A USECASE ON TRAVEL APP

Dear Friends,

Welcome Back....

Day by Day I am learning different thing which I like to share with you all. 

As a great person said " Learning is a journey not a Destination".

I was little busy last few day, acquiring different know-how on Hadoop. I was asked whether I can solve this usecase by one of the Travel app provider to know where is the demand of their services so that they can take decision on how to keep offers/discounts to lure the customers to use their services. 

In this blog, I am using this usecase to solve the problem and help the client to take decision for better business development. This usecase can be used to find the same for a particular state or a region. You can this blog as reference and modify this according to your need. Hope you all enjoy solving this usecase.

Eclipse IDE :- Neon.1

Hadoop Version:- 1.2.1

Ubuntu Version:- 12.04 LTS

Jars Used:- Apache POI 3.15

Problem Statement:

Travel app agency have loads and loads of data about various trips made all over India. They are unable to check where is the most demand and where they can use offers or discounts to lure customers in using the existing data.

1. Find the area/place where more and least demand is there so that appropriate offers and discounts can be given.

2. Remove duplicate entries and segregate customers according to area/place.

3. Use Graphical representation for decision making.

The data is in EXCEL format for which I used third party jar from Apache to parse excel data. (You can find the same from my previous blogs). I have created a small list of data as example for this usecase, which you can modify and bring a large set of data for more precise workflow/understanding.

The data is in following format:- (Download the data file HERE)

In our usecase the data is in excel format which we will load to HDFS for storing and using MR we will extract the data using a custom input format. After extraction of required data we will then load it into HIVE table using partition to divide it into different regions and the perform Count query to find the highest and lowest customers demand regions. Using Tableau to represent it graphically.

I kept this usecase as simple as I can. you can try different variation on the same to get desired result. For example; in this usecase I was asked to map the output along with the driver ID to know the best performing driver.

1. LOAD THE DATA IN HDFS:-

Our first step is to load the data in HDFS, Hope you all know that by this time. Still as a part of my Blog I will keep it.

To load the data in HDFS give the below command:-

Command > hadoop fs -mkdir /Input               (To make a directory)

Command > hadoop fs -put Trip.xlsx /Input               (To put the file in HDFS)

2.     MAP REDUCE CODES:-

(DRIVER CLASS)

package com.poc.trip;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

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;

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

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

import org.apache.hadoop.util.GenericOptionsParser;

public class PocDriver {

static public int count = 0;

public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {

Configuration conf = new Configuration();

GenericOptionsParser parser = new GenericOptionsParser(conf, args);

args = parser.getRemainingArgs();

Job job = new Job(conf, "Trip_Log");

job.setJarByClass(PocDriver.class);

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(Text.class);

job.setInputFormatClass(ExcelInputFormat.class);

job.setOutputFormatClass(TextOutputFormat.class);

LazyOutputFormat.setOutputFormatClass(job, TextOutputFormat.class);

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

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

// job.setNumReduceTasks(0);

job.setMapperClass(MyMap.class);

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

}

}

MY MAPPER 

(HAVING MAPPER LOGIC)

package com.poc.trip;

import java.io.IOException;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.output.MultipleOutputs;

public class MyMap extends Mapper<LongWritable, Text, Text, Text> {
MultipleOutputs<Text, Text> mos;

@Override
public void setup(Context context) {
mos = new MultipleOutputs<Text, Text>(context);
}

@Override
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String[] str1 = value.toString().split("\t");
String sr1 = str1[0] + "\t" + str1[1] + "\t" + str1[3] + "\t" + str1[5] + "\t" + str1[6] + "\t" + str1[7];

if (str1[3].contains("Bihar")) {
mos.write(new Text("Bihar"), new Text(sr1), ("/TripData/Bihar"));
} else if (str1[3].contains("Pondicherry")) {
mos.write(new Text("Pondicherry"), new Text(sr1), ("/TripData/Pondicherry"));

