[Zodb-checkins] CVS: ZODB4/BDBStorage/Doc - README.txt:1.1
Wed, 4 Dec 2002 14:25:07 -0500
Update of /cvs-repository/ZODB4/BDBStorage/Doc
In directory cvs.zope.org:/tmp/cvs-serv12940
Updated for the high level ZODB4 port.
=== Added File ZODB4/BDBStorage/Doc/README.txt ===
BerkeleyDB Storages for ZODB
This package contains implementations for ZODB storages based on
Sleepycat Software's BerkeleyDB and the PyBSDDB3 Python wrapper
module. These storages save ZODB data to a bunch of BerkeleyDB
tables, relying on Berkeley's transaction machinery to provide
reliability and recoverability.
Note that the Berkeley based storages are not "set and forget". The
underlying Berkeley database technology requires maintenance, careful
system resource planning, and tuning for performance. You should have
a good working familiarity with BerkeleyDB in general before trying to
use these storages in a production environment. It's a good idea to
read Sleepycat's own documentation, available at
See also our operating notes below.
Inside the BDBStorage package, there are two storage implementations:
- BDBFullStorage.py is a complete storage implementation, supporting
transactional undo, versions, application level conflict resolution,
packing, and automatic reference counting garbage collection. You
must pack this storage in order to get rid of old object revisions
and cyclic garbage, but there is also a new "autopack" strategy
which packs the storage in a separate thread.
- BDBMinimalStorage.py is an implementation of an undo-less,
version-less storage, which implements a reference counting garbage
collection strategy to remove unused objects. You still need to
pack to remove cyclic garbage, but this also implements a new
autopack strategy for packing the storage in a separate thread.
As of this writing (04-Dec-2002) it is recommended that at least
Python 2.2.2 be used with these storages. BDBFullStorage and
BDBMinimalStorage should also work with Python 2.3 (as yet
It is recommended that you use BerkeleyDB 4.0.14 and PyBSDDB
3.4.1 or later. Earlier versions of both packages had bugs that could
crash or hang your application. PyBSDDB suport for BerkeleyDB 4.1.24
is still experimental, so for now stick with 4.0.14. However, it is
highly recommended that you apply the patch for 4.0.14 in the file
We've seen corrupt databases that couldn't be recovered without this
patch. Sleepycat claims the patch fixes this problem and we've not
seen a repeat of it since applying the patch (note though that this
bug was hard to reproduce in the first place).
You must install Sleepycat BerkeleyDB and PyBSDDB separately.
To obtain the BerkeleyDB 4.0.14, see the Sleepycat Software site for
older releases of their software::
To obtain the latest source release of the PyBSDDB package, see::
Install both BerkeleyDB and PyBSDDB as per the instructions that come
with those packages. For BerkeleyDB, it's generally wise to accept
the default configure options and do a "make install" as root. This
will install BerkeleyDB in /usr/local/BerkeleyDB.4.0
Note that because Berkeley installs itself in a non-standard location,
the dynamic linker ld.so may not be able to find it. This could
result in link errors during application startup. For systems that
support ldconfig, it is highly recommended that you add
/usr/local/BerkeleyDB.4.0/lib to /etc/ld.so.conf and run ldconfig. An
alternative approach is given below.
PyBSDDB comes with a standard distutils-based setup script which will
do the right thing.
If you've extended your ld.so.conf file as above, you can build
PyBSDDB like so::
% python setup.py build_ext -i --berkeley-db=/usr/local/BerkeleyDB.4.0
Otherwise, here's the build command I've used with some success for
the PyBSDDB distribution::
% python setup.py build_ext -i --berkeley-db=/usr/local/BerkeleyDB.4.0/ --lflags="-Xlinker -rpath -Xlinker /usr/local/BerkeleyDB.4.0/lib"
Then install the package like so::
% python setup.py install
When you can run the tests which ship with PyBSDDB, you'll know you've
been successful at both BerkeleyDB and PyBSDDB installation.
