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Query Comparison: SQL vs FlureeQL

Simple SELECT​

SQL:


SELECT name, email FROM users WHERE id = 1;

FlureeQL:


{
"select": { "?user": ["schema:name", "schema:email"] },
"where": { "@id": "ex:users/1", "schema:name": "?name", "schema:email": "?email" }
}

JOIN​

SQL:


SELECT u.name, o.total
FROM users u
JOIN orders o ON u.id = o.user_id;

FlureeQL:


{
"select": ["?userName", "?total"],
"where": [
{ "@id": "?order", "ex:customer": "?user", "ex:total": "?total" },
{ "@id": "?user", "schema:name": "?userName" }
]
}

No explicit JOIN—graph traversal is natural.

GROUP BY with COUNT​

SQL:


SELECT user_id, COUNT(*)
FROM orders
GROUP BY user_id;

FlureeQL:


{
"select": ["?user", "(count ?order)"],
"where": { "@id": "?order", "ex:customer": "?user" },
"groupBy": "?user"
}

Subquery​

SQL:


SELECT * FROM users
WHERE id IN (SELECT user_id FROM orders WHERE total > 100);

FlureeQL:


{
"select": { "?user": ["*"] },
"where": [
{ "@id": "?order", "ex:customer": "?user", "ex:total": "?total" },
["filter", "(> ?total 100)"]
]
}

The graph model eliminates the need for subqueries.