Hello all. Due to a recent post on comp.lang.python "Large Dictionaries" I have been looking at BoboPOS as a potential solution. I have been trying to muck through the available documentation to arrive at some simple, minimal-code versions of basic tasks (sort of a mini-howto for large Dictionaries). Basic Task #1 Store/Retrieve simple picklable objects from BoboPOS import PickleDictionary from BoboPOS import Persistence pd = PickleDictionary("/tmp/pickle_dict") # begin a transaction get_transaction().begin() pd[1] = "This is a test for entry 1" pd[2] = "This is a test for entry 2" # commit the changes to the database get_transaction().commit() del pd # did this work? pd = PickleDictionary("pickle_dict") print pd[1] print pd[2] del pd The above code works well (it, perhaps, is not minimal). The problem I am having is that the above implementation will not cache. Suppose the transaction is in a loop: for i in range(10000): pd[i] = "Entry for key int(%i)"%i The memory used by this implementation is the same as the size of the "pickle_dict" file. I have looked through the source code and noticed that what I might actually want to use is a Transactional Pickle Dictionary which apparently does call the internal jar.cache.minimize() . Alghough the cache.minimize is only called during the abort() function which confuses me. The question is: How do I make large pickle dictionaries cache? Of course, this particular thread might be well used in a how-to. If such a beast already exists, please point me in the right direction. In any case, the results of this query will probably be posted to comp.lang.python as a service to the community and to promote Zope. Thanks. -- Brian Kelley w 505 995-8188 Bioreason, Inc f 505 995-8186 309 Johnson Av Santa Fe, NM 87501 kelley@bioreason.com