MySQL: Indexing for Better Performance
description
Transcript of MySQL: Indexing for Better Performance
By: Jehad Keriaki
DBA
MySQL: Indexing for Better Performance 1
MySQL: Indexing for Better Performance
Jehad Keriaki 2014
What is an Index Data structure to improve the speed of data
retrieval from DBs.
MySQL: Indexing for Better Performance 2
Jehad Keriaki 2014
Why Would We Use Indexes Speed, Speed, and Speed
Constraints (Uniqueness)
IO Optimization
MAX, MIN
Sorting, Grouping
MySQL: Indexing for Better Performance 3
Jehad Keriaki 2014
Index Types Primary Key (PK), Unique, Key
Primary Key vs Unique
Unique can be NULL
InnoDB is clustered based on PK
MySQL: Indexing for Better Performance 4
Jehad Keriaki 2014
Types (Algorithm) B-Tree, R-Tree, Hash, Full text R-Tree: Geo-spatial Hash: Memory only, fast for equality, whole key is used,
no range Full-text: For MyISAM, and as of 5.6 for InnoDB too. SELECT * WHERE MATCH(description) AGAINST ('toshiba') boolean , with query expansion, stop words, short words,
50% rule A better choice would be to use a search server like Sphinx
MySQL: Indexing for Better Performance 5
Jehad Keriaki 2014
Types (Algorithm) [cont'd] B-Tree:
For comparison operations (<>=..etc)
Range (Between)
Like, which is a special case of range when used with %
It is the DEFAULT in MySQL
In B-Tree, data are stored in the leaf nodes
MySQL: Indexing for Better Performance 6
Jehad Keriaki 2014
Types (Structure) One column
Multi-Column [composite]
Partial [prefix]
Any one of them can be "Covering Index", except 'partial'
MySQL: Indexing for Better Performance 7
Jehad Keriaki 2014
What Indexes to Create? PK is a must Best to be unsigned [smallest int] auto increment
PK and InnoDB (Clustered) InnoDB tables are clustered based on PKs Each secondary index has the PK in it. example: INDEX(name) is in fact (name, id)
AVOID long PKs. Why?
AVOID md5(), uuid(), etc.
MySQL: Indexing for Better Performance 8
Jehad Keriaki 2014
MyISAM and InnoDB In MyISAM:
Index entry tells the physical offset of the row in the data file
In InnoDB:
PK index has the data. Secondary indexes store PK as a pointer. Key on field F is (F, PK) - good for sorting and covering index
MySQL: Indexing for Better Performance 9
Jehad Keriaki 2014
Cardinality and Selectivity Cardinality: Number of distinct values
Selectivity: Cardinality / total number of rows
What values are better
Optimize Stats Update
MySQL: Indexing for Better Performance 10
Jehad Keriaki 2014
One Column Index This index is on one column only
Query example: SELECT * FROM employee WHERE first_name LIKE 'stephane';
Index solution: ALTER TABLE employee ADD INDEX (first_name);
Notes: Index the first n char of the char/varchar/text fields Do not use a function. i.e.
WHERE md5(field)='1bc29b36f623ba82aaf6724fd3b16718'
MySQL: Indexing for Better Performance 11
Jehad Keriaki 2014
Multi Column Index What is it: Index that involves more than one column.
Higher cardinality field goes first, with exceptions.
What 'left most' term is. [INDEX (A, B, C)]
Query example: SELECT * FROM employee WHERE department = 5 AND last_name LIKE 'tran';
Index solution: ALTER TABLE employee ADD INDEX (last_name, department);
{WHY NOT (department, last_name)??}
MySQL: Indexing for Better Performance 12
Jehad Keriaki 2014
Multi Column Index [Cont’d] Query example:
SELECT * FROM employee WHERE department = 5 and hiring_date>='2014-01-01';
Index solution: ALTER TABLE employee ADD INDEX (department, hiring_date);
Notes Should it be (hiring_date, department)? Is this an
exception? Order of columns IS important WILL NOT USE THE INDEX:
SELECT * FROM employee WHERE hiring_date>='2014-01-01';
MySQL: Indexing for Better Performance 13
Jehad Keriaki 2014
Partial Index What is it: Index on the first n char of a field.
Query example: email: varchar(255); SELECT * FROM users WHERE email like '[email protected]';
Index solution ALTER TABLE users ADD INDEX (email(12));
vs
ALTER TABLE users ADD INDEX (email);
Notes: Save space, efficient writing, same performance SELECT COUNT(DISTINCT(LEFT(field, 20))) FROM table 85% threshold? 90% maybe?
