The site has a relatively low daily traffic of 1.5K units and is hosted on a VPS with 512 RAM + 1GHz CPU. It also utilizes a mysql database with MyISAM tables. The tables involved in the queries contain several hundred thousand records on average, except for one table, which holds nearly a million records.



To optimize query performance, BTREE type indexes and unique primary keys have been added to the fields of the associated tables.

I've been maintaining a mysql_slow log, which logs slow queries. Upon recent examination, I was alarmed to discover that seemingly simple queries (without sorting, grouping, searching, limits, subqueries) are taking an exceptionally long time to execute. For instance, a query similar to the one below took almost 8 seconds to complete:

SELECT mp3_id3.artist, mp3_id3.album, mp3_id3.year, mp3_id3.title, mp3_genres.name AS genre, mp3_main.size, mp3_main.duration
FROM (mp3_id3)
JOIN mp3_main ON (mp3_id3.song_id = mp3_main.song_id)
JOIN mp3_genres ON (mp3_id3.genre = mp3_genres.number)
WHERE mp3_id3.song_id = 52596131;
What could be causing such a significant drop in productivity? Is it necessary to adjust any parameters in the mysql config? Are there any tools available to pinpoint the root of this issue?

There are several factors that could be contributing to the slow query execution.

Firstly, the server specs might be a limiting factor. The VPS with 512MB of RAM and a 1GHz CPU may struggle to handle complex queries, especially when dealing with large datasets. Upgrading the server resources could potentially improve the query performance.

Secondly, the choice of MyISAM tables in the MySQL database could be a contributing factor. MyISAM is known to have performance limitations, especially with write-intensive workloads or high-concurrency scenarios. Consider converting the tables to the more modern InnoDB format, which provides better support for ACID transactions and offers improved performance for complex queries.

Additionally, the indexing strategy should be reviewed. While you've mentioned the use of BTREE indexes and unique primary keys, it's essential to ensure that the indexes are optimized for the specific queries being executed. Adding indexes to fields involved in join conditions and the WHERE clause can significantly speed up query execution.

Furthermore, examining the MySQL configuration parameters is critical. Adjusting settings such as `key_buffer_size`, `query_cache_size`, and `innodb_buffer_pool_size` based on the available system resources and workload characteristics can enhance overall database performance.

To diagnose the root cause of the slow queries, utilizing tools like the MySQL Performance Schema, EXPLAIN statement, and query profiling can provide insights into query execution plans, index usage, and potential bottlenecks within the database engine.
Optimizing query performance requires a holistic approach encompassing server resources, database schema design, indexing strategy, configuration tuning, and thorough analysis using specialized database performance tools. By addressing these aspects, you can work towards mitigating the significant drop in productivity and improving the overall efficiency of the MySQL database system.

I'd like to emphasize the impact of record volume on MySQL performance. With a million records, MySQL can become less efficient, especially when dealing with complex operations such as key sampling. It's essential to evaluate the necessity of using join operations. While not inherently complex, utilizing three select queries by key may offer faster performance compared to a join, with the added benefit of increased chances to retrieve values from the cache.

Moreover, it's crucial to investigate potential issues with locks. Analyzing the logs can reveal if requests are frequently waiting for table lock permissions, which might be impacting overall system performance.

In addition, I recommend removing MyISAM at the table level and considering denormalization. For instance, storing the genre within a table with a song or using a SET could streamline operations. Furthermore, evaluating the use of inner join versus left join for table joins is essential to optimize query performance.
Furthermore, it's worth noting the explicit creation of a temporary table, which adds to the overall load. Experimenting with creating a view instead could potentially improve the system's efficiency.

It's crucial to constantly monitor the global status parameters and identify any abnormalities. There are useful scripts available to assist with this, such as mysqlreport and tuning-primer.sh, which can be easily found with a quick online search.

Issues like MyISAM locks and temporary disk tables often lead to performance slowdowns, possibly due to limited space for indexes or constraints on file descriptors. If you're unfamiliar with these topics, it's recommended to run the aforementioned scripts and thoroughly comprehend their output. Following this, if memory issues are ruled out, you can make significant improvements by identifying and addressing possible bottlenecks. Alternatively, if memory problems are detected, it's important to reduce unused resources. Overall, this process of iterative optimization is complex but essential.