| Nächste Überarbeitung | Vorhergehende Überarbeitung |
| modul:m290:learningunits:lu07:loesungen:l01 [2024/09/30 15:07] – angelegt vdemir | modul:m290:learningunits:lu07:loesungen:l01 [2024/10/17 12:44] (aktuell) – vdemir |
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| ====== LU07.S01 - Preparations for the assignment of the DML commands ====== | ====== LU08.S01 - Preparatory work====== |
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| ===== Preparation Assignments ===== | ===== A: Creating the data schema ===== |
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| ==== A: DATA SCHEMA ==== | |
| First of all we need a database schema (database workspace). Execute the following two lines on your MySQL installation. | First of all we need a database schema (database workspace). Execute the following two lines on your MySQL installation. |
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| After executing the three SQL commands above, the feedback from your database would look like in the image below: | After executing the three SQL commands above, the feedback from your database would look like in the image below: |
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| **Result in webstorm** | **Result in webstorm** |
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| | Please note, that the table is actually empty, that means that a "SELECT COUNT(*) FROM employees" leads to "0" results. |
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| {{:modul:m290:learningunits:lu07:aufgaben:m290.lu07.s01a.png?500|}} | {{:modul:m290:learningunits:lu07:aufgaben:m290.lu07.s01a.png?500|}} |
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| | ==== B: CREATE TABLE ===== |
| ==== B: CREATE TABLE ==== | |
| To exercise the DML commands, we need a suitable table including a reasonable amout of data. The following SQL statement will create a table **employee** regarding all necessary attributes of an "average employee". | To exercise the DML commands, we need a suitable table including a reasonable amout of data. The following SQL statement will create a table **employee** regarding all necessary attributes of an "average employee". |
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| CREATE TABLE EMPLOYEES ( | CREATE TABLE EMPLOYEES ( |
| employee_ID INT PRIMARY KEY, -- Employee ID as the primary key | employee_ID INT PRIMARY KEY, -- Employee ID as the primary key |
| name VARCHAR(50) NOT NULL, -- Name of the employee (max length 50 characters) | .... |
| surname VARCHAR(50) NOT NULL, -- Surname of the employee (max length 50 characters) | |
| birthdate DATE NOT NULL, -- Birthdate of the employee | **Result in Webstorm** |
| sex CHAR(1), -- Sex of the employee (M/F/O for other) | |
| pronomen VARCHAR(10), -- Pronoun of the employee | |
| employment_date DATE NOT NULL, -- Date when the employee was hired | |
| salary DECIMAL(10, 2) NOT NULL, -- Salary of the employee (up to 10 digits, 2 decimal places) | |
| department VARCHAR(50) NOT NULL -- Department where the employee works | |
| ); | |
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| After creating the table "employees" the result in our Webstorm ought to look like the figure above: | After creating the table "employees" the result in our Webstorm ought to look like the figure above: |
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| {{:modul:m290:learningunits:lu07:loesungen:m290.lu07.s01b.png?600|}} | {{:modul:m290:learningunits:lu07:loesungen:m290.lu07.s01b.png?600|}} |
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| ==== C1: Inital data import ==== | |
| Once we have created our 'employees' table, we need a reasonable amount of data to be able to make the assignments. The following SQL INSERTS will provide you with the inital data of 30 employees. Though execute the follwing INSERT statements on your MySQL. | |
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| As we already know there are two ways how to import the inital data cosisting of 30 rows of emloyee data: | |
