SQL> insert into test_pallets values (1),(2),(3),(4);
4 rows created.
SQL> create table test_boxes (pallet_id number, mass decimal);
Table created.
SQL> insert into test_boxes values (1,10),(1,20),(2, 5),(4, 5),(4, 50);
5 rows created.
SQL> create function pallet_payload_mass(
2 p dbms_tf.table_t,
3 b dbms_tf.table_t
4 ) return clob SQL_Macro as
5 begin
6 return q'{
7 select
8 p.pallet_id,
9 coalesce(sum(b.mass), 0) as payload_mass
10 from
11 p
12 left outer join
13 b on b.pallet_id = p.pallet_id
14 group by
15 p.pallet_id
16 }';
17 end;
18 /
Function created.
SQL> select payload_mass, pallet_id
2 from pallet_payload_mass(test_pallets, test_boxes)
3 order by payload_mass;
Oracle is now the leading JSON database, not only with a binary JSON datatype (allowing true piece-wise updates on disk and fast 'jump-navigation for super fast queries), but also JSON Schema support (for schema enforcement, type casting, to describe database objects and auto-generate relational views over JSON) and with JSON Duality it's possible to access (read/write) the same data as table as well as as (MongoDB compatible) collections.
https://docs.oracle.com/en/database/oracle/oracle-database/2...
SQL> create table test_pallets (pallet_id number);
Table created.
SQL> insert into test_pallets values (1),(2),(3),(4);
4 rows created.
SQL> create table test_boxes (pallet_id number, mass decimal);
Table created.
SQL> insert into test_boxes values (1,10),(1,20),(2, 5),(4, 5),(4, 50);
5 rows created.
SQL> create function pallet_payload_mass( 2 p dbms_tf.table_t, 3 b dbms_tf.table_t 4 ) return clob SQL_Macro as 5 begin 6 return q'{ 7 select 8 p.pallet_id, 9 coalesce(sum(b.mass), 0) as payload_mass 10 from 11 p 12 left outer join 13 b on b.pallet_id = p.pallet_id 14 group by 15 p.pallet_id 16 }'; 17 end; 18 /
Function created.
SQL> select payload_mass, pallet_id 2 from pallet_payload_mass(test_pallets, test_boxes) 3 order by payload_mass;
PAYLOAD_MASS PALLET_ID ------------ ---------- 0 3 5 2 30 1 55 4