Install HDFS and YARN on the cluster

From info319

Install Java

You need Java on each instance. We will use an older, stable version in case some of the tools are not upgraded to more recent versions: 

sudo apt update  # ('sudo apt upgrade' is also a good idea on a new instance)
sudo apt install -y openjdk-8-jdk-headless
export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64
cp ~/.bashrc ~/.bashrc-bkp-$(date --iso-8601=minutes --utc)
echo "export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64" >> ~/.bashrc

Explanation:

  • export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64: sets an environment variable temporarily
  • cp ~/.bashrc ~/.bashrc-bkp-$(date --iso-8601=minutes --utc): creates a time-stamped backup of the initialisation file ~/.bashrc
  • echo "export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64" >> ~/.bashrc: adds the export command to the initialisation so the environment variable becomes permanent

Install Hadoop

On each instance, go to http://hadoop.apache.org and download a recent binary distribution to ~/volume. For example: 

cd ~/volume
wget https://dlcdn.apache.org/hadoop/common/hadoop-3.3.4/hadoop-3.3.4.tar.gz
tar xzvf hadoop-3.3.4.tar.gz
rm hadoop-3.3.4.tar.gz
ln -fns hadoop-3.3.4 hadoop

On each instance, set the environment variable HADOOP_HOME and add Hadoop's bin folder to your PATH: 

export HADOOP_HOME=/home/ubuntu/volume/hadoop
export PATH=${PATH}:${HADOOP_HOME}/bin:${HADOOP_HOME}/sbin
cp ~/.bashrc ~/.bashrc-bkp-$(date --iso-8601=minutes --utc)
echo "export HADOOP_HOME=/home/ubuntu/volume/hadoop" >> ~/.bashrc
echo "export PATH=\${PATH}:\${HADOOP_HOME}/bin:\${HADOOP_HOME}/sbin" >> ~/.bashrc

If something goes wrong with a file update, install a text editor to fix it, such as sudo apt emacs or sudo apt nano.

On each instance, set additional environment variables for Hadoop: 

export HADOOP_MAPRED_HOME=${HADOOP_HOME}
export HADOOP_COMMON_HOME=${HADOOP_HOME}
export HADOOP_HDFS_HOME=${HADOOP_HOME}
export YARN_HOME=${HADOOP_HOME}
export HADOOP_CONF=${HADOOP_HOME}/etc/hadoop
export HADOOP_CONF_DIR=${HADOOP_CONF}
export LD_LIBRARY_PATH=${HADOOP_HOME}/lib/native:${LD_LIBRARY_PATH}
export HDFS_DATA=${HADOOP_HOME}/hdfs/data
cp ~/.bashrc ~/.bashrc-bkp-$(date --iso-8601=minutes --utc)
echo "export HADOOP_MAPRED_HOME=\${HADOOP_HOME}" >> ~/.bashrc
echo "export HADOOP_COMMON_HOME=\${HADOOP_HOME}" >> ~/.bashrc
echo "export HADOOP_HDFS_HOME=\${HADOOP_HOME}" >> ~/.bashrc
echo "export YARN_HOME=\${HADOOP_HOME}" >> ~/.bashrc
echo "export HADOOP_CONF=\${HADOOP_HOME}/etc/hadoop" >>  ~/.bashrc
echo "export HADOOP_CONF_DIR=\${HADOOP_CONF}" >>  ~/.bashrc
echo "export LD_LIBRARY_PATH=\${HADOOP_HOME}/lib/native:\${LD_LIBRARY_PATH}" >>  ~/.bashrc
echo "export HDFS_DATA=\${HADOOP_HOME}/hdfs/data" >>  ~/.bashrc

mkdir -p ${HDFS_DATA}  # this is where HDFS will store its data

Configure Hadoop

On each instance, extract further environment variables from /etc/hosts: 

export MASTER_NODE=$(grep spark-driver /etc/hosts | cut -d" " -f1)
export HADOOP_NAMENODE=${MASTER_NODE}
export NUM_WORKERS=$(grep spark- /etc/hosts | wc -l | cut -d" " -f1)
cp ~/.bashrc ~/.bashrc-bkp-$(date --iso-8601=minutes --utc)
echo "export MASTER_NODE=\$(grep spark-driver /etc/hosts | cut -d\" \" -f1)" >>  ~/.bashrc
echo "export HADOOP_NAMENODE=\${MASTER_NODE}" >>  ~/.bashrc
echo "export NUM_WORKERS=\$(grep spark- /etc/hosts | wc -l | cut -d\" \" -f1)" >>  ~/.bashrc

