How to optimize large database query accessed with an item ID? - sql

Question

I'm making a site that stores a large amount of data (8 data points for 313 item_ids every 10 seconds over 24 hr) and I serve that data to users on demand. The request is supplied with an item ID with which I query the database with something along the lines of SELECT * FROM current_day_data WHERE item_id = <supplied ID> (assuming the id is valid).
CREATE TABLE current_day_data (
"time" bigint,
item_id text NOT NULL,
-- some data,
id integer NOT NULL
);
CREATE INDEX item_id_lookup ON public.current_day_data USING btree (item_id);
This works fine, but the request takes about a third of a second, so I'm looking into either other database options to help optimize this, or some way to optimize the query itself.
My current setup is a PostgreSQL database with an index on the item ID column, but I feel like there's options in the realm of NoSQL (an area I'm unfamiliar with) due to it's similarity to a hash table.
My ideal solution would be a hash table with the item IDs as the key and the data as a JSON-like object but I don't know what options could achieve that.
tl;dr how to optimize SELECT * FROM current_day_data WHERE item_id = <supplied ID> through better querying or new database solution?
edit: here's the EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM current_day_data
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------
Seq Scan on current_day_data (cost=0.00..46811.09 rows=2584364 width=75) (actual time=0.013..291.667 rows=2700251 loops=1)
Buffers: shared hit=39058
Planning:
Buffers: shared hit=112
Planning Time: 0.584 ms
Execution Time: 446.622 ms
(6 rows)
EXPLAIN with a specified item id EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM current_day_data WHERE item_id = 'SUGAR_CANE';
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on current_day_info (cost=33.40..12099.27 rows=8592 width=75) (actual time=2.949..12.236 rows=8627 loops=1)
Recheck Cond: (product_id = 'SUGAR_CANE'::text)
Heap Blocks: exact=8570
Buffers: shared hit=8619
-> Bitmap Index Scan on prod_id_lookup (cost=0.00..32.97 rows=8592 width=0) (actual time=1.751..1.751 rows=8665 loops=1)
Index Cond: (product_id = 'SUGAR_CANE'::text)
Buffers: shared hit=12
Planning:
Buffers: shared hit=68
Planning Time: 0.339 ms
Execution Time: 12.686 ms
(11 rows)
Now this says 12.7ms which makes me think the 300ms has something to do with the library I'm using (SQLAlchemy), but that wouldn't really make sense since it's a popular library. More specifically, the line I'm using is:
results = CurrentDayData.query.filter(CurrentDayData.item_id == item_id).all()

Answer 1

That’s a very simple query that uses an index, therefore the only way to possible speed it up would be to improve the specification of your hardware.
Moving to a different form of database, on the same hardware, is not going to make a significant difference in performance to this type of query.