![]() Mainly throughout the course, we will use this operator with the SELECT command, which comes under Data Query Language (DQL). SELECT firstname, lastname FROM customer WHERE firstname LIKE Jen. There are numerous other ways to increase performance, which you can learn more about at my OSCON talk Speed Up You Database 300 Times.Īnna will be giving the talk Speed Up You Database 300 Times at OSCON 2017 in Austin, Texas. If you're interested in attending the conference use this discount code when you register, for our readers: PCOS. This LIKE operator is always used with WHERE clause in SELECT, UPDATE, and DELETE commands/statements. This tutorial shows you how to use PostgreSQL LIKE and ILIKE operator to query. Use EXPLAIN to confirm and remove any index that is not used in queries. So only add indexes that actually increase read performance. This doesn't mean that you should add indexes everywhere because each index makes it longer to write to the database. Each table uses a key for an optimal performance, making the query 380 times faster than the original. When these 100 albums are scanned, associated pictures are pinpointed using the album_id key. This time, the album table is not scanned in its entirety, but the right albums are quickly pinpointed using the user_id key. You can make sure that both tables use a key by adding the following index: ALTER TABLE album ADD INDEX(user_id) The query is also about 317 times faster than the original. This reduces the number of rows scanned to 200,000. After that, the pictures are quickly located using the indexed album_id column. First, all the albums are scanned to find the ones that belong to the user. Now if you run the query, the process no longer involves scanning the entire list of pictures. For example, you can add an index on picture.album_id like this: ALTER TABLE picture ADD INDEX(album_id) Use indexes to avoid unnecessary passes through tables. You can either flip through all the pages, or you can pull on the right letter tab to quickly locate the name you need. ![]() Think of data as being names in an address book. ![]() You can significantly increase performance by using indexes. However, you can make this process much more efficient. This means that it actually scans 40 billion rows for the album table. Prints string: SELECT FROM mytable LIMIT 20, 10 (in MySQL. It scans 2,000,000 pictures, then, for each picture, it scans 20,000 albums. Compiles the selection query just like builder->get() but does not run the query. The important pieces here are the table name, the key used, and the number of rows scanned during the execution of the query. You see a row for each table that was involved in the query: The result you get is an explanation of how data is accessed. LEFT JOIN album ON picture.album_id = album.id You simply prefix the query like this: EXPLAIN SELECT picture.id, picture.title It works with SELECT, DELETE, INSERT, REPLACE, and UPDATE statements. One tool that MySQL offers is the EXPLAIN keyword. Once you know which are the offending queries, you can start exploring what makes them slow.
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