The bookings schema contains all objects of the demo database. It means that when you access database objects, you either have to explicitly specify the schema name (for example: bookings.flights), or modify the search_path configuration parameter beforehand (for example: SET search_path = bookings, public;).

However, for the bookings.now function, you always have to specify the schema to distinguish this function from the standard now function.

To better understand the contents of the demo database, let's take a look at the results of several simple queries.

The results displayed below were received on a small database version (demo_small) of October 13, 2016. If the same queries return different data on your system, check your demo database version (using the bookings.now function). Some minor deviations may be caused by the difference between your local time and Moscow time, or your locale settings.

All flights are operated by several types of aircraft:

SELECT * FROM aircrafts;
    

 aircraft_code |        model        | range
---------------+---------------------+-------
 773           | Boeing 777-300      | 11100
 763           | Boeing 767-300      |  7900
 SU9           | Sukhoi SuperJet-100 |  3000
 320           | Airbus A320-200     |  5700
 321           | Airbus A321-200     |  5600
 319           | Airbus A319-100     |  6700
 733           | Boeing 737-300      |  4200
 CN1           | Cessna 208 Caravan  |  1200
 CR2           | Bombardier CRJ-200  |  2700
(9 rows)
    

For each aircraft type, a separate list of seats is supported. For example, in a small Cessna 208 Caravan, one can select the following seats:

SELECT   a.aircraft_code,
         a.model,
         s.seat_no,
         s.fare_conditions
FROM     aircrafts a
         JOIN seats s ON a.aircraft_code = s.aircraft_code
WHERE    a.model = 'Cessna 208 Caravan'
ORDER BY s.seat_no;
    

 aircraft_code |       model        | seat_no | fare_conditions
---------------+--------------------+---------+-----------------
 CN1           | Cessna 208 Caravan | 1A      | Economy
 CN1           | Cessna 208 Caravan | 1B      | Economy
 CN1           | Cessna 208 Caravan | 2A      | Economy
 CN1           | Cessna 208 Caravan | 2B      | Economy
 CN1           | Cessna 208 Caravan | 3A      | Economy
 CN1           | Cessna 208 Caravan | 3B      | Economy
 CN1           | Cessna 208 Caravan | 4A      | Economy
 CN1           | Cessna 208 Caravan | 4B      | Economy
 CN1           | Cessna 208 Caravan | 5A      | Economy
 CN1           | Cessna 208 Caravan | 5B      | Economy
 CN1           | Cessna 208 Caravan | 6A      | Economy
 CN1           | Cessna 208 Caravan | 6B      | Economy
(12 rows)
    

Bigger aircraft have more seats of various travel classes:

SELECT   s2.aircraft_code,
         string_agg (s2.fare_conditions || '(' || s2.num::text || ')',
                     ', ') as fare_conditions
FROM     (
          SELECT   s.aircraft_code, s.fare_conditions, count(*) as num
          FROM     seats s
          GROUP BY s.aircraft_code, s.fare_conditions
          ORDER BY s.aircraft_code, s.fare_conditions
         ) s2
GROUP BY s2.aircraft_code
ORDER BY s2.aircraft_code;
    

 aircraft_code |             fare_conditions
---------------+-----------------------------------------
 319           | Business(20), Economy(96)
 320           | Business(20), Economy(120)
 321           | Business(28), Economy(142)
 733           | Business(12), Economy(118)
 763           | Business(30), Economy(192)
 773           | Business(30), Comfort(48), Economy(324)
 CN1           | Economy(12)
 CR2           | Economy(50)
 SU9           | Business(12), Economy(85)
(9 rows)
    

The demo database contains the list of airports of almost all major Russian cities. Most cities have only one airport. The exceptions are:

