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<title>Berkeley DB Reference Guide: Access method tuning</title>
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<td><h3><dl><dt>Berkeley DB Reference Guide:<dd>Access Methods</dl></h3></td>
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<h1 align=center>Access method tuning</h1>
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<p>There are a few different issues to consider when tuning the performance
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of Berkeley DB access method applications.
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<p><dt>access method<dd>An application's choice of a database access method can significantly
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affect performance. Applications using fixed-length records and integer
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keys are likely to get better performance from the Queue access method.
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Applications using variable-length records are likely to get better
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performance from the Btree access method, as it tends to be faster for
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most applications than either the Hash or Recno access methods. Because
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the access method APIs are largely identical between the Berkeley DB access
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methods, it is easy for applications to benchmark the different access
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methods against each other. See <a href="../../ref/am_conf/select.html">Selecting an access method</a> for more information.
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<p><dt>cache size<dd>The Berkeley DB database cache defaults to a fairly small size, and most
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applications concerned with performance will want to set it explicitly.
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Using a too-small cache will result in horrible performance. The first
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step in tuning the cache size is to use the db_stat utility (or the
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statistics returned by the <a href="../../api_c/db_stat.html">DB->stat</a> function) to measure the
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effectiveness of the cache. The goal is to maximize the cache's hit
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rate. Typically, increasing the size of the cache until the hit rate
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reaches 100% or levels off will yield the best performance. However,
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if your working set is sufficiently large, you will be limited by the
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system's available physical memory. Depending on the virtual memory
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and file system buffering policies of your system, and the requirements
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of other applications, the maximum cache size will be some amount
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smaller than the size of physical memory. If you find that
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<a href="../../utility/db_stat.html">db_stat</a> shows that increasing the cache size improves your hit
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rate, but performance is not improving (or is getting worse), then it's
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likely you've hit other system limitations. At this point, you should
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review the system's swapping/paging activity and limit the size of the
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cache to the maximum size possible without triggering paging activity.
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Finally, always remember to make your measurements under conditions as
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close as possible to the conditions your deployed application will run
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under, and to test your final choices under worst-case conditions.
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<p><dt>shared memory<dd>By default, Berkeley DB creates its database environment shared regions in
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filesystem backed memory. Some systems do not distinguish between
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regular filesystem pages and memory-mapped pages backed by the
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filesystem, when selecting dirty pages to be flushed back to disk. For
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this reason, dirtying pages in the Berkeley DB cache may cause intense
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filesystem activity, typically when the filesystem sync thread or
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process is run. In some cases, this can dramatically affect application
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throughput. The workaround to this problem is to create the shared
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regions in system shared memory (<a href="../../api_c/env_open.html#DB_SYSTEM_MEM">DB_SYSTEM_MEM</a>) or in
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application private memory (<a href="../../api_c/env_open.html#DB_PRIVATE">DB_PRIVATE</a>).
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<p><dt>large key/data items<dd>Storing large key/data items in a database can alter the performance
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characteristics of Btree, Hash and Recno databases. The first parameter
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to consider is the database page size. When a key/data item is too
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large to be placed on a database page, it is stored on "overflow" pages
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that are maintained outside of the normal database structure (typically,
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items that are larger than one-quarter of the page size are deemed to
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be too large). Accessing these overflow pages requires at least one
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additional page reference over a normal access, so it is usually better
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to increase the page size than to create a database with a large number
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of overflow pages. Use the <a href="../../utility/db_stat.html">db_stat</a> utility (or the statistics
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returned by the <a href="../../api_c/db_stat.html">DB->stat</a> function) to review the number of overflow
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pages in the database.
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<p>The second issue is using large key/data items instead of duplicate data
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items. While this can offer performance gains to some applications
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(because it is possible to retrieve several data items in a single get
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call), once the key/data items are large enough to be pushed off-page,
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they will slow the application down. Using duplicate data items is
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usually the better choice in the long run.
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