Online mysql optimizer code#The cost here will be similar to the caching solutions mentioned above, primarily increased code complexity from keeping the search indices up-to-date with the database. Take the load off database by moving complex or heavy-text queries into a search engine like Elasticsearch. Supplement Queries With a Search Engine Like ElasticSearch This example works especially well with cases like monthly historical data, so it will be rarely updated. Rather than query this data every time the dashboard is loaded, save the data beforehand in a summary table. For example, a CRM dashboard might show the number of sales leads and closes by month by salesperson. Similarly, a summary table is a table that holds a variety of aggregate queries. Online mysql optimizer update#As such, we would need to update this unread_message_count every time a message gets read or new messages come in. For example, if a messages table is quite large and the number of unread messages for users are accessed frequently, it may be a good idea to store unread_message_count in the users table as a column. Cache Columns and Summary TablesĪ cache column on a table stores certain frequently accessed aggregate (ie. We cache the order query as instance variable, so we don’t run the query 3 times. If a query result is used multiple times during an operation, be sure to cache it as a variable. Many ORM frameworks already support prefetching such as ActiveRecord’s include method, so just be sure to use them. SELECT * FROM users WHERE account_id = 42 SELECT * FROM expenses WHERE user_id IN (3,5,27,38,99) Prefetching (aka eager-loading) means if we expect the application to also query for the expenses, then we batch query them by user_id, resulting in just 2 queries instead of 5. Here are the 5+1 queries SELECT * FROM users WHERE account_id = 42 # returns users 3,5,27,38,99 # Now select the expenses for each user SELECT * FROM expenses WHERE user_id = 3 SELECT * FROM expenses WHERE user_id = 5 SELECT * FROM expenses WHERE user_id = 27 SELECT * FROM expenses WHERE user_id = 38 SELECT * FROM expenses WHERE user_id = 99 Suppose, we want to fetch all 5 users from an account as well as each user’s expenses. If this definition is abstract and confusing, an example will clarify: The n+1 query is defined as a query that fetches n records from the database, then runs an addition query for each of those records. Prefetching Records to Prevent n+1 Queries But in high-write cases, caches are frequently missed, defeating the purpose of caching. This type of c aching works great for high-read-low-write caches, such as queries which populate the content for the front page of a popular media site. The next time a cache-able query is requested, it sees the cache is absent/invalid (aka cache miss), so it will query MySQL and cache the result. A common technique is when a table is updated, all its caches are deleted/invalidated. This complexity comes primarily from keeping the cache up-to-date with MySQL. Not every query should be cached, just those queries which are frequently accessed and/or slow.Īs with all caching solutions, this will increase code complexity and introduce potentially hard-to-find bugs. Cache Queries in RedisĬ aching database queries in an in-memory database like Redis will lead to fewer MySQL queries. These optimization techniques occur at the application level. For simplicity, we colloquially use MySQL to refer to these other databases as well. Note most of these techniques are also applicable to PostgreSQL and other SQL-RDBMSs. The techniques we discuss fall into these categories: Your MySQL-backed web application’s traffic just increased 100x (yay!) but now you’re starting to see timeouts (no!). As a heuristic, we use the following to define what it means to “scale MySQL”: This article presents techniques from all three of these perspectives. Online mysql optimizer how to#Or How to Stop Worrying When You See Timeouts and Learn to Love These Techniques What Does it Mean to Optimize and Scale a MySQL Web Application?Īsk an application developer, a database admin, and a DevOps engineer what it means to “optimize and scale MySQL” and you’ll probably get three different answers.
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