SELECT   a.airport_code as code,
         a.airport_name,
         a.city,
         a.longitude,
         a.latitude,
         a.timezone
FROM     airports a
WHERE    a.city IN (
            SELECT   aa.city
            FROM     airports aa
            GROUP BY aa.city
            HAVING   COUNT(*) > 1
         )
ORDER BY a.city, a.airport_code;
    

 code |    airport_name     |   city    | longitude | latitude  |   timezone
------+---------------------+-----------+-----------+-----------+---------------
 DME  | Домодедово          | Москва    | 37.906111 | 55.408611 | Europe/Moscow
 SVO  | Шереметьево         | Москва    | 37.414589 | 55.972642 | Europe/Moscow
 VKO  | Внуково             | Москва    | 37.261486 | 55.591531 | Europe/Moscow
 ULV  | Баратаевка          | Ульяновск |   48.2267 | 54.268299 | Europe/Samara
 ULY  | Ульяновск-Восточный | Ульяновск |   48.8027 |    54.401 | Europe/Samara
(5 rows)
    

To learn about your flying options from one point to another, it is convenient to use the routes materialized view that aggregates information on all flights. For example, here are the destinations where you can get from Volgograd on specific days of the week, with flight duration:

SELECT r.arrival_city as city,
       r.arrival_airport as airport_code,
       r.arrival_airport_name as airport_name,
       r.days_of_week,
       r.duration
FROM   routes r
WHERE  r.departure_city = 'Волгоград';
    

      city      | airport_code |  airport_name  |  days_of_week   | duration
----------------+--------------+----------------+-----------------+----------
 Москва         | SVO          | Шереметьево    | {1,2,3,4,5,6,7} | 01:15:00
 Челябинск      | CEK          | Челябинск      | {1,2,3,4,5,6,7} | 01:50:00
 Ростов-на-Дону | ROV          | Ростов-на-Дону | {1,2,3,4,5,6,7} | 00:30:00
 Москва         | VKO          | Внуково        | {1,2,3,4,5,6,7} | 01:10:00
 Чебоксары      | CSY          | Чебоксары      | {1,2,3,4,5,6,7} | 02:45:00
 Томск          | TOF          | Богашёво       | {3}             | 03:50:00
(6 rows)
    

The database was formed at the moment returned by the bookings.now() function:

SELECT bookings.now() as now;
    

          now
------------------------
 2016-10-13 17:00:00+03
    

In relation to this moment, all flights are classified as past and future flights:

SELECT   status,
         count(*) as count,
         min(scheduled_departure) as min_scheduled_departure,
         max(scheduled_departure) as max_scheduled_departure
FROM     flights
GROUP BY status 
ORDER BY min_scheduled_departure;
    

  status   | count | min_scheduled_departure | max_scheduled_departure
-----------+-------+-------------------------+-------------------------
 Arrived   | 16707 | 2016-09-13 00:50:00+03  | 2016-10-13 16:25:00+03
 Cancelled |   414 | 2016-09-16 10:35:00+03  | 2016-11-12 19:55:00+03
 Departed  |    58 | 2016-10-13 08:55:00+03  | 2016-10-13 16:50:00+03
 Delayed   |    41 | 2016-10-13 14:15:00+03  | 2016-10-14 16:25:00+03
 On Time   |   518 | 2016-10-13 16:55:00+03  | 2016-10-14 17:00:00+03
 Scheduled | 15383 | 2016-10-14 17:05:00+03  | 2016-11-12 19:40:00+03
(6 rows)
    

Let's find the next flight from Yekaterinburg to Moscow. The flight table is not very convenient for such queries, as it does not include information on the cities of departure and arrival. That is why we will use the flights_v view:

\x
SELECT   f.*
FROM     flights_v f
WHERE    f.departure_city = 'Екатеринбург'
AND      f.arrival_city = 'Москва'
AND      f.scheduled_departure > bookings.now()
ORDER BY f.scheduled_departure 
LIMIT    1;
    

-[ RECORD 1 ]-------------+-----------------------
flight_id                 | 10927
flight_no                 | PG0226
scheduled_departure       | 2016-10-14 07:10:00+03
scheduled_departure_local | 2016-10-14 09:10:00
scheduled_arrival         | 2016-10-14 08:55:00+03
scheduled_arrival_local   | 2016-10-14 08:55:00
scheduled_duration        | 01:45:00
departure_airport         | SVX
departure_airport_name    | Кольцово
departure_city            | Екатеринбург
arrival_airport           | SVO
arrival_airport_name      | Шереметьево
arrival_city              | Москва
status                    | On Time
aircraft_code             | 773
actual_departure          |
actual_departure_local    |
actual_arrival            |
actual_arrival_local      |
actual_duration           |
    

Note that the flights_v view shows both Moscow time and local time at the airports of departure and arrival.