Using BDBStorage with Zope
By default, Zope uses a FileStorage as its backend storage. To tell
Zope to use an alternate storage, you need to set up a custom_zodb.py
There is a sample custom_zodb.py file in the BDBStorage/Doc
subdirectory. The easiest way to get started with one of the Berkeley
storages is to copy custom_zodb.py file to your SOFTWARE_HOME
directory (your main Zope dir) and edit its contents to specify which
storage you want to use. If you use an INSTANCE_HOME setup, you'll
want to copy the file to the INSTANCE_HOME directory instead and do
If you choose to edit the contents of the custom_zodb.py file, you can
change the "envpath" string to point to a different environment
directory for BerkeleyDB. BerkeleyDB will store its support tables
and log files in this directory. The contents of this directory can
become quite large, even if your data needs are relatively modest (see
"BerkeleyDB Log Files" below).
You can also set up some tuning paramaters in the custom_zodb.py file.
See the comments in that file and in the BerkeleyBase.py file for
details. For better performance, you should consider at least setting
the config.logdir to point to a directory on a different disk than the
one your tables are stored on.
By default, the environment path is set in custom_zodb.py to a
subdirectory of your Zope's var subdirectory. You may change this to
any path that you have write permissions on. If the environment
directory doesn't exist, it will be created when you first run Zope
with one of the storages. It is recommended that you choose an
environment directory which does not contain any other files.
Additionally, you should not use BerkeleyDB on remotely mounted
filesystems such as NFS.
Using bsddb3Storage with ZEO
The Berkeley storages are compatible with ZEO. For general
information on how to use alternate storage implementations with ZEO,
see the "start.txt" file in the ZEO release documentation.
Using Berkeley storage outside of Zope
ZODB applications that use the Berkeley storages need to take care to
close the database gracefully, otherwise the underlying database could
be left in a corrupt, but recoverable, state.
By default, all the Berkeley storages open their Berkeley databases
with the DB_RECOVER flag, meaning if recovery is necessary
(e.g. because you didn't explicitly close it the last time you opened
it), then recover will be run automatically on database open. You can
also manually recover the database by running Berkeley's db_recover
The upshot of this is that a database which was not gracefully closed
can usually be recovered automatically, but this could greatly
increase the time it takes to open the databases. This can be
mitigated by periodically checkpointing the BerkeleyDB, since recovery
only needs to take place from the time of the last checkpoint (the
database is always checkpointed when it's closed).
You can configure the Berkeley storages to automatically checkpoint
the database every so often, by using the BerkeleyConfig class. The
"interval" setting determines how often, in terms of ZODB commits,
that the underlying database will be checkpointed. See the class
docstring for BerkeleyBase.BerkeleyConfig for details.
After Zope is started with one of the Berkeley storages, you will see
a number of different types of files in your BerkeleyDB environment
directory. There will be a number of "__db*" files, a number of
"log.*" files, and several files which have the prefix ``zodb_``. The
files which have the ``zodb_`` prefix are the actual BerkeleyDB
databases which hold the storage data. The "log.*" files are
write-ahead logs for BerkeleyDB transactions, and they are very
important. The "__db*" files are working files for BerkeleyDB, and
they are less important. It's wise to back up all the files in this
directory regularly. BerkeleyDB supports "hot-backup". Log files
need to be archived and cleared on a regular basis (see below).
You really want to store your database files on a file system with
large file support. See below for details.
BerkeleyDB log files
BerkeleyDB is a transactional database system. In order to maintain
transactional integrity, BerkeleyDB writes data to log files before
the data is committed. These log files live in the BerkeleyDB
environment directory unless you take steps to configure your
BerkeleyDB environment differently. There are good reasons to put the
log files on a different disk than the data files:
- The performance win can be huge. By separating the log and data
files, Berkeley can much more efficiently write data to disk. We
have seen performance improvements from between 2.5 and 10 times for
write intensive operations. You might also want to consider using
three separate disks, one for the log files, one for the data files,
and one for the OS swap.
- The log files can be huge. It might make disk space management
easier by separating the log and data files.
The log file directory can be changed by setting the "logfile"
attribute on the config object given to the various storage
constructors. Set this to the directory where BerkeleyDB should store
your log files. Note that this directory must already exist.
