MySQL is one of the most popular database management systems used today. Its scalability, high performance, security, cross-platform support, and compatibility with a range of other programming languages make it a top choice across a range of database applications including content management systems, cloud integration, and e-commerce sites. Knowing how to work with MySQL at a deeper level is essentail to taking advantage of its full capabilities. This exercise will go over MySQL optimization best practices and will use a bike shop dataset to show the impact proper performance tuning can have.

Importance of Performance Tuning

Performance tuning plays a crucial role in efficient and effective database utilization. Execution times, resource consumption, and overall user experience can be greatly enhanced through proper use:

• Faster Query Execution

• Efficient query execution can be the difference between waiting 0.0002 seconds and 20 minutes. As the database grows, these inefficiencies will further bog down the execution times.

• Resource Efficiency

• Minimizing resources like CPU and Memory can reduce server strain and in some cases save money with lower infrastructure costs.

• Better User Experience

• Application reliability, latency, and load times can be greatly reduced - creating a much smoother user experience.

System-Level Tuning

System-level tuning focuses on the hardware and operating system to enhance elements such as processing power, memory, and network configurations. The exact configurations depends heavily on the type of SQL processing being used (eg. whether or not the process is read-heavy or writing-heavy). The following should be considered for system-level tuning:

• Hardware

• CPU - Higher processing speed and greater caching capabilities can lead to faster queries and performance.
• MyISAM vs. InnoDB

• 	MyISAM	InnoDB
  Pros	fast read operations Less disk space consuption Lower memory usage	ACID (atomic, consistent, isolated, and durable) compliant Supports foreign keys Faster write-heavy applications Better crash recovery mechanisms Supports transactions
  Cons	Less reliable Prone to corruption Not ACID compliant Does not support foreign keys Does not support transactions	Requires more memory Requires more storage
  Best For	Simple read-heavy applications where data integrity is not important	All other applications

• SSDs and HDDs - SSDs are generally preferred due to greater data access speeds.

• Operating System

• Input/Output (I/O) Scheduling - Used to configure the order and priority of disk read and write operations.

• Network Configuration

• TCP Settings - Adjusting TCP buffer sizes can impact network-intensive MySQL environments with larger buffer sizes benefiting long-distance and high-latency networks. Adjusting TCP backlog settings can help with burst requests in MySQL by preventing connection drops and timeouts.

As the data and requirements grow, the hardware must be increased to keep up with the greater demand. Both vertical scaling (eg. higher CPU, more RAM, and faster or larger storage) and horizontal scaling (eg. server clustering and more distributed architecture) should be considered.

Software-Based Tuning

Increasing MySQL performance through software is done through configuring many of the MySQL settings which can greatly improve performance through efficiency and data processing speeds.

• Query Optimization

• Indexing - Proper indexing calls for faster querying by creating pointers to where data is stored
• Do not over index
• Create indexes where the WHERE clause and JOIN conditions are most frequently used
• Use composite indexes where queries involve multiple indexes
• Avoid using functions in WHERE clause as it can prevent use of indexes
• Joins - Efficiently join tables on primary and foreign keys
• Use the appropriate join type depending on your data relationships (INNER, LEFT, RIGHT, OUTER)
• Cross joins can lead to a large number of rows being processed

• Cache Configurations

• query caching often boosts performance in repetitive queries

• Adjustment Variable	Use	Recommended Configuration
  query_cache_size	Determines the amount of memory allocated to the query cache	0 disables the query cache (default), 1 allows caching all series, 2 enables caching only with the SQL_CACHE hint
  query_cache_type	Determines how query cache operates	10% to 20% of total memory is recommended to start

• InnoDB Storage Engine Configuration

• Adjustment Variable	Use	Recommended Configuration
  Innodb_buffer_pool_size	Adjusts server RAM for caching	50-70% of RAM
  Innodb_log_file_size	Adjust size of redo log files	Increase if more than 50% being utilized
  Innodb_flush_log_at_trx_commit	Adjust how transaction logs are written and flushed to the disk	Set to 1 for ACID compliance and durability; 0 or 2 for better performance at the cost of durability
  Innodb_flush_method	Determines how data and logs are flushed to disk	O_DIRECT for SSDs and fsync for HDD
  Innodb_file_per_table	Store each InnoDB table as its own file making scaling easier	Enable with ON
  Innodb_stats_on_metadata	Controls whether storage engine recalculates table statistics	Disable with OFF
  Innodb_buffer_pool_instances	Splits the buffer pool into multiple instances	1 instance per 1GB of buffer pool size with a limit of 8

• Database Configuration

• Defragmenting Tables: Use the OPTIMIZE TABLE command to reduce storage space and improve I/O efficiency.
• Archiving Data: Archiving old data that is no longer being used can
• Repairing Corrupted Tables: Use CHECK TABLE to see if the table is corrupted and REPAIR TABLE to repair the corrupted table.

