Skip to content

Update README.md - small corrections added #3

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
katjacb24 wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Update README.md - small corrections added #3

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
65 changes: 33 additions & 32 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ LIMIT 5;
That looks really familiar, because there's no difference between SQL and SQL++: we're `SELECT`-ing some fields `FROM` the collection (remember, that is equal to a table in RDBMS world) `airline`.
`WHERE` and `LIMIT` are exactly the same.

### Count the number of airports in each country, ordering the results by the airport count in descending order and limiting to the top 5 countries.
### Count the number of airports in each country, ordering the results by the airport count in descending order.
```sql
SELECT country, COUNT(*) AS airport_count
FROM airport
Expand Down Expand Up @@ -70,11 +70,11 @@ The route documents look like this (simplified):
]
}
```
Notice that the schedule field is an array of objects—each object represents a flight on a specific day and time.
Notice that the schedule field is an array of objects - each object represents a flight on a specific day and time.

What does `UNNEST` do here?
* **UNNEST** schedule sched takes the schedule array from each route document and flattens it.
* For each element in the `schedule` array, it creates a new row in the result, with sched representing each schedule entry.
* `**UNNEST** schedule sched` takes the schedule array from each route document and flattens it.
* For each element in the `schedule` array, it creates a new row in the result, with `sched` representing each schedule entry.

Summary

Expand Down Expand Up @@ -110,8 +110,8 @@ What `INNER NEST route r ON a.faa = r.sourceairport` does:
SELECT a.name, a.country,
COUNT(*) OVER (PARTITION BY a.country) AS country_airline_count
FROM airline a
ORDER BY country_airline_count DESC
LIMIT 10;
ORDER BY country_airline_count ASC
LIMIT 30;
```

**Explaination**
Expand All @@ -120,15 +120,15 @@ The line of interest here is `COUNT(*) OVER (PARTITION BY a.country) AS country_
This is a `window function`. Here’s what it does:
* For each airline, it counts **how many airlines are in the same country**.
* `PARTITION BY a.country` means the count is calculated separately for each country.
* The result is a new column, _country_airline_count_, showing the total number of airlines in that airline’s country.
* The result is a new field, _country_airline_count_, showing the total number of airlines in that airline’s country.

Key Point: What Does the `Window Function` Do?

For each airline, it counts the total number of airlines in that country. The result is not grouped (like with `GROUP BY`), so you still see each airline as a separate row, but with the country’s total airline count attached.

### Retrieve airport names and geo-coordinates, filtering based on latitude and longitude ranges.
```sql
SELECT ap.name, ap.geo.lat, ap.geo.lon
SELECT ap.airportname, ap.geo.lat, ap.geo.lon
FROM airport ap
WHERE ap.geo.lat BETWEEN 40 AND 50
AND ap.geo.lon BETWEEN -80 AND -70
Expand Down Expand Up @@ -164,7 +164,7 @@ Key Point: Accessing Nested Fields
In SQL++, you use dot notation (.) to access fields within nested objects.
`ap.geo.lat` means "go to the ap document, then go to the geo field, and then get the lat field."

### Join airline and route on the IATA code, groups by airline name, and counts the number of routes per airline.
### Join airline and route on the IATA code, group by airline name, and count the number of routes per airline.
```sql
SELECT a.name AS airline, COUNT(r.airline) AS route_count
FROM `travel-sample`.inventory.airline a
Expand Down Expand Up @@ -210,8 +210,7 @@ SELECT a.name, a.icao
FROM `travel-sample`.inventory.airline a
WHERE (a.name LIKE "A%"
OR REGEXP_CONTAINS(a.name, "^[Bb].*[Aa]ir"))
ORDER BY a.name
LIMIT 10;
ORDER BY a.name;
```
**Explaination**

Expand All @@ -220,12 +219,12 @@ LIMIT 10;
OR
`REGEXP_CONTAINS(a.name, "^[Bb].*[Aa]ir")`: uses regex to find airlines whose names either start with _"B"_ or _"b"_ (`^[Bb]`), have any characters in between (`.*`), end with _"Air"_ or _"air"_ (`[Aa]ir`)
* **SELECT**: `a.name` - returns the airline name, `a.icao` - returns the airline's ICAO code
* **ORDER BY** & **LIMIT**: sorts results alphabetically by airline name and returns only the first 10 matches
* **ORDER BY**: sorts results alphabetically by airline name


