solution to HackerRank sql exercise

sql-hackerrank.sql

/* Basic Select */
-- Revising the Select Query I
SELECT
    *
FROM
    CITY
WHERE
    POPULATION > 100000
    AND COUNTRYCODE = 'USA'




-- Revising the Select Query II
SELECT
    NAME
FROM
    CITY
WHERE
    POPULATION > 120000
    AND COUNTRYCODE = 'USA'




-- Select All
SELECT
    *
FROM
    CITY




-- Select By ID
SELECT
    *
FROM
    CITY
WHERE
    ID = 1661




-- Japanese Cities' Attributes
SELECT
    *
FROM
    CITY
WHERE
    COUNTRYCODE = 'JPN'




-- Weather Observation Station 1
SELECT
    CITY, STATE
FROM
    STATION
    




-- Weather Observation Station 3
SELECT
    distinct CITY
FROM
    STATION
WHERE
    (ID % 2) = 0
    




-- Weather Observation Station 4
SELECT
    COUNT(CITY) - COUNT(DISTINCT CITY) AS DIFF
FROM 
    STATION




-- Weather Observation Station 5
(SELECT
    CITY, LENGTH(CITY)
FROM STATION
ORDER BY LENGTH(CITY), CITY LIMIT 1)

UNION 

(SELECT
    CITY, LENGTH(CITY)
FROM STATION
ORDER BY LENGTH(CITY) DESC, CITY LIMIT 1)




-- Weather Observation Station 6
SELECT
    CITY
FROM
    STATION
WHERE
    REGEXP_LIKE(CITY, '^a|^e|^i|^o|^u')




-- Weather Observation Station 7
SELECT
    DISTINCT CITY
FROM
    STATION
WHERE
    REGEXP_LIKE(CITY, 'a$|e$|i$|o$|u$')




-- Weather Observation Station 8
SELECT
    DISTINCT CITY
FROM
    STATION
WHERE
    REGEXP_LIKE(CITY, '^[aeiou].*[aeiou]$', 'i')




-- Weather Observation Station 9
SELECT
    DISTINCT CITY
FROM
    STATION
WHERE
    REGEXP_LIKE(CITY, '^[^aeiou]', 'i')




-- Weather Observation Station 10
SELECT
    DISTINCT CITY
FROM
    STATION
WHERE
    REGEXP_LIKE(CITY, '[^aeiou]$', 'i')




-- Weather Observation Station 11
SELECT
    DISTINCT CITY
FROM
    STATION
WHERE
    REGEXP_LIKE(CITY, '^[^aeiou]|[^aeiou]$', 'i')




-- Weather Observation Station 12
SELECT
    DISTINCT CITY
FROM
    STATION
WHERE
    REGEXP_LIKE(CITY, '^[^aeiou].*[^aeiou]$', 'i')




-- Higher Than 75 Marks
SELECT
    NAME
FROM
    STUDENTS
WHERE
    MARKS > 75
ORDER BY RIGHT(NAME, 3), ID




-- Employee Names
SELECT 
    NAME
FROM
    EMPLOYEE
ORDER BY NAME




-- Employee Salaries
SELECT
    NAME
FROM
    EMPLOYEE
WHERE
    SALARY > 2000
    AND MONTHS < 10
ORDER BY EMPLOYEE_ID







































/* Advanced Select */
-- Type of Triangle
SELECT
    CASE
        WHEN (
            A + B <= C OR A + C <= B OR B + C <= A
        ) THEN 'Not A Triangle'
        WHEN (A = B AND B = C AND A = C) THEN 'Equilateral'
        WHEN (
            (A = B AND A != C) 
            OR (A = C AND A != B)
            OR (B = C AND A != B)
        ) THEN 'Isosceles'
        WHEN (A != B AND B != C AND A != C) THEN 'Scalene'
    END
FROM
    TRIANGLES




-- The PADS
SELECT
    CONCAT(NAME, '(', LEFT(OCCUPATION, 1), ')')
FROM
    OCCUPATIONS
ORDER BY NAME;


SELECT
    CONCAT('There are a total of ', COUNT(OCCUPATION), ' ', LOWER(OCCUPATION), 's.')
FROM
    OCCUPATIONS
GROUP BY OCCUPATION
ORDER BY COUNT(OCCUPATION), OCCUPATION;