} else if (str1[3].contains("Uttarakhand")) {
mos.write(new Text("Uttarakhand"), new Text(sr1), "/TripData/Uttarakhand");

} else if (str1[3].contains("Chhattisgarh")) {
mos.write(new Text("Chhattisgarh"), new Text(sr1), "/TripData/Chhattisgarh");

} else if (str1[3].contains("Goa")) {
mos.write(new Text("Goa"), new Text(sr1), "/TripData/Goa");

} else if (str1[3].contains("Assam")) {
mos.write(new Text("Assam"), new Text(sr1), "/TripData/Assam");

} else if (str1[3].contains("Himachal_Pradesh")) {
mos.write(new Text("Himachal_Pradesh"), new Text(sr1), "/TripData/Himachal_Pradesh");

} else {
mos.write(new Text("Other"), new Text(sr1), "/TripData/Other");

}
}

@Override
protected void cleanup(Context context) throws IOException, InterruptedException {
mos.close();
}
}

We are not using reducer as we are already doing the entire work in Map phase only. There is no reduce work needed.

3. EXECUTING THE MAP REDUCE CODE

Run the Jar file by below command:-

Command >  hadoop jar trip.jar com.poc.trip.PocDriver /Input/Trip.xlsx /Tripout

4. Create HIVE table and upload Processed data:-

In this blog I will be executing the hive command directly from terminal instead of going inside the HIVE shell. The execution time of the command will be more but I am doing this for a different approach. (You can go inside the HIVE shell and drive the commands)

First Creation of a Database and create a external table in the same to contain the MR data:-

You can achieve the same by the following command:-

Command >  hive -e 'create database Trip; use Trip;'

Create table in a script file and save it as .sql: 

Follow the below command for creation of table.

Command > nano hivetable.sql

Script        > hive -e 'create external table Trip (state string,sid int,tid int,address string,trip int)

                  > row format delimited

                  > fields terminated by '\t'

                  > stored as textfile location '/TripData';'

Now run the script file using the following command.

Command > hive -f hivetable.sql 

This will create an external table and store the data in that table.

Now its time to bring the data and count the number of trip made in each state. Follow the below command for the same.

Command > hive -e 'Select state, count(*) from trip group by state;' > /home/gopal/Desktop/Tripcount.txt

This will create a text file and store the output in that with tab delimited format.

To get the output in desired delimited format we can use a script for the same. Follow below command to get desired delimited format.

Command > nano hiveout.sql

Script > INSERT OVERWRITE LOCAL DIRECTORY '/home/gopal/Desktop/Tripcount'

           > ROW FORMAT DELIMITED

           > FIELDS TERMINATED BY ','

           > select state, count(*) from trip group by state;

Now run the script file to get desired output by below command.

Command > hive -f hiveout.sql

Now use Tableau software to give a graphical representation.

I have used the tableau software and exported the results into a PDF file.

Hope you all understood the procedures... 

Please do notify me for any corrections...

Kindly leave a comment for any queries/clarification...

ALL D BEST...

HIVE ON RESCUE- A HEALTHCARE USE_CASE ON STRUCTURED DATA

Dear Friends,

We know that Hadoop's HIVE component is very good for structured data processing. 

Structured data first depends on creating a data model – a model of the types of business data that will be recorded and how they will be stored, processed and accessed. This includes defining what fields of data will be stored and how that data will be stored: data type (numeric, currency, alphabetic, name, date, address).

Structured data has the advantage of being easily entered, stored, queried and analyzed. At one time, because of the high cost and performance limitations of storage, memory and processing, relational databases and spreadsheets using structured data were the only way to effectively manage data. Anything that couldn't fit into a tightly organized structure would have to be stored on paper in a filing cabinet. 

Hadoop has an advantage over this by using its component HIVE. Though HDFS solved the storing part but MR code writing & processing is bit lengthy. So, Hive is used for processing large amount of structured data more conveniently then any other component. Even though it uses MR as internal process the query writing is much simpler and easier comparing that with MR.