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| * multiple inserts | |
| * single insert | |
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| **Multiple inserts** | |
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| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (1, 'John', 'Doe', '1985-03-25', 'M', 'He/Him', '2015-01-15', 55000.00, 'Finance'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (2, 'Jane', 'Smith', '1990-07-19', 'F', 'She/Her', '2017-06-10', 60000.00, 'HR'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (3, 'Mike', 'Brown', '1982-11-02', 'M', 'He/Him', '2012-03-05', 75000.00, 'IT'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (4, 'Sarah', 'Johnson', '1970-01-14', 'F', 'She/Her', '2020-08-22', 50000.00, 'Marketing'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (5, 'Chris', 'Williams', '1987-04-11', 'M', 'He/Him', '2016-09-30', 65000.00, 'Finance'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (6, 'Anna', 'Lee', '1993-12-01', 'F', 'She/Her', '2018-04-12', 72000.00, 'IT'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (7, 'David', 'Kim', '1989-10-22', 'M', 'He/Him', '2014-11-08', 69000.00, 'Marketing'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (8, 'Laura', 'Clark', '1986-02-17', 'F', 'She/Her', '2013-05-01', 56000.00, 'HR'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (9, 'Jake', 'Lewis', '1962-09-05', 'M', 'He/Him', '2019-12-20', 59000.00, 'Finance'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (10, 'Emily', 'Martinez', '1996-06-13', 'F', 'She/Her', '2021-01-05', 61000.00, 'IT'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (11, 'Ryan', 'Turner', '1991-05-21', 'M', 'He/Him', '2017-07-19', 70000.00, 'Marketing'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (12, 'Sophia', 'Adams', '1988-12-06', 'F', 'She/Her', '2014-02-11', 62000.00, 'HR'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (13, 'Nathan', 'Garcia', '1994-03-29', 'M', 'He/Him', '2018-09-15', 66000.00, 'Finance'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (14, 'Olivia', 'Baker', '1983-08-11', 'F', 'She/Her', '2011-10-25', 78000.00, 'IT'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (15, 'Liam', 'Scott', '1958-12-18', 'M', 'He/Him', '2020-06-13', 53000.00, 'Marketing'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (16, 'Isabella', 'Carter', '1990-01-02', 'F', 'She/Her', '2016-03-30', 67000.00, 'HR'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (17, 'James', 'Rodriguez', '1984-05-14', 'M', 'He/Him', '2013-07-22', 72000.00, 'Finance'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (18, 'Mia', 'Lopez', '1998-09-27', 'F', 'She/Her', '2021-03-11', 51000.00, 'IT'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (19, 'Ethan', 'Harris', '1970-04-07', 'M', 'He/Him', '2019-10-05', 64000.00, 'Marketing'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (20, 'Charlotte', 'Moore', '1986-11-03', 'F', 'She/Her', '2014-12-30', 59000.00, 'HR'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (21, 'Lucas', 'Jackson', '1989-02-28', 'M', 'He/Him', '2015-08-18', 74000.00, 'Finance'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (22, 'Amelia', 'Young', '1991-06-23', 'F', 'She/Her', '2016-11-02', 62000.00, 'IT'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (23, 'Henry', 'King', '1993-10-08', 'M', 'He/Him', '2018-05-07', 67000.00, 'Marketing'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (24, 'Grace', 'Wright', '1987-03-19', 'F', 'She/Her', '2012-04-14', 71000.00, 'HR'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (25, 'Jack', 'White', '1962-08-15', 'M', 'He/Him', '2019-01-23', 69000.00, 'Finance'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (26, 'Chloe', 'Martin', '1996-12-31', 'F', 'She/Her', '2021-07-05', 54000.00, 'IT'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (27, 'Daniel', 'Green', '1985-09-25', 'M', 'He/Him', '2013-02-13', 76000.00, 'Marketing'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (28, 'Ella', 'Hall', '1994-04-30', 'F', 'She/Her', '2017-09-26', 63000.00, 'HR'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (29, 'Oliver', 'Nelson', '1993-07-21', 'M', 'He/Him', '2018-11-19', 64000.00, 'Finance'); | |
