MySQL InnoDB indexes slowing down sorts - mysql

I am using MySQL 5.6 on FreeBSD and have just recently switched from using MyISAM tables to InnoDB to gain advances of foreign key constraints and transactions.
After the switch, I discovered that a query on a table with 100,000 rows that was previously taking .003 seconds, was now taking 3.6 seconds. The query looked like this:
SELECT *
-> FROM USERS u
-> JOIN MIGHT_FLOCK mf ON (u.USER_ID = mf.USER_ID)
-> WHERE u.STATUS = 'ACTIVE' AND u.ACCESS_ID >= 8 ORDER BY mf.STREAK DESC LIMIT 0,100
I noticed that if I removed the ORDER BY clause, the execution time dropped back down to .003 seconds, so the problem is obviously in the sorting.
I then discovered that if I added back the ORDER BY but removed indexes on the columns referred to in the query (STATUS and ACCESS_ID), the query execution time would take the normal .003 seconds.
Then I discovered that if I added back the indexes on the STATUS and ACCESS_ID columns, but used IGNORE INDEX (STATUS,ACCESS_ID), the query would still execute in the normal .003 seconds.
Is there something about InnoDB and sorting results when referencing an indexed column in a WHERE clause that I don't understand?
Or am I doing something wrong?
EXPLAIN for the slow query returns the following results:
+----+-------------+-------+--------+--------------------------+---------+---------+---------------------+-------+---------------------------------------------------------------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+--------+--------------------------+---------+---------+---------------------+-------+---------------------------------------------------------------------+
| 1 | SIMPLE | u | ref | PRIMARY,STATUS,ACCESS_ID | STATUS | 2 | const | 53902 | Using index condition; Using where; Using temporary; Using filesort |
| 1 | SIMPLE | mf | eq_ref | PRIMARY | PRIMARY | 4 | PRO_MIGHT.u.USER_ID | 1 | NULL |
+----+-------------+-------+--------+--------------------------+---------+---------+---------------------+-------+---------------------------------------------------------------------+
EXPLAIN for the fast query returns the following results:
+----+-------------+-------+--------+---------------+---------+---------+----------------------+------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------+--------+---------------+---------+---------+----------------------+------+-------------+
| 1 | SIMPLE | mf | index | PRIMARY | STREAK | 2 | NULL | 100 | NULL |
| 1 | SIMPLE | u | eq_ref | PRIMARY | PRIMARY | 4 | PRO_MIGHT.mf.USER_ID | 1 | Using where |
+----+-------------+-------+--------+---------------+---------+---------+----------------------+------+-------------+
Any help would be greatly appreciated.

In the slow case, MySQL is making an assumption that the index on STATUS will greatly limit the number of users it has to sort through. MySQL is wrong. Presumably most of your users are ACTIVE. MySQL is picking up 50k user rows, checking their ACCESS_ID, joining to MIGHT_FLOCK, sorting the results and taking the first 100 (out of 50k).
In the fast case, you have told MySQL it can't use either index on USERS. MySQL is using its next-best index, it is taking the first 100 rows from MIGHT_FLOCK using the STREAK index (which is already sorted), then joining to USERS and picking up the user rows, then checking that your users are ACTIVE and have an ACCESS_ID at or above 8. This is much faster because only 100 rows are read from disk (x2 for the two tables).
I would recommend:
drop the index on STATUS unless you frequently need to retrieve INACTIVE users (not ACTIVE users). This index is not helping you.
Read this question to understand why your sorts are so slow. You can probably tune InnoDB for better sort performance to prevent these kind of problems.
If you have very few users with ACCESS_ID at or above 8 you should see a dramatic improvement already. If not you might have to use STRAIGHT_JOIN in your select clause.
Example below:
SELECT *
FROM MIGHT_FLOCK mf
STRAIGHT_JOIN USERS u ON (u.USER_ID = mf.USER_ID)
WHERE u.STATUS = 'ACTIVE' AND u.ACCESS_ID >= 8 ORDER BY mf.STREAK DESC LIMIT 0,100
STRAIGHT_JOIN forces MySQL to access the MIGHT_FLOCK table before the USERS table based on the order in which you specify those two tables in the query.
To answer the question "Why did the behaviour change" you should start by understanding the statistics that MySQL keeps on each index: http://dev.mysql.com/doc/refman/5.6/en/myisam-index-statistics.html. If statistics are not up to date or if InnoDB is not providing sufficient information to MySQL, the query optimiser can (and does) make stupid decisions about how to join tables.

