# How to Find the Closest Subset Sum with SQL

# How to Find the Closest Subset Sum with SQL

### In this article, we’ll compare the user’s imperative approach to the extremely elegant (Oracle) SQL approach.

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I stumbled upon this very interesting question on Stack Overflow, recently. Its title is:

[How to] compare a number with sum of subset of numbers

In this article, we’ll compare the user’s imperative approach to the extremely elegant (Oracle) SQL approach. We’ll be making use of any combination of these awesome SQL features:

- Window functions
- FIRST and LAST functions, using Oracle-specific syntax
`LATERAL JOIN`

(or`APPLY`

- Recursive SQL

### The Problem

The user alhashmiya who had asked this question, was looking for a solution to the problem of finding the “closest” sum of elements in a subset of numbers A to a set of “expected” sums B. More concretely, alhasmiya had the following two tables:

```
ID ASSIGN_AMT
--------------
1 25150
2 19800
3 27511
```

And…

```
ID WORK_AMT
------------
1 7120
2 8150
3 8255
4 9051
5 1220
6 12515
7 13555
8 5221
9 812
10 6562
```

The `ASSIGN_AMT`

value is the “expected” sum. What alhashmiya was looking for is the sum of a subset of `WORK_AMT`

values A, such that this sum is as close as possible to any of the “expected” sums. There are two ways to understand this problem:

- The possible “closest” sums are restricted to be the sums obtained in a strictly defined order, e.g. ordered by
`ID`

. An application of this understanding is to find out the exact moment when a well-defined, ordered running total (e.g. bank account balance) has exceeded a certain threshold - The possible “closest” sums are unrestricted. Any unordered subset qualifies to calculate such a sum. An application of this understanding is to find a combination of discrete values to reach a target value as closely as possible, e.g. to optimize a trade.

The second understanding is called the “subset sum problem”, for which there are only exponential algorithms if you’re looking for an exact solution. **It is important to notice that this algorithm will NOT scale well at all, regardless of the solution technique!**

But let’s look at the simpler understanding first:

## 1. Calculating a Sum of an Ordered Subset of Values

By strictly ordered we mean (in the sense of the question) that we want to order all `WORK_AMT`

values, e.g. by `ID`

, and allow only for sums that can appear in this particular order. I.e.:

```
ID WORK_AMT SUBSET_SUM
------------------------
1 7120 7120 (= WORK_AMT)
2 8150 15270 (= previous SUBSET_SUM + WORK_AMT)
3 8255 23525 (= previous SUBSET_SUM + WORK_AMT)
4 9051 32576 (= ...)
5 1220 33796
6 12515 46311
7 13555 59866
8 5221 65087
9 812 65899
10 6562 72461
```

The above `SUBSET_SUM`

value is the sum of all `WORK_AMT`

values with `ID <= "current" ID`

. We’ve seen this before on this blog, this is called a running total, and it is best calculated using window functions as follows:

```
WITH
VALS (ID, WORK_AMT) AS (
SELECT 1 , 7120 FROM DUAL
UNION ALL SELECT 2 , 8150 FROM DUAL
UNION ALL SELECT 3 , 8255 FROM DUAL
UNION ALL SELECT 4 , 9051 FROM DUAL
UNION ALL SELECT 5 , 1220 FROM DUAL
UNION ALL SELECT 6 , 12515 FROM DUAL
UNION ALL SELECT 7 , 13555 FROM DUAL
UNION ALL SELECT 8 , 5221 FROM DUAL
UNION ALL SELECT 9 , 812 FROM DUAL
UNION ALL SELECT 10, 6562 FROM DUAL
)
SELECT
ID,
WORK_AMT,
SUM (WORK_AMT) OVER (ORDER BY ID) AS SUBSET_SUM
FROM
VALS
ORDER BY
ID
```

The above window function calculates the sum of all `WORK_AMT`

values that are in the “window” of values where the `ID`

is smaller or equal to the current `ID`

.

### Finding the “Closest” of these Sums with Quantified Comparison Predicates

Now, the task at hand is to find for each value `ASSIGN_AMT`

in `25150`

, `19800`

, and `27511`

the closest value of `SUBSET_SUM`

. In a way, what we are trying to do is we want to minimise the expression `ABS(SUBSET_SUM - ASSIGN_AMT)`

.

