Data access is expensive for your application. It often requires CPU, network and disk access, all of which can take a lot of time. Using less computing resources, particularly in the cloud, results in decreased overall operational costs, so caches provide real value by avoiding using those resources. You need an efficient and reliable cache in order to achieve the desired result. Your end users also care about response times because this affects their work productivity or their enjoyment of your service. This post describes some of the most common cache methods for MySQL.
When the query cache is enabled, MySQL examines each query to see if the contents have been stored in the query cache. If the results have been cached they are used instead of actually running the query.. This improves the response time by avoiding the work of the query. If you are getting the impression that I’ve just introduced you to the query performance magic bullet, unfortunately, I haven’t.
The problem with the query cache is “coarse invalidation”. That is, as soon as you change a single row in any table, the query cache entries for every query which accessed that table must be invalidated. If a frequently executed or expensive query is invalidated, response time will be significantly impacted.
The invalidation frequency is controlled by the rate of change in the database tables. This results in unpredictable and therefore, undesirable, performance. The other big problem with the query cache is that it is protected by a single mutex. On servers with many cores, a high volume of queries can cause extensive mutex contention. Percona Server even has a state in the processlist ‘waiting on query cache mutex’ so that this is easier to spot.
In order to eliminate the unpredictable nature of the the query cache, external caches like Memcached are usually employed. When rows in the database change, the old query results remain available in the cache, but they are now “stale”. Until the cache key expires, the contents are available immediately and you avoid performing work in the database.
Eventually the cache contents expire. When this happens, the application attempts to get the cached value and fails. At this time, the application must compute the results, then place them into the cache. Additionally, memory pressure may cause unexpected invalidation of items, once again resulting in unpredictable, and therefore undesirable performance.
If the cache is emptied (perhaps due to a restart, crash, upgrade or power loss) then all of the results are invalidated, which can cause very poor performance.
With both of these cache methods, once the cache is invalidated or expires the entire result must be recalculated. This recalculation may be very expensive. For example, suppose we need to calculate the total count of items sold. A query may have to access many millions of rows of data to compute that result and this takes time.
Another problem which can occur is the “cache stampede” aka a “miss storm”. This happens when multiple requests need data for the same key, but the key recently expired. The stampede impacts performance because multiple requests try to recompute the contents at the same time. These cache stampedes are essentially the cause of the unpredictable performance of the MySQL query cache, since the rate of invalidation can not be controlled, and multiple cache entries may be invalidated by a single table change.
Peter’s advice in the miss storm blog post suggests that for best performance one should pre-compute the data for expensive keys. That is, the keys which are frequently accessed, or those that are expensive to recompute. For the queries that are expensive to recompute, the pre-computation normally takes the form of summary tables, which are discussed next.
If request frequency is the problem (mostly likely because the response time goes up due to increased concurrency) but the time to compute the contents is low, then using a mutex around the content generation is a possible solution to the problem. This forces one query to do the computation while others wait for the result. There are also probabilistic methods to enqueue items to be rebuilt with some increasing probability as the request time approaches the expiration time. This does not, however, offer very much improvement for keys which are not accessed very frequently.
Both Memcached and the MySQL query cache are limited in size. If you try to cache more information than you can store, space will need to be freed in order to store the new information in the cache. In order to ensure cache efficiency, you must only place information in the cache that you intend to retrieve again. Storing data in the cache that you won’t read again also increases response time in your application because it wastes a round trip to the cache server. It wastes CPU and other resources too.
If you can cache an entire HTML block instead of the rows used to create it, then do so. This avoids the CPU usage on your web server to create the block from the rows again and again. If you are paying for compute cycles in the cloud, this can be very beneficial as may reduce the number of instances you need.
Asking the cache for many different pieces of data to satisfy a single request is not very efficient. Use multi_get when possible. Once again, caching entire portions of pages is a good way to reduce the number of round trips to the cache.
Queries that access a lot of data usually face two bottlenecks: disk IO and CPU usage. Disk IO is expensive, and even if the disk bottleneck is eliminated, the sorting, aggregation and join operations are still CPU intensive and single threaded. In order to avoid these operations, the results can be pre-aggregated into a summary table.
Ideally, the summary tables can be updated with only the information that changed since they were last populated. MySQL includes two statements that make this easier: CREATE TABLE .. SELECT and INSERT .. SELECT. These SQL commands can be used to either replace the contents of, or insert new data into the summary table.
One advantage of summary tables is that they are persistent unlike the query cache or Memcached. There is no risk of unexpected invalidation, either. The summary table always exists, and should be fast to access, since it can be indexed appropriately for your queries.
The INSERT .. SELECT approach works best when there is some sort of log table, such as a log of clicks, a web access log, metric data from a monitoring system, or the like, which is to be aggregated over time. With this type of source data, one does not expect to see very many (if any) updates to the data once it has been collected.
This method does not usually work well when database tables may be updated or when rows may be deleted. Such changes may happen if a log is accidentally imported twice, for example and then the duplicate items are deleted. When this happens, decreasing the counts in summary tables may not be possible and thus they may be out of sync with the underlying data.
If there are frequent changes to the data then other options for maintaining summary tables must be used. Either they must be rebuilt from scratch each time (like a memcache miss) or they must be updated. Updating the summary tables efficiently is a hard problem to solve. My latest post on using Flexviews addresses this problem in a dedicated post.
If the least expensive queries are the ones you never run, then the most expensive queries very well may be the ones you have to run when the cache is empty. When talking about cache, the miss path is at least as important as the hit one. In order to make the miss path less expensive, use a layered approach to your caching. Cron jobs and summary tables can be used to make the miss path much less expensive. If you don’t pre-compute, and your website performance is dependent on your cache, then you could have a serious performance problem if your cache is unexpectedly emptied.
Effective caching is important. It is important to only cache things you know you will need again. Cache data in the form that it makes most sense to your application. Don’t cache the results of every query, simply because it is easy to do so.
Percona’s widely read Percona Data Performance blog highlights our expertise in enterprise-class software, support, consulting and managed services solutions for both MySQL® and MongoDB® across traditional and cloud-based platforms. The decades of experience represented by our consultants is found daily in numerous and relevant blog posts.
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