### Categorize airports by region based on their country and counts the number of airports in each region.
```sql
SELECT ap.name, ap.country,
SELECT ap.airportname, ap.country,
CASE
WHEN ap.country IN ["United States", "Canada", "Mexico"] THEN "North America"
WHEN ap.country IN ["United Kingdom", "France", "Germany", "Spain", "Italy"] THEN "Europe"
Expand All @@ -234,7 +233,7 @@ SELECT ap.name, ap.country,
END AS region,
COUNT(*) AS airport_count
FROM `travel-sample`.inventory.airport ap
GROUP BY ap.name, ap.country, region
GROUP BY ap.airportname, ap.country, region
ORDER BY airport_count DESC
LIMIT 10;
```
Expand All @@ -243,20 +242,20 @@ LIMIT 10;

* **FROM**: `airport ap` - references airport collection in the inventory scope, aliased as `ap`
* **SELECT**
* `ap.name`: returns the airport name
* `ap.airportname`: returns the airport name
* `ap.country`: returns the country of the airport
* **CASE ... END AS region** creates a new field called "region" based on the airport's country:
* `**CASE ... END AS region**`: creates a new field called "region" based on the airport's country:
* If the country is in _"United States", "Canada", "Mexico"_, the region is _"North America"_
* If the country is in _"United Kingdom", "France", "Germany", "Spain", "Italy"_, the region is _"Europe"_
* If the country is in _"China", "Japan", "India"_, the region is _"Asia"_
* Otherwise, the region is _"Other Regions"_
* **COUNT(*) AS airport_count**: counts the number of airports for each combination of airport name, country, and region
* `**COUNT(*) AS airport_count**`: counts the number of airports for each combination of airport name, country, and region
* **GROUP BY** & **LIMIT**: `ORDER BY airport_count DESC` sorts the results by the airport count in descending order, so the airport names with the most occurrences are listed first. `LIMIT 10` returns only the top 10 documents.