Related

MySQL returning only part of results from FULL TEXT SEARCH after version 5.6 to 5.7 upgrade

I have a query that consists of two full text searches in boolean mode (combined with OR operator) that worked just fine on MySQL 5.6 and that fails after bumping MySQL version 5.7. Both DBs have the exact same set of records, both are hosted on AWS (InnoDB, aurora).
Query below (don't pay too much attention to the table/column names as I tried to anonymise them):
SELECT
cars.id
FROM cars
INNER JOIN driver_licenses ON driver_licenses.car_id = cars.id
INNER JOIN drivers ON drivers.id = driver_licenses.driver_id AND drivers.noobie_driver = 0
WHERE (
(MATCH(cars.name) AGAINST ('mark*' IN BOOLEAN MODE))
OR (MATCH(drivers.first_name, drivers.last_name, drivers.email) AGAINST ('mark*' IN BOOLEAN MODE))
);
Of course I have the fulltext index on the [first_name, last_name, email] columns, as well a btree index on noobie_driver. There are two indices on cars.name - one btree and the other one fulltext.
Before the upgrade, query returned proper results (counted in hundreds compared to a few million records in total).
After the upgrade - it seems that the query/optimizer focuses only on the first condition and completely disregards the second full text search (by driver's names and email) and returns only few records - related directly to the search result of cars.name.
When queries are ran separately (first time for cars.name and then for drivers details) and then combined, they return same results as before the upgrade.
Also when I force to ignore noobie_driver index (or remove the noobie_driver condition), both full text search conditions are taken into consideration.
Running EXPLAIN in both DBs return the same results.
+----+-------------+---------------------------+------------+--------+------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+-----------------------------------------------------+------+----------+-------------+
| id | select_type | table | partitions | type | possible_keys | key | key_len | ref | rows | filtered | Extra |
+----+-------------+---------------------------+------------+--------+------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+-----------------------------------------------------+------+----------+-------------+
| 1 | SIMPLE | drivers | NULL | ref | PRIMARY,index_drivers_on_noobie_driver | index_drivers_on_noobie_driver | 1 | const | 6798 | 100.00 | NULL |
| 1 | SIMPLE | driver_licenses | NULL | ref | index_driver_licenses_on_car_id,index_driver_licenses_on_driver_id | index_driver_licenses_on_driver_id | 5 | Rental.drivers.id | 1 | 100.00 | Using where |
| 1 | SIMPLE | cars | NULL | eq_ref | PRIMARY | PRIMARY | 4 | Rental.driver_licenses.car_id | 1 | 100.00 | Using where |
+----+-------------+---------------------------+------------+--------+------------------------------------------------------------------------------------------------------+---------------------------------------------------+---------+-----------------------------------------------------+------+----------+-------------+
Tomorrow I'll be working on rebuilding the index/table(s) to see if that brings any changes to the behaviour on 5.7, once it's done I'll come back with more details. Running OPTIMIZE TABLE on all 3 tables haven't fixed anything here.
I'm wondering:
Have I missed something and it is a feature now in 5.7 now that it behaves this way?
How to overcome the issue and keep the exact same query (so without ignoring the index or performing two separate queries to combine the results afterwards)?
OK, dropping and recreating the index on noobie_driver column seems to do the trick on smoke-environment database that contains just few thousands records in the drivers table
DROP INDEX index_drivers_on_noobie_driver ON drivers;
CREATE INDEX index_drivers_on_noobie_driver USING BTREE ON drivers(noobie_driver);
BUT with production data that handles ~2kk records in the drivers" table, dropping and recreating an index did not help. I'm starting to believe it could be related to some bug strictly related to MySQL version.
Will be updating the question once I learn something new

