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SQL Exercises : SQL Practice with Solution for Beginners and Experienced

SQL ( Structured Query Language ) is a powerful tool used for managing and manipulating relational databases. Whether we are beginners or experienced professionals, practicing SQL exercises is important for improving your skills. Regular practice helps you get better at using SQL and boosts your confidence in handling different database tasks.

So, in this free SQL exercises page, we’ll cover a series of SQL practice exercises covering a wide range of topics suitable for beginners , intermediate , and advanced SQL learners. These exercises are designed to provide hands-on experience with common SQL tasks, from basic retrieval and filtering to more advanced concepts like joins window functions , and stored procedures.

Table of Content

SQL Exercises for Practice

Sql practice exercises for beginners, sql practice exercises for intermediate, sql practice exercises for advanced, more questions for practice.

Practice SQL questions to enhance our skills in database querying and manipulation. Each question covers a different aspect of SQL , providing a comprehensive learning experience.

We have covered a wide range of topics in the sections beginner , intermediate and advanced .

• Basic Retrieval
• Arithmetic Operations and Comparisons:
• Aggregation Functions
• Group By and Having
• Window Functions
• Conditional Statements
• DateTime Operations
• Creating and Aliasing
• Constraints
• Stored Procedures:
• Transactions

let’s create the table schemas and insert some sample data into them.

Create Sales table

Create Products table

This hands-on approach provides a practical environment for beginners to experiment with various SQL commands, gaining confidence through real-world scenarios. By working through these exercises, newcomers can solidify their understanding of fundamental concepts like data retrieval, filtering, and manipulation, laying a strong foundation for their SQL journey.

1. Retrieve all columns from the Sales table.

Explanation:

This SQL query selects all columns from the Sales table, denoted by the asterisk (*) wildcard. It retrieves every row and all associated columns from the Sales table.

2. Retrieve the product_name and unit_price from the Products table.

This SQL query selects the product_name and unit_price columns from the Products table. It retrieves every row but only the specified columns, which are product_name and unit_price.

3. Retrieve the sale_id and sale_date from the Sales table.

This SQL query selects the sale_id and sale_date columns from the Sales table. It retrieves every row but only the specified columns, which are sale_id and sale_date.

4. Filter the Sales table to show only sales with a total_price greater than $100.

This SQL query selects all columns from the Sales table but only returns rows where the total_price column is greater than 100. It filters out sales with a total_price less than or equal to $100.

5. Filter the Products table to show only products in the ‘Electronics’ category.

This SQL query selects all columns from the Products table but only returns rows where the category column equals ‘Electronics’. It filters out products that do not belong to the ‘Electronics’ category.

6. Retrieve the sale_id and total_price from the Sales table for sales made on January 3, 2024.

This SQL query selects the sale_id and total_price columns from the Sales table but only returns rows where the sale_date is equal to ‘2024-01-03’. It filters out sales made on any other date.

7. Retrieve the product_id and product_name from the Products table for products with a unit_price greater than $100.

This SQL query selects the product_id and product_name columns from the Products table but only returns rows where the unit_price is greater than $100. It filters out products with a unit_price less than or equal to $100.

8. Calculate the total revenue generated from all sales in the Sales table.

This SQL query calculates the total revenue generated from all sales by summing up the total_price column in the Sales table using the SUM() function.

9. Calculate the average unit_price of products in the Products table.

This SQL query calculates the average unit_price of products by averaging the values in the unit_price column in the Products table using the AVG() function.

10. Calculate the total quantity_sold from the Sales table.

This SQL query calculates the total quantity_sold by summing up the quantity_sold column in the Sales table using the SUM() function.

11. Retrieve the sale_id, product_id, and total_price from the Sales table for sales with a quantity_sold greater than 4.

This SQL query selects the sale_id, product_id, and total_price columns from the Sales table but only returns rows where the quantity_sold is greater than 4.

12. Retrieve the product_name and unit_price from the Products table, ordering the results by unit_price in descending order.

This SQL query selects the product_name and unit_price columns from the Products table and orders the results by unit_price in descending order using the ORDER BY clause with the DESC keyword.

13. Retrieve the total_price of all sales, rounding the values to two decimal places.

This SQL query calculates the total sales revenu by summing up the total_price column in the Sales table and rounds the result to two decimal places using the ROUND() function.

