SQL code copied to buffer
Group tasks by:
Support the project.
- 1. Get the actors
- 2. Retrieve Actor Names
- 3. Ordered Movie Titles
- 4. Top 10 Movies by Title
- 5. Get films list third page
- 6. Sort Movies by Multiple Fields
- 7. Get the longest movie
- 8. Find long movies
- 9. Find Long Comedies
- 10. Classic movies
- 11. Retrieve Actors by Name
- 12. Find duplicate actor names
- 13. Most Popular Actor Surname
- 14. Get the languages list
- 15. Ordered Languages List
- 16. Top 5 Longest Films
- 17. Find stuff members by condition
- 18. Get the sorted list of films with condition
- 19. Find clients starting with the letter "A"
- 20. Find clients starting with the letter "A" (2)
- 21. Find customers full names
- 22. Find addresses using sub-query
- 23. Find addresses using JOIN
- 24. Find all the actors in the film
- 25. Find all films of an actor
- 26. Find clients who rented the film
- 27. Find all films where HENRY BERRY did not participate
- 28. Find the number of films in which the actor took part
- 29. Find actors more popular than HENRY BERRY
- 30. Find the distribution of films by category
- 31. Find the average length of a movie
- 32. Find minimum, maximum and average film duration
- 33. Find long movie categories
- 34. Find the minimal and maximal film rental cost
- 35. Find company stores details
- 36. Average film rental duration for each customer
- 37. Find the average length of a movie by category
- 38. The average cost of renting a movie by category
- 39. Find sad actors
- 40. Find the most diverse actors
- 41. Analyze monthly payment
- 42. Find the best month
- 43. Find the films never been rented
- 44. Find the most popular film
- 45. Analyze rental data for film
- 46. Find customers who did not return rented discs
- 47. Find average daily film rentals
- 48. Calculate daily income for the month
- 49. Find movie distribution by store
- 50. Find the distribution of customer activity
- 51. Find movie popularity rating
- 52. Quarterly earnings analysis
- 53. Find the countries with the most customers
- 54. Find film names by description
- 55. Find most active customers
- 56. Generate dates table
- 57. Calculate the number of days off in a month
- 58. Calculate factorial
- 59. Find average disk idle time
- 60. Find the distribution by categories
- 61. Find the average client activity time
- 62. Find the average revenue
- 63. Find the store average revenue
- 64. Analyze monthly payments (2)
- 65. Calculate the area of a circle
- 66. Calculate circle perimeter
- 67. Find customer details
- 68. Find EMILY DEE fans
- 69. Clients who are not familiar with EMILY DEE films
- 70. Find the count of rented disks
- 71. Find the number of returns
- 72. Disc rental and return statistics
- 73. Find active customers
- 74. Find the least popular movies
- 75. Find the top spending customers
- 76. Films Without Available Inventory
- 77. Find languages not represented in films
- 78. Find movies that have never been rented
- 79. Movies with above-average rental rates
- 80. Clients with a high number of rentals
- 81. Movies with the highest replacement cost
- 82. Films with the highest replacement cost
- 83. Count the rental delays
- 84. Calculate the percentage of delays
- 85. Get movie cast lists
- 86. Extract address and domain from email
- 87. Get a list of actors - namesakes
- 88. List Movies with Their Categories
- 89. Average Rental Duration
- 90. Films with Low Rental Time
- 91. Movie rental prices by category
- 92. Get rolling payment amounts
- 93. Find the number of films in each category
- 94. Get list of clients
- 95. Analyze customer payments
- 96. Unique Movie Ratings
- 97. Find the most diverse clients
- 98. Find actors duets
- 99. Find film distribution
- 100. Find films that were out of stock
- 101. Payments analyze
- 102. Improve payments analyze
- 103. Get list of tables
- 104. Get table columns data
- 105. Get list of indexes
- 106. Distribution of clients by weekday
- 107. Client Rentals by Time of Day
- 108. Improve distribution of clients by weekday
- 109. Movies without cast records
- 110. Movies without actors records
- 111. Find all the actors in the film
- 112. Actors Excluding NC-17 Films
- 113. Analyze weekly rentals
- 114. Average rentals count
- 115. Find repeat rentals
- 116. Find the Horror films fans
- 117. Find the distribution of clients by country
- 118. Get the restricted films list
- 119. Get list of restricted films
- 120. Find never been delayed films
- 121. Find most delayed films
- 122. Create department table
- 123. NC-17 Films about DBA
- 124. Films about Dogs or Cats
- 125. Clients whose first name is the last name of another client
