![]() We'll cover these wildcard characters in more detail in the next section. The most common wildcard characters used in the LIKE clause are the percent sign ( %) and the underscore ( _). The pattern can include one or more wildcard characters that represent any character or set of characters. The pattern is the pattern you want to search for within the column(s). The table_name refers to the name of the table you want to search within. You can specify multiple columns by separating them with commas. In this syntax, column_name(s) refers to the column or columns you want to search within. The syntax for using the LIKE clause is as follows: SELECT column_name(s) FROM table_name WHERE column_name LIKE pattern The LIKE clause is used to search for patterns within a column or columns in a MySQL database. So, let's get started! Understanding the Syntax of LIKE Clause We'll also discuss performance considerations to keep in mind when using the LIKE clause to ensure your queries run efficiently.īy the end of this guide, you'll have a solid understanding of how to use the LIKE clause in MySQL to search for data within your database. Then, we'll dive into the different types of wildcards you can use in the LIKE clause and provide real-world scenarios where the LIKE clause can be useful. We'll start by explaining the syntax of the LIKE clause and how it works. In this article, we'll provide a comprehensive guide to using the LIKE clause in MySQL. The LIKE clause is a powerful tool that allows you to search for patterns within columns in a database. Coordinate this activity with the database administrators (DBAs) and make a backup of the original statistics.Are you struggling to find specific data in your MySQL database? Do you spend hours scrolling through rows of data looking for specific patterns or names? If so, the LIKE clause in MySQL can be a lifesaver. That might, however, also lead to unwanted side effects. My personal recommendation goes even further: after every index change, update the statistics for the base table and all its indexes. ![]() Such statistics will enable Oracle Database to correctly decide when to use the index. For this reason, the Oracle documentation recommends updating the table statistics after creating a function-based index:Īfter creating a function-based index, collect statistics on both the index and its base table using the DBMS_STATS package. ![]() Although the Oracle database collects the index statistics for new indexes automatically ( since release 10 g), it does not update the table statistics. Statistics for a function-based index (FBI) are also kept on table level as virtual columns. These figures are reused if a column is part of multiple indexes. The Oracle database maintains the information about the number of distinct column values as part of the table statistics. There are no dedicated operations or keywords for function-based indexes. The database traverses the B-tree and follows the leaf node chain. It is a regular INDEX RANGE SCAN as described in Chapter 1. Index Cond: (upper((last_name)::text) = 'WINAND'::text) Recheck Cond: (upper((last_name)::text) = 'WINAND'::text) |*2 | INDEX RANGE SCAN | EMP_UP_NAME | 40 | 1 |Ģ - access(UPPER("LAST_NAME")='WINAND') PostgreSQL QUERY PLAN | 1 | TABLE ACCESS BY INDEX ROWID| EMPLOYEES | 100 | 41 | See “ Full Table Scan” to see why that might make sense. This causes the optimizer to ignore the index and do a TBSCAN. When using UPPER('winand'), the optimizer does a gross misestimation and expects 4% of the table rows to be selected. The query was changed to WHERE UPPER(last_name) = 'WINAND' (no UPPER on the right hand side) to get the expected result. ![]() From the database’s perspective, that’s something entirely different.Ģ | FETCH EMPLOYEES | 1 of 1 (100.00%) | 13ģ | IXSCAN EMP_UP_NAME | 1 of 10000 (. Although there is an index on LAST_NAME, it is unusable-because the search is not on LAST_NAME but on UPPER(LAST_NAME). ![]() It is a return of our old friend the full table scan. Predicate Information (identified by operation id):ġ - filter(UPPER("LAST_NAME")='WINAND') PostgreSQL QUERY PLANįilter: (upper((last_name)::text) = 'WINAND'::text) |* 1 | TABLE ACCESS FULL| EMPLOYEES | 10 | 477 | ![]()
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