SQL code copied to buffer
Group tasks by:
Support the project.
- 1. Get the actors
- 2. Get the languages list
- 3. Retrieve Actor Names
- 4. Retrieve All Departments
- 5. Staff names
- 6. Product categories
- 7. Ordered Languages List
- 8. Top 5 Longest Films
- 9. Find stuff members by condition
- 10. Get the sorted list of films with condition
- 11. Find film names by description
- 12. Retrieve Actors by Name
- 13. Find the average length of a movie
- 14. Find foreign employees
- 15. Ordered Movie Titles
- 16. Find clients starting with the letter "A"
- 17. Find clients starting with the letter "A" (2)
- 18. Find the minimal and maximal film rental cost
- 19. Top 10 Movies by Title
- 20. Find long movies
- 21. Calculate the area of a circle
- 22. Calculate circle perimeter
- 23. Find active customers
- 24. Movies with the highest replacement cost
- 25. Get list of clients
- 26. Analyze customer payments
- 27. Unique Movie Ratings
- 28. Get the restricted films list
- 29. Get list of restricted films
- 30. Create new address record
- 31. Update the postal code
- 32. Remove customer records
- 33. Addresses Lacking Postal Codes
- 34. Addresses with Even Postal Codes
- 35. Build shared surnames list
- 36. Get airports data
- 37. Find long-range aircraft
- 38. Identify Palindrome Names
- 39. What is SQL?
- 40. What is DBMS?
- 41. What is RDBMS?
- 42. What is a Database?
- 43. What is ACID?
- 44. What are DQL commands?
- 45. What is index in SQL?
- 46. SQL Tables joins types
- 47. Choose join type
- 48. Perform price update
- 49. Update replacement cost
- 50. Order of execution of logical operators
- 51. Difference between UNION and UNION ALL
- 52. Display a list of departments
- 53. Find employee salary
- 54. Employees with high salaries
- 55. Employees with Above-Average Salaries
- 56. Find customers with even numbers
- 57. Find customers by phone prefix
- 58. Get unique customers list
- 59. How avoid accidental deletion?
- 60. What relation types exists in SQL?
- 61. Find non-Dollar/Euro countries
- 62. What is normalization in SQL?
- 63. What is a subquery?
- 64. Root categories list
- 65. Product List
- 66. Filtered list of products
- 67. Ten heaviest products
- 68. Determine the type of relationship
- 69. Sort the penguins
- 70. Find light weight penguins
- 71. Distribution penguins by islands
- 72. Find small penguins
- 73. Find small penguins species
- 74. Search by pattern
- 75. Flipper length to body mass rate
- 76. Penguins whose sex is unknown
- 77. Penguins with absent data
- 78. Heavy penguins
- 79. Count the penguins
- 80. Penguins and Islands
- 81. Medium sized bill Penguins
- 82. Penguins distribution
- 83. Common penguin species
- 84. Delete records
- 85. Delete employee records
- 86. Rename the table
- 87. Penguins stats
- 88. Create index
- 89. Create unique index
- 90. Drop table
- 91. Missing Data
- 92. Small bill Penguins
- 93. Not buyng customers
- 94. Penguins species
- 95. HAVING without aggregate
- 96. Duplicate Emails
- 97. Update Job Salaries
- 98. Create Penguins Table
- 99. Combine Penguin Lists
- 100. Unique Penguin List
- 101. Filter Little Penguins
- 102. Full-Text Index
- 103. Fare Conditions Types
- 104. Total Bookings Amount
- 1. Find customers full names
- 2. Find addresses using sub-query
- 3. Find addresses using JOIN
- 4. Find duplicate actor names
- 5. Most Popular Actor Surname
- 6. Find all the actors in the film
- 7. Find all films of an actor
- 8. Find the distribution of films by category
- 9. Find the average length of a movie by category
- 10. Find the number of films in which the actor took part
- 11. Find actors more popular than HENRY BERRY
- 12. Analyze monthly payment
- 13. Find the best month
- 14. Find the most popular film
- 15. Analyze rental data for film
- 16. Find the Managed Department
- 17. Employees on the Video Database Project
- 18. Find all customers with unshipped orders
- 19. Sort Movies by Multiple Fields
- 20. Get the longest movie
- 21. Get films list third page
- 22. Find the films never been rented
- 23. Find customers who did not return rented discs
- 24. Find average daily film rentals
- 25. Calculate daily income for the month
- 26. Generate dates table
- 27. Calculate the number of days off in a month
- 28. The average cost of renting a movie by category
- 29. Average film rental duration for each customer
- 30. Find Long Comedies
- 31. Find the distribution of customer activity
- 32. Find company stores details
- 33. Find clients who rented the film
- 34. Find minimum, maximum and average film duration
