Skip to main content

This Week's Best Picks from Amazon

Please see more curated items that we picked from Amazon here .

Group by and Having Clauses in SQL

To effectively work with SQL, it’s crucial to understand the logical order in which SQL executes its clauses. This understanding is essential for crafting queries that yield accurate and meaningful results. The logical execution order differs from the way queries are written, starting with the FROM clause, even though SELECT appears first in the syntax.

Logical Execution Order of SQL Clauses

The logical execution order is as follows:

Execution Order Clause
1 FROM
2 WHERE
3 GROUP BY
4 HAVING
5 SELECT
6 ORDER BY

Let’s use a real-world example involving sales data to illustrate these steps. Assume we have a table named sales_data with the following fields:

  • city: Name of the city
  • country: Country of the city
  • sales: Total sales in dollars

Step-by-Step Query Execution

Initial Data

The table sales_data contains the following data:

City Country Sales
New York USA 20,000
Los Angeles USA 15,000
Chicago USA 12,000
Beijing China 25,000
Shanghai China 18,000
Shenzhen China 10,000
Tokyo Japan 22,000
Osaka Japan 16,000

1. Filtering with WHERE

The WHERE clause filters cities with sales greater than 12,000:

SELECT city, country, sales
FROM sales_data
WHERE sales > 10000;

Result:

City Country Sales
New York USA 20,000
Los Angeles USA 15,000
Chicago USA 12,000
Beijing China 25,000
Shanghai China 18,000
Tokyo Japan 22,000
Osaka Japan 16,000

2. Grouping with GROUP BY

The GROUP BY clause aggregates the data by country:

SELECT country, SUM(sales) AS total_sales
FROM sales_data
WHERE sales > 10000
GROUP BY country;

Result:

Country Total Sales
USA 47,000
China 43,000
Japan 38,000

3. Filtering Groups with HAVING

The HAVING clause filters groups with total sales greater than 40,000:

SELECT country, SUM(sales) AS total_sales
FROM sales_data
WHERE sales > 10000
GROUP BY country
HAVING SUM(sales) > 40000;

Result:

Country Total Sales
USA 47,000

Key Takeaways

The WHERE clause filters rows before grouping, while the HAVING clause filters aggregated groups. Combining these effectively allows precise control over the data included in your query results.

Popular posts from this blog

Intelligent Agents and Their Application to Businesses

Intelligent agents, as a key technology in artificial intelligence (AI), have become central to a wide range of applications in both scientific research and business operations. These autonomous entities, designed to perceive their environment and adapt their behavior to achieve specific goals, are reshaping industries and driving innovation. This post provides a detailed analysis of the current state of intelligent agents, including definitions, theoretical and practical perspectives, technical characteristics, examples of business applications, and future prospects. Definitions and Terminology Intelligent agents are broadly defined as autonomous systems that can perceive and interact with their environments using sensors and actuators. Their autonomy enables them to make decisions and execute actions without constant human intervention. They operate with a specific goal or objective, which guides their decision-making processes. These entities may exi...
Read more

Data Visualization Communication Strategies

Data Visualization: Communicating Complex Information Effectively Data visualization plays a crucial role in communicating complex information in a clear and digestible manner. When effectively designed, visual representations of data enhance insight generation, facilitate decision-making, and persuade audiences to take action. The effectiveness of data visualization relies not only on the accuracy of the data but also on the strategic communication techniques employed in the design process (Kazakoff, 2022). This post examines three key data visualization communication strategies that improve audience engagement and understanding: audience-centered design, persuasive storytelling, and effective graph selection. The Importance of Audience-Centered Design A core component of effective data visualization is understanding the audience’s needs and preferences. The audience’s familiarity with the topic, their visual literacy, and their cognitive limitations influence how they interpret...
Read more

The Curse of Dimensionality: Why More Data Isn’t Always Better in Data Science

In data science, the phrase "more data leads to better models" is often heard. However, when "more data" means adding dimensions or features, it can lead to unexpected challenges. This phenomenon is known as the Curse of Dimensionality , a fundamental concept that explains the pitfalls of working with high-dimensional datasets. Let’s explore the mathematics behind it and practical techniques to overcome it. What is the Curse of Dimensionality? 1. Volume Growth in High Dimensions The volume of a space increases exponentially as the number of dimensions grows. For example, consider a unit hypercube with side length \(r = 1\). Its volume in \(d\)-dimensions is: \[ V = r^d = 1^d = 1 \] However, if the length of the side is slightly reduced, say \(r = 0.9\), the volume decreases drastically with increasing \(d\): \[ V = 0.9^d \] For \(d = 2\), \(V = 0.81\); for \(d = 10\), \(V = 0.35\); and for \(d = 100\), \(V = 0.00003\). This shows how...
Read more