Gemstone Data Compression with Hadoop

Introduction

Welcome to the Royale Academy of Magical Arts, a prestigious institution dedicated to the study and mastery of arcane knowledge. In this realm, a team of esteemed Gemstone Researchers is tasked with unlocking the secrets of enchanted gemstones, whose mystical properties hold the key to understanding the very fabric of reality.

Your role as a skilled Gemstone Researcher is to harness the power of the Hadoop ecosystem, specifically Hive, to analyze and compress vast troves of gemstone data. The goal is to optimize storage and processing efficiency, enabling you to unravel the intricate patterns and hidden mysteries within these enchanted artifacts.

Skills Graph

Set Up the Gemstone Data Repository

In this step, you will create a Hive table to store the gemstone data and populate it with sample records.

First, ensure you are logged in as the hadoop user by running the following command in the terminal:

su - hadoop

Then, launch the Hive shell by executing the following command:

hive

Now, create a new Hive database called gemstone_db:

CREATE DATABASE gemstone_db;

Use the new database:

USE gemstone_db;

Next, create a table named gemstones with columns for id, name, color, origin, and enchantment:

CREATE TABLE gemstones (
  id INT,
  name STRING,
  color STRING,
  origin STRING,
  enchantment STRING
) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';

Finally, load sample data from the /home/hadoop/gemstone_data.csv file into the gemstones table:

LOAD DATA LOCAL INPATH '/home/hadoop/gemstone_data.csv' OVERWRITE INTO TABLE gemstones;

Compress the Gemstone Data

To optimize storage and processing efficiency, we will compress the gemstone data using Hive's built-in compression capabilities.

First, create a new table gemstones_compressed with the same schema as the original gemstones table:

CREATE TABLE gemstones_compressed (
  id INT,
  name STRING,
  color STRING,
  origin STRING,
  enchantment STRING
) STORED AS ORC
TBLPROPERTIES ('orc.compress'='SNAPPY');

• Specify the STORED AS ORC clause to use the Optimized Row Columnar (ORC) file format, which provides efficient compression and storage.
• Set the orc.compress table property to SNAPPY, which enables Snappy compression for the ORC files.

Then, insert data from the original gemstones table into the gemstones_compressed table:

INSERT INTO TABLE gemstones_compressed SELECT * FROM gemstones;

Query the Compressed Data

Now that the gemstone data is compressed, you can query it efficiently using Hive.

First, execute a simple COUNT(*) query on the gemstones_compressed table to verify the data integrity:

SELECT COUNT(*) FROM gemstones_compressed;

Then, perform a GROUP BY query to count the number of gemstones for each color:

SELECT color, COUNT(*) AS count FROM gemstones_compressed GROUP BY color;

Summary

In this lab, you learned how to leverage Hive's compression capabilities to optimize storage and processing efficiency for large datasets. By creating a compressed ORC table and loading data into it, you were able to significantly reduce the storage footprint while maintaining query performance.

Throughout the process, you gained hands-on experience with creating Hive databases and tables, loading data, and querying compressed data. This practical knowledge will be invaluable as you continue your research into the mystical properties of enchanted gemstones, enabling you to uncover hidden patterns and insights more efficiently.