Commit 6a990e80 authored by Jeremy Hylton's avatar Jeremy Hylton

Add a lightly edited version of the berkeley storage docs from ZODB3.

I've removed all the details about Python versions prior to 2.3 and
Berkeley before 4.1.25.  I also removed all the configuration
instructions.  You "just" use the standard zconfig mechanism, which
may or may not be documented elsewhere.
parent ab3568e4
BerkeleyDB Storages for ZODB
============================
Introduction
------------
The BDBStorage package contains two types of ZODB storages based on
Sleepycat Software's BerkeleyDB library, and the PyBSDDB3 Python
wrapper module. These storages save ZODB data to a number of
BerkeleyDB tables, relying on BerkeleyDB's transaction machinery to
provide reliability and recoverability.
Note that the BerkeleyDB 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
http://www.sleepycat.com
See also our operating notes below.
Contents
--------
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,
but there is a new "autopack" strategy which packs the storage in a
separate thread and can eliminate the need for an explicit manual
pack operation.
- BDBMinimalStorage.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,
but this storage too implements an autopack strategy to collect such
cyclic garbage.
Compatibility
-------------
It is recommended that you use BerkeleyDB 4.1.25 and Python 2.3.3.
BDBStorage will not work with any BerkeleyDB version before 3.3.11 so
be careful if your Python or PyBSDDB is linking against earlier
BerkeleyDB 3.x versions. We have not tested with Berkeley DB 4.2.
Requirements
------------
You must install Sleepycat BerkeleyDB and perhaps PyBSDDB separately.
To obtain the BerkeleyDB 4.1.25, see the Sleepycat download page::
http://www.sleepycat.com/download/patchlogsdb.shtml
Install 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.4.1
Note that because BerkeleyDB 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.4.1/lib to /etc/ld.so.conf and run ldconfig.
If BerkeleyDB is installed, Python should find it and automatically
build the bsddb bindings.
Using Berkeley storage outside of Zope
--------------------------------------
ZODB applications that use the BerkeleyDB 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 BerkeleyDB storages open their 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, since recovery only needs to
take place from the time of the last checkpoint. The database is
always checkpointed when it's closed cleanly.
You can configure the BerkeleyDB 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 BerkeleyDB 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
http://www.sleepycat.com/docs/ref/transapp/reclimit.html.
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. 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 BDBStorages
mostly use BTree access method, although there are a few Queue tables
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 BerkeleyDB 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 BerkeleyDB 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 BerkeleyDB 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
http://www.sleepycat.com/docs/ref/transapp/archival.html
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::
http://www.sleepycat.com/docs/ref/transapp/recovery.html
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 and beyond 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:
http://www.python.org/doc/2.1.3/lib/posix-large-files.html
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
http://www.zope.org/Wikis/ZODB
Information about the BerkeleyDB storages in particular is at
http://www.zope.org/Wikis/ZODB/BerkeleyStorage
The email list zodb-dev@lists.zope.org are where all the
discussion about the Berkeley storages should take place.
Subscribe or view the archives at
http://lists.zope.org/mailman/listinfo/zodb-dev
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