1. 15 Ways to Optimize Your SQL Queries - Hungred Dot Com
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15 Ways to Optimize Your SQL Queries
Posted on October 27 by Clay
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Previous article was on 10 Ways To Destroy A SQL Database that sort of teaches 16
you what mistakes many company might make on their database that will 2
eventually lead to a database destroy. In this article, you will get to know 15
ways to optimize your SQL queries. Many ways are common to optimize a query while $10 - Advertise
others are less obvious.
Indexes
Index your column is a common way to optimize your search result. Nonetheless, one $10 - Advertise
must fully understand how does indexing work in each database in order to fully utilize
indexes. On the other hand, useless and simply indexing without understanding how it
work might just do the opposite.
SQL Server
Symbol Operator Training
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Join the
Symbol operator such as >,<,=,!=, etc. are very helpful in our query. We can optimize
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some of our query with symbol operator provided the column is indexed. For example, Community Today!
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1 SELECT * FROM TABLE WHERE COLUMN > 16
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optimized value will be Evaluation
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1 SELECT * FROM TABLE WHERE COLUMN >= 15
This way the DBMS might jump straight away to value 15 instead. It’s pretty much the Download Pl Sql
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http://hungred.com/useful-information/ways-optimize-sql-queries/[09/21/2012 4:05:29 PM]

2. 15 Ways to Optimize Your SQL Queries - Hungred Dot Com
Wildcard Visual SQL to
XML
Easy to use Data
In SQL, wildcard is provided for us with ‘%’ symbol. Using wildcard will definitely slow Mapping
down your query especially for table that are really huge. We can optimize our query with environment. Try
now!
wildcard by doing a postfix wildcard instead of pre or full wildcard. www.ecrion.com
1 #Full wildcard
2 SELECT * FROM TABLE WHERE COLUMN LIKE '%hello%';
3 #Postfix wildcard
4 SELECT * FROM TABLE WHERE COLUMN LIKE 'hello%';
5 #Prefix wildcard
6 SELECT * FROM TABLE WHERE COLUMN LIKE '%hello';
That column must be indexed for such optimize to be applied.
P.S: Doing a full wildcard in a few million records table is equivalence to killing the
database.
NOT Operator
Try to avoid NOT operator in SQL. It is much faster to search for an exact match
(positive operator) such as using the LIKE, IN, EXIST or = symbol operator instead of a
negative operator such as NOT LIKE, NOT IN, NOT EXIST or != symbol. Using a
negative operator will cause the search to find every single row to identify that they are
ALL not belong or exist within the table. On the other hand, using a positive operator
just stop immediately once the result has been found. Imagine you have 1 million record
in a table. That’s bad.
COUNT VS EXIST
Some of us might use COUNT operator to determine whether a particular data exist
1 SELECT COLUMN FROM TABLE WHERE COUNT(COLUMN) > 0
Similarly, this is very bad query since count will search for all record exist on the table to
determine the numeric value of field ‘COLUMN’. The better alternative will be to use the
EXIST operator where it will stop once it found the first record. Hence, it exist.
Wildcard VS Substr
Most developer practiced Indexing. Hence, if a particular COLUMN has been indexed, it
is best to use wildcard instead of substr.
1 #BAD
2 SELECT * FROM TABLE WHERE substr ( COLUMN, 1, 1 ) = 'value'.
The above will substr every single row in order to seek for the single character ‘value’. On
the other hand,
1 #BETTER
2 SELECT * FROM TABLE WHERE COLUMN = 'value%'.
Wildcard query will run faster if the above query is searching for all rows that contain
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3. 15 Ways to Optimize Your SQL Queries - Hungred Dot Com
‘value’ as the first character. Example,
1 #SEARCH FOR ALL ROWS WITH THE FIRST CHARACTER AS 'E'
2 SELECT * FROM TABLE WHERE COLUMN = 'E%'.
Index Unique Column
Some database such as MySQL search better with column that are unique and indexed.
Hence, it is best to remember to index those columns that are unique. And if the column
is truly unique, declare them as one. However, if that particular column was never used
for searching purposes, it gives no reason to index that particular column although it is
given unique.
Max and Min Operators
Max and Min operators look for the maximum or minimum value in a column. We can
further optimize this by placing a indexing on that particular column Misleading We can
use Max or Min on columns that already established such Indexes. But if that particular
column is frequently use, having an index should help speed up such searching and at the
same time speed max and min operators. This makes searching for maximum or
minimum value faster. Deliberate having an index just to speed up Max and Min is
always not advisable. Its like sacrifice the whole forest for a merely a tree.
Data Types
Use the most efficient (smallest) data types possible. It is unnecessary and sometimes
dangerous to provide a huge data type when a smaller one will be more than sufficient to
optimize your structure. Example, using the smaller integer types if possible to get
smaller tables. MEDIUMINT is often a better choice than INT because a MEDIUMINT
column uses 25% less space. On the other hand, VARCHAR will be better than longtext
to store an email or small details.
Primary Index
The primary column that is used for indexing should be made as short as possible. This
makes identification of each row easy and efficient by the DBMS.
String indexing
It is unnecessary to index the whole string when a prefix or postfix of the string can be
indexed instead. Especially if the prefix or postfix of the string provides a unique
identifier for the string, it is advisable to perform such indexing. Shorter indexes are
faster, not only because they require less disk space, but because they also give you more
hits in the index cache, and thus fewer disk seeks.
Limit The Result
Another common way of optimizing your query is to minimize the number of row return.
If a table have a few billion records and a search query without limitation will just break
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4. 15 Ways to Optimize Your SQL Queries - Hungred Dot Com
the database with a simple SQL query such as this.
1 SELECT * FROM TABLE
Hence, don’t be lazy and try to limit the result turn which is both efficient and can help
minimize the damage of an SQL injection attack.
1 SELECT * FROM TABLE WHERE 1 LIMIT 10
Use Default Value
If you are using MySQL, take advantage of the fact that columns have default values.
Insert values explicitly only when the value to be inserted differs from the default. This
reduces the parsing that MySQL must do and improves the insert speed.
In Subquery
Some of us will use a subquery within the IN operator such as this.
1 SELECT * FROM TABLE WHERE COLUMN IN (SELECT COLUMN FROM TABLE)
Doing this is very expensive because SQL query will evaluate the outer query first before
proceed with the inner query. Instead we can use this instead.
1 SELECT * FROM TABLE, (SELECT COLUMN FROM TABLE) as dummytable
WHERE dummytable.COLUMN = TABLE.COLUMN;
Using dummy table is better than using an IN operator to do a subquery. Alternative, an
exist operator is also better.
Utilize Union instead of OR
Indexes lose their speed advantage when using them in OR-situations in MySQL at least.
Hence, this will not be useful although indexes is being applied
1 SELECT * FROM TABLE WHERE COLUMN_A = 'value' OR COLUMN_B =
'value'
On the other hand, using Union such as this will utilize Indexes.
1 SELECT * FROM TABLE WHERE COLUMN_A = 'value'
2 UNION
3 SELECT * FROM TABLE WHERE COLUMN_B = 'value'
Hence, run faster.
Summary
Definitely, these optimization tips doesn’t guarantee that your queries won’t become your
system bottleneck. It will require much more benchmarking and profiling to further
optimize your SQL queries. However, the above simple optimization can be utilize by
anyone that might just help save some colleague rich bowl while you learn to write good
queries. (its either you or your team leader/manager)
http://hungred.com/useful-information/ways-optimize-sql-queries/[09/21/2012 4:05:29 PM]