- 126. Find clients who have met each other
- 127. Customers with matching name initials
- 128. Create new address record
- 129. Update the postal code
- 130. Update Canadian postal codes
- 131. Enter Woodridge postal code
- 132. Enter new staff record
- 133. Create customer addresses view
- 134. Movies in One Store
- 135. Find movies with no available copies
- 136. Staff performance analysis
- 137. Distribution of films by categories in JSON format
- 138. Generate a list of movies in JSON format
- 139. Remove customer records
- 140. Addresses Lacking Postal Codes
- 141. Addresses with Even Postal Codes
- 142. Category popularity analysis
- 143. Generate monthly bill
- 144. Build a overal email list
- 145. Build shared surnames list
- 146. Select clients without the letter "A"
- 147. Alter the staff table
- 148. Gap & Islands problem
- 149. Find customers who sow same films
- 150. Find films in several categories
- 151. Find last names with double letters
- 152. Analysis of the cost of film rental by category
- 153. Rental history
- 154. Find films to share
- 155. Film distribution by category and store
- 156. Delete films records
- 157. Rented Films
- 1. Get airports data
- 2. Get a list of airports
- 3. Find long-range aircraft
- 4. Find Boeing aircraft
- 5. Get flights departed from Domodedovo
- 6. Get a list of aircraft from Domodedovo
- 7. Aircraft usage analysis
- 8. Fare Conditions Types
- 9. Aircraft Lacking Business Class Seats
- 10. Find Aircraft with All Fare Conditions
- 11. Get counts of seats by class
- 12. Get rows and seats count
- 13. Calculate the number of seats on flight
- 14. Get the list of destination airports
- 15. Get a list of airports with direct connections
- 16. Get a list of airports without direct connections
- 17. Get a list of no-show passengers
- 18. Get a list of passengers
- 19. Find the flight delay
- 20. Get flights statistics
- 21. Rate airports
- 22. Find a list of flight options
- 23. Find the fastest flight
- 24. Calculate the daily number of flights
- 25. Obtain a list of passengers
- 26. Find average flight occupancy
- 27. Total Bookings Amount
- 28. Monthly Bookings Count
- 29. Find flight occupancy by fare
- 30. Get list of tables
- 31. Get information about the columns
- 32. Airports with one-way departures
- 33. Find airports relations
- 34. Find small airports
- 35. Get the passenger list
- 36. Get aircraft seat map
- 37. FInd the plane coordinates
- 38. Get a list of planes in the air
- 39. FInd the planes coordinates
- 40. Display a table of airports
- 41. Count departing passengers
- 42. Number of passengers with total
- 43. Display a table of departures
- 44. Get a list of airports with more than one direct flight
- 45. Distribution of flights by days of the week
- 46. Get list of tables (PostgreSQL)
- 47. Passenger names classification
- 48. Extract Airport Data as JSON
- 1. Display a list of departments
- 2. Find non-Dollar/Euro countries
- 3. A sub-departments list (JOIN)
- 4. Get a list of sub-departments
- 5. Find foreign employees
- 6. Find Employees by Department
- 7. Find employee salary
- 8. Employees with high salaries
- 9. Employees with Above-Average Salaries
- 10. Find the Managed Department
- 11. Employees on the Video Database Project
- 12. Staff Availability Report
- 13. Create a phonebook
- 14. Find all customers with unshipped orders
- 15. Find the number of employees
- 16. Find highly paid employees
- 17. Find employees by hire date
- 18. Get list of highly paid employees
- 19. Find highly paid employees
- 20. Reduced salaries
- 21. Find valuable employees
- 22. Salary Ratio Calculation
- 23. Create a salary rating
- 24. Jobs Without Specific Requirements
- 25. Orders shipped in the following month
- 26. Update project information
- 27. Find the median salary
- 28. Managed by Robert Nelson
- 29. Delete employee records
- 30. Overloaded employees
- 31. Update Job Salaries
- 32. Remove View from Database
- 1. Product categories
- 2. Product List
- 3. Filtered list of products
- 4. Ten heaviest products
- 5. Get list of tables (SQL Server)
- 6. Find customers with even numbers
- 7. Find customers by phone prefix
- 8. Find duplicate phone numbers
- 9. Get unique customers list
- 10. Duplicate Emails
- 11. Get product category color counts
- 12. Top states by population
- 13. List of subcategories
- 14. List of categories
- 15. Root categories list
- 16. Subcategories count
- 17. Products catalog
- 18. Products by category distribution
- 19. Large categories
- 20. Mountain Bikes catalog
- 21. Prepare mailing list
- 22. Customers without orders
- 23. Who ordered red helmet?
