At a customer site, I’ve recently encountered a report where a programmer needed to count quite a bit of stuff from a single table. The counts all differed in the way they used specific predicates. The report looked roughly like this (as always, I’m using the Sakila database for illustration):
-- Total number of films SELECT count(*) FROM film -- Number of films with a given length SELECT count(*) FROM film WHERE length BETWEEN 120 AND 150 -- Number of films with a given language SELECT count(*) FROM film WHERE language_id = 1 -- Number of films for a given rating SELECT count(*) FROM film WHERE rating = 'PG'
And then, unsurprisingly, combinations of these predicates were needed as well, i.e.
-- Number of films with a given length / language_id SELECT count(*) FROM film WHERE length BETWEEN 120 AND 150 AND language_id = 1 -- Number of films with a given length / rating SELECT count(*) FROM film WHERE length BETWEEN 120 AND 150 AND rating = 'PG' -- Number of films with a given language_id / rating SELECT count(*) FROM film WHERE language_id = 1 AND rating = 'PG' -- Number of films with a given length / language_id / rating SELECT count(*) FROM film WHERE length BETWEEN 120 AND 150 AND language_id = 1 AND rating = 'PG'
In the end, there were 32 queries in total (or 8 in my example) with all the possible combinations of predicates. Needless to say that running them all took quite a while, because the table had around 200M records and only one predicate could profit from an index.
But in fact, the improvement is really easy. There are several options to calculate all these counts in a single query
Simplest solution works in all databases: Filtered aggregate functions (or manual pivot)
This solution allows for calculating all results in a single query by using 8 different, explicit, filtered aggregate functions and no GROUP BY clause (none in this example. More complex cases where GROUP BY persists are sill imaginable).
This is how it works on all databases:
SELECT
count(*),
count(length),
count(language_id),
count(rating),
count(length + language_id),
count(length + rating),
count(language_id + rating),
count(length + language_id + rating)
FROM (
SELECT
CASE WHEN length BETWEEN 120 AND 150 THEN 1 END length,
CASE WHEN language_id = 1 THEN 1 END language_id,
CASE WHEN rating = 'PG' THEN 1 END rating
FROM film
) film
Which yields:
col1 col2 col3 col4 col5 col6 col7 col8 1000 224 1000 194 224 43 194 43
How to read the above query?
Instead of evaluating the three different predicates in a WHERE clause, we pre-calculate it in a derived table (subquery in the FROM clause) and translate the predicate in some random value (e.g. 1) if TRUE and NULL if FALSE. Note, I omitted the ELSE clause from the CASE expression, which means that we get NULLs per default. Running the nested select on its own…
SELECT CASE WHEN length BETWEEN 120 AND 150 THEN 1 END length, CASE WHEN language_id = 1 THEN 1 END language_id, CASE WHEN rating = 'PG' THEN 1 END rating FROM film
… yields something along the lines of:
length language_id rating --------------------------- NULL 1 1 NULL 1 NULL NULL 1 NULL NULL 1 NULL 1 1 NULL NULL 1 1 NULL 1 NULL ...
(Note, of course, we could have used actual BOOLEAN types, e.g. in PostgreSQL, but that wouldn’t work on all databases)
Now, in the outer query, we’re using once COUNT(*), which simply counts all the rows regardless of any predicates in the CASE expressions. The other COUNT(expr) aggregate functions do something that surprisingly few people are aware of (yet a lot of people use this form “by accident”). They count only the number of non-NULL rows. For instance:
SELECT
...
count(length),
...
FROM (
SELECT
CASE WHEN length BETWEEN 120 AND 150 THEN 1 END length,
...
FROM film
) film
Or also:
SELECT count(CASE WHEN length BETWEEN 120 AND 150 THEN 1 END) FROM film
These queries will count those films whose length is BETWEEN 120 AND 150 (because those rows produce the value 1, which is non-NULL, and thus counted), whereas all the other films are not being counted.
Finally, I just used a trick to combine nullable values to make sure they’re all non-NULL:
SELECT
...
count(length + language_id),
FROM (
SELECT
CASE WHEN length BETWEEN 120 AND 150 THEN 1 END length,
CASE WHEN language_id = 1 THEN 1 END language_id,
...
FROM film
) film
This counts those rows whose length BETWEEN 120 AND 150 and whose language_id = 1, because if either predicate was FALSE, the number would be NULL and thus the sum is NULL as well.
