There is a better way to narrow your searches.
#LIKE ILIKE SQL PLUS#
In this example table, there are only 19 rows but what if it was populated with perhaps 300 plus records? The time needed to search through all those records, one at a time by hand. Pouring over these records one at a time is not optimal. The mock table and generic data being used in this post will depict a Pipeline Weld Id or Weld X-Ray log containing the columns: id, weld_id, tech_name and date_completedįor the purpose of this blog post, suppose part of your task is to peruse this weld_log table and perform any number of operations with the weld_id field. Perhaps you need to review differences in the naming of any repaired Weld Ids? In the past, these repairs have been labeled with an ‘R’ at the end of the designated name but the naming in this table is mixed differently. I will be using Xubuntu Linux 16.04.2 LTS (Xenial Xerus), PostgreSQL 9.6.2 and pgAdmin for these exercises. It by no means depicts actual data belonging to or being used by any party or organization. Note: All data, names or naming found within the database presented in this post, are strictly used for practice, instruction and testing purposes.
Along with briefly visiting the PostgreSQL keyword ILIKE as well. In this post, I will demonstrate pattern matching examples using the powerful LIKE operator, found in both traditional SQL and PostgreSQL. Searching numerous columns of data for particular records is time-consuming and less than productive.
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