However, the `MIN()`

aggregate function won’t help us here, because that will simply return the minimum value of this difference. We want the value of `SUBSET_SUM`

that produces this difference in the first place.

One solution would be to use a **quantified comparison predicate**, a rarely used and not well-known comparison operator that works in all SQL databases:

```
-- The common table expressions are the same as
-- in the previous examples
WITH
ASSIGN(ID, ASSIGN_AMT) AS (
SELECT 1, 25150 FROM DUAL
UNION ALL SELECT 2, 19800 FROM DUAL
UNION ALL SELECT 3, 27511 FROM DUAL
),
VALS (ID, WORK_AMT) AS (
SELECT 1 , 7120 FROM DUAL
UNION ALL SELECT 2 , 8150 FROM DUAL
UNION ALL SELECT 3 , 8255 FROM DUAL
UNION ALL SELECT 4 , 9051 FROM DUAL
UNION ALL SELECT 5 , 1220 FROM DUAL
UNION ALL SELECT 6 , 12515 FROM DUAL
UNION ALL SELECT 7 , 13555 FROM DUAL
UNION ALL SELECT 8 , 5221 FROM DUAL
UNION ALL SELECT 9 , 812 FROM DUAL
UNION ALL SELECT 10, 6562 FROM DUAL
),
SUMS (ID, WORK_AMT, SUBSET_SUM) AS (
SELECT
VALS.*,
SUM (WORK_AMT) OVER (ORDER BY ID)
FROM
VALS
)
-- This is now the interesting part, where we
-- calculate the closest sum
SELECT
ASSIGN.ID,
ASSIGN.ASSIGN_AMT,
SUBSET_SUM
FROM
ASSIGN
JOIN
SUMS
ON
ABS (ASSIGN_AMT - SUBSET_SUM) <= ALL (
SELECT
ABS (ASSIGN_AMT - SUBSET_SUM)
FROM
SUMS
)
```

The **quantified comparison predicate** reads intuitively. We’re looking for *that specific SUBSET_SUM* whose difference to the “expected”

`ASSIGN_AMT`

is smaller or equal to all the other possible differences.The above query yields:

```
ID ASSIGN_AMT SUBSET_SUM
--------------------------
1 25150 23525
2 19800 23525
3 27511 23525
```

In this case, it’s always the same.

You may have noticed that the solution is not entirely correct in the event where `ASSIGN_AMT`

is allowed to be zero (let’s ignore the possibility of negative values) – in case of which we’ll produce duplicate values in the `JOIN`

. This can be achieved when replacing:

```
UNION ALL SELECT 4 , 0 FROM DUAL
```

Now, the result is:

```
ID ASSIGN_AMT SUBSET_SUM
--------------------------
1 25150 23525
2 19800 23525
2 19800 23525
3 27511 23525
```

One solution is to remove those duplicates using `DISTINCT`

(which is an anti-pattern. See #6 in this list). A better solution is to make the `JOIN`

predicate unambiguous by comparing `ID`

values as well, i.e.:

```
SELECT
ASSIGN.ID,
ASSIGN.ASSIGN_AMT,
SUBSET_SUM
FROM
ASSIGN
JOIN
SUMS
ON
(ABS (ASSIGN_AMT - SUBSET_SUM), SUMS.ID) <= ALL (
SELECT
ABS (ASSIGN_AMT - SUBSET_SUM),
ID
FROM
SUMS
)
```

The above unfortunately doesn’t work in Oracle (yet), which will report an error:

```
ORA-01796: this operator cannot be used with lists
```

Oracle supports comparing tuples / row value expressions only with equal comparators, not with less than / greater than comparators – which is a shame. The same query runs smoothlessly in PostgreSQL.

