## J.5. Usage

### J.5.1. Schema `bookings`

The `bookings` schema contains all objects of the demo database. When you connect to the database, `search_path` configuration parameter is automatically set to `bookings, public`, so you do not need to specify the schema name explicitly.

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

### J.5.2. Translations

By default, values of several translatable fields are shown in Russian. These are `airport_name` and `city` of the `airports` view, as well as `model` of the `aircrafts` view.

You can choose to display these fields in another language (although only the English translation is provided in the demo database). To switch to English, set the `bookings.lang` parameter to `en`. It may be convenient to choose the language at the database level:

    ALTER DATABASE demo SET bookings.lang = en;

You have to reconnect to the database for this command to take effect. For other methods of settings configuration parameters, see Section 18.1.

In the examples below, the English language is selected for translatable fields.

### J.5.3. Sample Queries

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 August 15, 2017. 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.coordinates
    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    |           coordinates
    ------+------------------------------------+-----------+-------------------------
     DME  | Domodedovo             | Moscow    | (37.9062995910645,55.4087982177734)
          | International Airport  |           |
     SVO  | Sheremetyevo           | Moscow    | (37.4146,55.972599)
          | International Airport  |           |
     VKO  | Vnukovo                | Moscow    | (37.2615013123,55.5914993286)
          | International Airport  |           |
     ULV  | Ulyanovsk              | Ulyanovsk | (48.2266998291,54.2682991028)
          | Baratayevka Airport    |           |
     ULY  | Ulyanovsk East Airport | Ulyanovsk | (48.8027000427246,54.4010009765625)
    (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 code,
           r.arrival_airport_name as airport_name,
           r.days_of_week,
           r.duration
    FROM   routes r
    WHERE  r.departure_city = 'Volgograd';

        city     | code |            airport_name            |  days_of_week   | duration
    -------------+------+------------------------------------+-----------------+----------
     Moscow      | SVO  | Sheremetyevo International Airport | {1,2,3,4,5,6,7} | 01:15:00
     Chelyabinsk | CEK  | Chelyabinsk Balandino Airport      | {1,2,3,4,5,6,7} | 01:50:00
     Rostov      | ROV  | Rostov-on-Don Airport              | {1,2,3,4,5,6,7} | 00:30:00
     Moscow      | VKO  | Vnukovo International Airport      | {1,2,3,4,5,6,7} | 01:10:00
     Cheboksary  | CSY  | Cheboksary Airport                 | {1,2,3,4,5,6,7} | 02:45:00
     Tomsk       | TOF  | Bogashevo Airport                  | {1}             | 03:50:00
    (6 rows)

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

    SELECT bookings.now() as now;

              now
    ------------------------
     2017-08-15 18:00:00+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 | 2017-07-16 01:50:00+03  | 2017-08-15 17:25:00+03
     Cancelled |   414 | 2017-07-19 11:35:00+03  | 2017-09-14 20:55:00+03
     Departed  |    58 | 2017-08-15 09:55:00+03  | 2017-08-15 17:50:00+03
     Delayed   |    41 | 2017-08-15 15:15:00+03  | 2017-08-16 17:25:00+03
     On Time   |   518 | 2017-08-15 17:55:00+03  | 2017-08-16 18:00:00+03
     Scheduled | 15383 | 2017-08-16 18:05:00+03  | 2017-09-14 20:40:00+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 = 'Yekaterinburg'
    AND      f.arrival_city = 'Moscow'
    AND      f.scheduled_departure > bookings.now()
    ORDER BY f.scheduled_departure
    LIMIT    1;

    -[ RECORD 1 ]-------------+-----------------------------------
    flight_id                 | 10927
    flight_no                 | PG0226
    scheduled_departure       | 2017-08-16 08:10:00+03
    scheduled_departure_local | 2017-08-16 10:10:00
    scheduled_arrival         | 2017-08-16 09:55:00+03
    scheduled_arrival_local   | 2017-08-16 09:55:00
    scheduled_duration        | 01:45:00
    departure_airport         | SVX
    departure_airport_name    | Koltsovo Airport
    departure_city            | Yekaterinburg
    arrival_airport           | SVO
    arrival_airport_name      | Sheremetyevo International Airport
    arrival_city              | Moscow
    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.

### J.5.4. Bookings

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   | 2017-07-05 17:08:00+03 |   1204500.00
     3AC131   | 2017-07-31 01:06:00+03 |   1087100.00
     65A6EA   | 2017-07-03 06:28:00+03 |   1065600.00
     D7E9AA   | 2017-08-08 05:29:00+03 |   1062800.00
     EF479E   | 2017-08-02 15:58:00+03 |   1035100.00
     521C53   | 2017-07-08 09:25:00+03 |    985500.00
     514CA6   | 2017-07-27 05:07:00+03 |    955000.00
     D70BD9   | 2017-07-05 12:47:00+03 |    947500.00
     EC7EDA   | 2017-07-02 16:13:00+03 |    946800.00
     8E4370   | 2017-07-28 02:04:00+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
    ------------+----------------------+----------------------+----------+-----------
     29.07.2017 | Moscow (SVO)         | Anadyr (DYR)         | Business | 185300.00
     02.08.2017 | Anadyr (DYR)         | Khabarovsk (KHV)     | Business |  92200.00
     03.08.2017 | Khabarovsk (KHV)     | Blagoveshchensk (BQS)| Business |  18000.00
     08.08.2017 | Blagoveshchensk (BQS)| Khabarovsk (KHV)     | Business |  18000.00
     12.08.2017 | Khabarovsk (KHV)     | Anadyr (DYR)         | Economy  |  30700.00
     17.08.2017 | Anadyr (DYR)         | Moscow (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
    ------------+----------------------+----------------------+-----------+---------
     29.07.2017 | Moscow (SVO)         | Anadyr (DYR)         | Arrived   | 5C
     02.08.2017 | Anadyr (DYR)         | Khabarovsk (KHV)     | Arrived   | 1D
     03.08.2017 | Khabarovsk (KHV)     | Blagoveshchensk (BQS)| Arrived   | 2C
     08.08.2017 | Blagoveshchensk (BQS)| Khabarovsk (KHV)     | Arrived   | 2D
     12.08.2017 | Khabarovsk (KHV)     | Anadyr (DYR)         | Arrived   | 20B
     17.08.2017 | Anadyr (DYR)         | Moscow (SVO)         | Scheduled |
    (6 rows)

### J.5.5. New Booking

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', 8525, 'Business', 0),
                ('_000000000001', 4967, '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', 8525, 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 | 2017-08-16 09:45:00
    scheduled_arrival_local   | 2017-08-16 10:35:00
    departure                 | St. Petersburg (LED)
    arrival                   | Moscow (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 | 2017-08-23 10:20:00
    scheduled_arrival_local   | 2017-08-23 11:10:00
    departure                 | Moscow (SVO)
    arrival                   | St. Petersburg (LED)
    status                    | Scheduled
    seat_no                   |

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