Closes #6460 Maybe optimize lookups of tables in DB to stop using SHOW TABLES LIKE

[Khadreal]

Description

Changed lookup table query.

Fixes #6460

Type of change

• Enhancement (non-breaking change which improves an existing functionality).

Detailed scenario

With a large site(mostly multisites), the query is run on every page loads and this slows down the page.
More information here https://wp-media.slack.com/archives/C43T1AYMQ/p1709561232039849

Technical description

Changed the query from show tables to select with a limit of 1

Documentation

Mandatory Checklist

Code validation

• I validated all the Acceptance Criteria. If possible, provide screenshots or videos.
• I triggered all changed lines of code at least once without new errors/warnings/notices.

Code style

• I wrote a self-explanatory code about what it does.
• I protected entry points against unexpected inputs.
• I did not introduce unnecessary complexity.

[codacy-production]

Coverage summary from Codacy

See diff coverage on Codacy

Coverage variation	Diff coverage
Report missing for 84982f81	✅ 50.00% (target: 50.00%)

Coverage variation details

	Coverable lines	Covered lines	Coverage
Common ancestor commit (84982f8)	Report Missing	Report Missing	Report Missing
Head commit (e951e59)	37565	9464	25.19%

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Diff coverage details

	Coverable lines	Covered lines	Diff coverage
Pull request (#6968)	4	2	50.00%

Diff coverage is the percentage of lines that are covered by tests out of the coverable lines that the pull request added or modified: <covered lines added or modified>/<coverable lines added or modified> * 100%

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[MathieuLamiot]

@wp-media/qa-team @Khadreal to discuss how to validate / test this.
A quick feedback here: the main risk I see is that we are not using "LIKE" anymore but a strictly equal table name. We would need to check on different setups (multi-site, a few hosting, etc.) that we always have the exact table name.
@Khadreal for instance, in most queries, we use $db->prefix, which does not seem to be the case in your PR. We'd need to ensure we are not missing a prefix mechanism here.

wp-rocket/inc/Engine/Preload/Database/Queries/Cache.php

Lines 310 to 311 in b898c2a

	$prefixed_table_name = $db->prefix . $this->table_name;
	$query = "SELECT id FROM `$prefixed_table_name` WHERE `last_accessed` <= date_sub(now(), interval $delay $unit)";

[Mai-Saad]

Test plan:

• confirm fixing the performance issue
• NRT plan execution https://wpmediaqa.testrail.io/index.php?/runs/view/898&group_by=cases:section_id&group_order=asc ✔️ (during execution, tried single site, multisite subdirectory nginx , subdomain multisite creating tables was good)

[Khadreal]

@wp-media/qa-team @Khadreal to discuss how to validate / test this. A quick feedback here: the main risk I see is that we are not using "LIKE" anymore but a strictly equal table name. We would need to check on different setups (multi-site, a few hosting, etc.) that we always have the exact table name. @Khadreal for instance, in most queries, we use $db->prefix, which does not seem to be the case in your PR. We'd need to ensure we are not missing a prefix mechanism here.

wp-rocket/inc/Engine/Preload/Database/Queries/Cache.php

Lines 310 to 311 in b898c2a

	$prefixed_table_name = $db->prefix . $this->table_name;
	$query = "SELECT id FROM `$prefixed_table_name` WHERE `last_accessed` <= date_sub(now(), interval $delay $unit)";

@MathieuLamiot
Tested this on multisite and its work as it should. $db->{$this->table_name} will return the table name with prefix. This $this->get_db() returns the db name with prefix

wp-rocket/inc/Engine/Common/Database/Queries/AbstractQuery.php

Line 234 in e951e59

$db = $this->get_db();

[Miraeld]

Hello hello,

I've conducted some tests and some logs of execution time between the old version and new one.

• For a database with 100 tables, it decreased from an average of 15ms to 5ms, representing a 66% improvement in performance.
• When I scaled up to a database with 500 tables, the improvement was even more significant. The execution time decreased from an average of 75ms to 15ms, which is an 80% improvement.
• Finally, in a large-scale scenario with a database containing 100,000 tables, the execution time went down from an average of 2 seconds to 20-30 ms.

So I confirm that it works as expected.