### Finding the “Closest” of these Sums with Oracle’s FIRST Function

Oracle has a very interesting function to keep the first (or last) values in a set of aggregate values of a group given any particular ordering, and calculating an aggregate function only on those values within the group. The following SQL statement will illustrate this:

```
SELECT
ASSIGN.ID,
ASSIGN.ASSIGN_AMT,
MIN (SUBSET_SUM) KEEP (
DENSE_RANK FIRST
ORDER BY ABS (ASSIGN_AMT - SUBSET_SUM)
) AS CLOSEST_SUM
FROM
ASSIGN
CROSS JOIN
SUMS
GROUP BY
ASSIGN.ID, ASSIGN.ASSIGN_AMT
```

Essentially, we’re grouping all values from the `SUMS`

table for each `ASSIGN_AMT`

. For each of those groups, we’ll look for the `"FIRST"`

row that appears when ordering rows within the group by our criteria `ABS(ASSIGN_AMT - SUBSET_SUM)`

. We `"KEEP"`

only those rows in the group, and retain from those rows the minimum `SUBSET_SUM`

.

This query will again yield:

```
ID ASSIGN_AMT SUBSET_SUM
--------------------------
1 25150 23525
2 19800 23525
3 27511 23525
```

This is an extremely nice functionality that can come in handy every once in a while.

Remember that we’ve seen a similar feature recently on this blog, when we were looking for `FIRST_VALUE`

(or `LAST_VALUE`

) within the `PARTITION`

of a window. In standard SQL, a similar thing can be achieved using window functions as such:

```
SELECT DISTINCT
ASSIGN.ID,
ASSIGN.ASSIGN_AMT,
FIRST_VALUE (SUBSET_SUM) OVER (
PARTITION BY ASSIGN.ID
ORDER BY ABS (ASSIGN_AMT - SUBSET_SUM)
) AS CLOSEST_SUM
FROM
ASSIGN
CROSS JOIN
SUMS
```

Unfortunately, these solutions all produce duplicates, which we have to remove either via `GROUP BY`

(`KEEP`

solution), or via `DISTINCT`

(`FIRST_VALUE`

solution).

### Finding the “Closest” of these Sums with LATERAL JOIN

A cleaner solution that doesn’t rely on the removal of duplicates is using Oracle 12c’s new `FETCH FIRST`

clause along with `CROSS JOIN LATERAL`

(or `CROSS APPLY`

, which is the same)

```
SELECT
ASSIGN.ID,
ASSIGN.ASSIGN_AMT,
CLOSEST_SUM
FROM
ASSIGN
CROSS JOIN LATERAL (
SELECT
SUBSET_SUM AS CLOSEST_SUM
FROM
SUMS
ORDER BY
ABS (ASSIGN.ASSIGN_AMT - SUBSET_SUM)
FETCH FIRST 1 ROW ONLY
) SUMS
```

What does this mean? We’re essentially joining for each value in `ASSIGN`

only the `FIRST 1 ROW`

in `SUMS`

, ordered by the usual criteria. We need `LATERAL`

(or `APPLY`

), because this allows us to reference columns from the *left side* of the `JOIN`

expression also in the *right side*, which wouldn’t be possible otherwise.

The same functionality is supported in SQL Server (only using `CROSS APPLY`

), or in PostgreSQL (only using `CROSS JOIN LATERAL`

).

Lateral can be very useful whenever the right hand side of a `JOIN`

depends on the left hand side. Unlike ordinary joins, this means that the `JOIN`

order will be set in stone from left to right, and the optimiser has a reduced set of join algorithm options. This is useful in examples like these (with `ORDER BY`

and `FETCH FIRST`

), or when joining unnested table-valued functions, which we’ll cover in a future blog post.

## 2. Calculating a Sum of any Subset of Values

So far, we’ve worked on a simplified version of the problem. This is probably not what alhashmiya meant on their Stack Overflow question. They probably wanted to solve the *Subset sum problem*, finding the “closest” sum of *any* subset of `WORK_AMT`

values.

We’ll use recursive SQL to calculate all the possible sums. First off, let’s remember how recursive SQL works:

Slide taken from the jOOQ advanced SQL Training. Contact us to learn more.

In recursive SQL, we need a `UNION ALL`

query in a common table expression (`WITH`

clause in Oracle, or `WITH RECURSIVE`

clause in PostgreSQL). The first subquery of `UNION ALL`

generates the “seed row(s)” of the recursion, and the second subqeury of `UNION ALL`

generates the recursion based on a `SELECT`

that selects from the table being declared, recursively.

