bash:

export HIVE_HOME=/path/to/hive

export PATH=$PATH:$HIVE_HOME/bin

Then, source the profile or restart your terminal:

Bash:

source ~/.bashrc

Configuring Hive:

1. Copy the Hive configuration template:

Bash:

cp $HIVE_HOME/conf/hive-default.xml.template $HIVE_HOME/conf/hive-site.xml

2. Edit the `hive-site.xml` file to configure Hive. For example, set the Hive metastore location:

Bash:

nano $HIVE_HOME/conf/hive-site.xml

Add the following properties:

xml

<configuration>

<property>

<name>javax.jdo.option.ConnectionURL</name>

<value>jdbc:derby:;databaseName=/path/to/hive/metastore_db;create=true</value>

<description>URL of the JDBC database used by Hive for metastore</description>

</property>

<!-- Other configurations as needed -->

</configuration>

Starting Hive:

1. Start the Hive Metastore:

Bash:

$HIVE_HOME/bin/schematool -dbType derby -initSchema

2. Start the Hive CLI (Command Line Interface):

Bash:

hive

Now, you have Hive up and running. You can start writing HiveQL queries in the Hive CLI. If you want to run Hive in server mode, you might need to configure additional settings and start the Hive server.

Please note that these steps provide a basic setup, and depending on your use case, you may need to configure Hive further for your specific environment and requirements. Always refer to the official Hive documentation for the most accurate and up-to-date information: https://cwiki.apache.org/confluence/display/Hive/Home

9.Write a HQL command (I) To create a table as employees with field name as eid,ename,eage,edepartment (II) Load data into table (III) Display the table content.

Certainly! Below are the HQL commands to perform the specified tasks:

I. Create a table named 'employees':

SQL:

CREATE TABLE employees (

eid INT,

ename STRING,

eage INT,

edepartment STRING

);

This command creates a table named 'employees' with columns `eid` (employee ID), `ename` (employee name), `eage` (employee age), and `edepartment` (employee department).

II. Load data into the 'employees' table:

Assuming you have a file named 'employee_data.txt' with data in the format (e.g., 1,John,25,IT), you can use the following command to load data into the 'employees' table:

SQL:

LOAD DATA LOCAL INPATH '/path/to/employee_data.txt' INTO TABLE employees;

Replace '/path/to/employee_data.txt' with the actual path to your data file.

III. Display the table content:

You can use a simple SELECT statement to display the content of the 'employees' table:

SQL:

SELECT * FROM employees;

This command retrieves all rows and columns from the 'employees' table and displays the result.

Remember to replace the file path in the 'LOAD DATA' command with the correct path to your data file. Also, ensure that your data file is in the appropriate format, and the table schema matches the structure of your data.

When you run these commands, make sure you are in the Hive CLI or another environment where you can execute HiveQL statements. Additionally, ensure that Hive is properly configured and running.

10.Downloading and installing HBASE and implement Start-up scripts and Configuration files.

HBase is a NoSQL database built on top of the Hadoop Distributed File System (HDFS). Below are the steps to download, install, and configure HBase, along with startup scripts and configuration files:

Prerequisites:

1. Hadoop should be installed and running on your system.

2. Java should be installed.

Downloading and Installing HBase:

1. Visit the Apache HBase website: https://hbase.apache.org/

2. Go to the "Downloads" section and select the version of HBase you want to install.

3. Download the binary version of HBase.

Installation Steps:

1. Extract the downloaded HBase archive to a directory of your choice:

Bash:

tar -xzvf hbase-x.x.x-bin.tar.gz

2. Move the extracted directory to a location of your choice:

Bash:

mv hbase-x.x.x /path/to/hbase

Configuring HBase:

1. Configure the environment variables by adding the following lines to your shell profile file (e.g., `.bashrc` or `.bash_profile`):

Bash:

export HBASE_HOME=/path/to/hbase

export PATH=$PATH:$HBASE_HOME/bin

Then, source the profile or restart your terminal:

Bash:

source ~/.bashrc

2. Edit the HBase configuration file (`hbase-site.xml`) to configure HBase.

Bash:

nano $HBASE_HOME/conf/hbase-site.xml

Add the following properties:

xml

<configuration>

<property>

<name>hbase.rootdir</name>

<value>hdfs://localhost:9000/hbase</value>

</property>

<property>

<name>hbase.cluster.distributed</name>

<value>true</value>

</property>

<!-- Other configurations as needed -->

</configuration>

Make sure to adjust the `hbase.rootdir` property to point to your HDFS location.