PostgreSQL and HSQLDB variant: FILTER
In PostgreSQL and HSQLDB (and in the SQL standard), there’s a special syntax for this. We can use the FILTER clause instead of encoding values in NULL / non-NULL like this:
SELECT
count(*),
count(*) FILTER (WHERE length IS NOT NULL),
count(*) FILTER (WHERE language_id IS NOT NULL),
count(*) FILTER (WHERE rating IS NOT NULL),
count(*) FILTER (WHERE length + language_id IS NOT NULL),
count(*) FILTER (WHERE length + rating IS NOT NULL),
count(*) FILTER (WHERE language_id + rating IS NOT NULL),
count(*) FILTER (
WHERE length + language_id + rating IS NOT NULL)
FROM (
SELECT
CASE WHEN length BETWEEN 120 AND 150 THEN 1 END length,
CASE WHEN language_id = 1 THEN 1 END language_id,
CASE WHEN rating = 'PG' THEN 1 END rating
FROM film
) film
Or even, writing out the entire predicates again:
SELECT
count(*),
count(*) FILTER (WHERE length BETWEEN 120 AND 150),
count(*) FILTER (WHERE language_id = 1),
count(*) FILTER (WHERE rating = 'PG'),
count(*) FILTER (
WHERE length BETWEEN 120 AND 150 AND language_id = 1),
count(*) FILTER (
WHERE length BETWEEN 120 AND 150 AND rating = 'PG'),
count(*) FILTER (
WHERE language_id = 1 AND rating = 'PG'),
count(*) FILTER (
WHERE length BETWEEN 120 AND 150
AND language_id = 1 AND rating = 'PG')
FROM film
Usually, the FILTER clause is more convenient, but both approaches are equivalent, and we’re running only a single query!
I also call this “manual PIVOT“, because it really works like a PIVOT table. And the good news is… There is a PIVOT syntax!
A more fancy solution: PIVOT
This solution is vendor-specific and only works in Oracle and with a bit less features in SQL Server. Here’s the Oracle version:
SELECT
a + b + c + d + e + f + g + h,
e + f + g + h,
c + d + g + h,
b + d + f + h,
g + h,
f + h,
d + h,
h
FROM (
SELECT
CASE WHEN length BETWEEN 120 AND 150
THEN 1 ELSE 0 END length,
CASE WHEN language_id = 1
THEN 1 ELSE 0 END language_id,
CASE WHEN rating = 'PG'
THEN 1 ELSE 0 END rating
FROM film
) film
PIVOT (
count(*) FOR (length, language_id, rating) IN (
(0, 0, 0) AS a,
(0, 0, 1) AS b,
(0, 1, 0) AS c,
(0, 1, 1) AS d,
(1, 0, 0) AS e,
(1, 0, 1) AS f,
(1, 1, 0) AS g,
(1, 1, 1) AS h
)
)
How to read this solution? There are 3 steps:
Step 1: The derived table
As in the previous example, we’re translating the desired predicates for our report into three columns that produce values 1 and 0. That’s understood so I won’t repeat the explanation.
Step 2: The PIVOT clause
The PIVOT clause can be applied to a table expression to “pivot” it in a similar way as we’re used from Microsoft Excel’s powerful pivot tables. It takes three parts:
- A list of aggregate functions
- An expression (
FORclause) - A list of expected values (
INclause)
The resulting table expression groups the PIVOT‘s input table by all the remaining columns (i.e. all the columns that are not part of the FOR clause, in our example, that’s no columns), and aggregates all the aggregate functions (in our case, only one) for all the values in the IN list.
If we SELECT * from this PIVOT table:
SELECT *
FROM (
SELECT
CASE WHEN length BETWEEN 120 AND 150
THEN 1 ELSE 0 END length,
CASE WHEN language_id = 1
THEN 1 ELSE 0 END language_id,
CASE WHEN rating = 'PG'
THEN 1 ELSE 0 END rating
FROM film
) film
PIVOT (
count(*) FOR (length, language_id, rating) IN (
(0, 0, 0) AS a,
(0, 0, 1) AS b,
(0, 1, 0) AS c,
(0, 1, 1) AS d,
(1, 0, 0) AS e,
(1, 0, 1) AS f,
(1, 1, 0) AS g,
(1, 1, 1) AS h
)
)
… we’ll get these values:
a b c d e f g h ------------------------------------ 0 0 625 151 0 0 181 43
As you can see, the column names are generated from the IN list of expected values and the values contained in these columns are aggregations for the different predicates. These aggregations are not exactly the ones we wanted. For instance, column G is all the films whose length BETWEEN 120 AND 150 and whose language_id = 1 and whose RATING != 'PG'.
