Transcript of "Southeast Linuxfest -- MySQL Queries-- - Damned Lies and Best Guesses"
MySQL Queries --
Lies and Best Guesses
Very few programmers really understand SQL or how to speed queries to their databases. This session covers
that basics of relational calculus (no actual math/calculus will be demanded of attendees), how a RDMS like
MySQL tries to optimize the query, and introduces query tuning.
SQL Structured Query Language
Structured Query Language (/ˈɛs kjuː ˈɛl/, or /ˈsiːkwəl/; (SQL)) is a
special-purpose programming language designed for managing data held in a
relational database management system (RDBMS).
Originally based upon relational algebra and tuple relational calculus, SQL consists of a data definition language
and a data manipulation language. The scope of SQL includes data insert, query, update and delete, schema
creation and modification, and data access control. Although SQL is often described as, and to a great extent is, a
declarative language (4GL), it also includesprocedural elements.
relational algebra is an offshoot of first-order logic and of algebra of sets concerned with operations over
finitary relations, usually made more convenient to work with by identifying the components of a tuple by a name
(called attribute) rather than by a numeric column index, which is called a relation in database terminology.
You send SQL to the server …
The mysqld process will take your input
and parse it for VALID syntax.
Then it will build a query plan on how best to
retrieve the data.
Finally it goes to fetch the data.
MySQL’s NoSQL queries that skip these steps are MUCH
1. Get the data that you need and only what
you need as fast as possible. No ‘SELECT *
2. Avoid unnecessary disk/memory reads and
3. Make data as compact as is usefull, no
BIGINTs for zipcodes.
Cost Based Optimizer
C.5.6. Optimizer-Related Issues
MySQL uses a cost-based optimizer to determine the best way to resolve a query. In many cases, MySQL can calculate the
best possible query plan, but sometimes MySQL does not have enough information about the data at hand and has to make
“educated” guesses about the data.
So MySQL wants to get your data as cheaply
as possible and plans accordingly.
Query Plan not lockable with MySQL
Each time MySQL gets a query it will optimise
It builds a list of statistics over time to help
keep track of data & speed retrieval of the
data (5.6 lets you save/restore this
EXPLAIN is a tool to ask the server how it
wants to optimize the query.
Prepend to a QUERY
A Quick Word on Indexes
Indexes allow you to go directly to the
record(s) you want (think SSN) instead of
reading all records to find the one(s) wanted.
But they require maintenance and overhead.
Not a panacea!
A more common example
Optimizer estimates 8 reads to get desired info
Could use the PRIMARY key but does not!!
Has to read all records
239 x 8 = 1,912 records to read
Slightly more complex query
SELECT a.Name as 'City',
b.Name as 'Country',
FROM City a
JOIN Country b
ON (a.CountryCode = b.Code)
WHERE a.population > 3000000
AND b.LifeExpectancy > 66
ORDER BY b.name, a.Population
Gee, we add all those qualifiers and it still has to read
All those records AND we get a temp table plus a file sort!
And we ONLY wanted 20 records!!!
MySQL 5.6 and
Workbench 6.1 use
JSON format output
to generate diagram.
Costs published with
5.7 and 6.1!
Yet a little deeper into complexity
SELECT CONCAT(customer.last_name, ', ', customer.first_name) AS
FROM rental INNER JOIN customer ON rental.customer_id =
INNER JOIN address ON customer.address_id = address.address_id
INNER JOIN inventory ON rental.inventory_id = inventory.inventory_id
INNER JOIN film ON inventory.film_id = film.film_id
WHERE rental.return_date IS NULL
AND rental_date + INTERVAL film.rental_duration DAY <
We can use this index searching on
1. City, State, and Zip
2. City, State
ALTER TABLE city ADD INDEX
country_idx (CountryCode, Population);
The INDEX contains all the data we are
searching for which means less data to
Musts for better queries
1. Read chapter 8 of the MySQL Manual
2. Join on like data types, INTs with INTS
3. Keep columns as small as practical
4. Maintain B-tree index with ANALYSE
TABLE when things are quiet
5. Keep looking for improvments
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