These two SQL standard language features are among the most powerful SQL features that are available from most other databases. I frequently include them in conference talks about SQL (see my article about 10 SQL Tricks That You Didn’t Think Were Possible), and as well in the Data Geekery SQL Masterclass. With MySQL 8.0 now supporting these exciting features, the masterclass will be including MySQL as well (along with Oracle, SQL Server, PostgreSQL, and DB2). And, of course, these features are now supported in the upcoming jOOQ 3.10 as well.

Then, connect to this instance and run this nice little query in it:

WITH RECURSIVE t(a, b) AS (
SELECT 1, CAST('a' AS CHAR(15))
UNION ALL
SELECT t.a + 1, CONCAT(t.b, 'a')
FROM t
WHERE t.a < 10
)
SELECT a, SUM(a) OVER (ORDER BY a) AS ∑, b
FROM t

And get this result:

a ∑ b
--------------------------
1 1 a
2 3 aa
3 6 aaa
4 10 aaaa
5 15 aaaaa
6 21 aaaaaa
7 28 aaaaaaa
8 36 aaaaaaaa
9 45 aaaaaaaaa
10 55 aaaaaaaaaa

Would you believe this is MySQL?

Bonus

A nice “hidden” feature is the support of new pessimistic locking clauses, in particular FOR UPDATE SKIP LOCKED. This has been available in Oracle for ages and since recently in PostgreSQL as well, and now in MySQL. A very useful feature when implementing simple message queues or reservation systems. More details in this article here:

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:

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

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 optimise 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.

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.

You may have noticed that the solution is not entirely correct in the event where WORK_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:

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

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.

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:

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,
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,
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:

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
),

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,
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

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:

Recursive SQL can be awesome, although a bit hard to read in its SQL standard beauty. Let’s assume you have some aggregated data with dates and a number of events per date:

As you may have noticed, there are no records for those dates with zero events (October 06). With recursive SQL, this is rather simple to achieve.

with recursive
-- Data could also be a regular table containing
-- the actual data
data(date, count) as (
select date '2013-10-01', 2 union all
select date '2013-10-02', 1 union all
select date '2013-10-03', 3 union all
select date '2013-10-04', 4 union all
select date '2013-10-05', 2 union all
select date '2013-10-06', 0 union all
select date '2013-10-07', 2
),
-- This is the recursive common table expression
-- It starts with all data where count > 0
-- ... and then recurses by subtracting one
recurse(date, count) as (
select date, count
from data
where count > 0
union all
select date, count - 1
from recurse
where count > 1
)
select date, count event_number from recurse
order by date asc, event_number asc;

See also this SQLFiddle to see the above CTE in action.

Incredibly, Oracle’s CONNECT BY clause doesn’t seem to be an option here. I challenge you to find a better solution, though! For instance, this beautiful solution that works with PostgreSQL:

with recursive
data(date, count) as (
select date '2013-10-01', 2 union all
select date '2013-10-02', 1 union all
select date '2013-10-03', 3 union all
select date '2013-10-04', 4 union all
select date '2013-10-05', 2 union all
select date '2013-10-06', 0 union all
select date '2013-10-07', 2
)
select date, generate_series(1, count) event_number
from data
where count > 0
order by date asc, event_number asc;