This is the use-case given to me by one of my friend and asked me not to use MR programming in this use-case.

Seriously, working with MR I am used to think like that but this POC made me to think in different way and I am glad that I solved it with using MR Programming. (Though MR is the background internal process happens in all Hadoop's components).

USE-CASE DEFINITION

Medicare Companies (Medical Insurance) tie up with different Hospitals for their customers to have cashless/problem free health checkups. Patients on recovery from certain heath issue don't go for checkup again (In fear of Cost/Travel/Time). They may no longer visit a hospital, or consult doctors altogether till they have some health issues again.

But a Medicare Companies shouldn’t give up on their customers without a fair try. Whether your customers have gone to the competition or just gone silent, they are worth your time and effort to revert them again with excellent proposals/policies.

There are number of Medicare-card holders who visit different Hospitals regularly. If some medicare-card holder doesn’t visit a particular Hospital for a period of time, that Hospital consider them as PASSIVE customer. Same way, if some Medicare-care holder visit a Hospital for first time, that customer is called ACTIVE. The problem is to find out ACTIVE & PASSIVE Mdicare-Card Holder for every Hospital from the data provided, on monthly basis to know how their business are performing ?

This will create a next steps for a luring that medicare-card holder who turned passive over a period of time by giving them offers, discounts etc.

Note # Passive period varies from Hospitals , For APPOLO, it may be 90 days, but for small Hospitals like DAVITA, DIALYSIS Care it may be 30 days.

PROBLEM STATEMENT

1. If PASSIVE period is 31 days & application is executing on 1 APRIL 2014 (New Financial Year) , Then the count of the Passive medicare-care holder that occurred as of 31st December 2013. It implies that the records to be used as quarterly basis.

2. If a Medicare-Card Holder is appearing first time in a Hospital or it has not appeared within period of 90 Days in same Hospital then it will be considered as new customer.

3. Output Report should contain daily Reports , Monthly Reports as well as Quarterly Reports.

4. Data to be used :

PASSIVE.info : Information about Passive period of a Hospital

HealthCare.csv: Total Transactions of the Hospitals WRT Medicare-Card Holder for the period of 2 years.

You can download the sample data used in this use-case from HERE.

USE_CASE SOLUTION ARCHITECTURE

In this Use-Case we will first sort the number of Hospitals for whom we need to find Active & Passive customers, by using HIVE partition table. Then using CTAS command in HIVE we will further divide each Hospital into Active & Passive customers  based on their last date of activity and keep them in a separate table.

After the above is done now based on our requirement we can put any query such as JOIN to find only Active/Passive customers or both, quarterly data,etc.

1. LOAD THE DATA IN HDFS:-

Our first step is to load the data in HDFS, Hope you all know that by this time. Still as a part of my Blog I will keep it.

To load the data in HDFS give the below command:-

Command > hadoop fs -mkdir /Health               (To make a directory)

Command > hadoop fs -put HealthCare.csv /Health         (To put the file in HDFS)

2. CREATE A DATABASE AND EXTERNAL TABLE TO KEEP THE DATA:-

Now as we have our data in HDFS and we need to work in HIVE, we have to create a database for the same. The benefit of creating an external table is that we don't have to copy the entire data to Hive's warehouse but can point to the location where the actual data is residing. To that if we delete the external table only table schema information will be lost not the actual data.

Below is the command for creating the database and external table:

Command > create database HealthCare;

Command > create external table health
                 > (cid int,card int,age int,disease string, hospital string,date date,address string,unit int)

                 > row format delimited               

                 > fields terminated by ','
                 > stored as textfile location '/Health'
                 > tblproperties ("skip.header.line.count"="1");            (Used to remove head line)

Check that all data is showing or not by below command.

