With growing online presence, more companies become concerned about reliability and performance of their systems. As a lot of system operations depend on databases, their seamless performance is crucial for overall system efficiency. There are a lot of factors that affect database working, including indexing, query structure, capacity of CPU and memory resources, etc. The practice of database optimization helps to define and tweak these factors enhancing database performance.
Query optimization is one of the primary methods used to boost database operation. It is aimed to find the most efficient way to execute SQL statement, avoiding processing excessive data. First and foremost, it includes defining and tuning of slow queries. Queries of that kind require plenty of system resources for the execution, which takes them longer to run and as a result delays data retrieval. To speed up data access it is recommended to use indexing.
Indexing the most searched for and ordered columns allows to select or sort your rows faster. Without indexing the search would go processing the whole data in the database until the required entry is found. Indexes implement special data structure that allows to process lesser data instead of searching through the whole bulk of information.
Another case of query optimization is correlated subqueries. A correlated subquery is a “query within another query”, where the inner one depends on outer or parent query. The case is usual with the command of WHERE from the outer query. Because of the correlation with a column of the outer query, the inner one gets re-executed for each row of the result. This affects the overall speed of the process slowing down database operation. Using JOINs, i.e. related data from another table, instead of correlated subqueries is a more efficient way to run SQL queries.
Another way to boost database performance is to avoid queries inside code loops. The problem with such queries is that every time the code loop is run it is followed by SQL query execution. As the number of iterations depends on how many objects another query returns, this could seriously hinder overall database performance, especially with large systems. This problem could be overcome by modifying the query by using JOINs, or unique UPDATE or INSERT commands with individual rows to avoid updating stored data.
Giving up using temporary tables in SQL queries can also speed up system processes. Temporary tables are used as a workspace to store intermediate results and are automatically deleted when your database connection is over. Its usage is preferable when working with large tables. For example, transferring data in a temp table for further operations would be far more efficient, as it has fewer rows to process than the initial table. However, creating temp tables requires a lot of system resources and slows down the speed of I/O operations, which impacts negatively the whole sequence. In turn, correct settings of certain database parameters and buffers allow to reduce disk I/O and boost performance efficiency while processing same queries or returning search results.
Sometimes the reason your database underperforms might be because of lack of hardware resources. Upgrading to a stronger CPU and allocating more memory could help your database operate faster and more efficient when tasked with multiple queries.
When it comes to huge scopes of data, even the slightest adjustments can have a huge difference. Implementing proper indexing, fine-tuning query structure or simply choosing the most suitable database type can have a dramatic effect on overall system performance. Consult our expert DBAs on regular database optimization and have your system run like clockwork without any delays.