14. Calculate the average total_price of sales in the Sales table.

This SQL query calculates the average total_price of sales by averaging the values in the total_price column in the Sales table using the AVG() function.

15. Retrieve the sale_id and sale_date from the Sales table, formatting the sale_date as ‘YYYY-MM-DD’.

This SQL query selects the sale_id and sale_date columns from the Sales table and formats the sale_date using the DATE_FORMAT() function to display it in ‘YYYY-MM-DD’ format.

16. Calculate the total revenue generated from sales of products in the ‘Electronics’ category.

This SQL query calculates the total revenue generated from sales of products in the ‘Electronics’ category by joining the Sales table with the Products table on the product_id column and filtering sales for products in the ‘Electronics’ category.

17. Retrieve the product_name and unit_price from the Products table, filtering the unit_price to show only values between $20 and $600.

This SQL query selects the product_name and unit_price columns from the Products table but only returns rows where the unit_price falls within the range of $50 and $200 using the BETWEEN operator.

18. Retrieve the product_name and category from the Products table, ordering the results by category in ascending order.

This SQL query selects the product_name and category columns from the Products table and orders the results by category in ascending order using the ORDER BY clause with the ASC keyword.

19. Calculate the total quantity_sold of products in the ‘Electronics’ category.

This SQL query calculates the total quantity_sold of products in the ‘Electronics’ category by joining the Sales table with the Products table on the product_id column and filtering sales for products in the ‘Electronics’ category.

20. Retrieve the product_name and total_price from the Sales table, calculating the total_price as quantity_sold multiplied by unit_price.

This SQL query retrieves the product_name from the Sales table and calculates the total_price by multiplying quantity_sold by unit_price, joining the Sales table with the Products table on the product_id column.

These exercises are designed to challenge you beyond basic queries, delving into more complex data manipulation and analysis. By tackling these problems, you’ll solidify your understanding of advanced SQL concepts like joins, subqueries, functions, and window functions, ultimately boosting your ability to work with real-world data scenarios effectively.

1. Calculate the total revenue generated from sales for each product category.

This query joins the Sales and Products tables on the product_id column, groups the results by product category, and calculates the total revenue for each category by summing up the total_price.

2. Find the product category with the highest average unit price.

This query groups products by category, calculates the average unit price for each category, orders the results by the average unit price in descending order, and selects the top category with the highest average unit price using the LIMIT clause.

3. Identify products with total sales exceeding 30.

This query joins the Sales and Products tables on the product_id column, groups the results by product name, calculates the total sales revenue for each product, and selects products with total sales exceeding 30 using the HAVING clause.

4. Count the number of sales made in each month.

This query formats the sale_date column to extract the month and year, groups the results by month, and counts the number of sales made in each month.

5. Determine the average quantity sold for products with a unit price greater than $100.

This query joins the Sales and Products tables on the product_id column, filters products with a unit price greater than $100, and calculates the average quantity sold for those products.

6. Retrieve the product name and total sales revenue for each product.

This query joins the Sales and Products tables on the product_id column, groups the results by product name, and calculates the total sales revenue for each product.

7. List all sales along with the corresponding product names.

This query joins the Sales and Products tables on the product_id column and retrieves the sale_id and product_name for each sale.

8. Retrieve the product name and total sales revenue for each product.

This query will give you the top three product categories contributing to the highest percentage of total revenue generated from sales. However, if you only have one category (Electronics) as in the provided sample data, it will be the only result.

9. Rank products based on total sales revenue.

This query joins the Sales and Products tables on the product_id column, groups the results by product name, calculates the total sales revenue for each product, and ranks products based on total sales revenue using the RANK () window function.

10. Calculate the running total revenue for each product category.

This query joins the Sales and Products tables on the product_id column, partitions the results by product category, orders the results by sale date, and calculates the running total revenue for each product category using the SUM() window function.

11. Categorize sales as “High”, “Medium”, or “Low” based on total price (e.g., > $200 is High, $100-$200 is Medium, < $100 is Low).

This query categorizes sales based on total price using a CASE statement. Sales with a total price greater than $200 are categorized as “High”, sales with a total price between $100 and $200 are categorized as “Medium”, and sales with a total price less than $100 are categorized as “Low”.

12. Identify sales where the quantity sold is greater than the average quantity sold.

This query selects all sales where the quantity sold is greater than the average quantity sold across all sales in the Sales table.

