- Berkeley DB Reference Guide:
- Access Methods
Access method tuning
There are a few different issues to consider when tuning the performance
of Berkeley DB access method applications.
- access method
- 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 Selecting an access method for more information.
- cache size
- 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 DB->stat 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
db_stat 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.
- shared memory
- 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 (DB_SYSTEM_MEM) or in
application private memory (DB_PRIVATE).
- large key/data items
- 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 db_stat utility (or the statistics
returned by the DB->stat function) to review the number of overflow
pages in the database.
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.
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