MySQL: Indexing for Better Performance 14
Jehad Keriaki 2014
Joins and Indexes Linking two or more tables to get related rows
Query example: SELECT employee.first_name, employee.last_name, FROM department INNER JOIN employee ON departmant.id = employee.department WHERE department.location='MTL';
Index solution: ALTER TABLE department ADD INDEX (location);
ALTER TABLE employee ADD INDEX (department);
Notes: The join could be on a non-indexed field on department, but an index has to exist on "employee's field"
MySQL: Indexing for Better Performance 15
Jehad Keriaki 2014
Multiple Indexes OR Multi-Col Index What is it:
ALTER TABLE ADD INDEX(field1), ADD INDEX(field2)
ALTER TABLE ADD INDEX(field1, field2)
Query example: WHERE field1=1 OR field2=2 [multiple indexes]
WHERE field1=1 AND field2=2 [multi-col index]
MySQL: Indexing for Better Performance 16
Jehad Keriaki 2014
Covering Index When the index has the required data, no need to
read data from table’s data!
Example: employee(id, first_name, last_name, email, phone, hiring_date)
SELECT email FROM employee WHERE phone='123456789';
ALTER TABLE employee ADD INDEX(phone, email);
min(), max() functions use the index only.
MySQL: Indexing for Better Performance 17
Jehad Keriaki 2014
Covering Index - Note only in InnoDB: myindex(col1,col2)
SELECT col1 FROM table1 WHERE col2 = 200 <<-- will use index
SELECT * FROM table1 where col2 = 200 <<-- will NOT use index.
MySQL: Indexing for Better Performance 18
Jehad Keriaki 2014
ICP (Index Condition Pushdown) [5.6] Lets the optimizer check in the index instead of checking in the
table's data. employee(id, first_name, last_name, department, phone, email, address)
INDEX(department, email)
SELECT * FROM employee WHERE department=5 AND email LIKE '%@beta.example%' [and address LIKE '%montreal%'];
Instead of stopping at department and then use where to check for email in the table's data, it will actually check in the index to see if the 2nd condition is satisfied, and then if yes, it will fetch the data from the table
MySQL: Indexing for Better Performance 19
Jehad Keriaki 2014
Using Index for Sorting ORDER BY x (index on x)
WHERE x ORDER BY y (index on x, y)
WHERE x ORDER BY x DESC, y DESC (index on x, y)
WHERE x ORDER BY x ASC, y DESC (Can't use index)
MySQL: Indexing for Better Performance 20
Jehad Keriaki 2014
Exceptions E.g. Date index with other less cardinal field.
Status or Gender special cases
MySQL: Indexing for Better Performance 21
Jehad Keriaki 2014
Overhead of indexing IO: Each DML operation will modify the indexes
Disk space
More indexes => Higher possibility of deadlock
MySQL: Indexing for Better Performance 22
Jehad Keriaki 2014
ABOUT EXPLAIN It lets us know the plan of query execution
What index would be used, if any
Rows to be scanned
MySQL: Indexing for Better Performance 23
MySQL: Indexing for Better Performance 24
QUESTIONS & EXAMPLES
MySQL: Indexing for Better Performance 25
mysql> explain select * from md_table where id=50000\G *************************** 1. row *************************** id: 1 select_type: SIMPLE table: md_table type: const possible_keys: PRIMARY key: PRIMARY key_len: 4 ref: const rows: 1 Extra: 1 row in set (0.00 sec) mysql> explain select id from md_table where id=50000\G *************************** 1. row *************************** id: 1 select_type: SIMPLE table: md_table type: const possible_keys: PRIMARY key: PRIMARY key_len: 4 ref: const rows: 1 Extra: Using index 1 row in set (0.00 sec)
MySQL: Indexing for Better Performance 26
mysql> explain select id from md_table where hashed_id='1017bfd4673955ffee4641ad3d481b1c'\G *************************** 1. row *************************** id: 1 select_type: SIMPLE table: md_table type: ALL possible_keys: NULL key: NULL key_len: NULL ref: NULL rows: 100000 Extra: Using where 1 row in set (0.00 sec) mysql> alter table md_table add index (hashed_id(15)); Query OK, 100000 rows affected (0.77 sec) Records: 100000 Duplicates: 0 Warnings: 0 mysql> explain select id from md_table where hashed_id='1017bfd4673955ffee4641ad3d481b1c'\G *************************** 1. row *************************** id: 1 select_type: SIMPLE table: md_table type: ref possible_keys: hashed_id key: hashed_id key_len: 46 ref: const rows: 1 Extra: Using where 1 row in set (0.01 sec)