13. Extract the month and year from the sale date and count the number of sales for each month.

14. Calculate the number of days between the current date and the sale date for each sale.

This query calculates the number of days between the current date and the sale date for each sale using the DATEDIFF function.

15. Identify sales made during weekdays versus weekends.

This query categorizes sales based on the day of the week using the DAYOFWEEK function. Sales made on Sunday (1) or Saturday (7) are categorized as “Weekend”, while sales made on other days are categorized as “Weekday”.

This section likely dives deeper into complex queries, delving into advanced features like window functions, self-joins, and intricate data manipulation techniques. By tackling these challenging exercises, users can refine their SQL skills and tackle real-world data analysis scenarios with greater confidence and efficiency.

1. Write a query to create a view named Total_Sales that displays the total sales amount for each product along with their names and categories.

This query creates a view named Total_Sales that displays the total sales amount for each product along with their names and categories.

2. Retrieve the product details (name, category, unit price) for products that have a quantity sold greater than the average quantity sold across all products.

This query retrieves the product details (name, category, unit price) for products that have a quantity sold greater than the average quantity sold across all products.

3. Explain the significance of indexing in SQL databases and provide an example scenario where indexing could significantly improve query performance in the given schema.

With an index on the sale_date column, the database can quickly locate the rows that match the specified date without scanning the entire table. The index allows for efficient lookup of rows based on the sale_date value, resulting in improved query performance.

4. Add a foreign key constraint to the Sales table that references the product_id column in the Products table.

This query adds a foreign key constraint to the Sales table that references the product_id column in the Products table, ensuring referential integrity between the two tables.

5. Create a view named Top_Products that lists the top 3 products based on the total quantity sold.

This query creates a view named Top_Products that lists the top 3 products based on the total quantity sold.

6. Implement a transaction that deducts the quantity sold from the Products table when a sale is made in the Sales table, ensuring that both operations are either committed or rolled back together.

The quantity in stock for product with product_id 101 should be updated to 5.The transaction should be committed successfully.

7. Create a query that lists the product names along with their corresponding sales count.

This query selects the product names from the Products table and counts the number of sales (using the COUNT() function) for each product by joining the Sales table on the product_id. The results are grouped by product name using the GROUP BY clause.

8. Write a query to find all sales where the total price is greater than the average total price of all sales.

The subquery (SELECT AVG(total_price) FROM Sales) calculates the average total price of all sales. The main query selects all columns from the Sales table where the total price is greater than the average total price obtained from the subquery.

9. Analyze the performance implications of indexing the sale_date column in the Sales table, considering the types of queries commonly executed against this column.

Query without indexing:.

Query with Indexing:

This format clearly displays the operations and details of the query execution plan before and after indexing.

Without indexing, the query performs a full table scan, filtering rows based on the sale date, which is less efficient. With indexing, the query uses the index to quickly locate the relevant rows, significantly improving query performance.

10. Add a check constraint to the quantity_sold column in the Sales table to ensure that the quantity sold is always greater than zero.

All rows in the Sales table meet the condition of the check constraint, as each quantity_sold value is greater than zero.

11. Create a view named Product_Sales_Info that displays product details along with the total number of sales made for each product.

This view provides a concise and organized way to view product details alongside their respective sales information, facilitating analysis and reporting tasks.

12. Develop a stored procedure named Update_Unit_Price that updates the unit price of a product in the Products table based on the provided product_id.

The above SQL code creates a stored procedure named Update_Unit_Price. This stored procedure takes two parameters: p_product_id (the product ID for which the unit price needs to be updated) and p_new_price (the new unit price to set).

13. Implement a transaction that inserts a new product into the Products table and then adds a corresponding sale record into the Sales table, ensuring that both operations are either fully completed or fully rolled back.

This will update the unit price of the product with product_id 101 to 550.00 in the Products table.

14. Write a query that calculates the total revenue generated from each category of products for the year 2024.

When you execute this query, you will get the total revenue generated from each category of products for the year 2024.

If you’re looking to sharpen your SQL skills and gain more confidence in querying database s, consider delving into these articles. They’re packed with query-based SQL questions designed to enhance your understanding and proficiency in SQL .

