How to Tune SQL Statements with CONCAT Operator for MySQL?

There may be some business requirements that need to compare concatenate strings and column with a given unknown length of the bind variable. Here is an example SQL that retrieves data from EMPLOYEE and DEPARTMENT tables where employee’s department ID must concatenate two strings before it is compared to an unknown length of variable @dpt_var

select * from employee,department
where concat(concat(‘A’,emp_dept),‘B’) = @dpt_var
and  emp_dept= dpt_id

Here the following are the query plans of this SQL, it takes 23.8 seconds to finish. The query shows a “Full Table Scan Employee” to nested loop Department table.

You can see that this SQL cannot utilize index scan even the emp_dept is an indexed field. Let me add a “force index(EMPS_DPT_INX) hints to the SQL and hope it can help MySQL SQL optimizer to use index scan, but it fails to enable the index scan anyway, so I add one more dummy condition emp_dept >= ” , it is an always true condition that emp_dept should be greater or equal to a smallest empty character. It is to fool MySQL SQL optimizer that emp_dept’s index is a reasonable step.

select  *
from  employee force index(EMPS_DPT_INX),
     department
where  concat(concat(‘A’,emp_dept),‘B’) = @dpt_var
     and emp_dept >= ”
     and emp_dept = dpt_id

Here is the query plan of the rewritten SQL and it is running faster. The new query plan shows that an Index Range Scan is used for Employee table first and then nested loop Department table.

This kind of rewrite can be achieved by Tosska SQL Tuning Expert for MySQL automatically, it shows that the rewrite is more than 3 times faster than the original SQL.

https://tosska.com/tosska-sql-tuning-expert-tse-for-mysql-2/

SQL Performance Tuning: Frequent Questions about Indexes

SQL performance tuning

A database is a piece of software operating on a computer, which means it is dependent and likely to face the same limitations as other software present on that computer. In other words, it will only be able to process as much data as the hardware can handle.

One of the best ways to speed up queries is to perform SQL performance tuning. In this post, we will answer some of the most frequent questions involving databases and indexes.

What is Indexing in SQL Query Optimization?

Indexing is one of the first things you may have come across while learning the ropes of your database. It is a wonderful tool that enables users to enhance the efficiency of their database. However, bear in mind that not every database requires indexing, and not all indexes are helpful in SQL performance tuning.

Let’s learn more about indexing: what it is and how it helps in enhancing database performance.

How do Indexes Affect SQL Query Performance?

An Index can locate data swiftly without having to go through each row in the table. This saves plenty of time! 

Certain data columns are required before you can create an index. These are –

  • The Search Key which holds a duplicate of the primary key
  • The Data Reference which has a set of pointers

All of these constitute the structure of one index. To understand how an index works, let us take an example. Suppose you need to look for a bit of data in your database. Rather than scour every line yourself, you make the computer search each row till it locates the information. Remember that the search is bound to take much longer if the requisite information is located at the end. Fortunately, you have the option to sort alphabetically to shorten the length of such queries.

What are the Types of Database Indexes?

Database indexes are of two kinds –

Clustered indexes – These arrange data using the primary key. The reason behind using a clustered index is to make sure the primary key is saved in ascending order. This is the same order in which the table stores memory.

A clustered index is automatically created when the primary key is set, which helps in SQL tuning for Oracle in the long run as well.

Non-clustered indexes – A non-clustered index is a data structure that boosts data fetching speed. It is different from clustered indexes, as they are made by data analysts or developers.

When and How Should We Use Indexes?

Since indexes are intended to accelerate database performance, you should apply them whenever you think they can simplify the use of the database. Although smaller databases may not have several opportunities to use indexes, they are likely to see the benefits of indexing as they grow into larger databases. 

You can make sure your indexes keep performing well, if you test run a set of queries on your database first. Clock the time those queries take to execute and begin creating your indexes after that. Keep rerunning these ‘tests’ for continuous improvements.

Conclusion

Indexing has its challenges, the biggest one being determining the best ones for every table.

For instance, heaps require clustered indexes because searching for a record in a heap table is comparable to finding a needle in a haystack: it’s inefficient and time-consuming, thanks to the heap’s unordered structure.

On the other hand, locating data is simpler and faster from a table that contains a proper clustered index, just like finding a name in a list that’s alphabetically ordered. DBAs, therefore, recommend that every SQL table contains a proper clustered index. Now that you know how indexes work and how they can optimize database performance, you should be able to use them to reduce query times substantially. If you would like more tips on how to use indexing, or you need a SQL query optimization tool for your database, let our experts know!