Addition of GROUP BY to simple query makes it 1000 slower

I am using test DB from https://github.com/datacharmer/test_db. It has a moderate size of 160 Mb. To run queries I use MySQL Workbench.
Following code runs in 0.015s
SELECT *
FROM employees INNER JOIN salaries ON employees.emp_no = salaries.emp_no
The similar code with GROUP BY added runs for 15.0s
SELECT AVG(salary), gender
FROM employees INNER JOIN salaries ON employees.emp_no = salaries.emp_no
GROUP BY gender
I checked the execution plan for both queries and found that in both cases query cost is similar and is about 600k. I should add that the employee table has 300K rows and the salary table is about 3M rows.
Can anyone suggest a reason why the difference in the execution time is soo huge? I need this explanation to understand the way SQL works better.
Problem solution: As I found due to comments and answers the problem was related to me not noticing that in the case of the first query my IDE was limiting result to 1000 rows. That's how I got 0.015s. In reality, it takes 10.0s to make a join in my case. If the index for gender is created (indices for employees.emp_no and salaries.emp_no already exist in this DB) it takes 10.0s to make join and group by. Without index for gender second query takes 18.0s.
The EXPLAIN for the first query shows that it does a table-scan (type=ALL) of 300K rows from employees, and for each one, does a partial primary key (type=ref) lookup to 1 row (estimated) in salaries.
mysql> explain SELECT * FROM employees
INNER JOIN salaries ON employees.emp_no = salaries.emp_no;
+----+-------------+-----------+------+---------------+---------+---------+----------------------------+--------+-------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-----------+------+---------------+---------+---------+----------------------------+--------+-------+
| 1 | SIMPLE | employees | ALL | PRIMARY | NULL | NULL | NULL | 299113 | NULL |
| 1 | SIMPLE | salaries | ref | PRIMARY | PRIMARY | 4 | employees.employees.emp_no | 1 | NULL |
+----+-------------+-----------+------+---------------+---------+---------+----------------------------+--------+-------+
The EXPLAIN for the second query (actually a sensible query to compute AVG() as you mentioned in your comment) shows something additional:
mysql> EXPLAIN SELECT employees.gender, AVG(salary) FROM employees
INNER JOIN salaries ON employees.emp_no = salaries.emp_no
GROUP BY employees.gender;
+----+-------------+-----------+------+---------------+---------+---------+----------------------------+--------+---------------------------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-----------+------+---------------+---------+---------+----------------------------+--------+---------------------------------+
| 1 | SIMPLE | employees | ALL | PRIMARY | NULL | NULL | NULL | 299113 | Using temporary; Using filesort |
| 1 | SIMPLE | salaries | ref | PRIMARY | PRIMARY | 4 | employees.employees.emp_no | 1 | NULL |
+----+-------------+-----------+------+---------------+---------+---------+----------------------------+--------+---------------------------------+
See the Using temporary; Using filesort in the Extra field? That means that the query has to build a temp table to accumulate the AVG() results per group. It has to use a temp table because MySQL can't know that it will scan all the rows for each gender together, so it must assume it will need to maintain running totals independently as it scans rows. It doesn't seem like that would be a big problem to track two (in this case) gender totals, but suppose it were postal code or something like that?
Creating a temp table is a pretty expensive operation. It means writing data, not only reading it as the first query does.
If we could make an index that orders by gender, then MySQL's optimizer would know it can scan all those rows with the same gender together. So it can calculate the running total of one gender at a time, then once it's done scanning one gender, calculate the AVG(salary) and then be guaranteed no further rows for that gender will be scanned. Therefore it can skip building a temp table.
This index helps:
mysql> alter table employees add index (gender, emp_no);
Now the EXPLAIN of the same query shows that it will do an index scan (type=index) which visits the same number of entries, but it'll scan in a more helpful order for the calculation of the aggregate AVG().
Same query, but no Using temporary note:
mysql> EXPLAIN SELECT employees.gender, AVG(salary) FROM employees
INNER JOIN salaries ON employees.emp_no = salaries.emp_no
GROUP BY employees.gender;
+----+-------------+-----------+-------+----------------+---------+---------+----------------------------+--------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-----------+-------+----------------+---------+---------+----------------------------+--------+-------------+
| 1 | SIMPLE | employees | index | PRIMARY,gender | gender | 5 | NULL | 299113 | Using index |
| 1 | SIMPLE | salaries | ref | PRIMARY | PRIMARY | 4 | employees.employees.emp_no | 1 | NULL |
+----+-------------+-----------+-------+----------------+---------+---------+----------------------------+--------+-------------+
And executing this query is a lot faster:
+--------+-------------+
| gender | AVG(salary) |
+--------+-------------+
| M | 63838.1769 |
| F | 63769.6032 |
+--------+-------------+
2 rows in set (1.06 sec)
The addition of the GROUP BY clause could easily explain the big performance drop that you are seeing.
From the documentation :
The most general way to satisfy a GROUP BY clause is to scan the whole table and create a new temporary table where all rows from each group are consecutive, and then use this temporary table to discover groups and apply aggregate functions (if any).
The additional cost incurred by the grouping process can be very expensive. Also, grouping happens even if no aggregate function is used.
If you don’t need an aggregate function, don’t group. If you do, ensure that you have a single index that references all grouped columns, as suggested by the documentation :
In some cases, MySQL is able to do much better than that and avoid creation of temporary tables by using index access.
PS : please note that « SELECT * ... GROUP BY »-like statements are not supported since MySQL 5.7.5 (unless you turn off option ONLY_FULL_GROUP_BY)
There is another reason as well as what GMB points out. Basically, you are probably looking at the timing of the first query until it returns the first row. I doubt it is returning all the rows in 0.015 seconds.
The second query with the GROUP BY needs to process all the data to derive the results.
If you added an ORDER BY (which requires processing all the data) to the first query, then you would see a similar performance drop.

