DB Quacks SQL Tutorial · Chapter 6
JSON Bourne: The Schema Identity
Jason Bourne Duck appears in tactical gear. Ive lost my data identity! Complex JSON with nested duck profiles - extract names and favorite foods from deep inside flexible schemas. Navigate the schema-free underworld!
Your task
Extract duck names and their favorite food from nested JSON data using the 'data/json_duck_profiles.json' file. You will be navigating nested JSON structures using JSON operators.
Hint
Look for operators that can access nested JSON properties and extract text values.
Expected result
A table showing duck names and their favorite foods extracted from JSON.
Tables
duck_diaries (10 rows)
| column | type |
|---|---|
diary_id | BIGINT |
duck_id | BIGINT |
entry_date | DATE |
diary_text | VARCHAR |
mood | VARCHAR |
duck_surveys (5000 rows)
| column | type |
|---|---|
survey_id | BIGINT |
duck_id | BIGINT |
age | BIGINT |
favorite_food | VARCHAR |
favorite_pond | BIGINT |
swimming_rating | BIGINT |
migration_preference | VARCHAR |
social_behavior | VARCHAR |
health_status | VARCHAR |
activity_level | VARCHAR |
survey_date | DATE |
survey_location | BIGINT |
ducks (15 rows)
| column | type |
|---|---|
id | BIGINT |
name | VARCHAR |
age | BIGINT |
species | VARCHAR |
birth_date | DATE |
arrival_date | DATE |
ponds (3 rows)
| column | type |
|---|---|
pond_id | BIGINT |
pond_name | VARCHAR |
location_lat | DOUBLE |
location_lng | DOUBLE |
capacity | BIGINT |
features | VARCHAR[] |
amenities | VARCHAR[] |
Files
data/json_duck_profiles.json (5 rows)
| column | type |
|---|---|
profile_id | BIGINT |
data | STRUCT(duck_id BIGINT, "name" VARCHAR, skills VARCHAR[], preferences STRUCT(favorite_food VARCHAR, favorite_activity VARCHAR, favorite_time VARCHAR), contact_info STRUCT(email VARCHAR, phone VARCHAR)) |
Database: https://dbquacks.com/data/databases/tutorial.duckdb
This page as markdown: https://dbquacks.com/tutorial/34.md