Kevin Grittner wrote:
In addition to what Mark pointed out, there is the possibility that a
is running which is scanning a large table or otherwise bringing in a
large number of pages from disk. That would first use up all available
unused cache space, and then may start replacing some of your
frequently used data.
An LRU cache is often a bad strategy for database applications. There are two
illustrations that show why.
1. You have an index that's used for EVERY search, but each search returns a
large and unique set of rows. If it happens that the rows returned exceed the
systems cache size, the part or all of your index will be flushed with EVERY
2. You do a sequential scan of a table that's one block bigger than the file
system cache, then you do it again. At the beginning of the second scan, the
first block of the table will have just been swapped out because it was the
oldest, so the file system brings it back in, replacing the second block, which
is now the oldest. As you scan the table, each block of the table is swapped
out JUST BEFORE you get to it. At the start of your query, the file system
might have had 99.9% of the relevant data in memory, but it swaps out 100% of
it as your query progresses.
Scenario 2 above is interesting because a system that is performing very well
can suddenly experience a catastrophic performance decline when the size of the
data exceeds a critical limit - the file system's avaliable cache.
LRU works well if your frequently-used data is used often enough to keep it in memory.
But many applications don't have that luxury. It's often the case that a single query
will exceed the file system's cache size. The file system cache is "dumb" --
it's strategy is too simple for a relational database.
What's needed is a way for the application developer to explicitely say, "This
object is frequenly used, and I want it kept in memory."
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