By practicing with these exercises, you’ll not only improve your SQL abilities but also boost your confidence in tackling various database-related tasks. The Questions are as follows:

• How to Insert a Value that Contains an Apostrophe in SQL?
• How to Select Row With Max Value in SQL?
• How to Efficiently Convert Rows to Columns in SQL?
• How To Use Nested Select Queries in SQL
• How to Select Row With Max Value on a Column in SQL?
• How to Specify Condition in Count() in SQL?
• How to Find the Maximum of Multiple Columns in SQL?
• How to Update Top 100 Records in SQL?
• How to Select the Last Records in a One-To-Many Relationship Using SQL Join
• How to Join First Row in SQL?
• How to Insert Row If Not Exists in SQL?
• How to Use GROUP BY to Concatenate Strings in SQL?
• How Inner Join works in LINQ to SQL
• How to Get the Identity of an Inserted Row in SQL
• How to Declare a Variable in SQL?

Mastering SQL requires consistent practice and hands-on experience. By working through these SQL practice exercises , you’ll strengthen your skills and gain confidence in querying relational databases.

Whether you’re just starting or looking to refine your expertise, these exercises provide valuable opportunities to hone your SQL abilities. Keep practicing , and you’ll be well-equipped to tackle real-world data challenges with SQL.

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10 Beginner SQL Practice Exercises With Solutions

• online practice
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Table of Contents

The Dataset

Exercise 1: selecting all columns from a table, exercise 2: selecting a few columns from a table, exercise 3: selecting a few columns and filtering numeric data in where, exercise 4: selecting a few columns and filtering text data in where, exercise 5: selecting a few columns and filtering data using two conditions in where, exercise 6: filtering data using where and sorting the output, exercise 7: grouping data by one column, exercise 8: grouping data by multiple columns, exercise 9: filtering data after grouping, exercise 10: selecting columns from two tables, that was fun now, time to do sql practice on your own.

Solve these ten SQL practice problems and test where you stand with your SQL knowledge!

This article is all about SQL practice. It’s the best way to learn SQL. We show you ten SQL practice exercises where you need to apply essential SQL concepts. If you’re an SQL rookie, no need to worry – these examples are for beginners.

Use them as a practice or a way to learn new SQL concepts. For more theoretical background and (even more!) exercises, there’s our interactive SQL Basics course. It teaches you how to select data from one or more tables, aggregate and group data, write subqueries, and use set operations. The course comprises 129 interactive exercises so there is no lack of opportunities for SQL practice, especially if you add some of the 12 ways of learning SQL online to it.

Speaking of practice, let’s start with our exercises!

The question is always where to find data for practicing SQL. We’ll use our dataset for all exercises. No need to limit yourself to this, though – you can find other free online datasets for practicing SQL .

Our dataset consists of two tables.

The table distribution_companies lists movie distribution companies with the following columns:

• id – The ID of the distribution company. This is the primary key of the table.
• company_name – The name of the distribution company.

The table is shown below.

The second table is movies . These are the columns:

• id – The ID of the movie. This is the primary key of the table.
• movie_title – The movie title.
• imdb_rating – The movie rating on IMDb.
• year_released – The year the movie was released.
• budget – The budget for the movie in millions of dollars.
• box_office – The earnings of the movie in millions of dollars.
• distribution_company_id – The ID of the distribution company, referencing the table distribution_companies (foreign key).
• language – The language(s) spoken in the movie.

Exercise: Select all data from the table distribution_companies .

Solution explanation: Select the data using the SELECT statement. To select all the columns, use an asterisk ( * ). The table from which the data is selected is specified in the FROM clause.

Solution output:

Exercise: For each movie, select the movie title, the IMDb rating, and the year the movie was released.

Solution explanation: List all the columns needed ( movie_title , imdb_rating , and year_released ) in the SELECT statement, separated by the comma. Reference the table movies in the FROM clause.

Exercise: Select the columns movie_title and box_office from the table movies . Show only movies with earnings above $300 million.

Solution explanation: List the columns in SELECT and reference the table in FROM . Use a WHERE clause to filter the data – write the column box_office and use the ‘greater than’ operator ( > ) to show only values above $300 million.

Exercise: Select the columns movie_title , imdb_rating , and year_released from the table movies . Show movies that have the word ‘Godfather’ in the title.

Solution explanation: List the columns in SELECT and reference the table in the FROM clause. Use a WHERE clause to filter the data. After writing the column name, use the LIKE logical operator to look for ‘Godfather’ in the movie title, written in single quotes. To find the word anywhere in the movie title, place the wildcard character ( % ) before and after the word.

