Sample and Practice code from the training session

Environment setup

If you are using it for first time, then please install

1. Setup your local PC for git
2. Clone the Repo:

git clone https://github.com/becloudready/hadoop-tutorials.git

Instructions for Module 4

Gitbash Once cloned the repo, make sure the code works by doing a local map reduce

echo "foo foo quux labs foo bar quux" | ./mapper.py | sort -k1,1 | ./reducer.py
bar	1
foo	3
labs	1
quux	2

Download the sample file

wget http://www.gutenberg.org/files/5000/5000-8.txt

Upload it to HDFS

hdfs dfs -mkdir /user/ssh
hdfs dfs -copyFromLocal 5000-8.txt /user/ssh

Run the Map reduce job [ Azure HDInsight ]

hadoop jar /usr/hdp/current/hadoop-mapreduce-client/hadoop-streaming-2.7.3.2.6.5.3015-8.jar \
-file /home/sshuser/hadoop-tutorials/Module4/mapper.py    -mapper /home/sshuser/hadoop-tutorials/Module4/mapper.py \
-file /home/sshuser/hadoop-tutorials/Module4/reducer.py   -reducer /home/sshuser/hadoop-tutorials/Module4/reducer.py \
-input /user/ssh/5000-8.txt -output /user/ssh/output

Run the MapReduce job [AWS EMR ]

hadoop jar /usr/lib/hadoop-mapreduce/hadoop-streaming-2.8.4-amzn-1.jar \
-file /home/hadoop/mapper.py    -mapper /home/hadoop/mapper.py \
-file /home/hadoop/reducer.py   -reducer /home/hadoop/reducer.py \
-input /user/ssh/5000-8.txt -output /user/ssh/output

Run the MapReduce job [ Cloudera Sandbox ]

hadoop jar /usr/lib/hadoop-0.20-mapreduce/contrib/streaming/hadoop-streaming-2.6.0-mr1-cdh5.12.0.jar -file /home/cloudera/mapper.py    -mapper /home/cloudera/mapper.py -file /home/cloudera/reducer.py   -reducer /home/cloudera/reducer.py -input /user/cloudera/*.py -output /user/cloudera/output

Instructions for Module 5 - Spark

WordCount Spark Example:

Upload a large file into HDFS

wget http://www.gutenberg.org/files/5000/5000-8.txt
hdfs dfs -mkdir /user/sshuser
hdfs dfs -copyFromLocal 5000-8.txt /user/sshuser

Start pyspark interactive session

pyspark

Initialize the Spark Context

text_file = sc.textFile("/user/sshuser/5000-8.txt")

Create flatmap variable

counts = text_file.flatMap(lambda line: line.split(" ")).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a + b)

Save the RDD into File

counts.saveAsTextFile("/user/sshuser/output")

Output will be stored in the directory

hdfs dfs -ls /user/sshuser/output
Found 3 items
-rw-r--r--   1 sshuser supergroup          0 2018-03-17 16:08 /user/sshuser/output/_SUCCESS
-rw-r--r--   1 sshuser supergroup     270851 2018-03-17 16:08 /user/sshuser/output/part-00000
-rw-r--r--   1 sshuser supergroup     269236 2018-03-17 16:08 /user/sshuser/output/part-00001

Spark SQL Example

pyspark --packages com.databricks:spark-csv_2.10:1.4.0
df1 = sqlContext.sql("SELECT * FROM default.hivesampletable")
df1.show()

Load Databrick CSV parsing library/module

df = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('/user/sshuser/cars.csv')
df.show()
df.printSchema()
df.select("mpg").show()
df.select(df['mpg'],df['hp']+ 1).show()
df.filter(df['mpg'] > 9).show()
df.groupBy("_c0").count().show()

HomeWork:

1. Take a large text file 10 GB, and run the word count program and compare the performance between MapReduce and Spark
2. Take a CSV file create hive table out of it, compare performance between SparkSQL and Hive queries

Module 6

Pig Examples

Download the data set

wget https://raw.githubusercontent.com/hortonworks/data-tutorials/master/tutorials/hdp/beginners-guide-to-apache-pig/assets/driver_data.zip