5. 15 Ways to Optimize Your SQL Queries - Hungred Dot Com
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About Clay
I am Clay who is the main writer for this website. I own a small web hosting
company in Malaysia and i'm available to be hired as individual contractor on
elance or odesk. You can find me on twitter.
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This entry was posted in Developer, How-to, Informative, SQL, Tips And Tricks, Web Development and tagged SQL.
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9 Responses to 15 Ways to Optimize Your SQL Queries
Veera says:
October 27 at 12:24 PM
I couldn’t understand the ‘Symbol Operator’ point. Could you please explain it a
little further?
Greg Jorgensen says:
October 27 at 1:25 PM
You are wrong about the NOT opertor. If you think about it you will realize that
you can determine if there are NO black marbles in a bowl just as fast as you can
determine if there is at least one black marble. There is no need to examine every
marble; you can stop as soon as I find one black marble.
NOT EXISTS is exactly that: an EXISTS test that is logically negated. It’s possible
that a NOT EXISTS (or NOT LIKE or NOT IN) test will examine every
row/character/list member if the searched item is not present, but that will
happen for both EXISTS and NOT EXISTS.
Greg Jorgensen says:
October 27 at 1:30 PM
MAX and MIN do not “look for the maximum or minimum value in a column,”
and they aren’t operators. The MIN and MAX functions are aggregate functions
http://hungred.com/useful-information/ways-optimize-sql-queries/[09/21/2012 4:05:29 PM]

