Data Modelling and Databases Exercise dates: March 20/March 27, 2017 Ce Zhang, Gustavo Alonso Last update: February 17, 2018.

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1 Data Modelling and Databases Exercise dates: March 20/March 27, 2017 Ce Zhang, Gustavo Alonso Last update: February 17, 2018 Spring Semester 2018 Head TA: Ingo Müller Datasets set-up This assignment will be discussed during the exercise slots indicated above. If you want feedback for your copy, hand it in during the lecture on the Wednesday before (preferably stapled and with your address). You can also annotate your copy with questions you think should be discussed during the exercise session. If you have questions that are not answered by the solution we provide, send them to our mailing list. This is a guide how to import three datasets into a PostgresSQL server, on which exercises of this and following weeks will be based. Tutorials describing how to install the database server and set up the pgadmin administration tool on Linux, MacOS, and Windows, are available on the course webpage 1. 1 Employee Dataset The Employee dataset is a synthetic sample dataset consisting of six tables modeling a simple HR application for managing employees together with their salaries, titles, and department affiliations. All of these can change over time, and all changes are recorded. Some employees can also be promoted to managers of a specific department. 1.1 Database Setup Download the provided example zip archive from the course website and decompress it into a folder of your choice. To create a new database in pgadmin, right-click on the Databases list item, and select the New Database entry from context menu. In the next dialog, you have to specify the name of new database. In this example, the database is called employee. 1

2 After a new database has been created, we define a schema and load the data. Start the SQL editor and open the provided schema.sql file. Read through the schema definition: it consists of Create Table statements. Execute the queries by pressing the Execute button. Then open the inserts.sql file, which contains Copy statements. The Copy instruction is used to load the data from a file (*.tbl) into the database. Edit the path provided to each Copy instruction (six in total) to point to the location where you extracted the zip archive.

3 After the query terminated successfully, you will be able to see the newly created tables under the Schemas/Tables list entry. 2 ZVV Dataset This dataset is a subset of the real-life ZVV dataset that is publicly available 2. The dataset contains tram stops in Zurich, and the trips the trams were scheduled to make on a specific day 2

4 (Monday, March ). Each stop has a stop_id, stop_name (e.g. Zürich, ETH/Universitätsspital ), and a location expressed in latitude and longitude floating points. A tram trip has a trip_id, a tram_number, and a trip_headsign, which indicates the direction in which the tram is heading. Each trip has multiple stops, where for each stop a tram makes, there is an arrival_time, a departing_time, a stop_id and a stop_sequence. 2.1 Database Setup Set up the ZVV database using the same steps as in Task TPC-H Dataset This dataset corresponds to the relational schema used in Task 1 in the previous exercise. It is a subset of an official benchmark suite 3 designed to test relational databases for performance of analytical queries. 3.1 Database Setup Set up the TPC-H database using the same steps as in Task Verification To verify that your database setup is working correctly, and that all the necessary data has been loaded, run the following three queries and check if your answers match. To run the queries, open the SQL editor and type in the following queries. 4.1 Employee Example Query: Return the first name, last name, department, salary, and title of all the current managers. SELECT FROM 3 e.first_name, e.last_name, d.dept_name, s.salary, t.title

5 JOIN JOIN JOIN JOIN WHERE employees e dept_manager m ON m.emp_no = e.emp_no departments d ON m.dept_no = d.dept_no salaries s ON s.emp_no = e.emp_no titles t ON t.emp_no = e.emp_no m.to_date > NOW() AND s.to_date > NOW() AND t.to_date > NOW() Result: first_name last_name dept_name salary title Vishwani Minakawa Marketing Manager Isamu Legleitner Finance Manager Karsten Sigstam Human Resources Manager Oscar Ghazalie Production Manager Leon DasSarma Development Manager Dung Pesch Quality Management Manager Hauke Zhang Sales Manager Hilary Kambil Research Manager Yuchang Sedman Customer Service Manager

6 4.2 ZVV Example Query: This query returns a sample timetable for a tram stop at a given time. Specifically, it returns the stop name, tram number, tram headsign, and departure time of all the trams leaving Zurich Central between 13:15 and 13:23. SELECT FROM s.stop_name, t.tram_number, t.trip_headsign, st.departure_time stops s, stop_times st, trips t WHERE t.trip_id = st.trip_id AND s.stop_id = st.stop_id AND s.stop_name like %Central AND st.departure_time between 13:15:00 and 13:23:00 ORDER BY st.departure_time Result: stop_name tram_number trip_headsign departure_time Zürich, Central 10 Zürich Flughafen, Fracht 13:15:48 Zürich, Central 4 Zürich, Bahnhof Altstetten N 13:16:06 Zürich, Central 15 Zürich, Klusplatz 13:16:12 Zürich, Central 6 Zürich, Bahnhof Enge 13:16:24 Zürich, Central 7 Zürich, Wollishofen 13:17:36 Zürich, Central 6 Zürich, Zoo 13:18:12 Zürich, Central 3 Zürich, Klusplatz 13:18:12 Zürich, Central 15 Zürich, Bucheggplatz 13:19:24 Zürich, Central 3 Zürich, Albisrieden 13:20:00 Zürich, Central 7 Zürich, Bahnhof Stettbach 13:20:12 Zürich, Central 4 Zürich, Bahnhof Tiefenbrunnen 13:20:42 Zürich, Central 10 Zürich, Bahnhofplatz/HB 13:22:06

7 4.3 TPC-H Example Query: Return how many of the most expensive part(s) were ordered per customer region SELECT FROM cust_r.regionname, sum(olquantity) AS ordered_quantity orderline ol, customer c, part p, supplypart sp, supplier s, orders o, nation supp_n, nation cust_n, region cust_r WHERE ol.orderid = o.orderid AND ol.partid = sp.partid AND ol.supplierid = sp.supplierid AND o.customerid = c.customerid AND c.nationid = cust_n.nationid AND cust_n.regionid = cust_r.regionid AND sp.partid = p.partid AND sp.supplierid = s.supplierid AND s.nationid = supp_n.nationid AND p.partretailprice IN (SELECT MAX(partretailprice) FROM part) GROUP BY cust_r.regionname ORDER BY ordered_quantity DESC; Result: regionname ordered_quantity AMERICA 224 AFRICA 210 MIDDLE EAST 106 ASIA 94 EUROPE 48