Previous article was on 10 Ways To Destroy A SQL Database that sort of teaches you what mistakes many company might make on their database that will 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 others are less obvious.
Index your column is a common way to optimize your search result. Nonetheless, one 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.
Symbol operator such as >,<,=,!=, etc. are very helpful in our query. We can optimize some of our query with symbol operator provided the column is indexed. For example,
SELECT * FROM TABLE WHERE COLUMN > 16
Now, the above query is not optimized due to the fact that the DBMS will have to look for the value 16 THEN scan forward to value 16 and below. On the other hand, a optimized value will be
SELECT * FROM TABLE WHERE COLUMN >= 15
This way the DBMS might jump straight away to value 15 instead. It’s pretty much the same way how we find a value 15 (we scan through and target ONLY 15) compare to a value smaller than 16 (we have to determine whether the value is smaller than 16; additional operation).
In SQL, wildcard is provided for us with ‘%’ symbol. Using wildcard will definitely slow down your query especially for table that are really huge. We can optimize our query with wildcard by doing a postfix wildcard instead of pre or full wildcard.
#Full wildcard SELECT * FROM TABLE WHERE COLUMN LIKE ‘%hello%’; #Postfix wildcard SELECT * FROM TABLE WHERE COLUMN LIKE ‘hello%’; #Prefix wildcard 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.
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
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.
#BAD 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,
#BETTER SELECT * FROM TABLE WHERE COLUMN = ‘value%’.
Wildcard query will run faster if the above query is searching for all rows that contain ‘value’ as the first character. Example,
#SEARCH FOR ALL ROWS WITH THE FIRST CHARACTER AS ‘E’ 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 columnMisleading 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.
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.
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.
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 the database with a simple SQL query such as this.
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.
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.
Some of us will use a subquery within the IN operator such as this.
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.
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
SELECT * FROM TABLE WHERE COLUMN_A = ‘value’ OR COLUMN_B = ‘value’
On the other hand, using Union such as this will utilize Indexes.
SELECT * FROM TABLE WHERE COLUMN_A = ‘value’ UNION SELECT * FROM TABLE WHERE COLUMN_B = ‘value’
Hence, run faster.
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)