Starting HBase:

1. Start HBase in distributed mode:

Bash:

start-hbase.sh

If you want to start HBase in standalone mode, you can use:

Bash:

start-hbase.sh

2. To verify that HBase is running, you can access the HBase shell:

Bash:

hbase shell

This will open the HBase shell where you can execute HBase commands.

These are the basic steps to download, install, and configure HBase. Depending on your use case and environment, you might need to adjust additional configuration settings. Always refer to the official HBase documentation for the most accurate and up-to-date information: https://hbase.apache.org/book.html

11.Downloading and installing Thrift and Generate HBASE thrift binding and interact with HBASE

To interact with HBase using Thrift, you'll need to follow these general steps:

1. Download and Install Thrift:

Visit the Apache Thrift website: https://thrift.apache.org/ and download the binary distribution of Thrift.

Bash:

tar -xzvf thrift-x.x.x.tar.gz

mv thrift-x.x.x /path/to/thrift

Configure your environment variables:

Bash:

export THRIFT_HOME=/path/to/thrift

export PATH=$PATH:$THRIFT_HOME/bin

2. Generate HBase Thrift Binding:

Assuming you have HBase installed and running, you need to generate the HBase Thrift binding using the Thrift compiler. The HBase Thrift definition file is typically located in the HBase source distribution under `src/main/resources/org/apache/hadoop/hbase/thrift/Hbase.thrift`. You can copy this file to a location of your choice.

Bash:

thrift --gen java /path/to/Hbase.thrift

This command generates Java source files in the `gen-java` directory. You can then compile these files and use them in your Java project.

3. Interact with HBase using the Generated Code:

Here is a simple Java program that uses the generated Thrift code to interact with HBase:

java

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.hbase.thrift.generated.*;

import org.apache.thrift.TException;

import org.apache.thrift.transport.TSocket;

import org.apache.thrift.transport.TTransport;

import org.apache.thrift.protocol.TBinaryProtocol;

public class HBaseThriftExample {

public static void main(String[] args) {

try {

TTransport transport = new TSocket("localhost", 9090);

transport.open();

TBinaryProtocol protocol = new TBinaryProtocol(transport);

Hbase.Client client = new Hbase.Client(protocol);

// Perform HBase operations using the Thrift client

// Example: Get list of tables

System.out.println("Tables: " + client.getTableNames());

transport.close();

} catch (TException x) {

x.printStackTrace();

}

}

}

Make sure to adjust the connection details (`"localhost"`, `9090`) based on your HBase setup.

4. Compile and Run:

Compile your Java program with the generated Thrift files and HBase dependencies:

Bash:

javac -cp .:/path/to/thrift/lib/libthrift-0.14.2.jar:/path/to/hbase/lib/* HBaseThriftExample.java gen-java/*.java

Run the compiled Java program:

Bash:

java -cp .:/path/to/thrift/lib/libthrift-0.14.2.jar:/path/to/hbase/lib/* HBaseThriftExample

This is a basic example, and you may need to handle exceptions, perform more sophisticated operations, and adjust the code based on your specific requirements.

Remember to replace version numbers in paths with the actual versions you have downloaded. Adjust the classpath (`-cp`) based on your specific setup. Always refer to the official documentation for the versions you are using.

12.Write HBASE command (I) To create a table as student with two column field. (II) Insert data into the table. (III) Get data from the table. (IV) Delete data from the table.

To perform the specified tasks in HBase, you can use the HBase Shell. Below are the HBase shell commands for each task:

I. Create a table named 'student' with two columns:

Bash:

create 'student', 'personal_data', 'academic_data'

This command creates a table named 'student' with two column families: 'personal_data' and 'academic_data'.

II. Insert data into the 'student' table:

Bash:

put 'student', 'row1', 'personal_data:name', 'John'

put 'student', 'row1', 'personal_data:age', '25'

put 'student', 'row1', 'academic_data:grade', 'A'

These commands insert data into the 'student' table for a row with the key 'row1'. You can insert additional rows with different keys and data.