Exercise: Select the columns movie_title , imdb_rating , and year_released from the table movies . Show movies that were released before 2001 and had a rating above 9.

Solution explanation: List the columns in SELECT and reference the table in FROM . Set the first condition that the year released is before 2001 using the ‘less than’ ( < ) operator. To add another condition, use the AND logical operator. Use the same logic as the first condition, this time using the ‘greater than’ operator with the column imdb_rating .

Exercise: Select the columns movie_title , imdb_rating , and year_released from the table movies . Show movies released after 1991. Sort the output by the year released in ascending order.

Solution explanation: List the columns in SELECT and reference the table in FROM . Filter the data with WHERE by applying the ‘greater than’ operator to the column year_released . To sort the data, use an ORDER BY clause and write the column name by which you wish to sort. The type of sorting is specified by writing ASC (ascending) or DESC (descending). If the type is omitted, the output is sorted in ascending order by default.

Exercise: Show the count of movies per each language category.

Solution explanation: Select the column language from the table movies . To count the number of movies, use the aggregate function COUNT() . Use the asterisk ( * ) to count the rows, which equals the count of movies. To give this column a name, use the AS keyword followed by the desired name. To show the count by language, you need to group the data by it, so write the column language in the GROUP BY clause.

Exercise: Show the count of movies by year released and language. Sort results by the release date in ascending order.

Solution explanation: List the columns year_released and language from the table movies in SELECT . Use COUNT(*) to count the number of movies and give this column a name using the AS keyword. Specify the columns by which you want to group in the GROUP BY clause. Separate each column name with a comma. Sort the output using ORDER BY with the column year_released and the ASC keyword.

Exercise: Show the languages spoken and the average movie budget by language category. Show only the languages with an average budget above $50 million.

Solution explanation: Select the column language from the table movies . To compute the average budget, use the aggregate function AVG() with the column budget in parentheses. Name the column in the output by using the AS keyword. Group the data by rating using GROUP BY . To filter the data after grouping, use a HAVING clause. In it, use the same AVG() construct as in SELECT and set the values to be above 50 using the ‘greater than’ operator.

Exercise: Show movie titles from the table movies , each with the name of its distribution company.

Solution explanation: List the columns movie_title and company_name in SELECT . In the FROM clause, reference the table distribution_companies . Give it an alias dc to shorten its name for use later. The AS keyword is omitted here; you may use it if you wish. To access the data from the other table, use JOIN (it may also be written as INNER JOIN ) and write the table name after it. Give this table an alias also. The join used here is an inner type of join; it returns only the rows that match the joining condition specified in the ON clause. The tables are joined where the column id from the table distribution_companies is equal to the column distribution_company_id from the table movies . To specify which column is from which table, use the corresponding alias of each table.

These ten SQL practice exercises give you a taste of what practicing SQL looks like. Whether you are at the beginner, intermediate, or advanced level, it’s the same. What changes is the complexity of the problems you solve and of the code you write.

Look for more challenges in the SQL Basics course and the Monthly SQL Practice track. Both are excellent for your SQL practice online. This is true, especially if you do not have an opportunity to use SQL on a daily basis in your job.

So, don’t try to test how long it takes to forget what you once knew in SQL! Use every opportunity to solve as many SQL practice problems as possible.

You may also like

How Do You Write a SELECT Statement in SQL?

What Is a Foreign Key in SQL?

Enumerate and Explain All the Basic Elements of an SQL Query

• ▼SQL Exercises
• Introduction
• Retrieve data from tables
• Boolean and Relational Operators
• Wildcard and Special operators
• Aggregate Functions
• Formatting query output
• Query on Multiple Tables
• FILTERING and SORTING on HR Database
• SUBQUERIES on HR Database
• JOINS on HR Database
• SQL User Management
• ▼Movie Database
• BASIC QUERIES
• ▼Soccer Database
• ▼Hospital Database
• ▼Employee Database
• ▼AdventureWorks Database
• ▼SQL Challenges
• Challenges-1
• ..More to come..

SQL Exercises, Practice, Solution

What is sql.

SQL stands for Structured Query Language and it is an ANSI standard computer language for accessing and manipulating database systems. It is used for managing data in relational database management system which stores data in the form of tables and relationship between data is also stored in the form of tables. SQL statements are used to retrieve and update data in a database.