MySQL has indexed tables and EXPLAIN looks good, but still not using index

I am trying to optimize a query, and all looks well when I got to "EXPLAIN" it, but it's still coming up in the "log_queries_not_using_index".
Here is the query:
SELECT t1.id_id,t1.change_id,t1.like_id,t1.dislike_id,t1.user_id,t1.from_id,t1.date_id,t1.type_id,t1.photo_id,t1.mobile_id,t1.mobiletype_id,t1.linked_id
FROM recent AS t1
LEFT JOIN users AS t2 ON t1.user_id = t2.id_id
WHERE t2.active_id=1 AND t1.postedacommenton_id='0' AND t1.type_id!='Friends'
ORDER BY t1.id_id DESC LIMIT 35;
So it grabs like a 'wallpost' data, and then I joined in the USERS table to make sure the user is still an active user (the number 1), and two small other "ANDs".
When I run this with the EXPLAIN in phpmyadmin it shows
id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra
1 | SIMPLE | t1 | index | user_id | PRIMARY | 4 | NULL | 35 | Using where
1 | SIMPLE | t2 | eq_ref | PRIMARY,active_id | PRIMARY | 4 | hnet_user_info.t1.user_id | 1 | Using where
It shows the t1 query found 35 rows using "WHERE", and the t2 query found 1 row (the user), using "WHERE"
So I can't figure out why it's showing up in the log_queries_not_using_index report.
Any tips? I can post more info if you need it.
tldr; ignore the "not using index warning". A query execution time of 1.3 milliseconds is not a problem; there is nothing to optimize here - look at the entire performance profile to find bottlenecks.
Trust the database engine. The database query planner will use indices when it determines that doing so is beneficial. In this case, due to the low cardinality estimates (35x1), the query planner decided that there was no reason to use indices for the actual execution plan. If indices were used in a trivial case like this it could actually increase the query execution time.
As always, use the 97/3 rule.

how can I optimize a query with multiple joins (already have indexes)?