III. Get data from the 'student' table:

Bash:

get 'student', 'row1'

This command retrieves all data for the row with the key 'row1' in the 'student' table.

IV. Delete data from the 'student' table:

Bash:

delete 'student', 'row1', 'personal_data:age'

This command deletes the 'age' column from the 'personal_data' column family for the row with the key 'row1' in the 'student' table. You can adjust the column family and column names based on your specific data structure.

Note: HBase commands are case-sensitive, and column family and column names are often separated by a colon (`:`). Additionally, you may need to add more details to the commands based on your specific use case.

These commands are executed in the HBase Shell, which you can access by running the following command:

Bash:

hbase shell

After entering the HBase Shell, you can run the commands mentioned above to perform the specified tasks.

13.Write a HIVE DDL commands.

Certainly! Below are some common Hive Data Definition Language (DDL) commands that you can use to create, alter, and manage tables in Hive:

1. Create a Table:

SQL:

CREATE TABLE employee (

emp_id INT,

emp_name STRING,

emp_salary DOUBLE,

emp_department STRING

);

This command creates a table named `employee` with columns `emp_id`, `emp_name`, `emp_salary`, and `emp_department`.

2. Create an External Table:

SQL:

CREATE EXTERNAL TABLE external_employee (

emp_id INT,

emp_name STRING,

emp_salary DOUBLE,

emp_department STRING

)

LOCATION '/user/hive/external/employee';

This command creates an external table named `external_employee`. External tables store their data outside the Hive warehouse directory, and you need to specify the data location.

3. Create a Partitioned Table:

SQL:

CREATE TABLE partitioned_employee (

emp_id INT,

emp_name STRING,

emp_salary DOUBLE

)

PARTITIONED BY (emp_department STRING);

This command creates a partitioned table named `partitioned_employee` where data is partitioned based on the `emp_department` column.

4. Describe a Table:

SQL:

DESCRIBE employee;

This command provides information about the structure of the `employee` table, including column names, data types, and comments.

5. Show Tables:

SQL:

SHOW TABLES;

This command lists all the tables in the current Hive database.

6. Alter Table (Add Columns):

SQL:

ALTER TABLE employee

ADD COLUMNS (emp_location STRING);

This command adds a new column named `emp_location` to the existing `employee` table.

7. Rename Table:

SQL:

ALTER TABLE employee

RENAME TO new_employee;

This command renames the table from `employee` to `new_employee`.

8. Drop Table:

SQL:

DROP TABLE IF EXISTS employee;

This command deletes the `employee` table. The `IF EXISTS` clause prevents an error if the table doesn't exist.

9. Truncate Table:

SQL:

TRUNCATE TABLE employee;

This command removes all the data from the `employee` table, but retains the table structure.

These are basic examples of Hive DDL commands. Depending on your use case, you might need additional options and clauses. Always refer to the official Hive documentation for detailed information: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL

14.Write a HIVE DML commands

Certainly! Hive Data Manipulation Language (DML) commands are used to query, insert, update, and delete data in Hive tables. Below are some common Hive DML commands:

1. Select Data:

SQL:

SELECT * FROM employee;

This command retrieves all rows and columns from the `employee` table.

2. Select with Filtering:

SQL:

SELECT emp_name, emp_salary FROM employee WHERE emp_department = 'IT';

This command retrieves the names and salaries of employees in the 'IT' department from the `employee` table.

3. Insert Data:

SQL:

INSERT INTO TABLE employee VALUES (101, 'John Doe', 50000, 'HR');

This command inserts a new row into the `employee` table.

4. Insert Data into Partitioned Table:

SQL:

INSERT INTO TABLE partitioned_employee PARTITION (emp_department='Marketing') VALUES (102, 'Jane Smith', 60000);

This command inserts a new row into the `partitioned_employee` table with the specified department partition.

5. Update Data:

SQL:

UPDATE employee SET emp_salary = 55000 WHERE emp_id = 101;

This command updates the salary for the employee with `emp_id` 101 in the `employee` table.

6. Delete Data:

SQL:

DELETE FROM employee WHERE emp_id = 101;

This command deletes the row for the employee with `emp_id` 101 from the `employee` table.