The best way we learn anything is by practice and exercise questions. We have started this section for those (beginner to intermediate) who are familiar with SQL . Hope, these exercises help you to improve your SQL skills. Currently following sections are available, we are working hard to add more exercises. Happy Coding!

You may read our SQL tutorial before solving the following exercises.

List of SQL Exercises

• SQL Retrieve data from tables [33 Exercises]
• SQL Boolean and Relational operators [12 Exercises]
• SQL Wildcard and Special operators [22 Exercises]
• SQL Aggregate Functions [25 Exercises]
• SQL Formatting query output [10 Exercises]
• SQL Quering on Multiple Tables [8 Exercises]
• FILTERING and SORTING on HR Database [38 Exercises]
• SQL JOINS [29 Exercises]
• SQL JOINS on HR Database [27 Exercises]
• SQL SUBQUERIES
• SQL SUBQUERIES [39 Exercises]
• SQL SUBQUERIES on HR Database [55 Exercises]
• SQL Union[9 Exercises]
• SQL View[16 Exercises]
• SQL User Account Management [16 Exercise]
• Movie Database
• BASIC queries on movie Database [10 Exercises]
• SUBQUERIES on movie Database [16 Exercises]
• JOINS on movie Database [24 Exercises]
• Soccer Database
• BASIC queries on soccer Database [29 Exercises]
• SUBQUERIES on soccer Database [33 Exercises]
• JOINS queries on soccer Database [61 Exercises]
• Hospital Database
• BASIC, SUBQUERIES, and JOINS [41 Exercises]
• Employee Database
• BASIC queries on employee Database [115 Exercises]
• SUBQUERIES on employee Database [77 Exercises]
• AdventureWorks Database:
• AdventureWorks Database [200 Exercises]
• SQL Challenges-1:
• SQL Challenges-1 [77 Exercises]
• More to come!

Structure of inventory database :

Structure of HR database :

Structure of movie database :

Structure of soccer database :

Structure of employee database :

Structure of hospital database :

Syntax diagram of SQL SELECT statement

You may download the structure and data of the tables of database on which SQL Exercises are built.

Please note that PostgreSQL 9.4 is used and the file which you would download is generated using pg_dump

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Learn SQL: Practice SQL Queries

Today is the day for SQL practice #1. In this series, so far, we’ve covered most important SQL commands ( CREATE DATABASE & CREATE TABLE , INSERT , SELECT ) and some concepts ( primary key , foreign key ) and theory ( stored procedures , user-defined functions , views ). Now it’s time to discuss some interesting SQL queries.

Let’s take a quick look at the model we’ll use in this practice.

You can expect that in real-life situations (e.g., interview), you’ll have a data model at your disposal. If not, then you’ll have the description of the database (tables and data types + additional description of what is stored where and how the tables are related).

The worst option is that you have to check all the tables first. E.g., you should run a SELECT statement on each table and conclude what is where and how the tables are related. This won’t probably happen at the interview but could happen in the real-life, e.g., when you continue working on an existing project.

Before We Start

The goal of this SQL practice is to analyze some typical assignments you could run into at the interview. Other places where this might help you are college assignments or completing tasks related to online courses.

The focus shall be on understanding what is required and what is the learning goal behind such a question. Before you continue, feel free to refresh your knowledge on INNER JOIN and LEFT JOIN , how to join multiple tables , SQL aggregate functions , and the approach to how to write complex queries . If you feel ready, let’s take a look at the first 2 queries (we’ll have some more in upcoming articles). For each query, we’ll describe the result we need, take a look at the query, analyze what is important for that query, and take a look at the result.

SQL Practice #1 – Aggregating & LEFT JOIN

Create a report that returns a list of all country names (in English), together with the number of related cities we have in the database. You need to show all countries as well as give a reasonable name to the aggregate column. Order the result by country name ascending.

Let’s analyze the most important parts of this query:

• We’ve used LEFT JOIN ( LEFT JOIN city ON country.id = city.country_id ) because we need to include all countries, even those without any related city
• We must use COUNT(city.id) AS number_of_cities and not only COUNT(*) AS number_of_cities because COUNT(*) would count if there is a row in the result (LEFT JOIN creates a row no matter if there is related data in other table or not). If we count the city.id , we’ll get the number of related cities
• The last important thing is that we’ve used GROUP BY country.id, country.country_name_eng instead of using only GROUP BY country.country_name_eng . In theory (and most cases), grouping by name should be enough. This will work OK if the name is defined as UNIQUE. Still, including a primary key from the dictionary, in cases similar to this one, is more than desired

You can see the result returned in the picture below.