### Calculate the distance between each hotel and New York City using geo-coordinates.
```sql
SELECT h.name AS hotel_name,
h.address.city AS city,
h.address AS address,
h.geo.lat AS latitude,
h.geo.lon AS longitude,
ROUND(
Expand All @@ -281,12 +280,12 @@ LIMIT 10;

- **SELECT**:
- `h.name AS hotel_name`: returns the hotel name.
- `h.address.city AS city`: returns the city from the nested address field.
- `h.address AS address`: returns the address of the hotel.
- `h.geo.lat AS latitude`: returns the latitude from the nested geo field.
- `h.geo.lon AS longitude`: returns the longitude from the nested geo field.
- `ROUND(DEGREES(ACOS(...)) * 69.09) AS miles_from_nyc`: calculates the distance from New York City (latitude 40.7128, longitude -74.0060) using the Haversine formula. The result is rounded and converted to miles.

- **WHERE**: filters out hotels missing geo-coordinates:
- **WHERE**: filters out hotels with missing geo-coordinates:
- `h.geo IS NOT MISSING`
- `h.geo.lat IS NOT MISSING`
- `h.geo.lon IS NOT MISSING`
Expand Down Expand Up @@ -475,7 +474,7 @@ Click `Options` in the query editor:
And then set
- `Transaction timeout` to `120 seconds`
- `Scan Consistency` to `not_bounded`
- Add a _Named Parameter_ `durability_level` with the value `"none"`, don't forgtet the value's quotes!
- Add a _Named Parameter_ `durability_level` with the value `"none"`, don't forget the value's quotes!

Hit `Save`

Expand All @@ -501,7 +500,7 @@ COMMIT;
**Verification**


Once finished, let's switch to the `Documents` tab, set the context to the bucket `travel-sample`, the scope `tenant_agent_00` and the collection `booking`.
Once finished, let's switch to the `Documents` tab, set the context to the bucket `travel-sample`, the scope `tenant_agent_00` and the collection `bookings`.
Fetch the document by its key - simply type `booking_1` into the `DOC ID` field and hit `Get Documents`:

<img width="1172" alt="image" src="https://github.com/user-attachments/assets/63688ad8-29a8-43e7-a077-06c764a89b02" />
Expand Down Expand Up @@ -543,23 +542,25 @@ This transaction demonstrates SQL++'s ability to:


### Time Series Querying
For this exercise you will need to download the regular Time Series dataset time_series_regular.json (that can be found in this repository).
For this exercise you will need to download the regular Time Series dataset `time_series_regular.json` (that can be found in this repository).
The dataset contains daily values for minimum and maximum temperature in 2024 for several locations within Munich.
The data in this dataset were already pre-converted into Time Series documents ready for querying in Couchbase (each JSON document contains data per location per month).

#### Import the Time Series dataset into Capella

In Capella UI: use the Import tool to import the regular Time Series dataset:
1. Go to the `Data Tools` tab → `Import` tab.
2. In the Import tab, select `Load from your browser` and choose the file ‘time_series_regular.json’ from your computer (please download it from this repository).
3. In the `Choose your target` step, click on `+ Create new target collection` and proceed to create a new bucket with `time_series` as the New Bucket Name, `time` as the New Scope Name and `weather` as the New Collection Name.
<img width="742" alt="image" src="https://github.com/user-attachments/assets/dd3abaff-92b8-46e8-9c19-411cf786e6c9" />
5. Click `Create`.
6. Select `Field` and `cbmid` as the Field name to use the value from the cbmid field inside of imported data as document keys.
7. Click `Import`.
2. In the Import tab, select `Load from your browser` and choose the file ‘time_series_regular.json’ from your computer (please, download it from this repository first).
3. In the `Choose your target` step, click on `+ Create new target collection` and proceed to create a `New` bucket with `time_series` as the New Bucket Name, `time` as the New Scope Name and `weather` as the New Collection Name.
<img width="732" height="859" alt="Screenshot 2025-11-02 at 14 56 20" src="https://github.com/user-attachments/assets/78ccf6d5-ffb9-4abc-a9ba-581bd6c7a414" />
4. Click `Create`.
5. Select `Field` and `cbmid` as the Field name to use the value from the cbmid field inside of the imported data as document keys.
6. Click `Import`.


In Capella UI: switch from the `Import` tab to the `Documents` tab and check that the documents were imported correctly.
<img width="1125" alt="Screenshot 2025-06-03 at 20 42 22" src="https://github.com/user-attachments/assets/5795a7a6-6f9b-453e-915a-f52c6fb7340e" />
<img width="1121" height="756" alt="Screenshot 2025-11-02 at 14 58 19" src="https://github.com/user-attachments/assets/c9b97700-8f19-4f1e-a52a-6cd9ab77634d" />


#### Query the regular Time Series data using SQL++

Expand All @@ -571,15 +572,15 @@ CREATE INDEX idx_temp ON weather(location, ts_end, ts_start);
```
<br>

##### Show the daily low and high temperatures for the time period from Jan, 1st 2024 till Jan 10th 2024 for the 'Olympia' location.
#### Show the daily low and high temperatures for the time period from Jan, 1st 2024 till Jan 10th 2024 for the 'Olympia' location.
In Capella Query Workbench:

```sql
-- Define the start and end range for the query
WITH range_start AS (1704067200000), -- Start timestamp in milliseconds (01.01.2024)
range_end AS (1704927600000) -- End timestamp in milliseconds (11.01.2024)
-- Select the required fields from the weather collection
SELECT MILLIS_TO_TZ(t._t,"UTC") AS day, -- Convert timestamp to UTC
SELECT d.location, MILLIS_TO_TZ(t._t,"UTC") AS day, -- Convert timestamp to UTC
t._v0 AS low, -- Low temperature
t._v1 AS high -- High temperature
FROM weather AS d -- Alias `for` the weather collection
Expand Down