Measuring Performance

Measuring performance is essential for knowing what steps to take in optimizing MySQL servers. Performance monitoring can be categorized into two main types - resource metrics (software, hardware, network resources consumed) and workload metrics (output a database can produce). Within these broader categories, there are a number of different ways to measure performance:

• Monitoring Query Performance

• EXPLAIN is used to get information about a query execution and returns the following column items:

• Column	Description	Output values
  id	Identifies the query step	integer (eg. 1)
  select_type	Identifies the type of query	SIMPLE - a select without using unions or subqueries PRIMARY - an outermost select UNION - second SELECT query type in a UNION DEPENDENT UNION - union dependent on outer query UNION RESULT - the result of a union SUBQUERY - a select query used in another select query (not using FROM clause) DEPENDENT SUBQUERY - subquery dependent on outer query DERIVED - a select query using FROM clause DEPENDENT DERIVED - derived table dependent on another table MATERIALIZED - temporary subquery table generated in memory UNCACHEABLE SUBQUERY - a subquery where the result cannot be cached and must be re-evaluated for each outer query row UNCACHEABLE UNION - a union in an UNCACHEABLE SUBQUERY
  table	Table name being accessed	string (eg. store_sales.orders)
  partitions	Lists partitions being used (how the query is being split)	partition identifier (eg. p1)
  type	Identifies the join type	ALL - fully scans table index - full index scan range - scans a range of rows using indexse ref - search rows with matching indexed values eq_ref - joins using a unique key const - uses a constant row or system table * join type listed from least to most efficient
  possible_keys	Identifies indexes being considered for query	Key type (eg. PRIMARY)
  key	Returns the index being used in the listed step	Key type (eg. PRIMARY)
  key_len	Returns the length of the key being used	integer (eg. 10)
  ref	Returns the column constant used with the index	Column name (eg. store_sales.order.order_id)
  rows	Returns the number of rows being queried	integer (eg. 2000)
  filtered	Returns a percentage of rows being filtered	integer (eg. 100)
  extra	Returns additional information	Using index - the query only uses indexes to get data Using where - retrieves all rows and then applies filter Using temporary - creates a temporary table Using filesort - an external sort is made

• Tracking Server Metrics

• Return server metrics using SHOW [GLOBAL|SESSION] STATUS;
• Using GLOBAL returns aggregated values across all connection
• Using SESSION returns current connection values
• SHOW [GLOBAL|SESSION] STATUS; returns a number of metrics including:

• Status Variable Category	Status Variable	Description
  Connections	Connections	Total number of connections
  	Threads_connected	Number of active connections
  	Threads_running	Number of threads actively running queries
  	Aborted_connects	Failed connection attempts
  Queries	Queries	Total number of queries
  	Slow_queries	Number of queries exceeding configured query speed threshold
  	Com_select	Number of SELECT operations
  	com_insert	Returns the length of the key being used
  Table and Row Operations	Handler_read_rnd	Number of random reads performed
  	Handler_read_key	Number of reads using indexes
  	Handler_write	Number of rows written
  	Handler_delete	Number of deleted rows
  Cache and Buffer Utilization	Qcache_hits	Number of query cache hits
  	Qcache_inserts	Number of queries added to query cache
  	Innodb_buffer_pool_read_requests	Number of read requests to the InnoDB buffer pool

  * Note: not all variables are covered in this table. CLick HERE for a full list of server status variable explanations.

There are a multitude of ways to measure performance. SHOW [GLOBAL|SESSION] STATUS; alone will return over a hundred status variables. They key is to know which status variables are important for which metric you want to focus on and how to read its output. It's important to also note that there are several third party applications such as ManageEngine, DigitalOcean, and Percona which can help monitor your MYSQL performance - each with their own strengths and weaknesses depending on your use case.

Applying Performance Tuning Practices to Bike Shop Dataset

This section will apply some of the mentioned MySQL performance strategies using a bike shop dataset.

Query Optimization

Let's start with a basic query trying to retrieve order data from a specific store:

Using EXPLAIN, we can get information of the query execution:

Whenever calling WHERE in a query, it is important to check where indexes can be useful. This query is looking for store_name = 'Santa Cruz Bikes' and joins the two tables on the store_id column. Knowing this, it would be useful to have indexes for store_name and store_id:

Rerunning EXPLAIN on the query, there are a few changes to note. Firstly, We can see the newly indexed rows in the query under the key column. The join type (under the type column) has also changed from "ALL" to "ref". This means the join went from fully scanning the whole table to only searching rows with matching index values - a much more efficient approach. Under the rows column we can see the number of rows being queried has been reduced significantly compared the non-indexed query:

Now that the tables are indexed, it is important to optimize the JOIN and SELECT portions of the query. We want to return rows that have matching values in both tables. This is already done with JOIN, but could be clearer syntactically using INNER JOIN (which does the same thing). The query currently returns all columns in both the stores and orders table - this returns unneeded information including many id columns. To clean up the query selection, we will also use SELECT only on the columns we need:

Conclusion

MySQL is a powerful database management tool with high performance, scalability, data security, and an abundance of documentation and support. Learning MySQL can be a challenge, but being able to optimize performance is essential in getting the most out of the program. Hands-on experience, tutorials, and familiarizing yourself with the documentation is the best way to learn MySQL and fast-track yourself towards fluency.