SQL problem solving for interviews

Practice solving a LeetCode SQL interview problem by selecting product IDs that are both low fats and recyclable with a simple where clause in MySQL.

Learn to find customers not referred by the customer with id two by handling null referee_id values in SQL with an or referee_id is null condition.

Solve a SQL problem on the world table by listing countries with area of at least 3 million or population of at least 25 million, returning name, population, and area.

Identify authors who viewed their own articles by querying the views table for rows where author_id equals viewer_id, selecting distinct author IDs, article IDs, and sorting by ID.

Identify all invalid tweets by selecting tweet id from the tweets table where the content length exceeds 15 characters using len or length.

join employees and employee uni on id to display each user's unique id, using a left join to include all employees and show null when missing.

Join the sales and product tables on product_id to provide each cell id's product name and price, illustrating a simple inner join and field projection.

Identify customers who visited but made no transactions by using a left join between visits and transactions, filtering for null transactions, and counting visits per customer with group by.

Perform a self-join on the weather table (w1 and w2) to compare each id's temperature with the previous date using date_add, and return ids where the temperature is higher.

Compute the average duration of each machine's processes by self-joining the activity table on machine and process IDs. Subtract start timestamps from end timestamps and round to three decimals.

Learn to use a left join between the employee and bonus tables to list each employee with a bonus less than 1000 or no bonus, showing their name and bonus.

Learn to count a student’s exams per subject using SQL. Generate all student-subject pairs with a Cartesian join, attach exams via a left join, and aggregate counts with group by.

Execute a self-join on the employee table to link managers with their direct reports, then group and apply having count at least five to identify managers.

Learn to compute per-user confirmation rates by left joining signups and confirmations, count timeouts and confirms, coalesce nulls, and round to two decimals.

Query the cinema table to fetch movies with an odd ID and a description not containing the word boring, selecting all columns and ordering by rating in descending order.

Compute the average selling price per product by left joining prices and unit_sold, matching purchase dates to start and end dates, aggregate price times units and round to two decimals.

Compute the average experience years per project by joining the project and employee tables on project_id and employee_id, grouping by project_id, and rounding the result to two decimals.

Calculate the percentage of users registered in each contest by joining users and register, counting per contest, dividing by total users, rounding to two decimals, and ordering by percentage.

Compute per-query quality as the average of rating divided by position, and determine the poor query percentage for rating below three using a filtered, rounded SQL query.

Explore monthly transactions by country and month, computing four metrics: total transactions, total amount, approved transactions, and approved amount from the transactions table using date formatting and case logic.

Compute the percentage of immediate first orders using a subquery on the delivery table. Compare order date with customer preferred delivery date and round results to two decimals.

Analyze a SQL workflow to compute the percentage of players who log in again the day after their first login, using subqueries, date arithmetic, and distinct counts.

Group by teacher_id and count distinct subject_id to count unique subjects per teacher, noting that the same teacher teaching the same subject in different departments counts as one.

Calculate daily active users over a 30-day window ending on 2019-07-27 by counting distinct user IDs per activity date.

Learn to extract each product’s first year, quantity, and price from the sales table using min(year) and group by product_id; know when to join the product table for missing sales.

Count students per class from the courses table, group by class, and filter with having count >= 5 to identify classes with at least five students.

Learn to identify customers who bought all products by comparing each customer's distinct product keys to the total product count using a subquery and group by.

Learn to perform a self-join on the employees table using reportsto to map each manager to their supervisees, counting reports and averaging ages with grouping, rounding, and ordering.

Select each employee's primary department using two sets (primary flag and single department) and unite results with union all.

Identify triangles in SQL data by applying the triangle inequality to x, y, and z with a case statement, labeling rows as triangle or not triangle.

Learn to use window functions like lag and lead to report numbers that appear at least three consecutive times, ordered by the auto-increment id.

Learn to retrieve each product's price on a given date from a products table with change dates, using subqueries, left joins, and coalesce for missing values.

Use a sum window function to build a running total of weights in boarding order, filter by 1000, and identify the last person who boards.

Categorize incomes into low, average, and high using a case statement, then count accounts per category, ensuring all categories appear with zeros using unions and a left join.

Learn to query the employees table to find IDs of staff earning under 30000 whose managers have left by filtering salary and using a not in subquery on employee IDs.

Learn to exchange seats in pairs using window functions and ceil, generating ranks to swap 1 with 2, 3 with 4, and so on, then assign final IDs with row_number.

Identify the user who rated the most movies. Find the February 2020 movie with the highest average rating using joins, group by, subqueries, and union all.

Develop SQL problem solving for interviews by computing the daily total amount paid and a seven-day moving average using sum over a window, then round the average to two decimals.

Join the employee and department tables to show department name, employee, and salary, then use dense rank partition by department to extract the top three salaries per department.

Learn to fix names in a users table by transforming to proper case with substring, upper and lower, and concat, then alias as name and order by user id.

Identify patients with type one diabetes by matching the condition field to the db1 prefix using like wildcards. Ensure the db1 appears as a separate word to handle multi-word conditions.

Learn to delete duplicate emails in a person table by joining the table to itself and keeping the row with the smallest ID.

Learn how to retrieve the second highest salary from an employee table using a simple subquery, filtering out the top salary and understanding the from, where, and select execution order.

Learn to group sold products by date, count distinct products per day, and list product names using group_concat, ordered lexicographically, from the activities table.

Join the products and orders tables on product_id, group by product name, and sum units sold. Filter for February 2020 with total units at least 100.

Learn to solve sql interview questions by filtering the users table with a regex to find valid emails, enforcing a letter-starting prefix and the leetcode.com domain.

Celebrate your hard work and progress as you near the end of sql problem solving for interviews. Wishing you the best in your future endeavors and your next interview.