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The caching and buffers subsystem is responsible for ensuring that the most frequently used data (or structures, as you will see) are available in the most efficient manner possible. In other words, the data must be resident or ready to read at all times. The caches dramatically increase the response time for requests for that data because the data is in memory and thus no additional disk access is necessary to retrieve it. The cache subsystem was created to encapsulate all of the caching and buffering into a loosely coupled set of library functions. Although you will find the caches implemented in several different source code files, they are considered part of the same subsystem. A number of caches are implemented in this subsystem. Most of the cache mechanisms use the same or similar concept of storing data as structures in a linked list. The caches are implemented in different portions of the code to tailor the implementation to the type of data that is being cached. Let s look at each of the caches.

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If multiple logging aspects are present and if they dominate all other aspects, the precedence between the logging aspects themselves is not determined If such precedence between logging aspects is important to your system, you can make certain logging aspects dominate other specific logging aspects However, this can get complex A better solution is to design the logging aspects so that only one logging aspect advises each join point In practice, you rarely need such precise control over the logging aspect precedence 554 Changing the underlying logging mechanism In most situations, you will be using just one logging API However, in certain situations you may need to support multiple underlying logging APIs One common situation calling for such support is during the development of components, when you may need to support the possibility of multiple logging packages.

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This technique works best if the pivot table layouts are static. If the layout changes and extends Tip

The table cache was created to minimize the overhead in opening, reading, and closing tables (the .FRM files on disk). For this reason, the table cache is designed to store metadata about the tables in memory. This makes it much faster for a thread to read the schema of the table without having to reopen the file every time. Each thread has its own list of table cache structures. This permits the threads to maintain their own views of the tables so that if one thread is altering the schema of a table (but has not committed the changes) another thread may use that table with the original schema. The structure used is a simple one that includes all of the metadata information for a table. The structures are stored in a linked list in memory and associated with each thread.

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The simplest solution is to use a wrapper logging toolkit such as the Jakarta Commons Logging toolkit (see http://jakartaapacheorg/commons/logginghtml) This kit provides an abstract logging API and delegates the actual logging to standard Java logging toolkit, log4j, or Systemout The other solution is to separate the definition of logging join points and the advice to those join points You can then write multiple aspects advising the join points in a toolkit-specific manner In building the system, you can then include the aspects of the required toolkit Any changes to the definition of logging join points are isolated from the logging toolkit implementation Yet another choice is to use around advice to bridge between multiple APIs With such a style, you advise each invocation of the source logging method and bypass it with the call to the target logging kit.

The record cache was created to enhance sequential reads from the storage engines. Thus the record cache is usually only used during table scans. It works like a read-ahead buffer by retrieving a block of data at a time, thus resulting in fewer disk accesses during the scan. Fewer disk accesses generally equates to improved performance. Interestingly, the record cache is also used in writing data sequentially by writing the new (or altered) data to the cache first and then writing the cache to disk when full. In this way write performance is improved as well. This sequential behavior (called locality of reference) is the main reason the record cache is most often used with the MyISAM storage engine, although it is not limited to MyISAM. The record cache is implemented in an agnostic manner that doesn t interfere with the code used to access the storage engine API. Developers don t have to do anything to take advantage of the record cache as it is implemented within the layers of the API.

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