Each booking can include several tickets, one for each passenger. The ticket, in its turn, can include several flight segments. The complete information about the booking is stored in three tables: bookings, tickets, and ticket_flights.

Let's find several most expensive bookings:

SELECT   *
FROM     bookings
ORDER BY total_amount desc
LIMIT    10;
    

 book_ref |       book_date        | total_amount
----------+------------------------+--------------
 3B54BB   | 2016-09-02 16:08:00+03 |   1204500.00
 3AC131   | 2016-09-28 00:06:00+03 |   1087100.00
 65A6EA   | 2016-08-31 05:28:00+03 |   1065600.00
 D7E9AA   | 2016-10-06 04:29:00+03 |   1062800.00
 EF479E   | 2016-09-30 14:58:00+03 |   1035100.00
 521C53   | 2016-09-05 08:25:00+03 |    985500.00
 514CA6   | 2016-09-24 04:07:00+03 |    955000.00
 D70BD9   | 2016-09-02 11:47:00+03 |    947500.00
 EC7EDA   | 2016-08-30 15:13:00+03 |    946800.00
 8E4370   | 2016-09-25 01:04:00+03 |    945700.00
(10 rows)
    

Let's take a look at the tickets included into the booking with code 521C53:

SELECT ticket_no,
       passenger_id,
       passenger_name
FROM   tickets
WHERE  book_ref = '521C53';
    

   ticket_no   | passenger_id |   passenger_name
---------------+--------------+--------------------
 0005432661914 | 8234 547529  | IVAN IVANOV
 0005432661915 | 2034 201228  | ANTONINA KUZNECOVA
(2 rows)
    

If we would like to know, which flight segments are included into Antonina Kuznecova's ticket, we can use the following query:

SELECT   to_char(f.scheduled_departure, 'DD.MM.YYYY') as when,
         f.departure_city || '(' || f.departure_airport || ')' as departure,
         f.arrival_city || '(' || f.arrival_airport || ')' as arrival,
         tf.fare_conditions as class,
         tf.amount
FROM     ticket_flights tf
         JOIN flights_v f ON tf.flight_id = f.flight_id
WHERE    tf.ticket_no = '0005432661915'
ORDER BY f.scheduled_departure;
    

    when    |     departure     |      arrival      |  class   |  amount
------------+-------------------+-------------------+----------+-----------
 26.09.2016 | Москва(SVO)       | Анадырь(DYR)      | Business | 185300.00
 30.09.2016 | Анадырь(DYR)      | Хабаровск(KHV)    | Business |  92200.00
 01.10.2016 | Хабаровск(KHV)    | Благовещенск(BQS) | Business |  18000.00
 06.10.2016 | Благовещенск(BQS) | Хабаровск(KHV)    | Business |  18000.00
 10.10.2016 | Хабаровск(KHV)    | Анадырь(DYR)      | Economy  |  30700.00
 15.10.2016 | Анадырь(DYR)      | Москва(SVO)       | Business | 185300.00
(6 rows)
    

As we can see, high booking cost is explained by multiple long-haul flights in business class.

Some of the flight segments in this ticket have earlier dates than the bookings.now() return value: it means that these flights had already happened. The last flight had not happened yet at the time of the database creation. After the check-in, a boarding pass with the allocated seat number is issued. We can check the exact seats occupied by Antonina (note the outer left join with table boarding_passes):

