Analytical function: sum the cumulative of previous column

All we need is an easy explanation of the problem, so here it is.

Using an analytical function, I want that the column ‘sum’ contains the
cumulative of the previous column.

But my code gets the total sum of all encounters.

Here is both table and data for testing:

CREATE TABLE users (
user_id INT PRIMARY KEY,
name CHARACTER VARYING(50)
);

CREATE TABLE orders_catalog (
order_code INT PRIMARY KEY,
order_desc CHARACTER VARYING(50) NOT NULL,
cost REAL NOT NULL
);

CREATE TABLE encounter (
encounter_id INT PRIMARY KEY,
user_id INT NOT NULL,
encounter_type CHARACTER VARYING(50) NOT NULL,

CONSTRAINT FK_encounter FOREIGN KEY (user_id) REFERENCES users(user_id)
);

CREATE TABLE orders (
order_id INT PRIMARY KEY,
order_code INT NOT NULL,
encounter_id INT NOT NULL,
created_dt TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,

CONSTRAINT FK_orders_catalog FOREIGN KEY (order_code) REFERENCES orders_catalog (order_code),
CONSTRAINT FK_orders_encounter FOREIGN KEY (encounter_id) REFERENCES encounter(encounter_id)
);
--

INSERT INTO users(user_id, name) VALUES(1, 'Peter');
INSERT INTO users(user_id, name) VALUES(2, 'Charles');
INSERT INTO users(user_id, name) VALUES(3, 'Eva') ;
INSERT INTO users(user_id, name) VALUES(4, 'John');
INSERT INTO users(user_id, name) VALUES(5, 'Helene');

INSERT INTO orders_catalog(order_code, order_desc, cost) VALUES (10000, 'Painting', 100.34);
INSERT INTO orders_catalog(order_code, order_desc, cost) VALUES (10001, 'Painting', 214.11);
INSERT INTO orders_catalog(order_code, order_desc, cost) VALUES (10002, 'Painting', 214.11);
INSERT INTO orders_catalog(order_code, order_desc, cost) VALUES (10003, 'Spare part', 181.03);
INSERT INTO orders_catalog(order_code, order_desc, cost) VALUES (10004, 'Sheet metal', 168.18);
INSERT INTO orders_catalog(order_code, order_desc, cost) VALUES (10005, 'Sheet metal', 240.02);
INSERT INTO orders_catalog(order_code, order_desc, cost) VALUES (10006, 'Sheet metal', 240.02);
INSERT INTO orders_catalog(order_code, order_desc, cost) VALUES (10007, 'Electricity', 146.85);
INSERT INTO orders_catalog(order_code, order_desc, cost) VALUES (10008, 'Spare part', 162.13);
INSERT INTO orders_catalog(order_code, order_desc, cost) VALUES (10009, 'Electricity', 140.02);
INSERT INTO orders_catalog(order_code, order_desc, cost) VALUES (10010, 'Electricity', 180.02);

INSERT INTO encounter(encounter_id, user_id, encounter_type) VALUES(100,1,'appointment');
INSERT INTO encounter(encounter_id, user_id, encounter_type) VALUES(101,2,'appointment');
INSERT INTO encounter(encounter_id, user_id, encounter_type) VALUES(102,3,'appointment');
INSERT INTO encounter(encounter_id, user_id, encounter_type) VALUES(103,4,'urgent');
INSERT INTO encounter(encounter_id, user_id, encounter_type) VALUES(104,5,'urgent');
INSERT INTO encounter(encounter_id, user_id, encounter_type) VALUES(105,1,'appointment');
INSERT INTO encounter(encounter_id, user_id, encounter_type) VALUES(106,2,'appointment');
INSERT INTO encounter(encounter_id, user_id, encounter_type) VALUES(107,3,'waiting');
INSERT INTO encounter(encounter_id, user_id, encounter_type) VALUES(108,4,'urgent');
INSERT INTO encounter(encounter_id, user_id, encounter_type) VALUES(109,5,'waiting');
INSERT INTO encounter(encounter_id, user_id, encounter_type) VALUES(110,1,'waiting');

