Exotic GROUP BY In MySQL - mysql

Question

Consider a typical GROUP BY statement in SQL: you have a table like
+------+-------+
| Name | Value |
+------+-------+
| A | 1 |
| B | 2 |
| A | 3 |
| B | 4 |
+------+-------+
And you ask for
SELECT Name, SUM(Value) as Value
FROM table
GROUP BY Name
You'll receive
+------+-------+
| Name | Value |
+------+-------+
| A | 4 |
| B | 6 |
+------+-------+
In your head, you can imagine that SQL generates an intermediate sorted table like
+------+-------+
| Name | Value |
+------+-------+
| A | 1 |
| A | 3 |
| B | 2 |
| B | 4 |
+------+-------+
and then aggregates together successive rows: the "Value" column has been given an aggregator (in this case SUM), so it's easy to aggregate. The "Name" column has been given no aggregator, and thus uses what you might call the "trivial partial aggregator": given two things that are the same (e.g. A and A), it aggregates them into a single copy of one of the inputs (in this case A). Given any other input it doesn't know what to do and is forced to begin aggregating anew (this time with the "Name" column equal to B).
I want to do a more exotic kind of aggregation. My table looks like
+------+-------+
| Name | Value |
+------+-------+
| A | 1 |
| BC | 2 |
| AY | 3 |
| AZ | 4 |
| B | 5 |
| BCR | 6 |
+------+-------+
And the intended output is
+------+-------+
| Name | Value |
+------+-------+
| A | 8 |
| B | 13 |
+------+-------+
Where does this come from? A and B are the "minimal prefixes" for this set of names: they occur in the data set and every Name has exactly one of them as a prefix. I want to aggregate data by grouping rows together when their Names have the same minimal prefix (and add the Values, of course).
In the toy grouping model from before, the intermediate sorted table would be
+------+-------+
| Name | Value |
+------+-------+
| A | 1 |
| AY | 3 |
| AZ | 4 |
| B | 5 |
| BC | 2 |
| BCR | 6 |
+------+-------+
Instead of using the "trivial partial aggregator" for Names, we would use one that can aggregate X and Y together iff X is a prefix of Y; in that case it returns X. So the first three rows would be aggregated together into a row with (Name, Value) = (A, 8), then the aggregator would see that A and B couldn't be aggregated and would move on to a new "block" of rows to aggregate.
The tricky thing is that the value we're grouping by is "non-local": if A were not a name in the dataset, then AY and AZ would each be a minimal prefix. It turns out that the AY and AZ rows are aggregated into the same row in the final output, but you couldn't know that just by looking at them in isolation.
Miraculously, in my use case the minimal prefix of a string can be determined without reference to anything else in the dataset. (Imagine that each of my names is one of the strings "hello", "world", and "bar", followed by any number of z's. I want to group all of the Names with the same "base" word together.)
As I see it I have two options:
1) The simple option: compute the prefix for each row and group by that value directly. Unfortunately I have an index on the Name, and computing the minimal prefix (whose length depends on the Name itself) prevents me from using that index. This forces a full table scan, which is prohibitively slow.
2) The complicated option: somehow convince MySQL to use the "partial prefix aggregator" for Name. This runs into the "non-locality" problem above, but that's fine as long as we scan the table according to my index on Name, since then every minimal prefix will be encountered before any of the other strings it is a prefix of; we would never try to aggregate AY and AZ together if A were in the dataset.
In a declarative programming language #2 would be rather easy: extract rows one at a time, in alphabetical order, keeping track of the current prefix. If your new row's Name has that as a prefix, it goes in the bucket you're currently using. Otherwise, start a new bucket with that as your prefix. In MySQL I am lost as to how to do it. Note that the set of minimal prefixes is not known beforehand.