Step 3: Summing the count values
So, in order to get the expected results, we have to sum all the partial counts as such:
SELECT
a + b + c + d + e + f + g + h,
e + f + g + h,
c + d + g + h,
b + d + f + h,
g + h,
f + h,
d + h,
h
FROM
...
The result is now the same.
A more fancy solution: GROUPING SETS
GROUPING SETS are a SQL standard and they’re supported in at least:
- DB2
- HANA
- Oracle
- PostgreSQL
- SQL Server
- Sybase SQL Anywhere
Simply put, GROUPING SETS allow for grouping a table several times and creating a UNION of all the results. For example, the following two queries are the same, conceptually, although the GROUPING SETS one is usually faster:
-- Grouping once by language_id, then by rating SELECT language_id, rating, count(*) FROM film GROUP BY GROUPING SETS ( (language_id), (rating) ) -- Grouping first by language_id SELECT language_id, NULL, count(*) FROM film GROUP BY language_id UNION ALL SELECT NULL, rating, count(*) FROM film GROUP BY rating
Both queries yield:
language_id rating count
1 1000 -- First grouping set / union subquery
G 178 \
PG 194 |
PG-13 223 | Second grouping set / union subquery
R 195 |
NC-17 210 /
Clearly, the GROUPING SETS variant is more concise. Let’s imagine, we’d like to add more combinations of grouping columns, e.g.
SELECT language_id, rating, count(*) FROM film GROUP BY GROUPING SETS ( (), (language_id), (rating), (language_id, rating) )
Now, we’re grouping by all the combinations of columns, and the result is:
language_id rating count
1000 -- First grouping set: ()
1 1000 -- Second grouping set: (language_id)
G 178 \
PG 194 |
PG-13 223 | Third grouping set: (rating)
R 195 |
NC-17 210 /
1 G 178 \
1 PG 194 |
1 PG-13 223 | Fourth grouping set: (language_id, rating)
1 R 195 |
1 NC-17 210 /
Of course, this would all be more impressive if we had more than one language in the system…
So, how do we solve the original problem with GROUPING SETS? Here’s how:
SELECT
GROUPING_ID (length, language_id, rating),
length,
language_id,
rating,
count(*)
FROM (
SELECT
CASE WHEN length BETWEEN 120 AND 150
THEN 1 ELSE 0 END length,
CASE WHEN language_id = 1
THEN 1 ELSE 0 END language_id,
CASE WHEN rating = 'PG'
THEN 1 ELSE 0 END rating
FROM film
) film
GROUP BY GROUPING SETS (
(),
(length),
(language_id),
(rating),
(length, language_id),
(length, rating),
(rating, language_id),
(length, language_id, rating)
)
HAVING COALESCE (length, 1) != 0
AND COALESCE (language_id, 1) != 0
AND COALESCE (rating, 1) != 0
ORDER BY GROUPING_ID (length, language_id, rating) DESC
Wow. How to read this? In 4 steps:
Step 1: Again, the derived table
This time, we’ll encode FALSE as 0, not NULL, because NULL already has a different meaning in GROUPING SETS. It means that for a given GROUPING SET, we didn’t group by that column. We’ll see that in step 3.
Step 2: The GROUPING SETS
In this section, we’re just listing all the possible combinations of GROUP BY columns that we want to use, which produces 8 distinct GROUPING SETS. I’ve already explained this in the previous introduction to GROUPING SETS, so this is no different.
Step 3: Filter out unwanted groupings
Just like in the PIVOT example, we’re also getting results for which the predicates are FALSE, but we don’t want those in the result. So we’re filtering them out in the HAVING clause:
SELECT ... HAVING COALESCE (length, 1) != 0 AND COALESCE (language_id, 1) != 0 AND COALESCE (rating, 1) != 0 ...
