2.0 beta 1 Release Information
BerkeleyDB Storages for ZODB ============================
Please see the LICENSE.txt file for terms and conditions.
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 bsddb3Storage package, there are three storage implementations:
- Full.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, but there is also a new "autopack" strategy which packs the storage in a separate thread.
- Minimal.py is an implementation of an undo-less, version-less storage, which implements a reference counting garbage collection strategy to remove unused objects. It is still possible for garbage objects to persist in the face of object cycles, although a future release will add automatic garbage collection.
- Packless.py is another, older implementation of an undo-less,
version-less, reference counting storage that also obviates the
need for packing, except in the presence of cyclic garbage.
Packless has two limitations which may make it less desirable
than Minimal.py (which will eventually replace Packless):
- Packless uses its own temporary commit log file, which can cause more disk I/O than Minimal.py
- Packless relies on the BerkeleyDB locking subsystem, so for very large transactions, you may run out of Berkeley locks.
You should consider Packless.py to be deprecated.
As of this writing (09-Nov-2002) it is recommended that at least Python 2.1.3 or Python 2.2.2 be used with these storages. Full and Minimal storages should also work with Python 2.3 (as yet unreleased).
Some testing has been conducted with both Zope 2.5.1, Zope 2.6, and the Zope 3 code base. These storages have primarily been tested on Linux.
It is recommended that you use at least 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. Since PyBSDDB is not yet compatible with BerkeleyDB 4.1.24, stick with 4.0.14.
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 bsddb3Storage 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 file.
There is a sample custom_zodb.py file in the docs/ subdirectory, shipped with this release. 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 the same.
If you choose to edit the contents of the custom_zodb.py file, you can change the "env" 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 program.
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.
BerkeleyDB files ----------------
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!
Tuning BerkeleyDB -----------------
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:
http://www.sleepycat.com/docs/ref/am_conf/cachesize.html http://www.sleepycat.com/docs/ref/am_misc/tune.html http://www.sleepycat.com/docs/ref/transapp/tune.html http://www.sleepycat.com/docs/ref/transapp/throughput.html
As you read these, it will be helpful to know that the bsddb3Storage databases mostly use BTree access method (Full storage has one Queue table to support packing).
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 archival.
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 storage server).
- back up the database files (the files prefixed with "zodb").
- back up all existing BerkeleyDB log files (the files prefixed "log").
- 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.
Disaster recovery -----------------
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 at:
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 available.
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 databases.
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 email@example.com are where all the discussion about the Berkeley storages should take place. Subscribe or view the archives at
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