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The Buffer Cache Hit Ratio
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Buffer reads are much faster than reads from disk. The all-important principle in appropriately sizing the buffer cache is summarized in the phrase touch as few blocks as possible, since disk I/Os necessary for reading data from Oracle blocks on disk are more time consuming than reading the data from the SGA. This is why the buffer cache hit ratio, which measures the percentage of time users accessed the data they needed from the buffer cache (rather than requiring a disk read), is such an important indicator of performance of the Oracle instance. You derive the buffer cache hit ratio as follows: hit rate = (1 (physical reads)/(logical reads)) * 100 In this calculation, the physical and logical reads (reads from disk and from memory, respectively) are accumulated from the start of the Oracle instance. So if you calculate the ratio on Monday morning after a restart on Sunday night, it will show a very low hit ratio. As the week progresses, the hit ratio could increase dramatically, because as more read requests come in, Oracle satisfies them with the data that is already in memory. Unfortunately, Oracle does not give you any reliable rules or guidelines to indicate how much memory you should allocate for your buffer cache ratio or the SGA. Some trial and error with data loads should give you a good idea about the right size. In 20, I present much more information on the tuning of the database buffer cache. A high buffer cache hit ratio doesn t always correlate with superior database performance. It is entirely possible for your database to have a very high hit ratio say, in the high 90s and still have a performance problem. For example, even if your total logical reads and hit ratio are high, your SQL queries could still be inefficient.
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The Shared Pool
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The shared pool is a very important part of the Oracle SGA, and sizing it appropriately for your instance will help avoid several types of Oracle instance bottlenecks. Unlike the database buffer cache, which holds actual data blocks, the shared pool holds executable PL/SQL code and SQL statements, as well as information regarding the data dictionary tables. The data dictionary is a set of key tables that Oracle maintains, and it contains crucial metadata about the database tables, users, privileges, and so forth. Proper sizing of the shared pool area benefits you in a couple of ways. First, your response times will be better because you re reducing processing time if you don t have to recompile the same Oracle code each time a user executes a query, you save time. Oracle will reuse the previously compiled code if it encounters the same code again. Second, more users can use the system because the reuse of code makes it possible for the database to serve more users with the same resources. Both the I/O rates and the CPU usage will diminish when your database uses its shared pool memory effectively. The following sections discuss the library cache and the data dictionary cache, both of which are components of the shared pool.
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The Library Cache
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All application code, whether it is pure SQL code or code embedded in the form of PL/SQL program units, such as procedures and packages, is parsed first and executed later. Oracle stores all compiled SQL statements in the library cache component of the shared pool. The library cache component of the shared pool memory is shared by all users of the database. Each time you issue a SQL statement, Oracle first checks the library cache to see if there is an already parsed and ready-to-execute form of the statement in there. If there is, Oracle uses the library cache version, reducing the processing time considerably this is called a soft parse. If Oracle doesn t find an execution-ready version of the SQL code in the library cache, the executable has to be built fresh this is called a hard parse. Oracle uses the library cache part of the shared pool memory for storing newly parsed code. If there isn t enough free memory in the shared pool, Oracle will jettison older code from the shared pool to make room for your new code. All hard parses involve the use of critical system resources, such as processing power, and internal Oracle structures, such as latches; you must make every attempt to reduce their occurrence. High hard-parse counts will lead to resource contention and a consequent slowdown of the database when responding to user requests. You should make decisions about the library cache size based on hit and miss ratios on the library cache as I explain in 20. If your system is showing more than the normal amount of misses (meaning that code is being reparsed or reexecuted often), it is time to increase the library cache memory. The way to do this is to increase the total memory allocated to the shared pool.
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