This document discusses indexing in MySQL databases to improve query performance. It begins by defining an index as a data structure that speeds up data retrieval from databases. It then covers various types of indexes like primary keys, unique indexes, and different indexing algorithms like B-Tree, hash, and full text. The document discusses when to create indexes, such as on columns frequently used in queries like WHERE clauses. It also covers multi-column indexes, partial indexes, and indexes to support sorting, joining tables, and avoiding full table scans. The concepts of cardinality and selectivity are introduced. The document concludes with a discussion of index overhead and using EXPLAIN to view query execution plans and index usage.

1. By: Jehad Keriaki
DBA
MySQL: Indexing for Better Performance1
MySQL: Indexing for Better Performance

2. Jehad Keriaki 2014
What is an Index
 Data structure to improve the speed of data
retrieval from DBs.
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3. Jehad Keriaki 2014
Why Would We Use Indexes
 Speed, Speed, and Speed
 Constraints (Uniqueness)
 IO Optimization
 MAX, MIN
 Sorting, Grouping
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4. Jehad Keriaki 2014
Index Types
 Primary Key (PK), Unique, Key
 Primary Key vs Unique
 Unique can be NULL
 InnoDB is clustered based on PK
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5. 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
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6. 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
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Types (Structure)
 One column
 Multi-Column [composite]
 Partial [prefix]
 Any one of them can be "Covering Index", except
'partial'
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8. 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.
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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
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10. Jehad Keriaki 2014
Cardinality and Selectivity
 Cardinality: Number of distinct values
 Selectivity: Cardinality / total number of rows
 What values are better
 Optimize  Stats Update
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11. 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'
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12. 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)??}
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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';
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14. 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 'richardmelo@yahoo.com';
 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?
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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"
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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]
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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.
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18. 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.
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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
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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)
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Exceptions
 E.g. Date index with other less cardinal field.
 Status or Gender special cases
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Overhead of indexing
 IO: Each DML operation will modify the indexes
 Disk space
 More indexes => Higher possibility of deadlock
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23. 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
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QUESTIONS & EXAMPLES

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mysql> explain select * from md_table where id=50000G
*************************** 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=50000G
*************************** 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)

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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)