match recognize collect row data into single column - sql

Question

I'm following the tutorial for match_recognize found here:
create or replace temporary table stock_price_history (company text, price_date date, price int);
insert into stock_price_history values
('ABCD', '2020-10-01', 50),
('ABCD', '2020-10-02', 50),
('ABCD', '2020-10-03', 51),
('ABCD', '2020-10-04', 51),
('ABCD', '2020-10-05', 51),
('ABCD', '2020-10-06', 52),
('ABCD', '2020-10-07', 71),
('ABCD', '2020-10-08', 80),
('ABCD', '2020-10-09', 90),
('ABCD', '2020-10-10', 63),
('XYZ' , '2020-10-01', 24),
('XYZ' , '2020-10-02', 24),
('XYZ' , '2020-10-03', 37),
('XYZ' , '2020-10-04', 63),
('XYZ' , '2020-10-05', 65),
('XYZ' , '2020-10-06', 66),
('XYZ' , '2020-10-07', 50),
('XYZ' , '2020-10-08', 54),
('XYZ' , '2020-10-09', 30),
('XYZ' , '2020-10-10', 32);
select * from stock_price_history
match_recognize(
partition by company
order by price_date
measures
match_number() as match_number,
price as all_price,
first(price_date) as start_date,
last(price_date) as end_date,
count(*) as rows_in_sequence,
count(row_with_price_stationary.*) as num_stationary,
count(row_with_price_increase.*) as num_increases
one row per match
after match skip to last row_with_price_increase
pattern(row_before_increase row_with_price_increase{1} row_with_price_stationary* row_with_price_increase{1})
define
row_with_price_increase as price > lag(price),
row_with_price_stationary as price = lag(price)
)
order by company, match_number;
The code above is my version of the tutorial code. Everything works fine except the price as all_price part in the measures clause. What I want to do is collect all prices in the pattern and return it as an array into a single column. I know I can do all rows per match to get all rows but that's not what I want.
How would I go about doing that?

Answer 1

You have to specify all rows per match or lose that information out of the match_recognize function. You can use array_agg within group to get the prices in a single array. Since this aggregates row counts down you may want to do the same for the dates of each of these prices - something like this:
select COMPANY
,array_agg(PRICE) within group (order by PRICE_DATE) as ALL_PRICE
,array_agg(PRICE_DATE) within group (order by PRICE_DATE) as ALL_PRICE_DATE
from stock_price_history
match_recognize(
partition by company
order by price_date
measures
match_number() as match_number,
price as all_price,
first(price_date) as start_date,
last(price_date) as end_date,
count(*) as rows_in_sequence,
count(row_with_price_stationary.*) as num_stationary,
count(row_with_price_increase.*) as num_increases
all rows per match
after match skip to last row_with_price_increase
pattern(row_before_increase row_with_price_increase{1} row_with_price_stationary* row_with_price_increase{1})
define
row_with_price_increase as price > lag(price),
row_with_price_stationary as price = lag(price)
)
group by company
order by company
;
COMPANY
ALL_PRICE
ALL_PRICE_DATE
ABCD
[ 50, 51, 51, 51, 52, 52, 71, 80 ]
[ "2020-10-02", "2020-10-03", "2020-10-04", "2020-10-05", "2020-10-06", "2020-10-06", "2020-10-07", "2020-10-08" ]
XYZ
[ 24, 37, 63, 63, 65, 66 ]
[ "2020-10-02", "2020-10-03", "2020-10-04", "2020-10-04", "2020-10-05", "2020-10-06" ]
If you want to keep all rows, you can use the window function version of array_agg:
select * exclude ALL_PRICE
,array_agg(PRICE) within group (order by PRICE_DATE)
over (partition by COMPANY) as ALL_PRICE
from stock_price_history
match_recognize(
partition by company
order by price_date
measures
match_number() as match_number,
price as all_price,
first(price_date) as start_date,
last(price_date) as end_date,
count(*) as rows_in_sequence,
count(row_with_price_stationary.*) as num_stationary,
count(row_with_price_increase.*) as num_increases
all rows per match
after match skip to last row_with_price_increase
pattern(row_before_increase row_with_price_increase{1} row_with_price_stationary* row_with_price_increase{1})
define
row_with_price_increase as price > lag(price),
row_with_price_stationary as price = lag(price)
)
order by company
;