For more information about BerkeleyDB log files, recoverability and
why it is advantageous to put your log files and your database files
on separate devices, see
You can reclaim some disk space by occasionally backing up and
removing unnecessary BerkeleyDB log files. Here's a trick that I use::
% db_archive | xargs rm
Be sure to read the db_archive manpages first!
BerkeleyDB has lots of knobs you can twist to tune it for your
application. Getting most of these knobs at the right setting is an
art, and will be different from system to system. We're still working
on recommendations with respect to the Full storage, but for the time
being, you should at least read the following Sleepycat pages::
As you read these, it will be helpful to know that the Berkeley
storage databases mostly use BTree access method, although a few
Queues are also used, depending on the specific storage.
One thing we can safely say is that the default BerkeleyDB cache size
of 256KB is way too low to be useful. The Berkeley storages
themselves default the cache size to 128MB which seems about optimal
on a 256MB machine. Be careful setting this too high though, as
performance will degrade if you tell Berkeley to consume more than the
available resources. You can change the cache size by setting the
"cachesize" attribute on the config object to the constructor.
Archival and maintenance
Log file rotation for Berkeley DB is closely related to database
BerkeleyDB never deletes "old" log files. Eventually, if you do not
maintain your Berkeley database by deleting "old" log files, you will
run out of disk space. It's necessary to maintain and archive your
BerkeleyDB files as per the procedures outlined in
It is advantageous to automate this process, perhaps by creating a
script run by "cron" that makes use of the "db_archive" executable as
per the referenced document. One strategy might be to perform the
following sequence of operations::
- shut down the process which is using BerkeleyDB (Zope or the ZEO
- back up the database files (the files prefixed with "zodb").
- back up all existing BerkeleyDB log files (the files prefixed
- run ``db_archive -h /the/environment/directory`` against your
environment directory to find out which log files are no longer
participating in transactions (they will be printed to stdout one
file per line).
- delete the log files that were reported by "db_archive" as no longer
participating in any transactions.
"Hot" backup and rotation of log files is slightly different. See the
above-referenced link regarding archival for more information.
To recover from an out-of-disk-space error on the log file partition,
or another recoverable failure which causes the storage to raise a
fatal exception, you may need to use the BerkeleyDB "db_recover"
executable. For more information, see the BerkeleyDB documentation
BerkeleyDB temporary files
BerkeleyDB creates temporary files in the directory referenced by the
$TMPDIR environment variable. If you do not have a $TMPDIR set, your
temp files will be created somewhere else (see
http://www.sleepycat.com/docs/api_c/env_set_tmp_dir.html for the
tempfile decision algorithm used by BerkeleyDB). These temporary
files are different than BerkeleyDB "log" files, but they can also
become quite large. Make sure you have plenty of temp space
Linux 2GB Limit
BerkeleyDB is effected by the 2GB single-file-size limit on 32-bit
Linux ext2-based systems. The Berkeley storage pickle database (by
default named "zodb_pickle"), which holds the bulk of the data for the
Berkeley storages is particularly susceptible to large growth.
If you anticipate your database growing larger than 2GB, it's
worthwhile to make sure your system can support files larger than 2GB.
Start with your operating system and file system. Most modern Linux
distributions have large file support.
Next, you need to make sure that your Python executable has large file
support (LFS) built in. Python 2.2.2 is automatically configured with
LFS, but for Python 2.1.3 you will need to rebuild your executable
according to the instructions on this page:
IMPORTANT NOTE: If any of your BerkeleyDB files reaches the 2GB limit
before you notice the failure situation, you will most likely need to
restore the database environment from a backup, putting the restored
files on a filesystem which can handle large files. This is due to
the fact that the database file which "hit the limit" on a 2GB-limited
filesystem will be left in an inconsistent state, and will probably be
rendered unusable. Be very cautious if you're dealing with large
For More Information
Information about ZODB in general is kept on the ZODB Wiki at
Information about the Berkeley storages in particular is at
The email list firstname.lastname@example.org are where all the
discussion about the Berkeley storages should take place.
Subscribe or view the archives at