How to read this? E.g. LENGTH can be any of:
1: The length predicate wasTRUE0: The length predicate wasFALSENULL: The length column is not considered for a givenGROUPING SET, e.g.()or(rating, language_id)
So, using COALESCE, we’re making sure that we include only 1 and NULL lengths, not 0 lengths.
Step 4: Ordering the results
This is optional, but in order to get the same output order as before, we can use the special GROUPING_ID() (or GROUPING() depending on the DB) function which returns an ID for each GROUPING SET. The output is:
grouping length language_id rating count
------------------------------------------------
7 NULL NULL NULL 1000
6 NULL NULL 1 194
5 NULL 1 NULL 1000
4 NULL 1 1 194
3 1 NULL NULL 224
2 1 NULL 1 43
1 1 1 NULL 224
0 1 1 1 43
Excellent! And hey, there’s even syntax sugar for “special” GROUPING SETS configurations like ours, where we list all the possible column permutations. In this case, we can use CUBE()!
SELECT
GROUPING_ID (length, language_id, rating),
length,
language_id,
rating,
count(*)
FROM (
SELECT
CASE WHEN length BETWEEN 120 AND 150
THEN 1 ELSE 0 END length,
CASE WHEN language_id = 1
THEN 1 ELSE 0 END language_id,
CASE WHEN rating = 'PG'
THEN 1 ELSE 0 END rating
FROM film
) film
GROUP BY CUBE (length, language_id, rating)
HAVING COALESCE(length, 1) != 0
AND COALESCE(language_id, 1) != 0
AND COALESCE(rating, 1) != 0
ORDER BY GROUPING_ID (length, language_id, rating) DESC
Performance
Such a comparison blog post wouldn’t be complete if we wouldn’t benchmark for performance. This time, I’ll be benchmarking only for Oracle, as PostgreSQL doesn’t support PIVOT and SQL Server’s PIVOT is more limited than Oracle’s.
Here’s the complete benchmark:
SET SERVEROUTPUT ON
DECLARE
v_ts TIMESTAMP WITH TIME ZONE;
v_repeat CONSTANT NUMBER := 2000;
BEGIN
-- Repeat the whole benchmark several times to avoid warmup penalty
FOR r IN 1..5 LOOP
-- Individual statements
v_ts := SYSTIMESTAMP;
FOR i IN 1..v_repeat LOOP
FOR rec IN (
SELECT count(*) FROM film
) LOOP
NULL;
END LOOP;
FOR rec IN (
SELECT count(*) FROM film
WHERE length BETWEEN 120 AND 150
) LOOP
NULL;
END LOOP;
FOR rec IN (
SELECT count(*) FROM film
WHERE language_id = 1
) LOOP
NULL;
END LOOP;
FOR rec IN (
SELECT count(*) FROM film
WHERE rating = 'PG'
) LOOP
NULL;
END LOOP;
FOR rec IN (
SELECT count(*) FROM film
WHERE length BETWEEN 120 AND 150
AND language_id = 1
) LOOP
NULL;
END LOOP;
FOR rec IN (
SELECT count(*) FROM film
WHERE length BETWEEN 120 AND 150
AND rating = 'PG'
) LOOP
NULL;
END LOOP;
FOR rec IN (
SELECT count(*) FROM film
WHERE language_id = 1
AND rating = 'PG'
) LOOP
NULL;
END LOOP;
FOR rec IN (
SELECT count(*) FROM film
WHERE length BETWEEN 120 AND 150
AND language_id = 1
AND rating = 'PG'
) LOOP
NULL;
END LOOP;
END LOOP;
dbms_output.put_line('Run ' || r ||', Statement 2 : ' || (SYSTIMESTAMP - v_ts));
-- Manual PIVOT
v_ts := SYSTIMESTAMP;
FOR i IN 1..v_repeat LOOP
FOR rec IN (
SELECT
count(*),
count(length),
count(language_id),
count(rating),
count(length + language_id),
count(length + rating),
count(language_id + rating),
count(length + language_id + rating)
FROM (
SELECT
CASE WHEN length BETWEEN 120 AND 150 THEN 1 END length,
CASE WHEN language_id = 1 THEN 1 END language_id,
CASE WHEN rating = 'PG' THEN 1 END rating
FROM film
) film