Command > select * from health;

(If everything is fine then you will see all results)

3. CREATE PARTITION TABLE FOR KEEPING EACH HOSPITAL'S RECORDS:-

Now we will create an external partition table to diving the entire records into individual hospitals and keep each records separately by below command:

Command > create external table HealthPart
                 > (cid int,card int,age int,disease string, phospital string,date date,address string,unit int)                      > PARTITIONED BY (hospital string)                                                                                                      > row format delimited                                                                      
                 > fields terminated by ','                                                                  
                 > stored as textfile location '/PartHealth';      
                                                                    

After creating the table insert data from Health table and it will automatically divide it into Hospital names. Below is the command for inserting data from Health table:

Command > insert overwrite table healthpart PARTITION (hospital)
                  > select cid,card,age,disease,hospital,date,address,unit,hospital from health;

4. CREATE TABLE FROM PARTITION TABLE FOR ACTIVE & PASSIVE CUSTOMERS FOR EACH HOSPITAL:-

Now we have to create two separate tables containing Active & Passive customers respectively. This can be achieved by CTAS(Create Table As Select), creating a table by selecting columns from partition table. Below is the command for achieving the same:-

Command > create table newdavita as select cid, card, age, disease, phospital, date, address, unit
                  > from healthpart
                  > where hospital='DAVITA'                                                                              
                  > and date>date_sub('2014-04-01',30) and date<'2014-04-01';

date_sub('yyyy-mm-dd',no. of days):- Is used for taking date with difference as no. of days. (For Davita its 30 and Appolo its 90, so on..)

(This command will take selected columns from partition table based on the date for Active customers)

Command > create table olddavita as select cid, card, age, disease, phospital, date, address, unit
                 > from healthpart
                 > where hospital='DAVITA'                                                                              
                 > and date<date_sub('2014-04-01',30) and date>'2014-01-01';

(This command will take selected columns from partition table based on the date for Passive customers). Here 30 is used as no. of days for Passiveness.

NOTE:- Do the same for each and every Hospital. You will get each Hospital's Active & Passive Customers.

(As APPOLO is having 90 days for passiveness all within that date range will be Active customer for that quarter and no Passive customer will be there.)

Check with command > select * from olddavita; to confirm about the data with date range.

NOTE:- You can workout with different date range according to the requirement, for month & year also.

5. REMOVE DUPLICATE ENTRIES FROM NEW & OLD TABLE:-

You have achieved the Active and Passive customer data from each Part-Hospital table. But the fact may be there a member may have come before Passive time difference and after passive time difference. So many Member Card holder will be registered in both New & Old table.

We will only consider the Member coming in Active data and remove them from Passive data for data overwrite or duplicate's. This can be achieve through JOIN Command, which you can find below:-

For Getting Active Data without overlapping from Passive Data:-

Command > select a.cid,a.card,a.date,a.phospital from newdavita a left outer join olddavita b
                  > on a.card=b.card WHERE a.card IS NULL;

(Here we are taking everything on left hand side ie; Active data and joining with right hand side ie; Passive data and using null for removing the same/duplicate data entries from Active data.)

OR Using semi join

Command > select a.cid,a.card,a.date,a.hospital from newdavita a left semi join olddavita b
                 > on a.card=b.card;

For Getting Passive Data without overlapping from Active Data:-

Command > select a.cid,a.card,a.date,a.phospital from newdavita a right outer join olddavita b
                  > on a.card=b.card WHERE a.card IS NULL;

(Here we are taking everything on left hand side ie; Active data and joining with right hand side ie; Passive data and using null for removing the same/duplicate data entries from Passive data.)

NOTE:- For my use-case it didn't worked as both data contained same members. You can try to make some changes and try. 

After removing duplicates/overlapping entries from Active & Passive data. Use group by option to group the same Member card number for display. Below is the command for the same.

Command > select card,max(date) from newdavita group by card;

This will remove repeating card number and return only 1 card number for each entries.

6. CONSOLIDATE EVERY NEW TABLE & OLD TABLE TO GET ACTIVE & PASSIVE CUSTOMERS BY CTAS & UNION ALL:-

Now, Once we have all Active & Passive customers for each Hospital, consolidate it to get Active & Passive Customers for the Quarter.

