The use of databases is extensive in the entire software development cycle, whether it is to run scenarios from the local server post code changes or update static data tables and provide data hotfixes. To operate DB, queries are needed. For instance, when a developer needs to check the DAO implementation from the codebase, he needs to fetch the queries executed so that he can understand the tables and columns where data is being updated post execution. Similarly, when he needs to run batches from the application or deal with data execution in a huge volume, a DB query is required to trigger packages in SQL.
Now, there are numerous ways in which a SQL query can be written and executed. If it is not written in a proper manner, not only will the execution time increase but there can be several other issues, like timeout, unique constraint validation, wrong data update, improper results, and so on. Therefore, a developer needs to focus on optimizing the SQL queries before execution so that the interim issues can be resolved and the desired operation is completed. Considering this, the following discussion will primarily illustrate a few practices for query optimization in SQL.
Appropriate indexing
When a query is executed to fetch a record or certain datasets in volume, several joins and tables need to be accessed interim. As a result, it takes a lot of time in the data retrieval process, which further leads to timeouts, duplicate records in the same column, and so on. To avoid such issues, using query indexing is the most feasible option. It is a form of a data structure that creates indexes on DB tables and concerned columns where the data required is present. When the indexes are created and the query is executed, tables and columns are called based on the index. This prevents timeout and fastens the entire data retrieval process.
Syntax for creating index: CREATE INDEX index_name ON table_1 (column 1, column 2, ……, column N);
Using minimal joins and linkages
Several types of joins are often used in SQL queries to access linked tables and extract the required datasets or records. Here, the joins are formed based on primary and foreign keys present in the linked tables. If there are too many outer and inner joins in a single query, not only does it increase the execution time but the PKs obtained can be wrong. Also, developers cannot implement joins while updating any table, like adding data or altering an existing record. So, it is better to limit the use of joins as much as possible. While writing the SQL query, it is best to know the exact tables that are called one after the other and use their PKs to fetch the desired result.
Utilizing SELECT command wisely
When SELECT command is used, it fetches the columns present in the tables mentioned in the query. Now, there are two ways to use this keyword.
- Select *: This command will fetch all the columns together present in the tables included in the query. So, if there are five tables, each having 10 columns, the output result will display all 50 columns together.
- Select column_name: When only the column name is used in the query instead of *, only those mentioned after the keyword will be displayed. Here, the columns mentioned are the ones present in the tables for which the query will be executed. So, when a developer writes 6 columns after the Select keyword, the output will fetch data from these 6 columns and display the same only instead of 50.
- Select ID: Another way to reduce the query execution time and retrieve unduplicated records is to use the SELECT ID command. It will fetch the records with the PKs provided as the input, which means there will be no duplicity. This process is mainly used while checking data versions, audit logs, and migrated records.
Avoiding loops and nested loops
Loops and nested loops can be used in SQL queries to fetch data, update any record, and perform several other tasks. Usually, such loops are used in update and insertion scripts, DB package execution, cyclic batch processing, XML file generation, and so on. If there is any value out of bounds of exception or the condition given for the loop is wrong, the control will continue to run inside it only, thereby leading to infinite execution. That’s why developers should limit the use of loops and nested loops while executing SQL queries at the most.
Using WHERE instead of HAVING for data searches
When a particular record needs to be fetched from the DB based on a certain value, it is crucial to mention the latter in a WHERE condition. The keyword will fetch records that meet the conditions specified after the Where keyword only. Although you can use HAVING keyword, it takes more time to compile and display the results. Besides, where keyword is more precise in sorting and retrieving the records.
Conclusion
In this article, we have discussed the best ways to optimize SQL queries and limit the errors that might occur. Besides, optimization will also reduce the execution time, thereby reducing the chances of timeout and package execution failures. While using SQL, it is suggested to work with simpler queries that won’t cause any conflict regarding the PKs, existing records, and duplicate records.