You want to find the largest row in each group of rows. An example is looking for the largest city in each state. While it is easy to find the MAX(population) ... GROUP BY state, it is hard to find the name of the city associated with that population. Alas, MySQL and MariaDB do not have any syntax to provide the solution directly.
This article is under construction, mostly for cleanup. The content is reasonably accurate during construction.
The article presents two "good" solutions. They differ in ways that make neither of them 'perfect'; you should try both and weigh the pros and cons.
Also, a few "bad" solutions will be presented, together with why they were rejected.
MySQL manual gives 3 solutions; only the "Uncorrelated" one is "good", the other two are "bad".
To show how the various coding attempts work, I have devised this simple task: Find the largest city in each Canadian province. Here's a sample of the source data (5493 rows):
Here's the desired output (13 rows):
One thing to consider is whether you want -- or do not want -- to see multiple rows for tied winners. For the dataset being used here, that would imply that the two largest cities in a province had identical populations. For this case, a duplicate would be unlikely. But there are many groupwise-max use cases where duplictes are likely.
The two best algorithms differ in whether they show duplicates.
Characteristics:
Superior performance or medium performance
It will show duplicates
Needs an extra index
Probably requires 5.6
An 'uncorrelated subquery':
But this also 'requires' an extra index: INDEX(province, population). In addition, MySQL has not always been able to use that index effectively, hence the "requires 5.6". (I am not sure of the actual version.)
Without that extra index, you would need 5.6, which has the ability to create indexes for subqueries. This is indicated by <auto_key0> in the EXPLAIN. Even so, the performance is worse with the auto-generated index than with the manually generated one.
With neither the extra index, nor 5.6, this 'solution' would belong in 'The Duds' because it would run in O(N*N) time.
Characteristics:
Good performance
Does not show duplicates (picks one to show)
Consistent O(N) run time (N = number of input rows)
Only one scan of the data
For your application, change the lines with comments.
*** 'correlated subquery' (from MySQL doc):**
O(N*N) (that is, terrible) performance
*** LEFT JOIN (from MySQL doc):**
Medium performance (2N-3N, depending on join_buffer_size).
For O(N*N) time,... It will take one second to do a groupwise-max on a few thousand rows; a million rows could take hours.
This is a variant on "groupwise-max" wherein you desire the largest (or smallest) N items in each group. Do these substitutions for your use case:
province --> your 'GROUP BY'
Canada --> your table
3 --> how many of each group to show
population --> your numeric field for determining "Top-N"
Output:
The performance of this is O(N), actually about 3N, where N is the number of source rows.
EXPLAIN EXTENDED gives
Explanation, shown in the same order as the EXPLAIN, but numbered chronologically: 3. Get the subquery id=2 (init) 4. Scan the output from subquery id=3 (x) 2. Subquery id=3 -- the table scan of Canada
Subquery id=2 -- init for simply initializing the two @variables
Yes, it took two sorts, though probably in RAM.
Main Handler values:
This variant is faster than the previous, but depends on city being unique across the dataset. (from openark.org)
Output. Note how there can be more than 3 cities per province:
Main Handler values:
(This does not need your table to be MyISAM, but it does need MyISAM tmp table for its 2-column PRIMARY KEY feature.) See previous section for what changes to make for your use case.
The main handler values (total of all operations):
Both "Top-n" formulations probably take about the same amount of time.
Hot off the press from Percona Live... has "windowing functions", which make "groupwise max" much more straightforward.
The code: TBD
Developed a first posted, Feb, 2015; Add MyISAM approach: July, 2015; Openark's method added: Apr, 2016; Windowing: Apr 2016
I did not include the technique(s) using GROUP_CONCAT. They are useful in some situations with small datasets. They can be found in the references below.
This has some of these algorithms, plus some others:
Other references:
Rick James graciously allowed us to use this article in the documentation.
has other useful tips, how-tos, optimizations, and debugging tips.
Original source:
This page is licensed: CC BY-SA / Gnu FDL
Change the SELECT and ORDER BY if you desire
DESC to get the 'largest'; ASC for the 'smallest'
Adding a large LIMIT to a subquery may make things work.
