Data Analysis Using MapReduce Program - Examining Weather Records to Identify Extreme Temperature Days

Data Analysis Using MapReduce Program - Examining Weather Records to Identify Extreme Temperature Days

In this article, we'll walk through creating a MapReduce program in Hadoop using Java to identify hot and cold days from large-scale NCEI weather data. Here's a step-by-step guide to help you get started.

1. Setup your Java project in Eclipse configured with the Hadoop MapReduce libraries.
2. Create a Mapper class that:
3. Extends .
4. Parses each weather record (a line from the input dataset) to extract:
   • The date.
   • The temperature measurement.
5. Emits key-value pairs with the date as the key and the temperature as the value.
6. Create a Reducer class that:
7. Extends .
8. Receives all temperature values for each date.
9. Compares the temperature to defined thresholds to classify the day as "hot", "cold", or "normal".
10. Emits the date and the classification result.
11. Create a Driver class that:
12. Configures the job.
13. Sets Mapper, Reducer, input and output formats.
14. Defines input and output paths (e.g., data on HDFS).
15. Submits the job to the Hadoop cluster or local Hadoop setup.

Detailed guidance for each component

Example Mapper outline

Example Reducer outline

Example Driver outline

Important notes

• Dataset format: NCEI weather data often includes temperature and date fields in CSV or fixed-width format. Adjust parsing logic in the Mapper accordingly.
• Temperature thresholds (e.g., 30°C for hot, 10°C for cold) can be customized based on your domain knowledge or requirements.
• Input and output on HDFS: Before running, upload your dataset to HDFS, and ensure output directory doesn’t exist to avoid errors.
• You can run and debug locally in Eclipse by installing Hadoop dependencies in your build path and running the function of the Driver class.
• The output of the MapReduce job is saved in a file named .
• To view the output, visit , navigate to , and download the result file.

This approach aligns with how the MapReduce framework processes data by emitting key-value pairs in the Mapper, grouping values by key, and then reducing those in the Reducer — precisely suited for large-scale weather data analysis. Java is the recommended language for Hadoop programming, and Eclipse is a suitable IDE for development.

The goal of this project is to identify temperature extremes (hot and cold days). To do so, hot days are defined as days with temperatures exceeding 30°C, while the definition of cold days is not explicitly provided in this context.

To implement this program, you'll need to export your project as a JAR file, and HDFS and YARN daemons need to be started for the program to run. Additionally, external JAR files (Hadoop Common and Hadoop MapReduce Core) need to be added to your project for correct package functionality. To check the Hadoop version, use the command "hadoop version".

Step 8: Run the MapReduce Job command is .

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