Killing a YARN application

yarn jar /usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar pi 5 10

yarn application -kill application_id

ssh -i ~/Documents/nkityd.pem hadoop@ec2-54-236-239-19.compute-1.amazonaws.com cat /etc/hive/conf/hive-site.xml | grep "thrift://" | sed 's/\<value>//g' | sed 's/\<\/value>//g' | awk '{print "HMS URI: " $1 }'

"""
A simple example demonstrating basic Spark SQL features using fictional
data inspired by a paper on determining the optimum length of chopsticks.
https://www.ncbi.nlm.nih.gov/pubmed/15676839
Run with:
  ./bin/spark-submit OptimumChopstick.py
"""
from __future__ import print_function
from pyspark.sql import SparkSession
from pyspark.sql.types import *
from pyspark.storagelevel import StorageLevel
#rdd.persist(StorageLevel.MEMORY_ONLY_SER)
​
# Get avg Food pinching effeciency by length
def AvgEffeciencyByLength(df):
    meansDf = df.groupby('ChopstickLength').mean('FoodPinchingEffeciency').orderBy('avg(FoodPinchingEffeciency)',ascending=0)
    return meansDf
​
# init
spark = SparkSession.builder.appName("Optimum Chopstick").getOrCreate()
sc = spark.sparkContext
input_loc = "s3://llubbe-gdelt-open-data/ChopstickEffeciency/"
​
# Read input by line
lines = sc.textFile(input_loc)
parts = lines.map(lambda l: l.split(","))
parts.persist(StorageLevel.MEMORY_ONLY_SER)
# Each line is converted to a tuple.
chopstickItems = parts.map(lambda p: (str(p[0]), float(p[1]), int(p[2]), int(p[3].strip())))
​
# Define a schema
fields = [StructField("TestID", StringType()),
          StructField("FoodPinchingEffeciency", DoubleType()), 
          StructField("Individual", IntegerType()), 
          StructField("ChopstickLength", IntegerType())]
schema = StructType(fields)
​
# Apply the schema to the RDD
chopsticksDF = spark.createDataFrame(chopstickItems, schema)
​
effeciencyByLength = AvgEffeciencyByLength(chopsticksDF)
effeciencyByLength.distinct().count()
​
moar_chopsticksDF = spark.read.load(input_loc, format="csv", schema=schema)
moar_effeciencyByLength = AvgEffeciencyByLength(moar_chopsticksDF)
moar_effeciencyByLength.distinct().count()
​
spark.stop()

beeline -u "jdbc:hive2://localhost:10000/default" -n hdfs

mysql -h ip-172-31-39-192.ec2.internal  -u hive -p 

hadoop jar /usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar teragen 10000000 /nkityd/teragendata

hadoop jar /usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar terasort /nkityd/teragendata /nkityd/teragensorteddata/

yarn jar /usr/lib/hadoop-mapreduce/hadoop-mapreduce-examples.jar pi 5 10

yarn application -kill application_id

yarn node -list -showDetails

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>
  <groupId>com.amazonaws.emr</groupId>
  <artifactId>knet</artifactId>
  <version>0.0.1-SNAPSHOT</version>
  <name>firstMapReduce</name>
  
   <properties>
    <maven-compiler-plugin.version>3.1</maven-compiler-plugin.version>
    <java.version>1.8</java.version>
    <hadoop.version>2.8.3</hadoop.version>
  </properties>
  <dependencies>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-common</artifactId>
      <version>${hadoop.version}</version>
      <scope>provided</scope>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-common</artifactId>
      <version>${hadoop.version}</version>
      <scope>provided</scope>
  </dependency>
</dependencies>
<build>
          <plugins>
            <plugin>
              <artifactId>maven-compiler-plugin</artifactId>
              <version>${maven-compiler-plugin.version}</version>
              <configuration>
                <source>${java.version}</source>
                <target>${java.version}</target>
              </configuration>
            </plugin>
    </plugins>
  </build>
  
</project>

package com.amazonaws.emr.knet;

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

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 IntSumReducer
       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 = new Job(conf,"word count");
    job.setJarByClass(WordCount.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(IntSumReducer.class);
    job.setReducerClass(IntSumReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

#!/bin/bash
sudo yum install -y gcc python-setuptools python-devel postgresql-devel
sudo easy_install psycopg2
sudo python -m pip install psycopg2-binary
sudo yum install postgresql -y
PGPASSWORD=root1234 psql --username=postgres --host=database-1.cxv0eh6uhsan.us-east-1.rds.amazonaws.com --command='CREATE DATABASE hive_db'
PGPASSWORD=root1234 psql --username=postgres --host=database-1.cxv0eh6uhsan.us-east-1.rds.amazonaws.com --command='CREATE DATABASE oozie_db'
PGPASSWORD=root1234 psql --username=postgres --host=database-1.cxv0eh6uhsan.us-east-1.rds.amazonaws.com --command='create database hue_db with lc_collate="en_US.UTF-8"'
cd /var/aws/emr/bigtop-deploy/puppet/modules/hadoop_hive/manifests
File="init.pp"
if [ -f "$File" ]; then
mkdir /home/hadoop/code
sudo mv "$File" /home/hadoop/code/
aws s3 cp s3://nkityd-bucket/init.pp /home/hadoop/
sudo cp /home/hadoop/init.pp "$File"
fi
echo "Successful"

[
    {
        "Classification": "hive-site",
        "Properties": {
            "javax.jdo.option.ConnectionUserName": "postgres",
            "javax.jdo.option.ConnectionDriverName": "org.postgresql.Driver",
            "javax.jdo.option.ConnectionPassword": "root1234",
            "javax.jdo.option.ConnectionURL": "jdbc:postgresql://database-1.cxv0eh6uhsan.us-east-1.rds.amazonaws.com:5432/hive_db"
        }
    },
    {
        "Classification": "hue-ini",
        "Properties": {},
        "Configurations": [
            {
                "Classification": "desktop",
                "Properties": {},
                "Configurations": [
                    {
                        "Classification": "database",
                        "Properties": {
                            "password": "root1234",
                            "engine": "postgres",
                            "port": "5432",
                            "host": "database-1.cxv0eh6uhsan.us-east-1.rds.amazonaws.com",
                            "name": "hue_db",
                            "user": "postgres"
                        }
                    }
                ]
            }
        ]
    },
    {
        "Classification": "oozie-site",
        "Properties": {
            "oozie.service.JPAService.jdbc.password": "root1234",
            "oozie.service.JPAService.jdbc.url": "jdbc:postgresql://database-1.cxv0eh6uhsan.us-east-1.rds.amazonaws.com:5432/oozie_db",
            "oozie.service.JPAService.jdbc.driver": "org.postgresql.Driver",
            "oozie.service.JPAService.jdbc.username": "postgres"
        }
    }
]