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<td><b><dl><dt>Berkeley DB Reference Guide:<dd>Access Methods</dl></b></td>
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<p align=center><b>Access method tuning</b></p>
<p>There are a few different issues to consider when tuning the performance
of Berkeley DB access method applications.</p>
<br>
<b>access method</b><ul compact><li>An application's choice of a database access method can significantly
affect performance.  Applications using fixed-length records and integer
keys are likely to get better performance from the Queue access method.
Applications using variable-length records are likely to get better
performance from the Btree access method, as it tends to be faster for
most applications than either the Hash or Recno access methods.  Because
the access method APIs are largely identical between the Berkeley DB access
methods, it is easy for applications to benchmark the different access
methods against each other.  See <a href="/ref/am_conf/select.html">Selecting an access method</a> for more information.</ul>
<b>cache size</b><ul compact><li>The Berkeley DB database cache defaults to a fairly small size, and most
applications concerned with performance will want to set it explicitly.
Using a too-small cache will result in horrible performance.  The first
step in tuning the cache size is to use the db_stat utility (or the
statistics returned by the <a href="/api_c/db_stat.html">DB-&gt;stat</a> function) to measure the
effectiveness of the cache.  The goal is to maximize the cache's hit
rate.  Typically, increasing the size of the cache until the hit rate
reaches 100% or levels off will yield the best performance.  However,
if your working set is sufficiently large, you will be limited by the
system's available physical memory.  Depending on the virtual memory
and file system buffering policies of your system, and the requirements
of other applications, the maximum cache size will be some amount
smaller than the size of physical memory.  If you find that
<a href="/utility/db_stat.html">db_stat</a> shows that increasing the cache size improves your hit
rate, but performance is not improving (or is getting worse), then it's
likely you've hit other system limitations.  At this point, you should
review the system's swapping/paging activity and limit the size of the
cache to the maximum size possible without triggering paging activity.
Finally, always remember to make your measurements under conditions as
close as possible to the conditions your deployed application will run
under, and to test your final choices under worst-case conditions.</ul>
<b>shared memory</b><ul compact><li>By default, Berkeley DB creates its database environment shared regions in
filesystem backed memory.  Some systems do not distinguish between
regular filesystem pages and memory-mapped pages backed by the
filesystem, when selecting dirty pages to be flushed back to disk.  For
this reason, dirtying pages in the Berkeley DB cache may cause intense
filesystem activity, typically when the filesystem sync thread or
process is run.  In some cases, this can dramatically affect application
throughput.  The workaround to this problem is to create the shared
regions in system shared memory (<a href="/api_c/env_open.html#DB_SYSTEM_MEM">DB_SYSTEM_MEM</a>) or application
private memory (<a href="/api_c/env_open.html#DB_PRIVATE">DB_PRIVATE</a>), or, in cases where this behavior
is configurable, to turn off the operating system's flushing of
memory-mapped pages.</ul>
<b>large key/data items</b><ul compact><li>Storing large key/data items in a database can alter the performance
characteristics of Btree, Hash and Recno databases.  The first parameter
to consider is the database page size.  When a key/data item is too
large to be placed on a database page, it is stored on "overflow" pages
that are maintained outside of the normal database structure (typically,
items that are larger than one-quarter of the page size are deemed to
be too large).  Accessing these overflow pages requires at least one
additional page reference over a normal access, so it is usually better
to increase the page size than to create a database with a large number
of overflow pages.  Use the <a href="/utility/db_stat.html">db_stat</a> utility (or the statistics
returned by the <a href="/api_c/db_stat.html">DB-&gt;stat</a> method) to review the number of overflow
pages in the database.
<p>The second issue is using large key/data items instead of duplicate data
items.  While this can offer performance gains to some applications
(because it is possible to retrieve several data items in a single get
call), once the key/data items are large enough to be pushed off-page,
they will slow the application down.  Using duplicate data items is
usually the better choice in the long run.</p></ul>
<br>
<p>A common question when tuning Berkeley DB applications is scalability.  For
example, people will ask why, when adding additional threads or
processes to an application, the overall database throughput decreases,
even when all of the operations are read-only queries.</p>
<p>First, while read-only operations are logically concurrent, they still
have to acquire mutexes on internal Berkeley DB data structures.  For example,
when searching a linked list and looking for a database page, the linked
list has to be locked against other threads of control attempting to add
or remove pages from the linked list.  The more threads of control you
add, the more contention there will be for those shared data structure
resources.</p>
<p>Second, once contention starts happening, applications will also start
to see threads of control convoy behind locks (especially on
architectures supporting only test-and-set spin mutexes, rather than
blocking mutexes).  On test-and-set architectures, threads of control
waiting for locks must attempt to acquire the mutex, sleep, check the
mutex again, and so on.  Each failed check of the mutex and subsequent
sleep wastes CPU and decreases the overall throughput of the system.</p>
<p>Third, every time a thread acquires a shared mutex, it has to shoot down
other references to that memory in every other CPU on the system.  Many
modern snoopy cache architectures have slow shoot down characteristics.</p>
<p>Fourth, schedulers don't care what application-specific mutexes a thread
of control might hold when de-scheduling a thread.  If a thread of
control is descheduled while holding a shared data structure mutex,
other threads of control will be blocked until the scheduler decides to
run the blocking thread of control again.  The more threads of control
that are running, the smaller their quanta of CPU time, and the more
likely they will be descheduled while holding a Berkeley DB mutex.</p>
<p>The results of adding new threads of control to an application, on the
application's throughput, is application and hardware specific and
almost entirely dependent on the application's data access pattern and
hardware.  In general, using operating systems that support blocking
mutexes will often make a tremendous difference, and limiting threads
of control to to some small multiple of the number of CPUs is usually
the right choice to make.</p>
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