Running Hadoop Services: Ensure Yarn resource manager, Namenode, Datanodes, and other crucial services are operational within the cluster.
Version Compatibility: Confirm your MapReduce application’s version compatibility with the cluster’s Hadoop version.
Application Development:
Map and Reduce Functions: Write your Map and Reduce functions in Java, Python, or another supported language. Implement the logic for processing data in the Map phase and aggregating/combining data in the Reduce phase.
Job Configuration: Specify the input and output paths for the data, the number of reducers (optional), compression codecs (optional), and other relevant settings.
2. Job Submission
Command-Line Submission: Use the hadoop jar command, mentioning the JAR file containing your compiled application, the main class to run, and the configuration arguments you defined.
Alternative Methods: Consider tools like YARN web UI or client APIs for a graphical or programmatic interface to submit and manage jobs.
3. Job Execution Flow
Job Scheduling: The JobTracker/Resource Manager in Yarn takes charge, dividing the input data into splits and assigning them to individual Map tasks on available nodes.
Map Phase: Each Map task processes its assigned split, invoking your Map function on each record within the split. This function generates key-value pairs as output, representing intermediate results.
Shuffle and Sort: The key-value pairs are shuffled across the cluster based on their keys (hashing function used) and sorted within each reducer’s input for efficient grouping.
Reduce Phase: Reduce tasks receive groups of key-value pairs with the same key. Your Reduce function is invoked on each group of values, aggregating or combining them to produce final output.
4. Monitoring and Analysis
Job Progress Tracking: Utilize cluster web UIs, command-line tools like mapred job -jobid <job_id> -status, or APIs to monitor the progress of Map and Reduce tasks, resource utilization, and overall job completion.
Output Analysis: Access the output files stored in HDFS (typically) and analyze the results based on your desired insights.
