Unpacking JSON into 14 columns without crosstab() and tablefunc?

Question

In a Postgres 12 database, I have a simple schema containing a JSON column. The JSON will always contain an object, and inside the object there are always 14 keys like so:

CREATE TABLE json_unpacking (foo text, bar json);
INSERT INTO json_unpacking VALUES
(
'ziggy',
'{"Baz (1998-03-12)":1.4285714286,"Baz (1998-03-13)":7.1428571429,"Baz (1998-03-14)":10.8571428571,"Baz (1998-03-15)":11.2857142857,"Baz (1998-03-16)":12.0,"Baz (1998-03-17)":18.1428571429,"Baz (1998-03-18)":2.0,"Baz (1998-03-19)":2.5714285714,"Baz (1998-03-20)":12.2857142857,"Baz (1998-03-21)":3.2857142857,"Baz (1998-03-22)":5.5734285714,"Baz (1998-03-23)":4.1428571429,"Baz (1998-03-24)":11.2857142857,"Baz (1998-03-25)":6.0}'
),
(
'zoggy',
'{"Baz (1998-04-12)":1.7285714286,"Baz (1998-04-13)":6.8578886549,"Baz (1998-04-14)":6.8574428571,"Baz (1998-04-15)":21.2857142857,"Baz (1998-04-16)":7.0,"Baz (1998-04-17)":3.1128571429,"Baz (1998-04-18)":5.0,"Baz (1998-04-19)":7.2214285714,"Baz (1998-04-20)":42.6857142857,"Baz (1998-04-21)":2.9857142857,"Baz (1998-04-22)":5.5734285714,"Baz (1998-04-23)":4.1428571429,"Baz (1998-03-24)":6.7537146517,"Baz (1998-04-25)":47.0}'
)
;

I'm able to query this table just fine, for instance:

SELECT *
FROM json_unpacking

Produces the output that you'd expect:

foo	bar
ziggy	{"Baz (1998-03-12)":1.4285714286,"Baz (1998-03-13)":7.1428571429,"Baz (1998-03-14)":10.8571428571,"Baz (1998-03-15)":11.2857142857,"Baz (1998-03-16)":12.0,"Baz (1998-03-17)":18.1428571429,"Baz (1998-03-18)":2.0,"Baz (1998-03-19)":2.5714285714,"Baz (1998-03-20)":12.2857142857,"Baz (1998-03-21)":3.2857142857,"Baz (1998-03-22)":5.5734285714,"Baz (1998-03-23)":4.1428571429,"Baz (1998-03-24)":11.2857142857,"Baz (1998-03-25)":6.0}
zoggy	{"Baz (1998-04-12)":1.7285714286,"Baz (1998-04-13)":6.8578886549,"Baz (1998-04-14)":6.8574428571,"Baz (1998-04-15)":21.2857142857,"Baz (1998-04-16)":7.0,"Baz (1998-04-17)":3.1128571429,"Baz (1998-04-18)":5.0,"Baz (1998-04-19)":7.2214285714,"Baz (1998-04-20)":42.6857142857,"Baz (1998-04-21)":2.9857142857,"Baz (1998-04-22)":5.5734285714,"Baz (1998-04-23)":4.1428571429,"Baz (1998-03-24)":6.7537146517,"Baz (1998-04-25)":47.0}

Now, I'd like to query this table and produce a structure with 15 total columns: "foo" plus 14 like "bar 1", "bar 2", ... "bar 14".

The JSON structure will always include a string pattern (which includes the date), and a numeric value. I do not want the key value, but I need the number values transposed into columns.

In other words, the output structure that I want is like so:

foo	bar 1	bar 2	bar 3	bar 4	bar 5	bar 6	bar 7	bar 8	bar 9	bar 10	bar 11	bar 12	bar 13	bar 14
ziggy	1.428571429	7.142857143	10.85714286	11.28571429	12	18.14285714	2	2.571428571	12.28571429	3.285714286	5.573428571	4.142857143	11.28571429	6
zoggy	1.728571429	6.857888655	6.857442857	21.28571429	7	3.112857143	5	7.221428571	42.68571429	2.985714286	5.573428571	4.142857143	6.753714652	47

I suppose I have two questions:

1. Is this possible?
2. Can it be done without any extension?

A colleague has suggested that the crosstab() function from the tablefunc extension might be required. I've tried various Postgres JSON functions, but have only succeeded in getting either the keys (e.g. using json_object_keys()) or null values (using json_to_record()).

Here's a DBfiddle of the problem: https://dbfiddle.uk/?rdbms=postgres_12&fiddle=22e9c99a954eafdadcde657b38f41b3a

Answer 1

You can use a JSON path query:

select foo, 
       items ->> 0 as bar_1, 
       items ->> 1 as bar_2, 
       items ->> 1 as bar_3, 
       .....
from ( 
  select foo, jsonb_path_query_array(bar::jsonb, '$.*') as items
  from json_unpacking
) as t 

Online example

---

Note that it's recommended to use jsonb over json (and in this case it would avoid the cast)

Answer 2

Unpack your JSON to separate rows using

SELECT json_unpacking.foo, 
       values.value, 
       ROW_NUMBER() OVER (PARTITION BY foo) value_number
FROM json_unpacking
CROSS JOIN json_each(json_unpacking.bar) values

Then combine it back into needed row structure using grouping and conditional aggregation.