Below is the command to join all Active customer data by UNION ALL and keep it in a table for further queries/analysis:-

Command > create table active_customer as select * from newdavita
                  > union all select * from newappolo;

NOTE:-  You can join multiple table using Union All to keep in one table like for eg; CTAS select * from new1 union all select * from new2 union all select * from new3 union all select * from new4 and so on....

STORE DATA IN HDFS:-

If you want to save the Active/Passive Customer Union data then below is the command:-

Command > insert overwrite directory '/NewCustomer'
                  > select * from newdavita union all      
                  > select * from newappolo;              

NOTE:- This will create a directory in HDFS and store the result. (But in Hive's format)

STORE DATA IN LFS:-

1. From Hive shell:-

If you want to keep it in .csv format give the below command:-

Command > insert overwrite local directory '/home/gopal/Desktop/NewCustomer'
                  > row format delimited                                            
                  > fields terminated by ','                                        
                  > select * from active_customer;    

2. From Terminal:-

If you want to keep it in .tsv format follow the below procedure:-

1. Close all active HIVE Shell.
2. Open a new terminal:-
Goto the directory where hive is running/Metastore is present and give the below command:-

Command > hive -e 'use healthcare; select * from newdavita union all select * from newappolo' > /home/gopal/Desktop/ActiveCustomer.tsv

Now go on and try different aspects of query to get variable results like monthly active and passive customers, count of each hospital customers, etc.

Hope you all understood the procedures... 

Please do notify me for any corrections...

Kindly leave a comment for any queries/clarification...

ALL D BEST...

WAYS TO BULK LOAD DATA IN HBASE

Dear Friends,

Going ahead with my post, this one was asked by one of my friend about HBase, for which I am sharing my thoughts and working procedure for the Loading of Bulk Data in HBase.

HBase is an open-source, NoSQL, distributed, column-oriented data store which has been implemented from Google BigTable that runs on top of HDFS. It was developed as part of Apache’s Hadoop project and runs on top of HDFS (Hadoop Distributed File System). HBase provides all the features of Google BigTable. We can call HBase a “Data Store” than a “Data Base” as it lacks many of the features available in traditional database, such as typed columns, secondary indexes, triggers, and advanced query languages, etc.
The Data model consists of Table name, row key, column family, columns, time stamp. While creating tables in HBase, the rows will be uniquely identified with the help of row keys and time stamp. In this data model the column family are static whereas columns are dynamic. Now let us look into the HBase Architecture. Hbase is a column oriented database where one has to specify what data belongs to which column family name.. So a Hbase table comprises of this minimum thing ie; A table Name and atleast 1 Column family name.

Apache HBase is all about giving you random, real-time, read/write access to your Big Data, but how do you efficiently get that data into HBase in the first place? Intuitively, a new user will try to do that via the client APIs or by using a MapReduce job with TableOutputFormat, but those approaches are problematic, as you will learn below. Instead, the HBase bulk loading feature is much easier to use and can insert the same amount of data more quickly.

In this blog I will take you through the number of ways to achieve Bulk Loading of Data in HBase.

There are basically 3 ways to Bulk load the data in HBase:-

1. Using ImportTsv Class to load txt Data to HBase.

2. Using Hive's HCatalog & Pig command.

3. Using MapReduce API.

You can download the Sample.txt file used in this blog HERE.

(NOTE:- While driving these examples, Please be sure to have your Hadoop Daemons & Hbase Daemons are up and running.)

1. Using ImportTsv Class to load txt Data to HBase:-

A) Uploading Sample.txt file to HDFS:-

Upload the sample file into HDFS by the following command:

Command > hadoop fs -put Sample.txt /Input

B) Create Table in HBase:-

For using this method we have to first create a table in HBase with number of column family according to the data. Here I am using 2 Column family in my data.

