Filling Sparse Data With the Previous Non-Empty Value in SQL
Want to learn how to fill sparse data sets with the "previous non-empty value"? Take a look at these two lean, SQL-based solutions.
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How do I fill the cells of a sparse data set with the “previous non-empty value”?
This is a very common problem in all data-related technologies, and we’re going to look into two very lean, SQL-based solutions for it.
The problem is really simple, and I’m reusing the example provided by Stack Overflow user aljassi to answer this question:
We have a table containing “sparse” data:
Col1 Col2 Col3 Col4 ---------------------- A 0 1 5 B 0 4 0 C 2 0 0 D 0 0 0 E 3 5 0 F 0 3 0 G 0 3 1 H 0 1 5 I 3 5 0
The above data set contains a couple of interesting data points that are non-zero, and some gaps modelled by the value zero. In other examples, we could replace zero by
NULL, but it would still be the same problem. The desired result is the following:
Col1 Col2 Col3 Col4 ---------------------- A 0 1 5 B 0 4 5 C 2 4 5 D 2 4 5 E 3 5 5 F 3 3 5 G 3 3 1 H 3 1 5 I 3 5 5
Note that all the generated values are highlighted in red, and they correspond to the most recent blue value.
How to do it with SQL? We’ll be looking at two solutions...
A Solution Using Window Functions
This is the solution you should be looking for, and there are two answers in the linked Stack Overflow question that both make use of window functions:
Both solutions are roughly equivalent. Here’s how they work (using Oracle syntax):
WITH t(col1, col2, col3, col4) AS ( SELECT 'A', 0, 1, 5 FROM DUAL UNION ALL SELECT 'B', 0, 4, 0 FROM DUAL UNION ALL SELECT 'C', 2, 0, 0 FROM DUAL UNION ALL SELECT 'D', 0, 0, 0 FROM DUAL UNION ALL SELECT 'E', 3, 5, 0 FROM DUAL UNION ALL SELECT 'F', 0, 3, 0 FROM DUAL UNION ALL SELECT 'G', 0, 3, 1 FROM DUAL UNION ALL SELECT 'H', 0, 1, 5 FROM DUAL UNION ALL SELECT 'I', 3, 5, 0 FROM DUAL ) SELECT col1, nvl(last_value(nullif(col2, 0)) IGNORE NULLS OVER (ORDER BY col1), 0) col2, nvl(last_value(nullif(col3, 0)) IGNORE NULLS OVER (ORDER BY col1), 0) col3, nvl(last_value(nullif(col4, 0)) IGNORE NULLS OVER (ORDER BY col1), 0) col4 FROM t
Now, let’s decompose these window functions:
This is just an easy way of producing
NULL values whenever we have what is an accepted “empty” value in our data set. So, instead of zeros, we just get
NULL. Applying this function to our data, we’re getting:
Col1 Col2 Col3 Col4 ---------------------- A NULL 1 5 B NULL 4 NULL C 2 NULL NULL D NULL NULL NULL E 3 5 NULL F NULL 3 NULL G NULL 3 1 H NULL 1 5 I 3 5 NULL
We’re doing this because now, we can make use of the useful
IGNORE NULLS clause that is available to some ranking functions, specifically
LAG(). We can now write:
last_value(...) IGNORE NULLS OVER (ORDER BY col1)
Where we take the last non-
NULL value that precedes the current row when ordering rows by
- If the current row contains a non-
NULLvalue, we’re taking that value.
- If the current row contains a
NULLvalue, we’re going “up” until we reach a non-
- If we’re going “up” and we haven’t reached any non-
NULLvalue, well, we get
This leads to the following result:
Col1 Col2 Col3 Col4 ---------------------- A NULL 1 5 B NULL 4 5 C 2 4 5 D 2 4 5 E 3 5 5 F 3 3 5 G 3 3 1 H 3 1 5 I 3 5 5
Note that with most window functions, once you specify an
ORDER BY clause, then the following frame clause is taken as a default:
last_value(...) IGNORE NULLS OVER ( ORDER BY col1 ROWS BETWEEN UNBOUNDED PRECEEDING AND CURRENT ROW )
That’s a lot of keywords, but their meaning is not really that obscure once you get a hang of window functions. We suggest reading the following blog posts to learn more about them:
- The Difference Between ROW_NUMBER(), RANK(), and DENSE_RANK()
- Don’t Miss out on Awesome SQL Power with FIRST_VALUE(), LAST_VALUE(), LEAD(), and LAG()
Finally, because we don’t want those
NULL values to remain in our results, we simply remove them using
COALESCE() in other databases):
nvl(last_value(...) IGNORE NULLS OVER (...), 0)
Easy, isn’t it? Note, that in this particular case,
LAST_VALUE() will have the same effect.