7. Merge Data (Starting Hive 3.0.0):

SQL:

MERGE INTO target AS T

USING source AS S

ON T.emp_id = S.emp_id

WHEN MATCHED THEN UPDATE SET T.emp_salary = S.emp_salary

WHEN NOT MATCHED THEN INSERT VALUES (S.emp_id, S.emp_name, S.emp_salary, S.emp_department);

This command performs a merge operation, updating existing rows and inserting new rows from the `source` table into the `target` table based on the matching condition.

8. Multi-Table Insert (Starting Hive 0.14.0):

SQL:

FROM source

INSERT INTO TABLE target1 SELECT col1, col2, col3

INSERT INTO TABLE target2 SELECT col1, col2, col4;

This command inserts data from the `source` table into two different target tables, `target1` and `target2`.

These are basic examples of Hive DML commands. Depending on your use case, you might need to customize these commands with additional clauses and conditions. Always refer to the official Hive documentation for detailed information: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DML

15.Write and execute the following HIVE commands 1. CREATE 2. SHOW 3. DESCRIBE 4. USE 5. DROP 6. ALTER 7. TRUNCATE

I can guide you on how to write and execute the Hive commands, but please note that you need to have a Hive environment set up and running to execute these commands. Ensure that Hive is properly configured and started before attempting to run the commands.

Here's an example of how you can write and execute each of the mentioned Hive commands:

1. CREATE:

SQL:

CREATE TABLE employee (

emp_id INT,

emp_name STRING,

emp_salary DOUBLE,

emp_department STRING

);

This command creates a table named `employee` with columns `emp_id`, `emp_name`, `emp_salary`, and `emp_department`.

2. SHOW:

SQL:

SHOW TABLES;

This command lists all the tables in the current Hive database.

3. DESCRIBE:

SQL:

DESCRIBE employee;

This command provides information about the structure of the `employee` table, including column names, data types, and comments.

4. USE:

SQL:

USE your_database_name;

This command switches to the specified Hive database. Replace `your_database_name` with the name of the database you want to use.

5. DROP:

SQL:

DROP TABLE IF EXISTS employee;

This command deletes the `employee` table. The `IF EXISTS` clause prevents an error if the table doesn't exist.

6. ALTER:

SQL:

ALTER TABLE employee

ADD COLUMNS (emp_location STRING);

This command adds a new column named `emp_location` to the existing `employee` table.

7. TRUNCATE:

SQL:

TRUNCATE TABLE employee;

This command removes all the data from the `employee` table but retains the table structure.

To execute these commands, you can save them in a Hive script file (e.g., `commands.hql`) and then run the script using the Hive command-line interface (CLI):

Bash:

hive -f commands.hql

Replace `commands.hql` with the actual name of your script file.

Remember to replace the placeholder names and values with your actual database and table names. Additionally, ensure that your Hive environment is correctly set up and running.

16.Write a JAVA program for Multiplication of two numbers with Hadoop Map Reduce

Mapper:

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

public class MultiplicationMapper extends Mapper<LongWritable, Text, Text, IntWritable> {

private Text outputKey = new Text();

private IntWritable outputValue = new IntWritable();

@Override

protected void map(LongWritable key, Text value, Context context)

throws IOException, InterruptedException {

// Input format: "num1 num2"

String[] numbers = value.toString().split(" ");

int num1 = Integer.parseInt(numbers[0]);

int num2 = Integer.parseInt(numbers[1]);

// Output key is a combination of num1 and num2

outputKey.set(num1 + " * " + num2);

// Output value is the product of num1 and num2

outputValue.set(num1 * num2);

context.write(outputKey, outputValue);

}

}

Reducer:

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

public class MultiplicationReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

private IntWritable result = new IntWritable();

@Override

protected void reduce(Text key, Iterable<IntWritable> values, Context context)

throws IOException, InterruptedException {

// Sum all values to get the total product

int product = 1;

for (IntWritable value : values) {

product *= value.get();

}

result.set(product);

context.write(key, result);

}

}

driver:

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class MultiplicationDriver {

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

Configuration conf = new Configuration();

Job job = Job.getInstance(conf, "Multiplication");

job.setJarByClass(MultiplicationDriver.class);

job.setMapperClass(MultiplicationMapper.class);

job.setReducerClass(MultiplicationReducer.class);

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

// Set input and output paths

FileInputFormat.addInputPath(job, new Path(args[0]));

FileOutputFormat.setOutputPath(job, new Path(args[1]));