First go to HBase shell by giving below command and create a table with column family names:

Command > hbase shell      (To enter into HBase shell)

Command > create ‘Test′,’cf1’,'cf2'                 (To create a table with column family)

 C) Using ImportTsv Class  LOAD the Sample.txt file to HBase:-

Now we are set and ready to load the file in HBase. To load the file we will be using ImportTsv class from Hadoop/HBase jar file using the below command (goto hbase folder and give command):-

Command > ./bin/hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.separator=”,” -Dimporttsv.columns=HBASE_ROW_KEY,cf1,cf2 Test /Input/Sample.txt

Check the data is loaded in Hbase Table:-

Command >scan 'Test'

Here’s a explanation of the different configuration elements:

-Dimporttsv.separator= " ," specifies that the separator is a comma separated value.

-Dimporttsv.bulk.output=output is a relative path to where the HFiles will be written. Since your user on the VM is “cloudera” by default, it means the files will be in /user/cloudera/output. Skipping this option will make the job write directly to HBase. (We have not used but is useful).

-Dimporttsv.columns=HBASE_ROW_KEY,f:count is a list of all the columns contained in this file. The row key needs to be identified using the all-caps HBASE_ROW_KEY string; otherwise it won’t start the job. (I decided to use the qualifier “count” but it could be anything else.)

2. Using Hive's HCatalog & Pig command:-

In this method different jar files from PIG, HIVE and HCatalog is required which can be exported using HADOOP_CLASSPATH Command, else error: ClassNotFoundException will come with respective class details.
(For the safe-side and since my classpath command didn't worked, I copied all jar file from pig/lib, hive/lib & hive/hcatalog/lib to hadoop/lib, After which it worked fine without any error.)

A) Create a Script using HIVE SerDe & Table Properties:-

After loading the data in HDFS define the HBase schema for the data in HIVE shell. Continuing with the Sample example, create a script file called sample.ddl, which will contain the HBase schema for data used by HIVE. To do so write the below code in a file and name it as Sample.ddl:

Script Sample.ddl :- 

CREATE TABLE sample_hcat_load_table (id STRING, cf1 STRING, cf2 STRING) STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' 

WITH SERDEPROPERTIES ( 'hbase.columns.mapping' = 'd:cf1,d:cf2' ) 

TBLPROPERTIES ( 'hbase.table.name' = 'sample_load_table'); 

B) Now Create and register the HBase table in HCatalog.

To register the ddl file use HCatalog. (Hcatalog will be inside HIVE folder (/usr/local/hadoop/hive/hcatalog) export the Hcatalog home and path in ~/.bashrc file (Like you did in installing hive)). After that source ~/.bashrc file to update it by giving below command:

Command > source ~/.bashrc

Now register the ddl file using syntax :- hcat -f $HBase_Table_Name.

The following HCatalog command-line command runs the DDL script Sample.ddl:

Command > hcat -f sample.ddl

Goto HBase shell by giving below command to check whether the table is created or not:-

Command > hbase shell

C) Create the import file using PIG Script:-.

The following command/script instructs Pig to load data from Sample. and store it in sample_load_table.

Script Hbase-bulk-load.pig:-

A = LOAD '/Input/Sample.txt' USING PigStorage(',') AS (id:chararray, c1:chararray, c2:chararray);

STORE A INTO 'simple_hcat_load_table' USING org.apache.hive.hcatalog.pig.HCatStorer();

Use Pig command to populate the HBase table via HCatalog bulkload:-

Continuing with the example, execute the following command:

Command > pig -useHCatalog Hbase-bulk-load.pig

Command > pig Hbase-bulk-load.pig 

(Since in my system it failed to read the Sample.txt data from HDFS I used local storage for my ease of usage by giving command pig -x local Hbase-bulk-load.pig or pig -x local -useHCatalog Hbase-bulk-load.pig )

Goto HBase shell and give scan command to check the result:-

Below is another example for achieving the same (I have not tried it.).