6. 15 Ways to Optimize Your SQL Queries - Hungred Dot Com
that operate on the selected rows (or groups of rows if GROUP BY is used).
SELECT MAX(col) FROM table will find the maximum value of col, but the
functions are more general than that. Indexes are expensive to maintain, and
indexing columns just to speed up MIN and MAX is not great advice.
Clay says:
October 27 at 3:20 PM
@Greg : I agree with you that indexing columns just to speed up MIN and MAX
is not a good advice. May be there is a misunderstanding on that point. I meant
that MAX and MIN can be used on indexed column for better speed. Deliberating
indexing a column because of a MIN or MAX is pure, NO NO. Thanks for the
feedback
Well, regarding the NOT operator, if there are any algorithm available in the
world that work like a human. May be your theory might just hit the right spot.
Clay says:
October 27 at 4:52 PM
@Veera : To make thing simple. If there are 15 available in that column it will
directly point towards 15 (if there are no exact value, it will just be similar as <16)
instead of going through the whole row finding which is the highest value that is
smaller than 16 (might be 15,14,13,12,11, etc. the DBMS do not know until it look
through them). If there is a equal to symbol, it means that there is a probability
that it will jump directly to 15 and return the result directly to you.
unreal4u says:
October 28 at 1:08 AM
nice post! i had no idea about #2 and #4…
also, some discoveries i’ve made about queries:
- UNION is slow, try to use stored procedures: i’ve not tested this one on MySQL,
but in PostGreSQL, it is much faster to use a SP than an union. Example:
SP:
SELECT
getValue(column1) AS name,
Operation(column2) AS number_of_products
is much much faster than:
(SELECT
name,
0 AS number_of_products
http://hungred.com/useful-information/ways-optimize-sql-queries/[09/21/2012 4:05:29 PM]

7. 15 Ways to Optimize Your SQL Queries - Hungred Dot Com
FROM customer
WHERE a=b)
UNION
(SELECT
0 AS name,
number_of_products
FROM products
WHERE y=z)
However, like i said, i’ve not tested this one on MySQL.
- PHP and MySQL: ok, strictly this isn’t a part of query optimization, but it is
always better to just retrieve the rows we are interested in, instead of all rows.
Why? Memory (PHP and MySQL) and bandwidth consumption.
Example:
best:
SELECT id, name FROM customer
instead of:
SELECT * FROM customer
(Assuming “customer” haves an id, name, last_name, description, login,
password, e-mail, etc.)
It also makes the code cleaner.
INNER/LEFT/RIGHT JOIN: ok, this is an untested one. A while ago, i was asked
to optimize a MySQL query. Originally, i used joins, but turned out it was much
faster to use them in the WHERE part.
Example:
Slower:
SELECT * FROM customer AS a LEFT JOIN products AS b ON a.id = b.id_customer
Faster:
SELECT * FROM customer AS a, products AS b
WHERE a.id = b.id_customer
I don’t know how or why, but it turned out that first case was much slower (10
seconds) than the second case (3,4 seconds).
It was however, a system already in production, so i hadn’t the chance to play
much with the query or with indexes
The client however, was very happy with the results xD
Greetings
JW says:
October 28 at 1:49 AM
"However, if that particular column was never used for searching purposes, it
gives no reason to index that particular column although it is given unique"
http://hungred.com/useful-information/ways-optimize-sql-queries/[09/21/2012 4:05:29 PM]

8. 15 Ways to Optimize Your SQL Queries - Hungred Dot Com
Um, I would say this is misleading too. What about unique columns that are
frequently used in joins, but never searched?
Clay says:
October 28 at 6:40 AM
@JW : Yup, you don’t need an index when no search is being done on a
particular column. But if it is a unique and frequently use column, having an
index will perform better.
For join, it depends on what DBMS you use. In MySQL, indexes perform more
efficient when your join have the same data type and size. Although you don’t
need its result, columns that frequently require in joins need DBMS to search for
the matching partners. Hence, having indexes will be better in joins too but
criteria to make it efficient depends on each individual implementation of DBMS.
Clay says:
October 28 at 7:00 AM
@unreal4u : Thanks for sharing
I’m not really familiar with stored procedure in MySQL at the moment since its
quite new (MySQL v5.1 onwards). But in MySQL, stored procedure is used when
a query is frequently used and have no data change. Hence, using a stored
procedure will be more efficient. You can read Stored Procedure for more
information. But it should work closely similar to PostGreSQL since the concept
is the same.
Ya, its not good to always use *. For security reason too other than optimization
point of view. (i was writing an example so i was lazy, sorry about that)
On the INNER/LEFT/RIGHT join, i also experience such situation with a few
million of data table, when a join seems slower than a WHERE clauses. I read it
somewhere before but i forgotten why is that so (should be at the documentation
of MySQL).
@Mehedi : Welcome
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