Dynamically fetch columns as JSON in oracle - json

Question

I have to get some columns as is and some columns from a query as JSON document. The as is column names are known to me but rest are dynamic columns so there are not known beforehand.
Below is the query like
select col1,col2,col3,
sum(col4) as col4,
sum(col5) as col5
from my_table
group by col1,col2,col3;
here col4,col5 names are unknown to me as they are been fetched dynamically.
Suppost my_table data looks like
The expected result is like below
I tried
select JSON_OBJECT(*) from
(
select col1,col2,col3,
sum(col4) as col4,
sum(col5) as col5
from my_table
group by col1,col2,col3
);
But obviously it does not yield expected output.
I'm on 19c DB version 19.17
Any help or suggestion would be great help!

Answer 1

It's kinda hacky, but you could:
Use json_object(*) to convert the whole row to json
Pass the result of this json_transform*, which you can use to remove unwanted attributes
So you could do something like:
with rws as (
select mod ( level, 2 ) col1, mod ( level, 3 ) col2,
level col3, level col4
from dual
connect by level <= 10
), grps as (
select col1,col2,
sum(col3) as col3,
sum(col4) as col4
from rws
group by col1,col2
)
select col1,col2,
json_transform (
json_object(*),
remove '$.COL1',
remove '$.COL2'
) json_data
from grps;
COL1 COL2 JSON_DATA
---------- ---------- ------------------------------
1 1 {"COL3":8,"COL4":8}
0 2 {"COL3":10,"COL4":10}
1 0 {"COL3":12,"COL4":12}
0 1 {"COL3":14,"COL4":14}
1 2 {"COL3":5,"COL4":5}
0 0 {"COL3":6,"COL4":6}
json_transform is a 21c feature that's been backported to 19c in 19.10.

Answer 2

You may achieve this by using Polymorphic Table Function available since 18c.
Define the function that will project only specific columns and serialize others into JSON. An implementation is below.
PTF package (function implementation).
create package pkg_jsonify as
/*Package to implement PTF.
Functions below implement the API
described in the DBMS_TF package*/
function describe(
tab in out dbms_tf.table_t,
keep_cols in dbms_tf.columns_t
) return dbms_tf.describe_t
;
procedure fetch_rows;
end pkg_jsonify;
/
create package body pkg_jsonify as
function describe(
tab in out dbms_tf.table_t,
keep_cols in dbms_tf.columns_t
) return dbms_tf.describe_t
as
add_cols dbms_tf.columns_new_t;
new_col_cnt pls_integer := 0;
begin
for i in 1..tab.column.count loop
/*Initially remove column from the output*/
tab.column(i).pass_through := FALSE;
/*and keep it in the row processing (to be available for serialization*/
tab.column(i).for_read := TRUE;
for j in 1..keep_cols.count loop
/*If column is in a projection list, then remove it
from processing and pass it as is*/
if tab.column(i).description.name = keep_cols(j)
then
tab.column(i).pass_through := TRUE;
/*Skip column in the row processing (JSON serialization)*/
tab.column(i).for_read := FALSE;
end if;
end loop;
end loop;
/*Define new output column*/
add_cols := dbms_tf.columns_new_t(
1 => dbms_tf.column_metadata_t(
name => 'JSON_DOC_DATA',
type => dbms_tf.type_clob
)
);
/*Return the list of new cols*/
return dbms_tf.describe_t(
new_columns => add_cols
);
end;
procedure fetch_rows
/*Process rowset and serialize cols*/
as
rowset dbms_tf.row_set_t;
num_rows pls_integer;
new_col dbms_tf.tab_clob_t;
begin
/*Get rows*/
dbms_tf.get_row_set(
rowset => rowset,
row_count => num_rows
);
for rn in 1..num_rows loop
/*Calculate new column value in the same row*/
new_col(rn) := dbms_tf.row_to_char(
rowset => rowset,
rid => rn,
format => dbms_tf.FORMAT_JSON
);
end loop;
/*Put column to output*/
dbms_tf.put_col(
columnid => 1,
collection => new_col
);
end;
end pkg_jsonify;
/
PTF function definition based on the package.
create function f_cols_to_json(tab in table, cols in columns)
/*Function to serialize into JSON using PTF*/
return table pipelined
row polymorphic using pkg_jsonify;
/
Demo.
create table sample_tab
as
select
trunc(level/10) as id
, mod(level, 3) as id2
, level as val1
, level * level as val2
from dual
connect by level < 40
with prep as (
select
id
, id2
, sum(val1) as val1_sum
, max(val2) as val2_max
from sample_tab
group by
id
, id2
)
select *
from table(f_cols_to_json(prep, columns(id, id2)))
ID
ID2
JSON_DOC_DATA
0
1
{"VAL1_SUM":12, "VAL2_MAX":49}
0
2
{"VAL1_SUM":15, "VAL2_MAX":64}
0
0
{"VAL1_SUM":18, "VAL2_MAX":81}
1
1
{"VAL1_SUM":58, "VAL2_MAX":361}
1
2
{"VAL1_SUM":42, "VAL2_MAX":289}
1
0
{"VAL1_SUM":45, "VAL2_MAX":324}
2
2
{"VAL1_SUM":98, "VAL2_MAX":841}
2
0
{"VAL1_SUM":72, "VAL2_MAX":729}
2
1
{"VAL1_SUM":75, "VAL2_MAX":784}
3
0
{"VAL1_SUM":138, "VAL2_MAX":1521}
3
1
{"VAL1_SUM":102, "VAL2_MAX":1369}
3
2
{"VAL1_SUM":105, "VAL2_MAX":1444}
with prep as (
select
id
, id2
, sum(val1) as val1_sum
, max(val2) as val2_max
from sample_tab
group by
id
, id2
)
select *
from table(f_cols_to_json(prep, columns(id)))
ID
JSON_DOC_DATA
0
{"ID2":1, "VAL1_SUM":12, "VAL2_MAX":49}
0
{"ID2":2, "VAL1_SUM":15, "VAL2_MAX":64}
0
{"ID2":0, "VAL1_SUM":18, "VAL2_MAX":81}
1
{"ID2":1, "VAL1_SUM":58, "VAL2_MAX":361}
1
{"ID2":2, "VAL1_SUM":42, "VAL2_MAX":289}
1
{"ID2":0, "VAL1_SUM":45, "VAL2_MAX":324}
2
{"ID2":2, "VAL1_SUM":98, "VAL2_MAX":841}
2
{"ID2":0, "VAL1_SUM":72, "VAL2_MAX":729}
2
{"ID2":1, "VAL1_SUM":75, "VAL2_MAX":784}
3
{"ID2":0, "VAL1_SUM":138, "VAL2_MAX":1521}
3
{"ID2":1, "VAL1_SUM":102, "VAL2_MAX":1369}
3
{"ID2":2, "VAL1_SUM":105, "VAL2_MAX":1444}
fiddle