A = LOAD '/hbasetest.txt' USING PigStorage(',') as (id:chararray, c1:chararray, c2:chararray);

STORE A INTO 'hbase://mydata'  USING
org.apache.pig.backend.hadoop.hbase.HBaseStorage('mycf:intdata');

3. Using MapReduce API.

HBase's Put API can be used to insert the data into HDFS, but the data has to go through the complete HBase path as explained here. So, for inserting the data in bulk into HBase using the Put API is lot slower than the bulk loading option. There are some references to bulk loading (1, 2), but either they are incomplete or a bit too complicated.

1. Extract data from source(in our case from Text File).
2. Transform data into HFiles.
3. Loading the files into HBase by telling RegionServers where to find them.

Below is the coding I used for the same for my Sample.txt data file. You can modify it according to your requirement.

NOTE:- This code doesn't create a tablein HBase so, before ruuning this code in Hadoop environment, make sure to create a table in HBase using create command with coulmn families.

HBaseBulkLoadDriver

DRIVER CLASS

package com.poc.hbase;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.conf.Configured;

import org.apache.hadoop.fs.FileSystem;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.hbase.HBaseConfiguration;

import org.apache.hadoop.hbase.client.HTable;

import org.apache.hadoop.hbase.client.Put;

import org.apache.hadoop.hbase.io.ImmutableBytesWritable;

import org.apache.hadoop.hbase.mapreduce.HFileOutputFormat;

import org.apache.hadoop.mapreduce.Job;

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

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

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

import org.apache.hadoop.util.Tool;

import org.apache.hadoop.util.ToolRunner;

public class HBaseBulkLoadDriver extends Configured implements Tool {

    private static final String DATA_SEPERATOR = ",";

    private static final String TABLE_NAME = "sample-data";

    private static final String COLUMN_FAMILY_1="cf1";

    private static final String COLUMN_FAMILY_2="cf2";

  

    public static void main(String[] args) {

        try {

            int response = ToolRunner.run(HBaseConfiguration.create(), new HBaseBulkLoadDriver(), args);

            if(response == 0) {

                System.out.println("Job is successfully completed...");

            } else {

                System.out.println("Job failed...");

            }

        } catch(Exception exception) {

            exception.printStackTrace();

        }

    }

    @Override

    public int run(String[] args) throws Exception {

        int result=0;

        String outputPath = args[1];

        Configuration configuration = getConf();

        configuration.set("data.seperator", DATA_SEPERATOR);

        configuration.set("hbase.table.name",TABLE_NAME);

        configuration.set("COLUMN_FAMILY_1",COLUMN_FAMILY_1);

        configuration.set("COLUMN_FAMILY_2",COLUMN_FAMILY_2);

        Job job = new Job(configuration);

        job.setJarByClass(HBaseBulkLoadDriver.class);

        job.setJobName("Bulk Loading HBase Table::"+TABLE_NAME);

        job.setInputFormatClass(TextInputFormat.class);

        job.setMapOutputKeyClass(ImmutableBytesWritable.class);

        job.setMapperClass(HBaseBulkLoadMapper.class);

        FileInputFormat.addInputPaths(job, args[0]);

        FileSystem.getLocal(getConf()).delete(new Path(outputPath), true);

        FileOutputFormat.setOutputPath(job, new Path(outputPath));

        job.setMapOutputValueClass(Put.class);

        HFileOutputFormat.configureIncrementalLoad(job, new HTable(configuration,TABLE_NAME));

        job.waitForCompletion(true);

        if (job.isSuccessful()) {

            HBaseBulkLoad.doBulkLoad(outputPath, TABLE_NAME);

        } else {

            result = -1;

        }

        return result;