SELECT   to_char(f.scheduled_departure, 'DD.MM.YYYY') as when,
         f.departure_city || '(' || f.departure_airport || ')' as departure,
         f.arrival_city || '(' || f.arrival_airport || ')' as arrival,
         f.status,
         bp.seat_no
FROM     ticket_flights tf
         JOIN flights_v f ON tf.flight_id = f.flight_id
         LEFT JOIN boarding_passes bp ON tf.flight_id = bp.flight_id 
                                     AND tf.ticket_no = bp.ticket_no
WHERE    tf.ticket_no = '0005432661915'
ORDER BY f.scheduled_departure;
    

    when    |     departure     |      arrival      |  status   | seat_no
------------+-------------------+-------------------+-----------+---------
 26.09.2016 | Москва(SVO)       | Анадырь(DYR)      | Arrived   | 5C
 30.09.2016 | Анадырь(DYR)      | Хабаровск(KHV)    | Arrived   | 1D
 01.10.2016 | Хабаровск(KHV)    | Благовещенск(BQS) | Arrived   | 2C
 06.10.2016 | Благовещенск(BQS) | Хабаровск(KHV)    | Arrived   | 2D
 10.10.2016 | Хабаровск(KHV)    | Анадырь(DYR)      | Arrived   | 20B
 15.10.2016 | Анадырь(DYR)      | Москва(SVO)       | Scheduled |
(6 rows)
    

Let's try to send Aleksandr Radishchev from Saint Petersburg to Moscow — the route that made him famous. Naturally, he will travel for free and in business class. We have already found a flight for tomorrow, and a return flight a week later.

BEGIN;

INSERT INTO bookings (book_ref, book_date, total_amount)
VALUES      ('_QWE12', bookings.now(), 0);

INSERT INTO tickets (ticket_no, book_ref, passenger_id, passenger_name)
VALUES      ('_000000000001', '_QWE12', '1749 051790', 'ALEKSANDR RADISHCHEV');

INSERT INTO ticket_flights (ticket_no, flight_id, fare_conditions, amount)
VALUES      ('_000000000001', 9720, 'Business', 0),
            ('_000000000001', 6662, 'Business', 0);

COMMIT;
    

To avoid conflicts with the range of values present in the database, identifiers are started with an underscore.

We will check in Aleksandr for tomorrow's flight right away:

INSERT INTO boarding_passes (ticket_no, flight_id, boarding_no, seat_no)
VALUES      ('_000000000001', 9720, 1, '1A');
    

Now let's check the booking information:

SELECT   b.book_ref,
         t.ticket_no,
         t.passenger_id,
         t.passenger_name,
         tf.fare_conditions,
         tf.amount,
         f.scheduled_departure_local,
         f.scheduled_arrival_local,
         f.departure_city || '(' || f.departure_airport || ')' as departure,
         f.arrival_city || '(' || f.arrival_airport || ')' as arrival,
         f.status,
         bp.seat_no
FROM     bookings b
         JOIN tickets t ON b.book_ref = t.book_ref
         JOIN ticket_flights tf ON tf.ticket_no = t.ticket_no
         JOIN flights_v f ON tf.flight_id = f.flight_id
         LEFT JOIN boarding_passes bp ON tf.flight_id = bp.flight_id
                                     AND tf.ticket_no = bp.ticket_no
WHERE    b.book_ref = '_QWE12'
ORDER BY t.ticket_no, f.scheduled_departure;
    

-[ RECORD 1 ]-------------+---------------------
book_ref                  | _QWE12
ticket_no                 | _000000000001
passenger_id              | 1749 051790
passenger_name            | ALEKSANDR RADISHCHEV
fare_conditions           | Business
amount                    | 0.00
scheduled_departure_local | 2016-10-14 08:45:00
scheduled_arrival_local   | 2016-10-14 09:35:00
departure                 | Санкт-Петербург(LED)
arrival                   | Москва(SVO)
status                    | On Time
seat_no                   | 1A
-[ RECORD 2 ]-------------+---------------------
book_ref                  | _QWE12
ticket_no                 | _000000000001
passenger_id              | 1749 051790
passenger_name            | ALEKSANDR RADISHCHEV
fare_conditions           | Business
amount                    | 0.00
scheduled_departure_local | 2016-10-21 09:20:00
scheduled_arrival_local   | 2016-10-21 10:10:00
departure                 | Москва(SVO)
arrival                   | Санкт-Петербург(LED)
status                    | Scheduled
seat_no                   |
    

We hope that these simple examples helped you get an idea of this demo database.