-- Occupations
-- https://my.oschina.net/u/4600992/blog/4459062
WITH t AS (
    SELECT 
        OCCUPATION,
        ROW_NUMBER() OVER (
            PARTITION BY OCCUPATION
            ORDER BY NAME
        ) AS RN,
        NAME
    FROM
        OCCUPATIONS
)
SELECT 
    MIN(IF(OCCUPATION = 'Doctor', name, NULL)) AS Doctor,
    MIN(IF(OCCUPATION = 'Professor', name, NULL)) AS Professor,
    MIN(IF(OCCUPATION = 'Singer', name, NULL)) AS Singer,
    MIN(IF(OCCUPATION = 'Actor', name, NULL)) AS Actor
FROM
    t
GROUP BY RN




-- Binary Tree Nodes
SELECT
    N,
    CASE
        WHEN P IS NULL THEN 'Root'
        WHEN N in (SELECT P FROM BST) THEN 'Inner'
        ELSE 'Leaf'
    END
FROM
    BST
ORDER BY N




-- New Companies
SELECT employee.company_code, 
       company.founder, 
       COUNT(DISTINCT employee.lead_manager_code), 
       COUNT(DISTINCT employee.senior_manager_code), 
       COUNT(DISTINCT employee.manager_code), 
       COUNT(DISTINCT employee.employee_code) 
FROM   employee
       INNER JOIN company
               ON employee.company_code = company.company_code 
GROUP  BY employee.company_code, 
          company.founder 
ORDER  BY employee.company_code 







































/* Aggregations */
-- Revising Aggregations - The Count Function
SELECT
    COUNT(ID)
FROM
    CITY
WHERE
    POPULATION > 100000




-- Revising Aggregations - The Sum Function
SELECT
    SUM(POPULATION)
FROM
    CITY
WHERE
    DISTRICT = 'California'




-- Revising Aggregations - Averages
SELECT
    AVG(POPULATION)
FROM
    CITY
WHERE
    DISTRICT = 'California'




-- Average Population
SELECT
    ROUND(AVG(POPULATION), 0)
FROM
    CITY




-- Japan Population
SELECT
    SUM(POPULATION)
FROM
    CITY
WHERE
    COUNTRYCODE = 'JPN'




-- Population Density Difference
SELECT
    MAX(POPULATION) - MIN(POPULATION)
FROM
    CITY




-- The Blunder
SELECT
    CEIL(AVG(SALARY) - AVG(REPLACE(SALARY, '0', '')))
FROM
    EMPLOYEES




-- Top Earners
SELECT EMPLOYEE_WITH_MAX_SALARY.earning, 
       Count(*) 
FROM   (SELECT NAME, 
               salary * months AS EARNING 
        FROM   employee 
        WHERE  salary * months = (SELECT Max(TOTAL_SALARY.earning) 
                                  FROM   (SELECT months * salary AS EARNING 
                                          FROM   employee) AS TOTAL_SALARY)
       ) AS EMPLOYEE_WITH_MAX_SALARY 
GROUP  BY EMPLOYEE_WITH_MAX_SALARY.earning 




-- Weather Observation Station 2
SELECT
    ROUND(SUM(LAT_N), 2) AS lat,
    ROUND(SUM(LONG_W), 2) AS lon
FROM
    STATION




-- Weather Observation Station 13
SELECT
    ROUND(SUM(LAT_N), 4)
FROM
    STATION
WHERE
    LAT_N > 38.7880 AND LAT_N < 137.2345




-- Weather Observation Station 14
SELECT
    ROUND(MAX(LAT_N), 4)
FROM
    STATION
WHERE
    LAT_N < 137.2345




-- Weather Observation Station 15
SELECT
    ROUND(LONG_W, 4)
FROM
    STATION
WHERE
    LAT_N = (
        SELECT 
            MAX(LAT_N)
        FROM
            STATION
        WHERE
            LAT_N < 137.2345    
    )




-- Weather Observation Station 16
SELECT
    ROUND(MIN(LAT_N), 4)
FROM
    STATION
WHERE
    LAT_N > 38.7780




-- Weather Observation Station 17
SELECT
    ROUND(LONG_W, 4)
FROM
    STATION
WHERE
    LAT_N = 
    (SELECT 
        MIN(T.LAT_N)
     FROM
        (
            SELECT
                LAT_N
            FROM
                STATION
            WHERE
                LAT_N > 38.7880
        ) AS T
    )




-- Weather Observation Station 18
SELECT
    ROUND(ABS(MIN(LAT_N) - MAX(LAT_N)) + ABS(MIN(LONG_W) - MAX(LONG_W)) ,4)
FROM
    STATION




-- Weather Observation Station 19
SELECT
    ROUND(
        SQRT(
            POWER((MIN(LAT_N) - MAX(LAT_N)), 2) + POWER((MIN(LONG_W) - MAX(LONG_W)), 2)
        ), 4)
FROM
    STATION