What are the most effective methods for optimizing MySQL table and query performance to meet high-traffic and low-latency requirements?

To optimize MySQL for high-traffic and low-latency requirements, it's essential to focus on indexing, query optimization, and server configuration. As a database administrator, I'd recommend leveraging the power of indexing to reduce the number of rows that need to be scanned, thereby decreasing query execution time. This can be achieved by creating composite indexes, covering indexes, or even using indexing techniques like hash indexing or full-text indexing.

Additionally, optimizing queries is crucial, and this can be done by avoiding SELECT *, using LIMIT and OFFSET, and optimizing JOINs and subqueries. It's also vital to ensure that the MySQL server is properly configured, with adequate resources allocated to the buffer pool, sort buffer, and join buffer.

I'd also recommend implementing connection pooling, using a load balancer, and leveraging MySQL's built-in replication features to distribute the load and improve performance. Moreover, regular maintenance tasks like running ANALYZE TABLE and OPTIMIZE TABLE can help maintain optimal performance.

When troubleshooting sluggish query performance, leverage the EXPLAIN and EXPLAIN ANALYZE commands to pinpoint bottlenecks. For instance, if the output indicates a temporary table is being spun up, and the server lacks an SSD, first ensure the DBMS has sufficient RAM and optimal config settings to prevent disk thrashing. Moreover, the presence of BLOB and TEXT fields in the query result set can lead to disk I/O overhead.

If the analysis reveals slow sequential scans, it's likely that indexing is inadequate. Conversely, if updates and inserts are taking an eternity, the culprit might be an over-indexed table or an excessively large row count, making index maintenance a challenge (think millions or tens of millions of rows – a problem often mitigated by table partitioning).

When dealing with SELECT queries featuring LIKE conditions with percentages not anchored to the end of the string (i.e., searching for arbitrary substring occurrences), consider replacing this with a full-text search, especially if the use case involves word searches. Lastly, refrain from denormalizing the database unless absolutely necessary, as this can lead to long-term data management headaches and scalability issues.

When it comes to database table optimization, there are indeed a plethora of tools at our disposal, including phpMyAdmin, which offers a user-friendly interface for executing SQL queries and optimizing database performance. Additionally, DBAs and developers can leverage various tuning scripts, commands, and software programs to fine-tune their database tables and improve overall system efficiency.

In the realm of database administration, it's essential to employ a combination of indexing, caching, and query optimization techniques to ensure that database tables are running at peak performance. This can involve utilizing tools like MySQLTuner, a popular script for analyzing and optimizing MySQL databases, or PT-Online-Schema-Change, a tool for performing online schema changes without causing downtime.