    }

}

HBaseBulkLoadMapper

MAPPER CLASS

package com.poc.hbase;

import org.apache.hadoop.hbase.client.Put;
import org.apache.hadoop.hbase.util.Bytes;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
import org.apache.hadoop.mapreduce.Mapper;

public class HBaseBulkLoadMapper extends Mapper<LongWritable, Text, ImmutableBytesWritable, Put> {
    private String hbaseTable;
    private String dataSeperator;
    private String columnFamily1;
    private String columnFamily2;
    private ImmutableBytesWritable hbaseTableName;

    public void setup(Context context) {
        Configuration configuration = context.getConfiguration();
        hbaseTable = configuration.get("hbase.table.name");
        dataSeperator = configuration.get("data.seperator");
        columnFamily1 = configuration.get("COLUMN_FAMILY_1");
        columnFamily2 = configuration.get("COLUMN_FAMILY_2");
        hbaseTableName = new ImmutableBytesWritable(Bytes.toBytes(hbaseTable));
    }

    public void map(LongWritable key, Text value, Context context) {
        try {
            String[] values = value.toString().split(dataSeperator);
            String rowKey = values[0];
            Put put = new Put(Bytes.toBytes(rowKey));
            put.add(Bytes.toBytes(columnFamily1), Bytes.toBytes("cf1"), Bytes.toBytes(values[1]));
            put.add(Bytes.toBytes(columnFamily2), Bytes.toBytes("cf2"), Bytes.toBytes(values[2]));
            context.write(hbaseTableName, put);
        } catch(Exception exception) {
            exception.printStackTrace();
        }
    }
}

HBaseBulkLoad

HBASE CONFIGURATION CLASS

package com.poc.hbase;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.hbase.HBaseConfiguration;
import org.apache.hadoop.hbase.client.HTable;
import org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles;

public class HBaseBulkLoad {

public static void doBulkLoad(String pathToHFile, String tableName) {
try {
Configuration configuration = new Configuration();
configuration.set("mapreduce.child.java.opts", "-Xmx1g");
HBaseConfiguration.addHbaseResources(configuration);
LoadIncrementalHFiles loadFfiles = new LoadIncrementalHFiles(configuration);
HTable hTable = new HTable(configuration, tableName);
loadFfiles.doBulkLoad(new Path(pathToHFile), hTable);
System.out.println("Bulk Load Completed..");
} catch (Exception exception) {
exception.printStackTrace();
}
}
}

NOTE:- To create a table you can tweak and use below coding.

You have to create the table first using Java API. You can do it with the below code

//Create table and do pre-split
HTableDescriptor descriptor = new HTableDescriptor(
Bytes.toBytes(tableName)
);

descriptor.addFamily(
new HColumnDescriptor(Constants.COLUMN_FAMILY_NAME)
);

HBaseAdmin admin = new HBaseAdmin(config);

byte[] startKey = new byte[16];
Arrays.fill(startKey, (byte) 0);

byte[] endKey = new byte[16];
Arrays.fill(endKey, (byte)255);

admin.createTable(descriptor, startKey, endKey, REGIONS_COUNT);
admin.close();

Run the Jar File

Compile the above coding in eclipse with including HBase jars while compilation and export the jar file and run.

Command > hadoop jar hbase.jar com/poc/hbase/HBaseBulkLoadDriver /Input/Sample.txt /Out

Now goto HBase terminal to check data is loaded.

Hbase shell > scan 'sample-table'

That's all friends...

Now go ahead and tweak the coding to learn more about  HBase working Mechanism.

References:-

http://hbase.apache.org/book.html#_preface

https://docs.hortonworks.com/HDPDocuments/HDP2/HDP-2.3.2/bk_importing_data_into_hbase_guide/content/ch_importing_data_into_hbase_chapter.html

http://blog.cloudera.com/blog/2013/09/how-to-use-hbase-bulk-loading-and-why/

http://blogs.perficient.com/delivery/blog/2015/09/09/some-ways-load-data-from-hdfs-to-hbase/

http://salsahpc.indiana.edu/ScienceCloud/hbase_hands_on_1.htm#r_hbase

http://www.rswebsols.com/tutorials/programming/bulk-load-big-data-hadoop-hbase-table

Hope you all understood the procedures... 

Please do notify me for any corrections...

Kindly leave a comment for any queries/clarification...

(Detailed Description of each phase to be added soon).

ALL D BEST...