Database Tuning refers to a group of activities used for homogenizing and optimizing the performance of a database. Usually, it protrudes with query tuning but describes the design of the database files, configuration of the databases’ environment such as CPU, operating systems, etc., and configuration of the DBMS (Database Management System) application. Mainly, the database performance tuning is done to maximize the use of system resources so that application or the system works faster and efficiently as possible. Most systems are designed in a way that they can manage their use of system resources. However, in order to improve their efficiency, there still exists room for improvement which can be done by configuring the database and the DBMS and customizing their settings. Today, we are going to learn some of the best practices implemented by the DBAs and developers to improve SQL query performance or database performance. Here you go- Creating Optimized Indexes Indexing acts as a backbone for any RDBMS (relational database management system). No index directly impacts the functioning of a SELECT statement, while the queries will slow down if you put an excess of indexes. The fields and their order in indexing play a vital role during the database performance tuning. While indexing, the most important information to consider is the number of unique values present in a column of the table. For instance, thousands of first names in a column might maximize specific SELECT statements but it also introduce extra index retrieval time due to the big index pages. Therefore, it’s essential to optimize the index to balance between the two processing times. Pre-determine the Expected Growth Indexes create a negative impact on DML (INSERT, UPDATE, and DELETE) queries. When an index is created, the data for indexed columns gets stored on the disk. When the values in the indexed column are changed or new rows of data are inserted into the table, the database needs to reorganize the data storage to create room for the new rows. The reorganization of data storage can take extra troll on DML queries. However, if you need new rows in any table on a frequent basis, you need to specify the expected growth for an index. Also, the terminology for the predetermined growth varies in every database. Select Limited Data The less data you retrieve, the faster the query runs. Instead of filtering on the client, add as much filters you can on the server-end. As a result, less data will be sent on the wire and you will get results much faster. Try to eradicate any computed or obvious columns. Avoid Foreign Key Constraints The term is mainly used for ensuring data integrity. But, in turn, it affects the performance of your database. So, if your first priority includes performance, you need to modify the process of database performance tuning. The best example of a database avoiding the foreign key constraints is the system tables containing meta-data information about the user databases. However, it is seen that most users are concerned about data integrity. In such cases, they perform the data integrity process in the application layer and use foreign key only in development or QA environment as an error detection feature. This method will not affect the database performance and the data integrity is also protected. No Indexes before Loading Data Dropping indexes on a table before loading a large quantity of data allows the INSERT statement to run faster. Once the insert is completed, it can be re-indexed. You can use a temporary table to load data when thousands of rows in an online system is being inserted. Also, ensure that this temp table doesn’t contain any index.