SELECT citing.article_id as citing, lac_a.year, r.id_when_cited, cited_issue.country, citing.num_citations
FROM isi_lac_authored_articles as lac_a
JOIN isi_articles citing ON (lac_a.article_id = citing.article_id)
JOIN isi_citation_references r ON (citing.article_id = r.article_id)
JOIN isi_articles cited ON (cited.id_when_cited = r.id_when_cited)
JOIN isi_issues cited_issue ON (cited.issue_id = cited_issue.issue_id);
I have indexes on all the fields being JOINED on.
Is there anything I can do? My tables are large (some 1 Million records, the references tables has 500 million records, the articles table has 25 Million).
This is what EXPLAIN has to say:
+----+-------------+-------------+--------+--------------------------------------------------------------------------+---------------------------------------+---------+-------------------------------+---------+-------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-------------+--------+--------------------------------------------------------------------------+---------------------------------------+---------+-------------------------------+---------+-------------+
| 1 | SIMPLE | cited_issue | ALL | NULL | NULL | NULL | NULL | 1156856 | |
| 1 | SIMPLE | cited | ref | isi_articles_id_when_cited,isi_articles_issue_id | isi_articles_issue_id | 49 | func | 19 | Using where |
| 1 | SIMPLE | r | ref | isi_citation_references_article_id,isi_citation_references_id_when_cited | isi_citation_references_id_when_cited | 17 | mimir_dev.cited.id_when_cited | 4 | Using where |
| 1 | SIMPLE | lac_a | eq_ref | PRIMARY | PRIMARY | 16 | mimir_dev.r.article_id | 1 | |
| 1 | SIMPLE | citing | eq_ref | PRIMARY | PRIMARY | 16 | mimir_dev.r.article_id | 1 | |
+----+-------------+-------------+--------+--------------------------------------------------------------------------+---------------------------------------+---------+-------------------------------+---------+-------------+
5 rows in set (0.07 sec)
If you realy need all the returned data, I would suggest two things:
You, probably, know the data better than MySQL and you can try to make advantage of it if MySQL is not correct in its assumptions. Currently, MySQL thinks that it is easier to full scan the whole isi_issues table at the beginning, and if the result is really going to include all issues, than the assumption is correct. But if there are many issues that should not be in the result, you may want to force another order of the joins that you consider more correct. It is you, who knows which table applies the strongest restrictions and which are the smallest to full scan (you will anyway need to full scan something, since there is no WHERE clause).
You can make profit from covering indexes (that is indexes that contain enough data in itself and not needing to touch the row data). For example, having an index (article_id, num_citations) on isi_articles and (article_id, year) on isi_lac_authored_articles and even (country) on isi_issues will significantly speed up that query as long as the indexes fit in memory, but, from the other side, will make you indexes larger and slightly slow dow inserts into the table.
i think it's the best you can do. i mean at least it's not using nested/multiple queries. you should do a little benchmark on the sql. you could at least limit your results at the least as possible. 15-30 rows for a return set is pretty fine per page (this depends on the app, but 15-30 for me is the tolerance range)
i believe in mySQL (phpMyAdmin, console, GUI whatever) they return some sort of "execution time" which is the time that it took to the query to process. compare that with a benchmark of the query using your server-side code. then compare that with the query run using the server-side code and outputting it with your app interface included after that.
by this, you can see where your bottle-neck is - that is where you optimize.
Unless the result of your query is input to some other query or system, it is useless to return that much(3M) rows. That would be clever to return just an acceptable amount of rows per query(like 1000) that is for visualizing.
Looking at your SQL - the lack of a WHERE clause means it is pulling all rows from:
JOIN isi_issues cited_issue ON (cited.issue_id = cited_issue.issue_id)
You could look at partitioning the large isi_issues table, this would allow MySQL to perform a bit quicker (smaller files are easier to handle)
Or alternatively you can loop the statement and use a LIMIT clause.
LIMIT 0,100000
then
LIMIT 100001, 200000
This will let the statements run quicker and you can deal with the data in batches.