Unzip and upload it to HDFS

Hbase Examples

Switch to hbase user

su - hbase
hbase shell

Data Definition language commands in Hbase shell

hbase>create 'driver_dangerous_event','events'
hbase>list

Download the sample data file -- Run it on Linux Prompt

curl -o ~/data.csv https://raw.githubusercontent.com/hortonworks/data-tutorials/d0468e45ad38b7405570e250a39cf998def5af0f/tutorials/hdp/hdp-2.5/introduction-to-apache-hbase-concepts-apache-phoenix-and-new-backup-restore-utility-in-hbase/assets/data.csv

hdfs dfs -put ~/data.csv /tmp

Load the CSV file into Hbase -- Run it on Linux Prompt

hbase org.apache.hadoop.hbase.mapreduce.ImportTsv -Dimporttsv.separator=,  -Dimporttsv.columns="HBASE_ROW_KEY,events:driverId,events:driverName,events:eventTime,events:eventType,events:latitudeColumn,events:longitudeColumn,events:routeId,events:routeName,events:truckId" driver_dangerous_event hdfs://sandbox-hdp.hortonworks.com:/tmp/data.csv

Scan the table -- Run it on Hbase shell

scan 'driver_dangerous_event'

Insert Commands for hbase

put '<table_name>','row1','<column_family:column_name>','value'

put 'driver_dangerous_event','4','events:driverId','78'
put 'driver_dangerous_event','4','events:driverName','Carl'
put 'driver_dangerous_event','4','events:eventTime','2016-09-23 03:25:03.567'
put 'driver_dangerous_event','4','events:eventType','Normal'
put 'driver_dangerous_event','4','events:latitudeColumn','37.484938'
put 'driver_dangerous_event','4','events:longitudeColumn','-119.966284'
put 'driver_dangerous_event','4','events:routeId','845'
put 'driver_dangerous_event','4','events:routeName','Santa Clara to San Diego'
put 'driver_dangerous_event','4','events:truckId','637'

Select/Get Commands get '<table_name>','<row_number>' get 'table_name', 'row_number', {COLUMN ⇒ 'column_family:column-name '}

get 'driver_dangerous_event','1',{COLUMN => 'events:driverName'}
get 'driver_dangerous_event','1',{COLUMNS => ['events:driverName','events:routeId']}

Home work

1. Try pig and hbase examples using car.csv data file

Module 7

Sqoop Tutorial

Download Mysql java connector from

https://dev.mysql.com/downloads/connector/j/

Extract and copy the bin.jar to

sudo cp mysql-connector-java-5.1.46-bin.jar /usr/hdp/current/sqoop-client/lib/

Install MySQL on Azure Ubuntu box

sudo apt install mysql-server

It will prompt to choose mysql root password put test

Now you need to modify mysql to run on all ports so that MR jobs can access it from any node.

edit the file /etc/mysql/mysql.conf.d/mysqld.cnf and modify the bind-address to 0.0.0.0

bind-address            = 0.0.0.0

sudo cp my.cnf /etc/mysql/mysql.conf.d/mysqld.cnf

Restart Mysql

sudo /etc/init.d/mysql restart

Now login to mysql and grant priviledges

mysql -uroot -ptest

GRANT ALL ON *.* to root@'%' IDENTIFIED BY 'test'; 
GRANT ALL ON *.* to root@'localhost' IDENTIFIED BY 'test'; 

Make sure you can login to mysql using -h parameter example

mysql -h10.0.0.18 -uroot -ptest

Now clone the sample data from github

git clone https://github.com/datacharmer/test_db.git

Load the data into DB

cd test_db
mysql -uroot -ptest < employees.sql

Run ingest this data into Hive

sqoop import --connect jdbc:mysql://hn0-omni21.w3rnsdgdzaie1pas2ohmnw4jfd.cx.internal.cloudapp.net:3306/employees --username root -P --split-by emp_no --columns emp_no,first_name --table employees  --target-dir /user/sshuser/sq2 --fields-terminated-by "," --hive-import --create-hive-table --hive-table default.employee