MySQL `FORCE INDEX` use cases?

Question

Almost everywhere I read the use of FORCE INDEX is highly discouraged and I perfectly understand and know why - there are immensely huge chances that MySQL knows better what indexes to choose than the (average) developer.

However, recently I have found a case where FORCE INDEX improved my execution times in the range of hundred of times:

• JOIN on 4 tables
• first table has about 500 000 records
• 2 of the INNER JOINed table have more than 1 milion records
• first table has a field called published_date, stored as varchar in YMD format (could not changed to datetime)
• needed a range on published_date of at most 5 000 records
• some other GROUP BY and ORDER BY clauses on the first table on different fields than published_date were needed for this query

Although I've rewritten the query in many ways, I was not able to get execution times smaller than 130 seconds (with highest being over 700 ). After using FORCE INDEX with published_date, the execution time dropped below 5 seconds.

It took me a few days to remember about the infamous FORCE INDEX option.

Questions:

• What are other use cases you found where FORCE INDEX saved you?
• Do you have some best practices when you consider using FORCE INDEX?

Edit - Obsevations: I created this blog post with the question here also. All the answer you provide will appear there too - whith credits and all the stuff you want.

Edit 2

I applied the suggestions I received in your comments (ANALYZE TABLE and OPTIMIZE TABLE), below is the output of EXPLAIN applied on query - unfortunately the index selection is not at all better:

1. without FORCE INDEX on table with alias a:

id  select_type table   type    possible_keys   key key_len ref rows    Extra
1   SIMPLE  am2 range   PRIMARY,idx_meta_article    idx_meta_article    4   NULL    275228  Using where; Using index; Using temporary; Using f...
1   SIMPLE  a   eq_ref  PRIMARY,serial_issue_date_productid,pub_date,idx_d...   PRIMARY 4   mydb_toto.am2.ArticleID 1   Using where
1   SIMPLE  ai  ref PRIMARY,idx_iso_article PRIMARY 4   mydb_toto.a.serial  11523   Using where; Using index
1   SIMPLE  m   range   PRIMARY,meta_articles_type  meta_articles_type  4   NULL    96  Using where
1   SIMPLE  am  eq_ref  PRIMARY,idx_meta_article    PRIMARY 8   mydb_toto.a.serial,mydb_toto.m.meta_id  1   Using where; Using index

2. with FORCE INDEX on table with alias a:

id  select_type table   type    possible_keys   key key_len ref rows    Extra
1   SIMPLE  a   range   pub_date    pub_date    11  NULL    17679   Using where; Using temporary; Using filesort
1   SIMPLE  am2 ref PRIMARY,idx_meta_article    PRIMARY 4   mydb_toto.a.serial  21930   Using where; Using index
1   SIMPLE  ai  ref PRIMARY,idx_iso_article PRIMARY 4   mydb_toto.a.serial  11523   Using where; Using index
1   SIMPLE  m   range   PRIMARY,meta_articles_type  meta_articles_type  4   NULL    96  Using where
1   SIMPLE  am  eq_ref  PRIMARY,idx_meta_article    PRIMARY 8   mydb_toto.am2.ArticleID,mydb_toto.m.meta_id 1   Using where; Using index

3. after ANALYZE TABLE, without FORCE INDEX:

id  select_type table   type    possible_keys   key key_len ref rows    Extra
1   SIMPLE  am2 range   PRIMARY,idx_meta_article    idx_meta_article    4   NULL    275228  Using where; Using index; Using temporary; Using f...
1   SIMPLE  a   eq_ref  PRIMARY,serial_issue_date_productid,pub_date,idx_d...   PRIMARY 4   mydb_toto.am2.ArticleID 1   Using where
1   SIMPLE  ai  ref PRIMARY,idx_iso_article PRIMARY 4   mydb_toto.a.serial  11523   Using where; Using index
1   SIMPLE  m   range   PRIMARY,meta_articles_type  meta_articles_type  4   NULL    96  Using where
1   SIMPLE  am  eq_ref  PRIMARY,idx_meta_article    PRIMARY 8   mydb_toto.a.serial,mydb_toto.m.meta_id  1   Using where; Using index

4. after OPTIMIZE TABLE, without FORCE INDEX:

id  select_type table   type    possible_keys   key key_len ref rows    Extra
1   SIMPLE  am2 range   PRIMARY,idx_meta_article    idx_meta_article    4   NULL    275228  Using where; Using index; Using temporary; Using f...
1   SIMPLE  a   eq_ref  PRIMARY,serial_issue_date_productid,pub_date,idx_d...   PRIMARY 4   mydb_toto.am2.ArticleID 1   Using where
1   SIMPLE  ai  ref PRIMARY,idx_iso_article PRIMARY 4   mydb_toto.a.serial  11523   Using where; Using index
1   SIMPLE  m   range   PRIMARY,meta_articles_type  meta_articles_type  4   NULL    96  Using where
1   SIMPLE  am  eq_ref  PRIMARY,idx_meta_article    PRIMARY 8   mydb_toto.a.serial,mydb_toto.m.meta_id  1   Using where; Using index