So, a naive solution to this subset sum problem can be seen here:

```
-- Repetition of the previous data
WITH
ASSIGN (ID, ASSIGN_AMT) AS (
SELECT 1, 25150 FROM DUAL
UNION ALL SELECT 2, 19800 FROM DUAL
UNION ALL SELECT 3, 27511 FROM DUAL
),
WORK (ID, WORK_AMT) AS (
SELECT 1 , 7120 FROM DUAL
UNION ALL SELECT 2 , 8150 FROM DUAL
UNION ALL SELECT 3 , 8255 FROM DUAL
UNION ALL SELECT 4 , 9051 FROM DUAL
UNION ALL SELECT 5 , 1220 FROM DUAL
UNION ALL SELECT 6 , 12515 FROM DUAL
UNION ALL SELECT 7 , 13555 FROM DUAL
UNION ALL SELECT 8 , 5221 FROM DUAL
UNION ALL SELECT 9 , 812 FROM DUAL
UNION ALL SELECT 10, 6562 FROM DUAL
),
-- A new way of calculating all possible sums by
-- Recursively adding up all the sums
SUMS (SUBSET_SUM, MAX_ID) AS (
SELECT
WORK_AMT,
ID
FROM
WORK
UNION ALL
SELECT
WORK_AMT + SUBSET_SUM,
GREATEST (MAX_ID, WORK.ID)
FROM
SUMS
JOIN
WORK
ON
SUMS.MAX_ID < WORK.ID
)
-- The same technique to match the "closest" sum
-- As before
SELECT
ASSIGN.ID,
ASSIGN.ASSIGN_AMT,
MIN (SUBSET_SUM) KEEP (
DENSE_RANK FIRST
ORDER BY ABS (ASSIGN_AMT - SUBSET_SUM)
) AS CLOSEST_SUM
FROM
SUMS
CROSS JOIN
ASSIGN
GROUP BY
ASSIGN.ID, ASSIGN.ASSIGN_AMT
```

The recursion is simple. In the first subquery of the recursion (the “seed row”), we select each individual row in `WORK`

:

```
SELECT
WORK_AMT,
ID
FROM
WORK
```

In the second subquery of the recursion (the “recusion rows”), we join the value of the previous recursion step (`SUMS`

) with all the remaining values (`WORK`

), i.e. all the values that have a higher `ID`

:

```
SELECT
WORK_AMT + SUBSET_SUM,
GREATEST (MAX_ID, WORK.ID)
FROM
SUMS
JOIN
WORK
ON
SUMS.MAX_ID < WORK.ID
```

In this combination, we calculate the intermediate sum (which is also a running total, by the way) and we calculate the highest summed-up ID thus far, to reduce the number of combinations. The latter, we can do because summing is commutative.

The main difference in this solution compared to previous approaches is the fact that we’re now generating a lot (a huge lot) more different values in the `SUMS`

table.

After a still acceptable 0.112s for 10 different `WORK_AMT`

values, the database calculated:

```
ID ASSIGN_AMT CLOSEST_SUM
---------------------------
1 25150 25133
2 19800 19768
3 27511 27488
```

But beware, as soon as you start adding values to the `VALS`

table, this algorithm starts exploding in time and space complexity. Running the same query with the following, only slightly bigger `WORK`

table already requires 16.3 seconds to yield a result:

```
WORK(ID, WORK_AMT) AS (
SELECT 1 , 7120 FROM DUAL
UNION ALL SELECT 2 , 8150 FROM DUAL
UNION ALL SELECT 3 , 8255 FROM DUAL
UNION ALL SELECT 4 , 9051 FROM DUAL
UNION ALL SELECT 5 , 1220 FROM DUAL
UNION ALL SELECT 6 , 12515 FROM DUAL
UNION ALL SELECT 7 , 13555 FROM DUAL
UNION ALL SELECT 8 , 5221 FROM DUAL
UNION ALL SELECT 9 , 812 FROM DUAL
UNION ALL SELECT 10, 6562 FROM DUAL
UNION ALL SELECT 11, 1234 FROM DUAL
UNION ALL SELECT 12, 61 FROM DUAL
UNION ALL SELECT 13, 616 FROM DUAL
UNION ALL SELECT 14, 2456 FROM DUAL
UNION ALL SELECT 15, 5161 FROM DUAL
UNION ALL SELECT 16, 414 FROM DUAL
UNION ALL SELECT 17, 516 FROM DUAL
UNION ALL SELECT 18, 617 FROM DUAL
UNION ALL SELECT 19, 146 FROM DUAL
),
```

