Postgres: Optimize a huge GROUP BY - postgresql

Question

I have such a table:
CREATE TABLE values (first_id varchar(26), sec_id int, mode varchar(6), external1_id varchar(23), external2_id varchar(26), x int, y int);
There may be multiple values having the same first_id, my goal is to flatten (into json) for each first_id, all the related rows, into another table.
I do this this way:
INSERT INTO othervalues(first_id, results)
SELECT first_id, json_agg(values) AS results FROM values GROUP BY first_id;
In the results column, I have a json array of all the rows, that I can use later as it is.
My problem is that this is very slow, with a huge table: with about 100 000 000 rows in values, this slows down my computer (I actually test locally) until it dies (this is an Ubuntu).
Using EXPLAIN I noticed that is used a GroupPartitioner, I added:
SET work_mem = '1GB';
Now it uses a HashPartitioner, but this still kills my computer. An explain gives me:
Insert on othervalues (cost=2537311.89..2537316.89 rows=200 width=64)
-> Subquery Scan on "*SELECT*" (cost=2537311.89..2537316.89 rows=200 width=64)
-> HashAggregate (cost=2537311.89..2537314.39 rows=200 width=206)
-> Seq Scan on values (cost=0.00..2251654.26 rows=57131526 width=206)
Any idea how to optimize it?

Answer 1

The solution I finally use is to split the GROUP BY into multiple:
First I create a temporary table with the unique IDs of the stuff I want to group. This allow to get only a part of the results - like with OFFSET and LIMIT - but these there can be very slow with huge results sets (big offset mean the execution tree will yet browse the first results):
CREATE TEMP TABLE tempids AS SELECT ROW_NUMBER() OVER (ORDER BY theid), theid FROM (SELECT DISTINCT theid FROM sourcetable) sourcetable;
Then in a WHILE loop:
DO $$DECLARE
r record;
i INTEGER := 0;
step INTEGER := 500000;
size INTEGER := (SELECT COUNT(*) FROM tempids);
BEGIN
WHILE i <= size
LOOP
INSERT INTO target(theid, theresult)
SELECT ...
WHERE tempids > i AND tempids < i + step
GROUP BY tempids.theid;
This looks like usual coding, this is not nice sql, but this works.