Create these four files on your local computer: 

File "core-site.xml":

<configuration>
    <property>
        <name>fs.defaultFS</name>
        <value>hdfs://${HADOOP_NAMENODE}:9000</value>
    </property>
</configuration>


File "hdfs-site.xml":

<configuration>
    <property>
        <name>dfs.replication</name>
        <value>${NUM_WORKERS}</value>
    </property>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>file://${HDFS_DATA}</value>
    </property>
    <property>
        <name>dfs.datanode.data.dir</name>
        <value>file://${HDFS_DATA}</value>
    </property>
</configuration>


File "yarn-site.xml":

<configuration>
    <property>
        <name>yarn.nodemanager.aux-services</name>
        <value>mapreduce_shuffle</value>
    </property>
    <property>
        <name>yarn.nodemanager.aux-services.mapreduce.shuffle.class</name>
        <value>org.apache.hadoop.mapred.ShuffleHandler</value>
    </property>
    <property>
       <name>yarn.resourcemanager.hostname</name>
       <value>${HADOOP_NAMENODE}</value>
    </property>
</configuration>


File mapred-site.xml:

<configuration>
    <property>
        <name>mapreduce.jobtracker.address</name>
        <value>${HADOOP_NAMENODE}</value>
    </property>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>
</configuration>

On your local machine: 

scp {core,hdfs,yarn}-site.xml spark-driver:
scp {core,hdfs,yarn}-site.xml spark-worker-1:
...
scp mapred-site.xml spark-driver:  # mapred-site.xml is only needed on the driver

On each instance, use the envsubst command to substitute variables names like ${NUM_WORKERS} in the xml-files with their values in the local environment: 

for name in core hdfs yarn mapred; do
    cp ${HADOOP_CONF}/${name}-site.xml ${HADOOP_CONF}/${name}-site.xml.original;
done
cd ~
for name in *-site.xml; do
    envsubst < ${name} > ${HADOOP_CONF}/${name};
done
rm ~/*-site.xml

On each instance, define the master and worker IPv4 addresses, and do a few other preparations: 

grep spark-driver /etc/hosts | cut -d" " -f2 > ${HADOOP_CONF}/masters
grep spark- /etc/hosts | cut -d" " -f2 > ${HADOOP_CONF}/workers
echo "JAVA_HOME=$JAVA_HOME" >> $HADOOP_CONF/hadoop-env.sh
mkdir -p -m 0777 $HADOOP_HOME/logs

Run HDFS on the cluster

On spark-driver: 

hdfs namenode -format  # answer 'Y' if you are asked about reformatting
start-dfs.sh

On any instance, test it with 

hdfs dfs -mkdir -p /user/ubuntu/raw-data
hdfs dfs -ls /user/ubuntu/  # on any machine in the spark-* cluster

From your local machine (this file is available at mitt.uib.no): 

scp head-100000-latest-all.json spark-ANY-INSTANCE:

On spark-ANY-INSTANCE: 

hdfs dfs -put ~/head-100000-latest-all.json raw-data

From another instance, use hdfs dfs -ls ... to check that the file is there.

On spark-driver, stop HDFS again: 

stop-dfs.sh

Run HDFS and YARN on the cluster

You are now ready to start YARN and HDFS together. On spark-drive run: 

start-all.sh

When you want to stop YARN and HDFS, use: 

stop-all.sh

Web UIs

If you have opened the ports on spark-driver, you can now access

(assuming that 158.39.201.197 is the IPv4 address of spark-driver).

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