) LOOP
NULL;
END LOOP;
END LOOP;
dbms_output.put_line('Run ' || r ||', Statement 2 : ' || (SYSTIMESTAMP - v_ts));
-- PIVOT
v_ts := SYSTIMESTAMP;
FOR i IN 1..v_repeat LOOP
FOR rec IN (
SELECT
a + b + c + d + e + f + g + h,
e + f + g + h,
c + d + g + h,
b + d + f + h,
g + h,
f + h,
d + h,
h
FROM (
SELECT
CASE WHEN length BETWEEN 120 AND 150 THEN 1 ELSE 0 END length,
CASE WHEN language_id = 1 THEN 1 ELSE 0 END language_id,
CASE WHEN rating = 'PG' THEN 1 ELSE 0 END rating
FROM film
) film
PIVOT (
count(*) FOR (length, language_id, rating) IN (
(0, 0, 0) AS a,
(0, 0, 1) AS b,
(0, 1, 0) AS c,
(0, 1, 1) AS d,
(1, 0, 0) AS e,
(1, 0, 1) AS f,
(1, 1, 0) AS g,
(1, 1, 1) AS h
)
)
) LOOP
NULL;
END LOOP;
END LOOP;
dbms_output.put_line('Run ' || r ||', Statement 3 : ' || (SYSTIMESTAMP - v_ts));
-- GROUPING SETS
v_ts := SYSTIMESTAMP;
FOR i IN 1..v_repeat LOOP
FOR rec IN (
SELECT
GROUPING_ID (length, language_id, rating),
length,
language_id,
rating,
count(*)
FROM (
SELECT
CASE WHEN length BETWEEN 120 AND 150 THEN 1 ELSE 0 END length,
CASE WHEN language_id = 1 THEN 1 ELSE 0 END language_id,
CASE WHEN rating = 'PG' THEN 1 ELSE 0 END rating
FROM film
) film
GROUP BY CUBE (length, language_id, rating)
HAVING COALESCE (length, 1) != 0
AND COALESCE (language_id, 1) != 0
AND COALESCE (rating, 1) != 0
ORDER BY GROUPING_ID (length, language_id, rating) DESC
) LOOP
NULL;
END LOOP;
END LOOP;
dbms_output.put_line('Run ' || r ||', Statement 4 : ' || (SYSTIMESTAMP - v_ts));
END LOOP;
END;
/
And the results:
Run 1, Statement 1 : +000000000 00:00:01.928497000 Run 1, Statement 2 : +000000000 00:00:01.136341000 Run 1, Statement 3 : +000000000 00:00:02.751679000 Run 1, Statement 4 : +000000000 00:00:00.797529000 Run 2, Statement 1 : +000000000 00:00:01.695543000 Run 2, Statement 2 : +000000000 00:00:01.004073000 Run 2, Statement 3 : +000000000 00:00:02.490895000 Run 2, Statement 4 : +000000000 00:00:00.838979000 Run 3, Statement 1 : +000000000 00:00:01.634047000 Run 3, Statement 2 : +000000000 00:00:01.016266000 Run 3, Statement 3 : +000000000 00:00:02.566895000 Run 3, Statement 4 : +000000000 00:00:00.790159000 Run 4, Statement 1 : +000000000 00:00:01.669844000 Run 4, Statement 2 : +000000000 00:00:01.015502000 Run 4, Statement 3 : +000000000 00:00:02.574646000 Run 4, Statement 4 : +000000000 00:00:00.807804000 Run 5, Statement 1 : +000000000 00:00:01.653498000 Run 5, Statement 2 : +000000000 00:00:00.980375000 Run 5, Statement 3 : +000000000 00:00:02.556186000 Run 5, Statement 4 : +000000000 00:00:00.890283000
Very disappointingly, the PIVOT solution is the slowest every time. I’m assuming there’s some substantial temporary object overhead which wouldn’t be as severe if the table were much larger, but clearly, the manual PIVOT solution (COUNT(CASE ...)) and the GROUPING SETS solution heavily outperform the initial attempt, where we calculate 8 counts individually.
To get back to the original report where 32 counts were calculated: The report was roughly 20x as fast with manual PIVOT on 200M rows and imagine if you need to JOIN – you definitely want to avoid those 32 individual queries and calculate everything in one go.
Cheers!
Filed under: sql Tagged: Aggregate Function, Analytics, COUNT, cube, grouping sets, OLAP, PIVOT, Reporting, sql, SQL Performance