INSERT INTO orders(order_id, order_code, encounter_id, created_dt) VALUES (1000,10000,100,'2009-06-16 09:12');
INSERT INTO orders(order_id, order_code, encounter_id, created_dt) VALUES (1001,10001,101,'2009-06-16 09:12');
INSERT INTO orders(order_id, order_code, encounter_id, created_dt) VALUES (1002,10002,102,'2009-06-16 09:12');
INSERT INTO orders(order_id, order_code, encounter_id, created_dt) VALUES (1003,10003,103,'2009-12-03 09:50');
INSERT INTO orders(order_id, order_code, encounter_id, created_dt) VALUES (1004,10004,104,'2010-02-24 12:21');
INSERT INTO orders(order_id, order_code, encounter_id, created_dt) VALUES (1005,10005,105,'2010-03-27 23:54');
INSERT INTO orders(order_id, order_code, encounter_id, created_dt) VALUES (1006,10006,106,'2010-03-22 12:43');
INSERT INTO orders(order_id, order_code, encounter_id, created_dt) VALUES (1007,10007,107,'2010-02-24 12:21');
INSERT INTO orders(order_id, order_code, encounter_id, created_dt) VALUES (1008,10008,108,'2010-03-04 08:55');
INSERT INTO orders(order_id, order_code, encounter_id, created_dt) VALUES (1009,10009,109,'2010-03-06 09:25');
INSERT INTO orders(order_id, order_code, encounter_id, created_dt) VALUES (1010,10010,110,'2010-03-22 11:18');

And here is the query that I tried:

SELECT
u.user_id,
name,
ROW_NUMBER() OVER (
PARTITION BY u.user_id ORDER BY o.created_dt ASC
) AS position,
SUM (c.cost) OVER (
PARTITION BY o.encounter_id
) AS cost,
SUM (cost) OVER (
PARTITION BY u.user_id
) AS sum
FROM
users u
INNER JOIN
encounter e USING (user_id)
INNER JOIN
orders o USING (encounter_id)
INNER JOIN
orders_catalog c USING (order_code)
ORDER BY name, position;

But the output is not what I expect:

user_id name position cost sum
2 Charles 1 214.11 454.13
2 Charles 2 240.02 454.13
3 Eva 1 214.11 360.96002
3 Eva 2 146.85 360.96002
5 Helene 1 168.18 308.2
5 Helene 2 140.02 308.2
4 John 1 181.03 343.16
4 John 2 162.13 343.16
1 Peter 1 100.34 520.38
1 Peter 2 180.02 520.38
1 Peter 3 240.02 520.38

Since I would want something like:

user_id name position cost sum
1 Peter 1 100.34 100.34
1 Peter 2 180.02 280.36
1 Peter 3 240.02 520.38

Fiddle

How to solve :

I know you bored from this bug, So we are here to help you! Take a deep breath and look at the explanation of your problem. We have many solutions to this problem, But we recommend you to use the first method because it is tested & true method that will 100% work for you.

Method 1

The sum definition lacks an important component, the key detail that turns a regular window aggregate into a running total, or cumulative aggregate if you will – the ORDER BY clause. In this case, the ORDER BY criterion should be the same as the one used in the position definition, i.e. o.created_dt ASC.

Therefore, your complete query should look like this:

SELECT
u.user_id,
name,
ROW_NUMBER() OVER (
PARTITION BY u.user_id ORDER BY o.created_dt ASC
) AS position,
SUM (c.cost) OVER (
PARTITION BY o.encounter_id
) AS cost,
SUM (cost) OVER (
PARTITION BY u.user_id ORDER BY o.created_dt ASC
) AS sum
FROM
users u
INNER JOIN
encounter e USING (user_id)
INNER JOIN
orders o USING (encounter_id)
INNER JOIN
orders_catalog c USING (order_code)
ORDER BY name, position;

Output:

user_id name position cost sum
2 Charles 1 214.11 214.11
2 Charles 2 240.02 454.13
3 Eva 1 214.11 214.11
3 Eva 2 146.85 360.96
5 Helene 1 168.18 168.18
5 Helene 2 140.02 308.2
4 John 1 181.03 181.03
4 John 2 162.13 343.16
1 Peter 1 100.34 100.34
1 Peter 2 180.02 280.36
1 Peter 3 240.02 520.38

This solution can be tested and played with in a live demo at db<>fiddle.

Note: Use and implement method 1 because this method fully tested our system.
Thank you 🙂