SQL Practice #2 – Combining Subquery & Aggregate Function

Write a query that returns customer id and name and the number of calls related to that customer. Return only customers that have more than the average number of calls of all customers.

The important things I would like to emphasize here are:

• Please notice that we’ve used aggregate functions twice, once in the “main” query, and once in the subquery. This is expected because we need to calculate these two aggregate values separately – once for all customers (subquery) and for each customer separately (“main” query)
• The aggregate function in the “main” query is COUNT(call.id) . It’s used in the SELECT part of the query, but we also need it in the HAVING part of the query (Note: HAVING clause is playing the role of the WHERE clause but for aggregate values)
• Group is created by id and customer name. These values are the ones we need to have in the result
• In the subquery, we’ve divided the total number of rows ( COUNT(*) ) by the number of distinct customers these calls were related to ( COUNT(DISTINCT customer_id) ). This gave us the average number of calls per customer
• The last important thing here is that we used the CAST operator ( CAST(… AS DECIMAL(5,2)) ). This is needed because the final result would probably be a decimal number. Since both COUNTs are integers, SQL Server would also return an integer result. To prevent this from happening, we need to CAST both divider and the divisor as decimal numbers

Let’s take a look at what the query actually returned.

In today’s SQL practice, we’ve analyzed only two examples. Still, these two contain some parts you’ll often meet at assignments – either in your work, either in a testing (job interview, college assignments, online courses, etc.). In the next part, we’ll continue with a few more interesting queries that should help you solve problems you might run into.

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Where can I find exercises to practice SQL statements? [closed]

Do you know where I can find some practice SQL problems where I can write select statements?

• 1 Look at my comments below the chosen answer. You might get better stuff there than the junk at sql.ru, w3 schools etc. –  Trojan.ZBOT Commented Dec 30, 2013 at 3:38
• HackeRank has a good collection of questions : hackerrank.com/domains/sql/select –  saruftw Commented Feb 20, 2017 at 9:41
• Check out my book SQL Practice Problems , available on Amazon. I wrote it specifically because it's easy to find SQL keyword and syntax info, but difficult to find good real-world practice in writing select statements. –  Sylvia Commented Jun 7, 2017 at 16:24

4 Answers 4

SQL exercises or you can create a test table with fake data and manipulate that. Personally, I learn better with hands-on activity, by playing with the SELECT statements myself before even practicing an online guide. However, not everyone is the same.

Here are a few other links to check out:

• SQLCourse - Interactive for beginners.
• SQLCourse2 - It's awesome you can try and sharpen your skills.

• 2 In one word - yuck ! Only if you can get the databases used, then go for examples like these - google.com/search?q=sql+assignments+with+solutions Or based on the public AdventureWorks DB for MS-SQL server - cuttingedgecourse.com/CIS310/Assignments.htm –  Trojan.ZBOT Commented Dec 30, 2013 at 3:26
• Or search google like this - sql assignment site:.edu –  Trojan.ZBOT Commented Dec 30, 2013 at 3:33
• 1 This website, for example has IMDB (movies) website data and questions - courses.cs.washington.edu/courses/cse444/06wi/hw/hw1.htm data for questions is here - courses.cs.washington.edu/courses/cse444/06wi/hw/IMDB.zip –  Trojan.ZBOT Commented Dec 30, 2013 at 3:35
• 3 This is the most complex query in page 10 of the "advanced course" - SELECT customers.customerid, customers.firstname, customers.lastname, items_ordered.order_date, items_ordered.item, items_ordered.price FROM customers, items_ordered WHERE customers.customerid = items_ordered.customerid; Glance these sqlCourse links, but don't expect to get any real knowledge out of them. –  Trojan.ZBOT Commented Dec 30, 2013 at 4:19

Look the line of books by Celko, it has lots of examples and exercises.

You can find some here as well.

• 1 Although really, I would download SQL Server Express, download the AdventureWorks db, and get a beginning SQL book like "Beginning SQL" –  davecoulter Commented Mar 9, 2010 at 23:46

Try SQL Exercises . Free and interactive.

• 2 Already posted in accepted answer. –  ctomek Commented Feb 18, 2017 at 20:29

Not the answer you're looking for? Browse other questions tagged sql or ask your own question .

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