- 35. Find long movie categories
- 36. Find the number of employees
- 37. Find movie distribution by store
- 38. Find highly paid employees
- 39. Find employees by hire date
- 40. Get list of highly paid employees
- 41. Find valuable employees
- 42. Find the average client activity time
- 43. Find the average revenue
- 44. Find the store average revenue
- 45. Create a salary rating
- 46. Quarterly earnings analysis
- 47. Find the countries with the most customers
- 48. Find customer details
- 49. Find the count of rented disks
- 50. Find the number of returns
- 51. Find the top spending customers
- 52. Films Without Available Inventory
- 53. Find languages not represented in films
- 54. Find movies that have never been rented
- 55. Movies with above-average rental rates
- 56. Clients with a high number of rentals
- 57. Films with the highest replacement cost
- 58. Count the rental delays
- 59. Calculate the percentage of delays
- 60. Get movie cast lists
- 61. Extract address and domain from email
- 62. Get a list of actors - namesakes
- 63. List Movies with Their Categories
- 64. Average Rental Duration
- 65. Movie rental prices by category
- 66. Get rolling payment amounts
- 67. Find the number of films in each category
- 68. Find the most diverse clients
- 69. Find film distribution
- 70. Payments analyze
- 71. Get list of tables
- 72. Get table columns data
- 73. Get list of indexes
- 74. Distribution of clients by weekday
- 75. Client Rentals by Time of Day
- 76. Improve distribution of clients by weekday
- 77. Movies without actors records
- 78. Movies without cast records
- 79. Actors Excluding NC-17 Films
- 80. Average rentals count
- 81. Find repeat rentals
- 82. Find the distribution of clients by country
- 83. Find never been delayed films
- 84. Find most delayed films
- 85. Create department table
- 86. NC-17 Films about DBA
- 87. Films about Dogs or Cats
- 88. Clients whose first name is the last name of another client
- 89. Update Canadian postal codes
- 90. Enter Woodridge postal code
- 91. Enter new staff record
- 92. Create customer addresses view
- 93. Distribution of films by categories in JSON format
- 94. Generate a list of movies in JSON format
- 95. Category popularity analysis
- 96. Build a overal email list
- 97. Select clients without the letter "A"
- 98. Alter the staff table
- 99. Find films in several categories
- 100. Get a list of airports
- 101. Find Boeing aircraft
- 102. Get flights departed from Domodedovo
- 103. Get a list of aircraft from Domodedovo
- 104. Aircraft usage analysis
- 105. Find last names with double letters
- 106. Customers with matching name initials
- 107. Format Customer Names
- 108. Aircraft Lacking Business Class Seats
- 109. Get counts of seats by class
- 110. Get rows and seats count
- 111. Calculate the number of seats on flight
- 112. Get a list of airports with direct connections
- 113. Get a list of no-show passengers
- 114. Get a list of passengers
- 115. Find the flight delay
- 116. Get flights statistics
- 117. Find the fastest flight
- 118. Calculate the daily number of flights
- 119. Obtain a list of passengers
- 120. Get list of tables
- 121. Get information about the columns
- 122. Airports with one-way departures
- 123. Find airports relations
- 124. Get the passenger list
- 125. Get aircraft seat map
- 126. Get a list of planes in the air
- 127. What are DDL commands?
- 128. What are DML commands?
- 129. How is data stored in a relational database?
- 130. What is a constraint in SQL?
- 131. SQL constraints types
- 132. Choose tables join type
- 133. Calculate the tax
- 134. Get formatted list of films
- 135. What is a primary key?
- 136. Update customer's address
- 137. Adjust the rental cost
- 138. Find the tomorrow date
- 139. Start and End Dates of Current Month
- 140. Find the first and last dates of week
- 141. Display a table of airports
- 142. Count departing passengers
- 143. Number of passengers with total
- 144. Display a table of departures
- 145. Get a list of airports with more than one direct flight
- 146. Get list of tables (PostgreSQL)
- 147. A sub-departments list (JOIN)
- 148. Get a list of sub-departments
- 149. Find Employees by Department
- 150. Get list of tables (SQL Server)
- 151. Find duplicate phone numbers
- 152. What is a covering index?
- 153. What is a view in SQL?
- 154. Move film between categories
- 155. Find a June 2005 hit
- 156. Get product category color counts
- 157. Find films to share
- 158. How to find common rows in SQL?
