Hadoop and Spark Installation on Raspberry Pi-3 Cluster – Part-4

In this part we will see the configuration of Slave Node. Here are the steps

  1. Mount second Raspberry Pi-3 device on the nylon standoffs (on top of Master Node)
  2. Load the image from part2 into a sd_card
  3. Insert the sd_card into one Raspberry Pi-3 (RPI) device
  4. Connect RPI to the keyboard via USB port
  5. Connect to monitor via HDMI cable
  6. Connect to Ethernet switch via ethernet port
  7. Connect to USB switch via micro usb slot
  8. Hadoop related changes on Slave node

Here Steps1-7 are all physical and hence I am skipping them.

Once the device is powered on, login via external keyboard and monitor and change the hostname from rpi3-0 (which comes from base image) to rpi3-1

Step #8: Hadoop Related Configuration

  • Setup HDFS
sudo mkdir -p /hdfs/tmp  
sudo chown hduser:hadoop /hdfs/tmp  
chmod 750 /hdfs/tmp  
hdfs namenode -format
  • Update /etc/hosts file	localhost	rpi3-0	rpi3-1	rpi3-2	rpi3-3
  • Repeat the above steps for each of the slave node. And for every addition of slave node, ensure
  • ssh is setup from master node to slave node
  • slaves file on master is updated
  • /etc/hosts file on both master and slave is updated

Start the hadoop/spark cluster

    • Start dfs and yarn services
cd /opt/hadoop-2.7.3/sbin 
    • On master node “jps” should show following
hduser@rpi3-0:~ $ jps
20421 ResourceManager
20526 NodeManager
19947 NameNode
20219 SecondaryNameNode
24555 Jps
20050 DataNode
    • On Slave Node “jps” should show following processes
hduser@rpi3-3:/opt/hadoop-2.7.3/logs $ jps
2294 NodeManager
2159 DataNode
2411 Jps
    • To verify the successful installation, run a hadoop and spark job in cluster mode and you will see the Application Master tracking URL.
    • Run spark Job
      • spark-submit –class com.learning.spark.SparkWordCount –master yarn –executor-memory 512m ~/word_count-0.0.1-SNAPSHOT.jar /ntallapa/word_count/text 2
    • Run example mapreduce job
      • hadoop jar /opt/hadoop-2.7.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.3.jar wordcount /ntallapa/word_count/text /ntallapa/word_count/output

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