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mysql perf eval

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Sears Russell 2005-03-26 02:20:40 +00:00
parent 6751ba0204
commit 0b71e13bff
1 changed files with 13 additions and 5 deletions
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18
doc/paper2/LLADD.tex
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@ -1483,7 +1483,7 @@ record.
%We then calculate the $(page,slot)$ offset within that region. %We then calculate the $(page,slot)$ offset within that region.
\subsection{Bucket Overflow} \subsection{Bucket List}
%\eab{don't get this section, and it sounds really complicated, which is counterproductive at this point -- Is this better now? -- Rusty} %\eab{don't get this section, and it sounds really complicated, which is counterproductive at this point -- Is this better now? -- Rusty}
% %
@ -1831,7 +1831,7 @@ application servers.
However, one drawback of many such schemes is that any update requires However, one drawback of many such schemes is that any update requires
a full serialization of the entire object. In some application a full serialization of the entire object. In some application
scenarios, this can be extremely inefficient, as it may be the case scenarios this can be extremely inefficient as it may be the case
that only a single field from a large complex object has been that only a single field from a large complex object has been
modified. modified.
@ -1842,12 +1842,13 @@ The backing store also
maintains a separate in-memory buffer pool with the serialized versions of maintains a separate in-memory buffer pool with the serialized versions of
some objects, as a cache of the on-disk data representation. some objects, as a cache of the on-disk data representation.
Accesses to objects that are only present in the serialized buffers Accesses to objects that are only present in the serialized buffers
pool incur medium latency, as they must be unmarshalled (deserialized) pool incur significant latency, as they must be unmarshalled (deserialized)
before the application may access them. before the application may access them.
There may even be a third copy of this data resident in the filesystem There may even be a third copy of this data resident in the filesystem
buffer cache, accesses to which incur latency of both system call overhead and buffer cache, accesses to which incur latency of both system call overhead and
the unmarshalling cost. the unmarshalling cost.
To maximize performance we want to maximize the size of the in-memory object cache.
However, naively constraining the size of the data store's buffer pool However, naively constraining the size of the data store's buffer pool
causes performance degradation. Most transactional layers causes performance degradation. Most transactional layers
(including ARIES) must read a page (including ARIES) must read a page
@ -1877,14 +1878,21 @@ custom log entries, it is trivial to have it store deltas to
the log instead of writing the entire object during an update. the log instead of writing the entire object during an update.
Such an optimization would be difficult to achieve with Berkeley DB Such an optimization would be difficult to achieve with Berkeley DB
since the only diff-based mechanism it supports requires changes to since the only diff-based mechanism it supports requires changes to
span contiguous, which is not necessarily the case for arbitrary span contiguous regions of a record, which is not necessarily the case for arbitrary
object updates. object updates.
In a database server context, this type of optimization can be In a database server context, this type of optimization can be
supported if the fields of the object are broken into database table supported if the fields of the object are broken into database table
columns. and a SQL update query only modifies a subset of the fields. columns. and a SQL update query only modifies a subset of the fields.
However, as we have seen in some preliminary evaluation, However, as we have seen in some preliminary evaluation,
the overheads associated with a SQL based interface outweigh the the overheads associated with a SQL based interface outweigh the
advantages of this optimization. advantages of this optimization. To avoid RPC costs, we ran the
benchmark using libmysqld, which bypasses RPC costs by linking MySQL
into an application binary. We used an InnoDB table, which seemed
to perform better than tables backed by Berkeley DB. Still, MySQL
was significantly slower then native Berkeley DB. If we do not link
MySQL the MySQL server into the application binary then the cost of local
interprocess communication dominates the test, causing the benchmark to
take too long to be reported here.
%% \footnote{It is unclear if %% \footnote{It is unclear if
%% this optimization would outweigh the overheads associated with an SQL %% this optimization would outweigh the overheads associated with an SQL