- 159. Jobs Without Specific Requirements
- 160. Orders shipped in the following month
- 161. Update project information
- 162. Top states by population
- 163. What is a materialized view?
- 164. Reduced salaries
- 165. List of categories
- 166. List of subcategories
- 167. What is a SQL transaction?
- 168. What is a correlated subquery?
- 169. Create a phonebook
- 170. Staff Availability Report
- 171. What is "PIVOT" in SQL?
- 172. Film distribution by category and store
- 173. Find highly paid employees
- 174. Find the hypotenuse of a triangle
- 175. Find the median booking amount
- 176. Find the median film's length
- 177. Prepare mailing list
- 178. Penguins list
- 179. Daily income by source
- 180. Index usage
- 181. Using a covering index
- 182. Island with the minimum penguins mass
- 183. The most populated island
- 184. Classic movies
- 185. Penguins stats table
- 186. Managed by Robert Nelson
- 187. Tables joining algorithms in SQL
- 188. Delete films records
- 189. Analyze bill length
- 190. Analyze flipper length
- 191. What is denormalization in RDB?
- 192. Create functional index
- 193. Staff information
- 194. Customers without orders
- 195. Overloaded employees
- 196. Rank Penguins by Body Mass
- 197. Who ordered red helmet?
- 198. Who ordered helmet?
- 199. What bought Jon Grande?
- 200. The most popular product
- 201. Products catalog
- 202. Mountain Bikes catalog
- 203. Products by category distribution
- 204. Large categories
- 205. Set Last Service Date
- 206. Refurbished machines
- 207. Data migration
- 208. Penguins with low body weight
- 209. Most Frequent Co-Purchase
- 210. Most popular products
- 211. Average Sales Delay
- 212. Distance between cities
- 213. Penguins habitat
- 214. Extract Geometry as Text
- 215. Manhattan's subway stations
- 216. Area of the Neighborhood
- 217. Area of the Neighborhood
- 218. Neighborhood Average Area
- 219. Extract Geometry as JSON
- 220. New York streets length
- 221. Rented Films
- 222. What is FULL-TEXT index?
- 223. Find Aircraft with All Fare Conditions
- 224. Is the index fit for query?
- 225. Is the index fit for queries?
- 1. Find most active customers
- 2. Find sad actors
- 3. Find the most diverse actors
- 4. Find all films where HENRY BERRY did not participate
- 5. Calculate factorial
- 6. Find average disk idle time
- 7. Find the distribution by categories
- 8. Salary Ratio Calculation
- 9. Analyze monthly payments (2)
- 10. Find movie popularity rating
- 11. Find EMILY DEE fans
- 12. Clients who are not familiar with EMILY DEE films
- 13. Find the least popular movies
- 14. Films with Low Rental Time
- 15. Find actors duets
- 16. Find films that were out of stock
- 17. Improve payments analyze
- 18. Find all the actors in the film
- 19. Analyze weekly rentals
- 20. Find the Horror films fans
- 21. Movies in One Store
- 22. Find movies with no available copies
- 23. Staff performance analysis
- 24. Generate monthly bill
- 25. Gap & Islands problem
- 26. Find customers who sow same films
- 27. Get the list of destination airports
- 28. Get a list of airports without direct connections
- 29. Rate airports
- 30. Find a list of flight options
- 31. Rental history
- 32. Find average flight occupancy
- 33. Find flight occupancy by fare
- 34. Find small airports
- 35. FInd the plane coordinates
- 36. FInd the planes coordinates
- 37. What is a subset of the SQL language?
- 38. SQL set operators
- 39. Find a 2005 hits
- 40. Analysis of the cost of film rental by category
- 41. Distribution of flights by days of the week
- 42. Subcategories count
- 43. Find the median salary
- 44. Actual statistics
- 45. Actual statistics 2
- 46. Cumulative payment analysis
- 47. Country Area
- 48. Population Distribution (Pivot)
- 49. Little Italy Stations
- 50. Frequently Purchased Product Pairs
- 51. Sales by Category Percentage
- 1. The Storage Engine
- 2. The Release Strategy
- 3. The MariaDB Foundation Role
- 4. Evolution and Compatibility
- 5. Passenger names classification
- 6. Monthly Bookings Count
- 7. Extract Airport Data as JSON
- 8. Remove View from Database
- 9. Salary Bucketing
- 10. Product Weight Buckets
- 11. Get Bookings by Date
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Rename the table little_penguins to small_penguins.
Use SQLite syntax to write your answer. Descriptions of tables are provided in the right pane.
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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"} |