System.exit(job.waitForCompletion(true) ? 0 : 1);

}

}

Bash:

$ javac -classpath $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-client-core-<version>.jar -d . Multiplication*.java

$ jar cf Multiplication.jar Multiplication*.class

$ hadoop jar Multiplication.jar MultiplicationDriver input_path output_path

17.Write a JAVA program for addition of two numbers with Hadoop Map Reduce

Mapper:

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

public class AdditionMapper extends Mapper<LongWritable, Text, Text, IntWritable> {

private Text outputKey = new Text();

private IntWritable outputValue = new IntWritable();

@Override

protected void map(LongWritable key, Text value, Context context)

throws IOException, InterruptedException {

// Input format: "num1 num2"

String[] numbers = value.toString().split(" ");

int num1 = Integer.parseInt(numbers[0]);

int num2 = Integer.parseInt(numbers[1]);

// Output key is a combination of num1 and num2

outputKey.set(num1 + " + " + num2);

// Output value is the sum of num1 and num2

outputValue.set(num1 + num2);

context.write(outputKey, outputValue);

}

}

Reducer:

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

public class AdditionReducer extends Reducer<Text, IntWritable, Text, IntWritable> {

private IntWritable result = new IntWritable();

@Override

protected void reduce(Text key, Iterable<IntWritable> values, Context context)

throws IOException, InterruptedException {

// Sum all values to get the total sum

int sum = 0;

for (IntWritable value : values) {

sum += value.get();

}

result.set(sum);

context.write(key, result);

}

}

driver:

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class AdditionDriver {

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

Configuration conf = new Configuration();

Job job = Job.getInstance(conf, "Addition");

job.setJarByClass(AdditionDriver.class);

job.setMapperClass(AdditionMapper.class);

job.setReducerClass(AdditionReducer.class);

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

// Set input and output paths

FileInputFormat.addInputPath(job, new Path(args[0]));

FileOutputFormat.setOutputPath(job, new Path(args[1]));

System.exit(job.waitForCompletion(true) ? 0 : 1);

}

}

Bash:

$ javac -classpath $HADOOP_HOME/share/hadoop/mapreduce/hadoop-mapreduce-client-core-<version>.jar -d . Addition*.java

$ jar cf Addition.jar Addition*.class

$ hadoop jar Addition.jar AdditionDriver input_path output_path

18.Write a HQL command to create a table employee with your own attribute and execute the following command 1. SELECT all fields 2. GROUP BY department 3. WHERE any condition 4. HAVING 5. ORDER BY employee number

Certainly! Below is an example of a HiveQL command to create a table named `employee` with some sample attributes. Following that are examples of HiveQL queries for various operations.

1. Create Table:

SQL:

CREATE TABLE employee (

emp_id INT,

emp_name STRING,

emp_salary DOUBLE,

emp_department STRING,

emp_location STRING

);

This command creates a table named `employee` with attributes `emp_id`, `emp_name`, `emp_salary`, `emp_department`, and `emp_location`.

2. SELECT All Fields:

SQL:

SELECT * FROM employee;

This query selects all fields from the `employee` table.

3. GROUP BY Department:

SQL:

SELECT emp_department, COUNT(*) as employee_count

FROM employee

GROUP BY emp_department;

This query groups the employees by department and counts the number of employees in each department.

4. WHERE Any Condition:

SQL:

SELECT * FROM employee

WHERE emp_salary > 50000 AND emp_department = 'IT';

This query selects all fields for employees with a salary greater than 50,000 in the 'IT' department.

5. HAVING:

SQL:

SELECT emp_department, AVG(emp_salary) as avg_salary

FROM employee

GROUP BY emp_department

HAVING AVG(emp_salary) > 60000;

This query groups employees by department and calculates the average salary for each department. It then selects only those departments where the average salary is greater than 60,000.

6. ORDER BY Employee Number:

SQL:

SELECT * FROM employee

ORDER BY emp_id;

This query selects all fields and orders the result by the employee number (`emp_id`).

Please note that these are basic examples, and you may need to adjust the column names and conditions based on your specific table structure and data. Always refer to the official Hive documentation for more detailed information on HiveQL: https://cwiki.apache.org/confluence/display/Hive/LanguageManual+SelectClause

19.Write a command to implement file management tasks in HDFS.