5. after OPTIMIZE TABLE and ANALYZE TABLE, with FORCE INDEX:

id  select_type table   type    possible_keys   key key_len ref rows    Extra
1   SIMPLE  a   range   pub_date    pub_date    11  NULL    17679   Using where; Using temporary; Using filesort
1   SIMPLE  am2 ref PRIMARY,idx_meta_article    PRIMARY 4   mydb_toto.a.serial  21930   Using where; Using index
1   SIMPLE  ai  ref PRIMARY,idx_iso_article PRIMARY 4   mydb_toto.a.serial  11523   Using where; Using index
1   SIMPLE  m   range   PRIMARY,meta_articles_type  meta_articles_type  4   NULL    96  Using where
1   SIMPLE  am  eq_ref  PRIMARY,idx_meta_article    PRIMARY 8   mydb_toto.am2.ArticleID,mydb_toto.m.meta_id 1   Using where; Using index

Answer 1

I have noticed that FORCE INDEX helps when you have multiple joins and sub-queries on VARCHAR fields where both the FK and the referenced value are not the primary key, while at the same time having where clause on a DATE field.

Something like:

SELECT NAME, a.reference_no, i.value, p.value FROM customers AS c
INNER JOIN accounts AS a ON c.id = a.customer_id
INNER JOIN invoices AS i ON i.reference_no = a.reference_no
INNER JOIN payments AS p ON p.invoice_no = i.invoice_no
WHERE payments.date >= '2011-09-01' AND DATE < '2011-10-01';

mysql will always use the PKs and FKs, where you would it to use the payment_date index on the payments table first as it is the largest one. So a FORCE INDEX(payment_date) on the payments table join would help a lot.

This an example from the third-party billing database that we use at work. We had huge issues with optimisation, and FORCE INDEX did the job most of the times. Usually we found the slow quires with mysqladmin, tested them with FORCE INDEX and send them to the vendors to rewrite them in the source code of the app.

Here are the four tables to get a better grip on the example:

CREATE TABLE `customers` (
 `id` int(11) NOT NULL AUTO_INCREMENT,
 `name` varchar(100) NOT NULL,
 PRIMARY KEY (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=3 DEFAULT CHARSET=latin1;

CREATE TABLE `accounts` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `customer_id` int(11) NOT NULL,
  `reference_no` varchar(10) NOT NULL,
  PRIMARY KEY (`id`),
  UNIQUE KEY `reference_no_uniq` (`reference_no`),
  KEY `FK_accounts` (`customer_id`),
  CONSTRAINT `FK_accounts` FOREIGN KEY (`customer_id`) REFERENCES `customers` (`id`)
) ENGINE=InnoDB AUTO_INCREMENT=9 DEFAULT CHARSET=latin1;

CREATE TABLE `invoices` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `reference_no` varchar(10) NOT NULL,
  `invoice_no` varchar(10) NOT NULL,
  `value` int(11) NOT NULL,
  PRIMARY KEY (`id`),
  UNIQUE KEY `invoice_no_uniq` (`invoice_no`),
  KEY `FK_invoices` (`reference_no`),
  CONSTRAINT `FK_invoices` FOREIGN KEY (`reference_no`) REFERENCES `accounts` (`reference_no`)
) ENGINE=InnoDB AUTO_INCREMENT=10 DEFAULT CHARSET=latin1;

CREATE TABLE `payments` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `invoice_no` varchar(10) NOT NULL,
  `value` int(11) NOT NULL,
  `date` datetime DEFAULT NULL,
  PRIMARY KEY (`id`),
  KEY `FK_payments` (`invoice_no`),
  KEY `payment_date` (`date`),
  CONSTRAINT `FK_payments` FOREIGN KEY (`invoice_no`) REFERENCES `invoices` (`invoice_no`)
) ENGINE=InnoDB AUTO_INCREMENT=7 DEFAULT CHARSET=latin1;

Answer 2

I notice through your EXPLAIN plans that the table sequence has changed, with the first two tables reversed, which may well be where your performance improvements are coming from, in addition to the use of the date index.

Have you looked into the use of STRAIGHT_JOIN within the queries to force the sequence of the tables?

I have worked on a large database schema, where the optimal join configuration used STRAIGHT_JOINs all the way through the query, and the performance was 100 fold improved over the INNER JOIN equivalents.

Unfortunately, I don't have access to the system any more to get some example EXPLAIN plans, but the optimal table sequence went something like this;

Table 1           10 rows              1 analysed
Table 2           500 rows             50 analysed
Table 3           1,000,000 rows       300,000 analysed
Table 4           500,000,000 rows     4,000,000 analysed

Using STRAIGHT_JOINs to keep this sequence resulted in query performance far greater than the INNER JOIN equivalent, which essentially just reversed the sequence of the tables.

Go back to your original query, remove the force index, and replace the INNER JOINs with STRAIGHT_JOINs and see what the explain plan gives you.

You may also want to create a composite index on the a table using pub_date and serial, which I think will improve the query even further.