SQL Query Optimization

I am trying to speed up this django app (note: I didn't design this... just stuck maintaining it) and the biggest bottle neck seems to be these queries that are being generated by the admin. We have a content class that 4-5 other sub-classes inherit from and anytime the master list is pulled up in the admin a query like this is generated:
SELECT `content_content`.`id`,
`content_content`.`issue_id`,
`content_content`.`slug`,
`content_content`.`section_id`,
`content_content`.`priority`,
`content_content`.`group_id`,
`content_content`.`rotatable`,
`content_content`.`pub_status`,
`content_content`.`created_on`,
`content_content`.`modified_on`,
`content_content`.`old_pk`,
`content_content`.`content_type_id`,
`content_image`.`content_ptr_id`,
`content_image`.`caption`,
`content_image`.`kicker`,
`content_image`.`pic`,
`content_image`.`crop_x`,
`content_image`.`crop_y`,
`content_image`.`crop_side`,
`content_issue`.`id`,
`content_issue`.`special_issue_name`,
`content_issue`.`web_publish_date`,
`content_issue`.`issue_date`,
`content_issue`.`fm_name`,
`content_issue`.`arts_name`,
`content_issue`.`comments`,
`content_section`.`id`,
`content_section`.`name`,
`content_section`.`audiodizer_id`
FROM `content_image`
INNER
JOIN `content_content`
ON `content_image`.`content_ptr_id` = `content_content`.`id`
INNER
JOIN `content_issue`
ON `content_content`.`issue_id` = `content_issue`.`id`
INNER
JOIN `content_section`
ON `content_content`.`section_id` = `content_section`.`id`
WHERE NOT ( `content_content`.`pub_status` = -1 )
ORDER BY `content_issue`.`issue_date` DESC LIMIT 30
I ran an EXPLAIN on this and got the following:
+----+-------------+-----------------+--------+-------------------------------------------------------------------------------------------------+---------+---------+--------------------------------------+-------+---------------------------------+
| id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra |
+----+-------------+-----------------+--------+-------------------------------------------------------------------------------------------------+---------+---------+--------------------------------------+-------+---------------------------------+
| 1 | SIMPLE | content_image | ALL | PRIMARY | NULL | NULL | NULL | 40499 | Using temporary; Using filesort |
| 1 | SIMPLE | content_content | eq_ref | PRIMARY,issue_id,content_content_issue_id,content_content_section_id,content_content_pub_status | PRIMARY | 4 | content_image.content_ptr_id | 1 | Using where |
| 1 | SIMPLE | content_section | eq_ref | PRIMARY | PRIMARY | 4 | content_content.section_id | 1 | |
| 1 | SIMPLE | content_issue | eq_ref | PRIMARY | PRIMARY | 4 | content_content.issue_id | 1 | |
+----+-------------+-----------------+--------+-------------------------------------------------------------------------------------------------+---------+---------+--------------------------------------+-------+---------------------------------+
Now, from what I've read, I need to somehow figure out how to make the query to content_image not be terrible; however, I'm drawing a blank on where to start.
Currently, judging by the execution plan, MySQL is starting with content_image, retrieving all rows, and only thereafter using primary keys on the other tables: content_image has a foreign key to content_content, and content_content has foreign keys to content_issue and content_section. Also, only after all the joins are complete can it make much use of the ORDER BY content_issue.issue_date DESC LIMIT 30, since it can't tell which of these joins might fail, and therefore, how many records from content_issue will really be needed before it can get the first thirty rows of output.
So, I would try the following:
Change JOIN content_issue to JOIN (SELECT * FROM content_issue ORDER BY issue_date DESC LIMIT 30) content_issue. This will allow MySQL, if it starts with content_issue and works its way to the other tables, to grab a very small subset of content_issue.
Note: properly speaking, this changes the semantics of the query: it means that only records from at most the last 30 content_issues will be retrieved, and therefore that if some of those issues don't have published contents with images, then fewer than 30 records will be retrieved. I don't have enough information about your data to gauge whether this change of semantics would actually change the results you get.
Also note: I'm not suggesting to remove the ORDER BY content_issue.issue_date DESC LIMIT 30 from the end of the query. I think you want it in both places.
Add an index on content_issue.issue_date, to optimize the above subquery.
Add an index on content_image.content_ptr_id, so MySQL can work its way from content_content to content_image without doing a full table scan.