In Hadoop, you can use the `hadoop fs` command to perform various file management tasks in Hadoop Distributed File System (HDFS). Here are some common file management tasks and the corresponding `hadoop fs` commands:

1. List Files and Directories:

Bash:

hadoop fs -ls /path/in/hdfs

This command lists files and directories in the specified HDFS path.

2. Create a Directory:

Bash:

hadoop fs -mkdir /path/in/hdfs/new_directory

This command creates a new directory in HDFS.

3. Copy Local File to HDFS:

Bash:

hadoop fs -copyFromLocal localfile.txt /path/in/hdfs/

This command copies a local file (`localfile.txt`) to the specified HDFS path.

4. Copy HDFS File to Local File System:

Bash:

hadoop fs -copyToLocal /path/in/hdfs/file.txt localfile.txt

This command copies a file from HDFS to the local file system.

5. Move File or Directory within HDFS:

Bash:

hadoop fs -mv /path/in/hdfs/source /path/in/hdfs/destination

This command moves a file or directory within HDFS.

6. Remove File or Directory:

Bash:

hadoop fs -rm /path/in/hdfs/file.txt

This command removes a file from HDFS.

Bash:

hadoop fs -rm -r /path/in/hdfs/directory

This command removes a directory and its contents recursively from HDFS.

7. Change Permissions:

Bash:

hadoop fs -chmod 755 /path/in/hdfs/file.txt

This command changes the permissions of a file in HDFS.

8. Change Owner:

Bash:

hadoop fs -chown user:group /path/in/hdfs/file.txt

This command changes the owner of a file in HDFS.

9. Display File Content:

Bash:

hadoop fs -cat /path/in/hdfs/file.txt

This command displays the content of a file in HDFS.

These are basic examples of file management tasks in HDFS using the `hadoop fs` command. Adjust the paths and options based on your specific use case. Always ensure that your Hadoop cluster is running, and the HDFS URI (`hdfs://localhost:9000`) is configured correctly according to your Hadoop setup.

20.Write and execute the following HIVE commands 1. UPDATE 2. EXPORT 3. IMPORT 4. SORT BY 5. DISTRIBUTE BY 6. CLUSTER BY

Hive, as of my last knowledge update in January 2022, does not support the UPDATE command for updating existing records in a table. However, I'll provide examples for the other commands you mentioned: EXPORT, IMPORT, SORT BY, DISTRIBUTE BY, and CLUSTER BY.

Before executing the commands, make sure that you have appropriate permissions and that your Hive environment is properly configured.

1. EXPORT:

The EXPORT command is used to export the contents of a table or partition to HDFS. Here's an example:

SQL:

EXPORT TABLE employee TO 'hdfs://localhost:9000/user/hive/exported_data';

This command exports the 'employee' table to the specified HDFS location.

2. IMPORT:

The IMPORT command is used to import data from HDFS into a Hive table. Here's an example:

SQL:

IMPORT TABLE imported_employee FROM 'hdfs://localhost:9000/user/hive/exported_data';

This command imports data from the specified HDFS location into the 'imported_employee' table.

3. SORT BY:

The SORT BY clause is used to sort the result of a SELECT query based on one or more columns. Here's an example:

SQL:

SELECT * FROM employee

SORT BY emp_salary DESC;

This command selects all fields from the 'employee' table and sorts the result by 'emp_salary' in descending order.

4. DISTRIBUTE BY:

The DISTRIBUTE BY clause is used to control the distribution of data to reducers during a MapReduce job. Here's an example:

SQL:

INSERT OVERWRITE TABLE distributed_employee

SELECT * FROM employee

DISTRIBUTE BY emp_department;

This command inserts data into the 'distributed_employee' table, distributing it based on the 'emp_department' column.

5. CLUSTER BY:

The CLUSTER BY clause is similar to DISTRIBUTE BY but also sorts the data within each reducer. Here's an example:

SQL:

INSERT OVERWRITE TABLE clustered_employee

SELECT * FROM employee

CLUSTER BY emp_department;

This command inserts data into the 'clustered_employee' table, clustering and sorting it based on the 'emp_department' column.

Remember to adjust the commands according to your specific use case, and ensure that your Hive environment is set up and running before executing these commands. Also, note that the availability and syntax of certain features may vary depending on the Hive version.