- 24. Who ordered helmet?
- 25. What bought Jon Grande?
- 26. The most popular product
- 27. Most Frequent Co-Purchase
- 28. Most popular products
- 29. Not buyng customers
- 30. Average Sales Delay
- 31. Frequently Purchased Product Pairs
- 32. Sales by Category Percentage
- 33. Product Sales Analysis
- 1. Retrieve All Departments
- 2. Staff names
- 3. Sort the penguins
- 4. Penguins species
- 5. Find light weight penguins
- 6. Penguins list
- 7. Distribution penguins by islands
- 8. Population Distribution (Pivot)
- 9. Find small penguins
- 10. Find small penguins species
- 11. Medium sized bill Penguins
- 12. Small bill Penguins
- 13. Penguins with low body weight
- 14. Search by pattern
- 15. Flipper length to body mass rate
- 16. Penguins whose sex is unknown
- 17. Heavy penguins
- 18. Penguins with absent data
- 19. Penguins and Islands
- 20. Count the penguins
- 21. Island with the minimum penguins mass
- 22. The most populated island
- 23. Penguins distribution
- 24. Penguins stats table
- 25. Common penguin species
- 26. Penguins habitat
- 27. Penguins stats
- 28. Staff information
- 29. Delete records
- 30. Rank Penguins by Body Mass
- 31. Set Last Service Date
- 32. Missing Data
- 33. Refurbished machines
- 34. Data migration
- 35. Create Penguins Table
- 36. Combine Penguin Lists
- 37. Unique Penguin List
- 38. Filter Little Penguins
Dear user,
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You and many others enjoy this site for free. Our only sources of funding are advertising and donations. To help us keep this project free for everyone, please consider not blocking ads on the site.
If ads are an issue for you, please let me know, and I'll turn them off for your account.
For every record in the machine table, update the JSON content within the details column. You need to add or update the last_service field within this JSON to reflect today's date.
Use SQLite syntax to write your answer. Descriptions of tables are provided in the right pane.
Write your request in the field below and click the "Check it!" button.
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Querynomicon (SQLite)
A compact database for learning the basics of SQL.The Querynomicon database tables:
department - table of departments.
- identDepartment ID
- nameDepartment name
- buildingBuilding name
| ident | name | building |
|---|---|---|
| gen | Genetics | Chesson |
little_penguins - table of little penguins.
- speciesPenguin species
- islandIsland of residence
- bill_length_mmBill length, mm
- bill_depth_mmBill depth, mm
- flipper_length_mmFlipper length, mm
- body_mass_gBody mass, g
- sexSex
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex |
|---|---|---|---|---|---|---|
| Gentoo | Biscoe | 52.1 | 17 | 230 | 5550 | MALE |
penguins - table of penguins.
- speciesPenguin species
- islandIsland of residence
- bill_length_mmBill length, mm
- bill_depth_mmBill depth, mm
- flipper_length_mmFlipper length, mm
- body_mass_gBody mass, g
- sexSex
| species | island | bill_length_mm | bill_depth_mm | flipper_length_mm | body_mass_g | sex |
|---|---|---|---|---|---|---|
| Gentoo | Biscoe | 52.1 | 17 | 230 | 5550 | MALE |
staff - table of employees.
- identEmployee number
- personalEmployee first name
- familyEmployee last name
- deptDepartment
- ageAge
| ident | personal | family | dept | age |
|---|---|---|---|---|
| 7 | Abram | Chokshi | gen | 23 |
machine - table of machines.
- identMachine ID
- nameMachine name
- detailsJSON with details
| ident | name | details |
|---|---|---|
| 1 | WY401 | {"acquired": "2023-05-01"} |
| 2 | Inphormex | {"acquired": "2021-07-15", "refurbished": "2023-10-22"} |
| 3 | AutoPlate 9000 | {"note": "needs software update"} |