+------------------+----------------+------------+
| province | city | population |
+------------------+----------------+------------+
| Saskatchewan | Rosetown | 2309 |
| British Columbia | Chilliwack | 51942 |
| Nova Scotia | Yarmouth | 7500 |
| Alberta | Grande Prairie | 41463 |
| Quebec | Sorel | 33591 |
| Ontario | Moose Factory | 2060 |
| Ontario | Bracebridge | 8238 |
| British Columbia | Nanaimo | 84906 |
| Manitoba | Neepawa | 3151 |
| Alberta | Grimshaw | 2560 |
| Saskatchewan | Carnduff | 950 |
...+---------------------------+---------------+------------+
| province | city | population |
+---------------------------+---------------+------------+
| Alberta | Calgary | 968475 |
| British Columbia | Vancouver | 1837970 |
| Manitoba | Winnipeg | 632069 |
| New Brunswick | Saint John | 87857 |
| Newfoundland and Labrador | Corner Brook | 18693 |
| Northwest Territories | Yellowknife | 15866 |
| Nova Scotia | Halifax | 266012 |
| Nunavut | Iqaluit | 6124 |
| Ontario | Toronto | 4612187 |
| Prince Edward Island | Charlottetown | 42403 |
| Quebec | Montreal | 3268513 |
| Saskatchewan | Saskatoon | 198957 |
| Yukon | Whitehorse | 19616 |
+---------------------------+---------------+------------+SELECT c1.province, c1.city, c1.population
FROM Canada AS c1
JOIN
( SELECT province, MAX(population) AS population
FROM Canada
GROUP BY province
) AS c2 USING (province, population)
ORDER BY c1.province;SELECT
province, city, population -- The desired columns
FROM
( SELECT @prev := '' ) init
JOIN
( SELECT province != @prev AS first, -- `province` is the 'GROUP BY'
@prev := province, -- The 'GROUP BY'
province, city, population -- Also the desired columns
FROM Canada -- The table
ORDER BY
province, -- The 'GROUP BY'
population DESC -- ASC for MIN(population), DESC for MAX
) x
WHERE first
ORDER BY province; -- Whatever you likeSELECT province, city, population
FROM Canada AS c1
WHERE population =
( SELECT MAX(c2.population)
FROM Canada AS c2
WHERE c2.province= c1.province
)
ORDER BY province;SELECT c1.province, c1.city, c1.population
FROM Canada AS c1
LEFT JOIN Canada AS c2 ON c2.province = c1.province
AND c2.population > c1.population
WHERE c2.province IS NULL
ORDER BY province;SELECT
province, n, city, population
FROM
( SELECT @prev := '', @n := 0 ) init
JOIN
( SELECT @n := if(province != @prev, 1, @n + 1) AS n,
@prev := province,
province, city, population
FROM Canada
ORDER BY
province ASC,
population DESC
) x
WHERE n <= 3
ORDER BY province, n;+---------------------------+------+------------------+------------+
| province | n | city | population |
+---------------------------+------+------------------+------------+
| Alberta | 1 | Calgary | 968475 |
| Alberta | 2 | Edmonton | 822319 |
| Alberta | 3 | Red Deer | 73595 |
| British Columbia | 1 | Vancouver | 1837970 |
| British Columbia | 2 | Victoria | 289625 |
| British Columbia | 3 | Abbotsford | 151685 |
| Manitoba | 1 | ...+----+-------------+------------+--------+---------------+------+---------+------+------+----------+----------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+------------+--------+---------------+------+---------+------+------+----------+----------------+
| 1 | PRIMARY | <derived2> | system | NULL | NULL | NULL | NULL | 1 | 100.00 | Using filesort |
| 1 | PRIMARY | <derived3> | ALL | NULL | NULL | NULL | NULL | 5484 | 100.00 | Using where |
| 3 | DERIVED | Canada | ALL | NULL | NULL | NULL | NULL | 5484 | 100.00 | Using filesort |
| 2 | DERIVED | NULL | NULL | NULL | NULL | NULL | NULL | NULL | NULL | No tables used |
+----+-------------+------------+--------+---------------+------+---------+------+------+----------+----------------+| Handler_read_rnd | 39 |
| Handler_read_rnd_next | 10971 |
| Handler_write | 5485 | -- #rows in Canada (+1)SELECT province, city, population
FROM Canada
JOIN
( SELECT GROUP_CONCAT(top_in_province) AS top_cities
FROM
( SELECT SUBSTRING_INDEX(
GROUP_CONCAT(city ORDER BY population DESC),
',', 3) AS top_in_province
FROM Canada
GROUP BY province
) AS x
) AS y
WHERE FIND_IN_SET(city, top_cities)
ORDER BY province, population DESC;| Alberta | Calgary | 968475 |
| Alberta | Edmonton | 822319 |
| Alberta | Red Deer | 73595 |
| British Columbia | Vancouver | 1837970 |
| British Columbia | Victoria | 289625 |
| British Columbia | Abbotsford | 151685 |
| British Columbia | Sydney | 0 | -- Nova Scotia's second largest is Sydney
| Manitoba | Winnipeg | 632069 || Handler_read_next | 5484 | -- table size
| Handler_read_rnd_next | 5500 | -- table size + number of provinces
| Handler_write | 14 | -- number of provinces (+1)-- build tmp table to get numbering
-- (Assumes auto_increment_increment = 1)
CREATE TEMPORARY TABLE t (
nth MEDIUMINT UNSIGNED NOT NULL AUTO_INCREMENT,
PRIMARY KEY(province, nth)
) ENGINE=MyISAM
SELECT province, NULL AS nth, city, population
FROM Canada
ORDER BY population DESC;
-- Output the biggest 3 cities in each province:
SELECT province, nth, city, population
FROM t
WHERE nth <= 3
ORDER BY province, nth;
+---------------------------+-----+------------------+------------+
| province | nth | city | population |
+---------------------------+-----+------------------+------------+
| Alberta | 1 | Calgary | 968475 |
| Alberta | 2 | Edmonton | 822319 |
| Alberta | 3 | Red Deer | 73595 |
| British Columbia | 1 | Vancouver | 1837970 |
| British Columbia | 2 | Victoria | 289625 |
| British Columbia | 3 | Abbotsford | 151685 |
| Manitoba | ...
SELECT FOR CREATE:
+----+-------------+--------+------+---------------+------+---------+------+------+----------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+--------+------+---------------+------+---------+------+------+----------------+
| 1 | SIMPLE | Canada | ALL | NULL | NULL | NULL | NULL | 5484 | Using filesort |
+----+-------------+--------+------+---------------+------+---------+------+------+----------------+
Other SELECT:
+----+-------------+-------+-------+---------------+---------+---------+------+------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+-------+---------------+---------+---------+------+------+-------------+
| 1 | SIMPLE | t | index | NULL | PRIMARY | 104 | NULL | 22 | Using where |
+----+-------------+-------+-------+---------------+---------+---------+------+------+-------------+| Handler_read_rnd_next | 10970 |
| Handler_write | 5484 | -- number of rows in Canada (write tmp table)