A Solution Using the MODEL Clause
Whenever you have a problem in (Oracle) SQL that becomes hard to solve with window functions, the Oracle
MODEL clause might offer an “easy” solution to it. I’m using quotes on “easy”, because the syntax is a bit hard to remember, but the essence of it is really not that hard.
MODEL clause is nothing else than an Oracle-specific dialect for implementing spreadsheet-like logic in the database. I highly recommend reading the relevant Whitepaper by Oracle, which explains the functionality very well:
Here’s how you could tackle the problem with
MODEL (and bear with me):
WITH t(col1, col2, col3, col4) AS ( SELECT 'A', 0, 1, 5 FROM DUAL UNION ALL SELECT 'B', 0, 4, 0 FROM DUAL UNION ALL SELECT 'C', 2, 0, 0 FROM DUAL UNION ALL SELECT 'D', 0, 0, 0 FROM DUAL UNION ALL SELECT 'E', 3, 5, 0 FROM DUAL UNION ALL SELECT 'F', 0, 3, 0 FROM DUAL UNION ALL SELECT 'G', 0, 3, 1 FROM DUAL UNION ALL SELECT 'H', 0, 1, 5 FROM DUAL UNION ALL SELECT 'I', 3, 5, 0 FROM DUAL ) SELECT * FROM t MODEL DIMENSION BY (row_number() OVER (ORDER BY col1) rn) MEASURES (col1, col2, col3, col4) RULES ( col2[any] = DECODE(col2[cv(rn)], 0, NVL(col2[cv(rn) - 1], 0), col2[cv(rn)]), col3[any] = DECODE(col3[cv(rn)], 0, NVL(col3[cv(rn) - 1], 0), col3[cv(rn)]), col4[any] = DECODE(col4[cv(rn)], 0, NVL(col4[cv(rn) - 1], 0), col4[cv(rn)]) )
There are three clauses that are of interest here:
The DIMENSION BY Clause
Like in a Microsoft Excel spreadsheet,
DIMENSION corresponds to the consecutive, distinct index of each spreadsheet cell, by which we want to access the cell. In Excel, there are always two dimensions (one written with letters A..Z, AA..ZZ, …) and the other one written with numbers (1..infinity).
MODEL, you can specify as many dimensions as you want. In our example, we’ll only use one, the row number of each row, ordered by
col1 (another use-case for a window function).
The MEASURES Clause
MEASURES clause specifies the individual cell values for each “cell”. In Microsoft Excel, a cell can have only one value. In Oracle’s
MODEL clause, we can operate on many values at once, within a “cell”.
In this case, we’ll just make all of the columns our cells.
The RULES Clause
This is the really interesting part in the
MODEL clause. Here, we specify by what rules we want to calculate the values of each individual cell. The syntax is simple:
RULES ( <rule 1>, <rule 2>, ..., <rule N> )
Each individual rule can implement an assignment of the form:
RULES ( cell[dimension(s)] = rule )
In our case, we’ll repeat the same rule for cells
col4, and for any value of the dimension
rn (for row number). So, the left-hand side of the assignment is:
RULES ( col2[any] = rule, col3[any] = rule, col4[any] = rule, )
The right hand side is a trivial (though not so trivial-looking) expression:
Let’s decompose again.
DECODE is a simple and useful Oracle function that takes a first argument, compares it with argument 2, and if they’re the same, returns argument 3, otherwise argument 4. It works like a
CASE, which is a bit more verbose:
|1 2 3 4 5|| |
cv() is a
MODEL specific “function” that means “current value”. On the left-hand side of the assignment, we used
"any" as the dimension specifier, so we’re applying this rule for “any” value of
rn. In order to access a specific
rnvalue, we’ll simply write
cv(rn), or the “current value of rn”.
RULES of the
MODEL clause are allowed to span a recursive tree (although not a graph, so no cycles are allowed), where each cell can be defined based on a previous cell, which is again defined based on its predecessor. We’re doing this via
col2[cv(rn) - 1], where
cv(rn) - 1means the “current row number minus one”.
Easy, right? Granted. The syntax isn’t straight-forward and we’re only scratching the surface of what’s possible with
SQL provides cool ways to implementing data-driven, declarative specifications of what your data should be like. The
MODEL clause is a bit eerie, but at the same time extremely powerful. Much easier and also a bit faster are window functions, a tool that should be in the tool chain of every developer working with SQL.
In this article, we’ve shown how to fill gaps in sparse data using window functions or
MODEL. A similar use-case are running totals. If this article has triggered your interest, I suggest reading about different approaches of calculating a running total in SQL.
Published at DZone with permission of Lukas Eder. See the original article here.
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