How do you perform query optimization?
Query Processing is Tasks related to obtaining information from a database. The purpose of QP is to convert a high-level language query into an accurate and effective execution strategy defined in a low-level language, then to execute that strategy to retrieve the needed data. The four primary phases are decomposition, optimization, code creation, and execution. Choosing a practical execution approach for processing queries is known as query optimization. QO aims to select the equivalent transformation that uses the fewest resources from the analogous changes of the same high-level query. Reduce the query’s total execution time in general. May also speed up the query’s answer. The chosen approach is reduced to finding the nearly ideal solution because the problem is computationally hard and has many relations.
- Instead of using SELECT * from, use only the columns you need in your results when utilizing SELECT statements. This will significantly shrink the size of the results and speed up your SQL query.
- Every time you nest one query/view inside another, a lot of data is returned, significantly slowing down your query. The database server might even time out occasionally and give no response.
- Use an IN-list predicate when searching an indexed column rather than a mathematical operator like “=“or a logical operator like AND/OR. Your SQL queries can run faster thanks to the IN predicate since the query optimizer sorts the IN-list according to the INDEX’s sort order.
- Create a temporary table that only contains the information needed for connecting with the smaller table if you are joining a huge table to a tiny one. To accomplish this, you can choose data from a large table, move it to a temporary table, and then join the small table to this temporary table.
- Due to logging, the UPDATE statement takes longer than the CASE statement. On the other side, the CASE statement speeds up your SQL queries and identifies what needs to be changed.
- Globally unique IDs should not be used because they will slow down your queries. Instead, use DATE or IDENTITY.
- JOINS take a long time because your database must check each matching record. Your database will require twice as long to match records if you also apply an OR condition in a JOIN. Use the IN operator instead, as was previously indicated.
- There could be instances where indexes are there but not being used. Implicit data type conversion can be one of the causes.