| INSERT INTO EMPLOYEES (employee_id, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES (30, 'Ava', 'Walker', '1958-02-11', 'F', 'She/Her', '2021-05-30', 52000.00, 'Marketing'); | |
| |
| **Single insert** | |
| INSERT INTO EMPLOYEES (employee_ID, name, surname, birthdate, sex, pronomen, employment_date, salary, department) | |
| VALUES | |
| (1, 'John', 'Doe', '1985-03-25', 'M', 'He/Him', '2015-01-15', 55000.00, 'Finance'), | |
| (2, 'Jane', 'Smith', '1990-07-19', 'F', 'She/Her', '2017-06-10', 60000.00, 'HR'), | |
| (3, 'Mike', 'Brown', '1982-11-02', 'M', 'He/Him', '2012-03-05', 75000.00, 'IT'), | |
| (4, 'Sarah', 'Johnson', '1970-01-14', 'F', 'She/Her', '2020-08-22', 50000.00, 'Marketing'), | |
| (5, 'Chris', 'Williams', '1987-04-11', 'M', 'He/Him', '2016-09-30', 65000.00, 'Finance'), | |
| (6, 'Anna', 'Lee', '1993-12-01', 'F', 'She/Her', '2018-04-12', 72000.00, 'IT'), | |
| (7, 'David', 'Kim', '1989-10-22', 'M', 'He/Him', '2014-11-08', 69000.00, 'Marketing'), | |
| (8, 'Laura', 'Clark', '1986-02-17', 'F', 'She/Her', '2013-05-01', 56000.00, 'HR'), | |
| (9, 'Jake', 'Lewis', '1962-09-05', 'M', 'He/Him', '2019-12-20', 59000.00, 'Finance'), | |
| (10, 'Emily', 'Martinez', '1996-06-13', 'F', 'She/Her', '2021-01-05', 61000.00, 'IT'), | |
| (11, 'Ryan', 'Turner', '1991-05-21', 'M', 'He/Him', '2017-07-19', 70000.00, 'Marketing'), | |
| (12, 'Sophia', 'Adams', '1988-12-06', 'F', 'She/Her', '2014-02-11', 62000.00, 'HR'), | |
| (13, 'Nathan', 'Garcia', '1994-03-29', 'M', 'He/Him', '2018-09-15', 66000.00, 'Finance'), | |
| (14, 'Olivia', 'Baker', '1983-08-11', 'F', 'She/Her', '2011-10-25', 78000.00, 'IT'), | |
| (15, 'Liam', 'Scott', '1958-12-18', 'M', 'He/Him', '2020-06-13', 53000.00, 'Marketing'), | |
| (16, 'Isabella', 'Carter', '1990-01-02', 'F', 'She/Her', '2016-03-30', 67000.00, 'HR'), | |
| (17, 'James', 'Rodriguez', '1984-05-14', 'M', 'He/Him', '2013-07-22', 72000.00, 'Finance'), | |
| (18, 'Mia', 'Lopez', '1998-09-27', 'F', 'She/Her', '2021-03-11', 51000.00, 'IT'), | |
| (19, 'Ethan', 'Harris', '1970-04-07', 'M', 'He/Him', '2019-10-05', 64000.00, 'Marketing'), | |
| (20, 'Charlotte', 'Moore', '1986-11-03', 'F', 'She/Her', '2014-12-30', 59000.00, 'HR'), | |
| (21, 'Lucas', 'Jackson', '1989-02-28', 'M', 'He/Him', '2015-08-18', 74000.00, 'Finance'), | |
| (22, 'Amelia', 'Young', '1991-06-23', 'F', 'She/Her', '2016-11-02', 62000.00, 'IT'), | |
| (23, 'Henry', 'King', '1993-10-08', 'M', 'He/Him', '2018-05-07', 67000.00, 'Marketing'), | |
| (24, 'Grace', 'Wright', '1987-03-19', 'F', 'She/Her', '2012-04-14', 71000.00, 'HR'), | |
| (25, 'Jack', 'White', '1962-08-15', 'M', 'He/Him', '2019-01-23', 69000.00, 'Finance'), | |
| (26, 'Chloe', 'Martin', '1996-12-31', 'F', 'She/Her', '2021-07-05', 54000.00, 'IT'), | |
| (27, 'Daniel', 'Green', '1985-09-25', 'M', 'He/Him', '2013-02-13', 76000.00, 'Marketing'), | |
| (28, 'Ella', 'Hall', '1994-04-30', 'F', 'She/Her', '2017-09-26', 63000.00, 'HR'), | |
| (29, 'Oliver', 'Nelson', '1993-07-21', 'M', 'He/Him', '2018-11-19', 64000.00, 'Finance'), | |
| (30, 'Ava', 'Walker', '1958-02-11', 'F', 'She/Her', '2021-05-30', 52000.00, 'Marketing'); | |
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| ===== Solution ===== | |
| [[modul:m290:learningunits:lu07:loesungen:l01|Lösung]] | |
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| ===== Vocabulary ===== | ===== Vocabulary ===== |
| ^ English ^ German ^ | ^ English ^ German ^ |
| | ... | ... | | | ought to | sollte | |
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| ---- | ---- |
| [[https://creativecommons.org/licenses/by-nc-sa/4.0/|{{https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png}}]] Volkan Demir | [[https://creativecommons.org/licenses/by-nc-sa/4.0/|{{https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png}}]] Volkan Demir |