And the result would be:

```
ID ASSIGN_AMT CLOSEST_SUM
---------------------------
1 25150 25150
2 19800 19800
3 27511 27511
```

Want proof about the actual sum? That’s easy as well with recursive SQL:

```
-- Repetition of the previous data
WITH
ASSIGN (ID, ASSIGN_AMT) AS (
SELECT 1, 25150 FROM DUAL
UNION ALL SELECT 2, 19800 FROM DUAL
UNION ALL SELECT 3, 27511 FROM DUAL
),
WORK (ID, WORK_AMT) AS (
SELECT 1 , 7120 FROM DUAL
UNION ALL SELECT 2 , 8150 FROM DUAL
UNION ALL SELECT 3 , 8255 FROM DUAL
UNION ALL SELECT 4 , 9051 FROM DUAL
UNION ALL SELECT 5 , 1220 FROM DUAL
UNION ALL SELECT 6 , 12515 FROM DUAL
UNION ALL SELECT 7 , 13555 FROM DUAL
UNION ALL SELECT 8 , 5221 FROM DUAL
UNION ALL SELECT 9 , 812 FROM DUAL
UNION ALL SELECT 10, 6562 FROM DUAL
),
-- A new way of calculating all possible sums by
-- Recursively adding up all the sums
SUMS (SUBSET_SUM, MAX_ID, CALC) AS (
SELECT
WORK_AMT,
ID,
TO_CHAR(WORK_AMT)
FROM WORK
UNION ALL
SELECT
WORK_AMT + SUBSET_SUM,
GREATEST (MAX_ID, WORK.ID),
CALC || '+' || WORK_AMT
FROM
SUMS
JOIN
WORK
ON
SUMS.MAX_ID < WORK.ID
)
-- The same technique to match the "closest" sum
-- As before
SELECT
ASSIGN.ID,
ASSIGN.ASSIGN_AMT,
MIN (SUBSET_SUM) KEEP (
DENSE_RANK FIRST
ORDER BY ABS (ASSIGN_AMT - SUBSET_SUM)
) AS CLOSEST_SUM,
MIN (CALC) KEEP (
DENSE_RANK FIRST
ORDER BY ABS (ASSIGN_AMT - SUBSET_SUM)
) AS CALCULATION
FROM
SUMS
CROSS JOIN
ASSIGN
GROUP BY
ASSIGN.ID, ASSIGN.ASSIGN_AMT
```

The above now yields:

```
ID ASSIGN_AMT CLOSEST_SUM CALCULATION
------------------------------------------------------------
1 25150 25133 7120 + 8150 + 9051 + 812
2 19800 19768 1220 + 12515 + 5221 + 812
3 27511 27488 8150 + 8255 + 9051 + 1220 + 812
```

## Conclusion

SQL is powerful. Extremely powerful. In this article, we’ve used various techniques to calculate the subset sum problem, or a simplification thereof. We’ve shown how to solve this problem in Oracle or PostgreSQL using a combination of these awesome SQL features:

- Window functions
`KEEP FIRST`

(in Oracle only)`LATERAL JOIN`

(or`APPLY`

- Recursive SQL

Did you like this article? Would you like to learn more about advanced SQL? Contact us to enquire about our advanced SQL training sessions, where we help you understand the simplicity of set-oriented thinking and calculations with SQL.

We’d like to point out that all of these solutions can be written in Java using jOOQ in a type safe manner as well.

Databases should be easy to deploy, easy to use, and easy to scale. If you agree, you should check out CockroachDB, a scalable SQL database built for businesses of every size. Check it out here.

Published at DZone with permission of Lukas Eder , DZone MVB. See the original article here.

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