Optimization Help Needed: Complex PostgreSQL Query with Multiple Joins and Filters

Question

I'm facing a performance issue with a complex SQL query in PostgreSQL and seeking your expertise to help optimize it. The query involves multiple tables and joins and seems to be taking longer to execute than expected. I believe there might be room for improvement to enhance its efficiency and speed up the results retrieval process. Details: Database: PostgreSQL Tables involved: customers, orders, order_items, and products Use case: This query aims to retrieve data about customers in the USA who placed orders for electronics products after January 1, 2022.

SELECT customers.customer_id, customers.name, products.product_id, products.product_name, 
       orders.order_id, orders.order_date, order_items.quantity, order_items.unit_price
FROM customers
JOIN orders ON customers.customer_id = orders.customer_id
JOIN order_items ON orders.order_id = order_items.order_id
JOIN products ON order_items.product_id = products.product_id
WHERE customers.country = 'USA'
AND orders.order_date >= '2022-01-01'
AND products.category = 'Electronics'
ORDER BY orders.order_date DESC;

I would greatly appreciate any insights or recommendations on how to optimize this query. My goal is to efficiently retrieve the required data while minimizing resource usage and query execution time. I'm particularly interested in any index suggestions, join optimizations, or other SQL techniques that could improve the overall performance.

Use the `EXPLAIN ANALYZE` in order to accumulate the related stats. https://www.postgresql.org/docs/current/sql-explain.html. It is impossible to guess what's wrong here, the query looks fine; far not the scariest SQL query I've seen. — Zazaeil, Jul 26 '23 at 11:01
This query seems to be pretty optimized. I do not see any big flaws. Just make sure that you have correct indexes on all the joining id's. If it's still too slow you might wanna implement a lazy loading for improving user experience. You could also work with a materialized view if the data does not need to be updated in real time. — Silvan, Jul 26 '23 at 11:26
Even better, use `EXPLAIN(ANALYZE, VERBOSE, BUFFERS, SETTINGS)` for you SQL statement to get the details about the planning and execution of the statement. — Frank Heikens, Jul 26 '23 at 14:51

score 1 · Answer 1 · answered Jul 26 '23 at 11:58

The DBMS will try to find a way to accumulate the data without building huge intermediate results. It may be looking out for the most limiting factor in your query. If you mainly have customers in Europe, customers.country = 'USA' may be the most limiting clause. The DBMS would pick those relatively few customers, look up their orders, etc. Or you may have orders since 1995 in your database. Then orders.order_date >= date '2022-01-01' would be a great way to start gathering the data. Get the relatively few orders since 2022, get their electronic items, and the US customers and be done with it. Or there may be 500 different categories, with electronics just being one of them. Or maybe none of the clauses alone is very helpful to reduce the accessed data significantly.

I don't know your data, and I don't know how well your DBMS knows your data. Here are some indexes you could try:

create index idx1 on customers (country) include customer_id, name;
create index idx2 on orders (customer_id, order_date) include order_id;
create index idx3 on order_items (order_id) include product_id, quantity, unit_price;
create index idx4 on products (product_id, category) include product_name;

create index idx5 on orders (order_date) include customer_id, order_id;
create index idx6 on customers (customer_id) include country, name;

create index idx7 on products (category) include product_id, product_name;
create index idx8 on order_items (product_id) include order_id, quantity, unit_price;
create index idx9 on orders (order_id, order_date) include customer_id;

Create these. Then check which indexes are being used and drop the unused ones.

score 0 · Answer 2 · answered Aug 05 '23 at 18:28

0

Try the following ways to solve it:

You should use the LIMIT clause to reduce the number of the returned rows.
Consider using EXPLAIN to analyze the query execution plan.

answered Aug 05 '23 at 18:28

adil shahid

125
4

score 0 · Answer 3 · answered Aug 17 '23 at 20:58

You can optimize the query in several ways:

Use Indexing and Check indexing execution by EXPLAIN.
Consider using tools like EXPLAIN ANALYZE to get actual execution times.
Try to get the required columns and rows from a table which also speeds up the execution time.

score -1 · Answer 4 · answered Jul 26 '23 at 17:15

Create Missing INDEX:

CREATE INDEX idx_customers_country ON customers (country);
CREATE INDEX idx_orders_order_date ON orders (order_date);
CREATE INDEX idx_products_category ON products (category);
CREATE INDEX idx_order_items_order_id ON order_items (order_id);
CREATE INDEX idx_order_items_product_id ON order_items (product_id);

Optimized Query:

WITH relevant_orders AS (
    SELECT order_id
    FROM orders
    WHERE order_date >= '2022-01-01'
)
SELECT c.customer_id, c.name, p.product_id, p.product_name, 
       o.order_id, o.order_date, oi.quantity, oi.unit_price
FROM customers c
JOIN relevant_orders ro ON c.customer_id = ro.customer_id
JOIN order_items oi ON ro.order_id = oi.order_id
JOIN products p ON oi.product_id = p.product_id
WHERE c.country = 'USA'
AND p.category = 'Electronics'
ORDER BY o.order_date DESC;

This is anti-optimization. CTEs impose an optimization fence and should be expressed as subqueries instead. — Reinderien, Jul 26 '23 at 21:04

score -1 · Answer 5 · answered Jul 26 '23 at 19:23

The query involves multiple tables and joins and seems to be taking longer to execute than expected.

To minimize the execution time of the query, check the planning and execution details using EXPLAIN ANALYZE to know exactly what's causing the execution time to take longer before any other steps to be taken.

Optimization Help Needed: Complex PostgreSQL Query with Multiple Joins and Filters

5 Answers5