Hadoop Date Mastery for Astronomers
Tags: coding, programming, tutorial, hadoop
Published: 2024-06-19 11:54:35


## Introduction

The year is 2285, and humanity has established a thriving space station orbiting the planet Mars. This research facility, known as the Martian Observatory, serves as a hub for scientific exploration and discovery. Among the many scientists stationed here is Dr. Emma Wilkins, a brilliant data analyst specializing in astronomical observations.

Dr. Wilkins has been tasked with analyzing vast amounts of data collected from various telescopes and instruments aboard the station. However, the data is in a raw format, and she needs to process and manipulate it to extract meaningful insights. One of the critical challenges she faces is working with date and time information, as many of the observations are time-sensitive and require accurate date calculations.

To tackle this challenge, Dr. Wilkins must leverage the powerful date operating functions available in Hadoop Hive, a data warehousing solution designed for big data processing. By mastering these functions, she can efficiently manipulate and analyze the date and time data, enabling her to uncover patterns, trends, and anomalies that could lead to groundbreaking discoveries in the field of astronomy.


## Setting Up the Hive Environment

In this step, we will set up the Hive environment and create a sample dataset for practicing date operating functions.

1. First, switch to the `hadoop` user by running the following command in the terminal:

```bash
su - hadoop
```

2. Now, launch the Hive shell by executing the following command:

```bash
hive
```

3. Create a new Hive table called `observations` with the following schema:

```sql
CREATE TABLE observations (
    observation_id INT,
    telescope STRING,
    observation_date STRING,
    observation_time STRING
)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY ','
STORED AS TEXTFILE;
```

4. Load some sample data into the `observations` table:

```sql
LOAD DATA LOCAL INPATH '/home/hadoop/resources/observations.csv' OVERWRITE INTO TABLE observations;
```

The `observations.csv` file contains sample observation data with columns for `observation_id`, `telescope`, `observation_date` (in the format `yyyy-MM-dd`), and `observation_time` (in the format `HH:mm:ss`).


## Using the year() Function

In this step, we will learn how to use the `year()` function in Hive to extract the year from a date string.

The `year()` function takes a date or timestamp string as input and returns the year component as an integer value.

1. Open the Hive console by running the `hive` command in the terminal.

2. Execute the following query to extract the year from the `observation_date` column:

```sql
SELECT observation_id, year(observation_date) AS observation_year
FROM observations;
```

This query will return the `observation_id` and the corresponding year for each observation in the table.

3. You can also use the `year()` function in combination with other date functions or clauses. For example, to filter observations from a specific year, you can use the following query:

```sql
SELECT *
FROM observations
WHERE year(observation_date) = 2022;
```

This query will return all observations where the year component of the `observation_date` is 2022.


## Using the datediff() Function

In this step, we will learn how to use the `datediff()` function in Hive to calculate the difference between two dates.

The `datediff()` function takes two date or timestamp strings as input and returns the number of days between them.

1. Open the Hive console if it's not already open.

2. Execute the following query to calculate the number of days between two observation dates:

```sql
SELECT observation_id,
       observation_date,
       '2022-12-31' AS reference_date,
       datediff('2022-12-31', observation_date) AS days_until_end_of_year
FROM observations;
```

This query will return the `observation_id`, `observation_date`, a reference date (`2022-12-31`), and the number of days between the `observation_date` and the reference date (`days_until_end_of_year`).

3. You can also use the `datediff()` function with other date functions or clauses. For example, to filter observations within a specific date range, you can use the following query:

```sql
SELECT *
FROM observations
WHERE datediff(observation_date, '2022-01-01') BETWEEN 0 AND 180;
```

This query will return all observations where the `observation_date` is between January 1, 2022, and June 30, 2022 (inclusive).


## Using the date_format() Function

In this step, we will learn how to use the `date_format()` function in Hive to convert a date string from one format to another.

The `date_format()` function takes two arguments: a date or timestamp string and a format pattern. It returns the date or timestamp string in the specified format pattern.

1. Open the Hive console if it's not already open.

2. Execute the following query to convert the `observation_date` column from the `yyyy-MM-dd` format to the `MMM dd, yyyy` format:

```sql
SELECT observation_id,
       observation_date,
       date_format(observation_date, 'MMM dd, yyyy') AS formatted_date
FROM observations;
```

This query will return the `observation_id`, the original `observation_date`, and the formatted date (`formatted_date`) in the `MMM dd, yyyy` format (e.g., `Jun 15, 2022`).

3. You can also use the `date_format()` function with other date functions or clauses. For example, to filter observations based on a specific date format, you can use the following query:

```sql
SELECT *
FROM observations
WHERE date_format(observation_date, 'yyyy/MM/dd') = '2022/06/15';
```

This query will return all observations where the `observation_date`, when formatted as `yyyy/MM/dd`, is equal to `2022/06/15`.


## Using the add_months() Function

In this step, we will learn how to use the `add_months()` function in Hive to add or subtract months from a date.

The `add_months()` function takes two arguments: a date or timestamp string and an integer value representing the number of months to add or subtract.

1. Open the Hive console if it's not already open.

2. Execute the following query to add six months to the `observation_date` column:

```sql
SELECT observation_id,
       observation_date,
       add_months(observation_date, 6) AS date_plus_six_months
FROM observations;
```

This query will return the `observation_id`, the original `observation_date`, and the date six months after the `observation_date` (`date_plus_six_months`).

3. You can also use the `add_months()` function with other date functions or clauses. For example, to filter observations within a specific month range, you can use the following query:

```sql
SELECT *
FROM observations
WHERE month(add_months(observation_date, 6)) BETWEEN 1 AND 6;
```

This query will return all observations where the month component of the date six months after the `observation_date` is between January and June (inclusive).


## Summary

In this lab, we explored the world of date operating functions in Hadoop Hive, a powerful data warehousing solution for big data processing. Through a captivating scenario set in a futuristic space station orbiting Mars, we followed the journey of Dr. Emma Wilkins, a brilliant data analyst tasked with analyzing astronomical observations.

By mastering date operating functions such as `year()`, `datediff()`, `date_format()`, and `add_months()`, Dr. Wilkins gained the ability to efficiently manipulate and analyze date and time data, enabling her to uncover patterns, trends, and anomalies that could lead to groundbreaking discoveries in the field of astronomy.

Throughout the lab, we delved into hands-on examples and provided checkers to ensure a seamless learning experience. The interactive nature of the lab allowed learners to practice and reinforce their understanding of these essential functions, laying a solid foundation for more advanced data analysis techniques.

Overall, this lab not only imparted valuable technical skills but also fostered a sense of wonder and curiosity about the vast expanse of the cosmos. By empowering learners with the tools to unlock the secrets hidden within astronomical data, we paved the way for future generations of scientists to push the boundaries of human knowledge and exploration.

---

## Want to learn more?

- 🚀 Practice [Hadoop Date Mastery for Astronomers](https://labex.io/tutorials/hadoop-hadoop-date-mastery-for-astronomers-288963)
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