Answer 1

Edit 2
It occurred to me that if the table is ordered by Name, this would be a lot easier (and faster). Since I don't know if your data is sorted, I've included a sort in this query, but if the data is sorted, you can strip out (SELECT * FROM table1 ORDER BY Name) t1 and just use FROM table1
SELECT prefix, SUM(`Value`)
FROM (SELECT Name, Value, #prefix:=IF(Name NOT LIKE CONCAT(#prefix, '_%'), Name, #prefix) AS prefix
FROM (SELECT * FROM table1 ORDER BY Name) t1
JOIN (SELECT #prefix := '~') p
) t2
GROUP BY prefix
Updated SQLFiddle
Edit
Having slept on the problem, I realised that there is no need to do the IN, it's enough to just have a WHERE NOT EXISTS clause on the JOINed table:
SELECT t1.Name, SUM(t2.Value) AS `Value`
FROM table1 t1
JOIN table1 t2 ON t2.Name LIKE CONCAT(t1.Name, '%')
WHERE NOT EXISTS (SELECT *
FROM table1 t3
WHERE t1.Name LIKE CONCAT(t3.Name, '_%')
)
GROUP BY t1.Name
Updated Explain (Name changed to UNIQUE key from PRIMARY)
id select_type table type possible_keys key key_len ref rows Extra
1 PRIMARY t1 index Name Name 11 NULL 6 Using where; Using index; Using temporary; Using filesort
1 PRIMARY t2 ALL NULL NULL NULL NULL 6 Using where; Using join buffer (Block Nested Loop)
3 DEPENDENT SUBQUERY t3 index NULL Name 11 NULL 6 Using where; Using index
Updated SQLFiddle
Original Answer
Here is one way you could do it. First, you need to find all the unique prefixes in your table. You can do that by looking for all values of Name where it does not look like another value of Name with other characters on the end. This can be done with this query:
SELECT Name
FROM table1 t1
WHERE NOT EXISTS (SELECT *
FROM table1 t2
WHERE t1.Name LIKE CONCAT(t2.Name, '_%')
)
For your sample data, that will give
Name
A
B
Now you can sum all the values where the Name starts with one of those prefixes. Note we change the LIKE pattern in this query so that it also matches the prefix, otherwise we wouldn't count the values for A and B in your example:
SELECT t1.Name, SUM(t2.Value) AS `Value`
FROM table1 t1
JOIN table1 t2 ON t2.Name LIKE CONCAT(t1.Name, '%')
WHERE t1.Name IN (SELECT Name
FROM table1 t3
WHERE NOT EXISTS (SELECT *
FROM table1 t4
WHERE t3.Name LIKE CONCAT(t4.Name, '_%')
)
)
GROUP BY t1.Name
Output:
Name Value
A 8
B 13
An EXPLAIN says that both of these queries use the index on Name, so should be reasonably efficient. Here is the result of the explain on my MySQL 5.6 server:
id select_type table type possible_keys key key_len ref rows Extra
1 PRIMARY t1 index PRIMARY PRIMARY 11 NULL 6 Using index; Using temporary; Using filesort
1 PRIMARY t3 eq_ref PRIMARY PRIMARY 11 test.t1.Name 1 Using where; Using index
1 PRIMARY t2 ALL NULL NULL NULL NULL 6 Using where; Using join buffer (Block Nested Loop)
3 DEPENDENT SUBQUERY t4 index NULL PRIMARY 11 NULL 6 Using where; Using index
SQLFiddle Demo

Answer 2

Here are some hints on how to do the task. This locates any prefixes that are useful. That's not what you asked for, but the flow of the query and the usage of #variables, plus the need for 2 (actually 3) levels of nesting, might help you.
SELECT DISTINCT `Prev`
FROM
(
SELECT #prev := #next AS 'Prev',
#next := IF(LEFT(city, LENGTH(#prev)) = #prev, #next, city) AS 'Next'
FROM ( SELECT #next := ' ' ) AS init
JOIN ( SELECT DISTINCT city FROM us ) AS dedup
ORDER BY city
) x
WHERE `Prev` = `Next` ;
Partial output:
+----------------+
| Prev |
+----------------+
| Alamo |
| Allen |
| Altamont |
| Ames |
| Amherst |
| Anderson |
| Arlington |
| Arroyo |
| Auburn |
| Austin |
| Avon |
| Baker |
Check the Al% cities:
mysql> SELECT DISTINCT city FROM us WHERE city LIKE 'Al%' ORDER BY city;
+-------------------+
| city |
+-------------------+
| Alabaster |
| Alameda |
| Alamo | <--
| Alamogordo | <--
| Alamosa |
| Albany |
| Albemarle |
...
| Alhambra |
| Alice |
| Aliquippa |
| Aliso Viejo |
| Allen | <--
| Allen Park | <--
| Allentown | <--
| Alliance |
| Allouez |
| Alma |
| Aloha |
| Alondra Park |
| Alpena |
| Alpharetta |
| Alpine |
| Alsip |
| Altadena |
| Altamont | <--
| Altamonte Springs | <--
| Alton |
| Altoona |
| Altus |
| Alvin |
+-------------------+
40 rows in set (0.01 sec)