Yes but
def storeTree( p ) :
cache.ram( p['treeId'], lambda:p['tree'], time_expire=0 )
and make sure p['treeId'] does not contain symbols that have a regex
meaning {}[]()=_*\.
On Jul 27, 10:12 am, "topher.baron" <topher.ba...@gmail.com> wrote:
> There is a potential of having more than one of these objects, and
> they are not shared across users. I've just implemented a cache.ram
> solution, but I don't think I quite understand how the time_expire
> works.
>
> These objects are trees. This is how I've implemented the cache.ram
> solution:
>
> def getTree( p ):
> return cache.ram( p['treeId'], lambda:None, time_expire = None )
>
> def clearTreeCache( p ):
> cache.ram.clear( regex = p['treeId'] )
>
> def storeTree( p ) :
> cache.ram( p['treeId'], lambda:p['tree'], None )
>
> Is this a reasonable implementation ?
>
> As for cPickle, I'm not sure what the problem is. The reason I
> believe is a file size problem is because I am able to pickle smaller
> objects, whereas the exception is thrown when I attempt to do it on a
> larger object. I'd be happy to copy the OSX exception dialogue if
> that would help. Also, I will try to pickle the object here :
> os.path.join(request.folder,'private') and let you know what happens.
> Thanks.
>
> On Jul 27, 1:53 am, mdipierro <mdipie...@cs.depaul.edu> wrote:
>
> > If you have only one such objects, shared between all users than
> > cache.ram may be a solution.
>
> > I am surprised cPickle fails since the data is not so large. Are you
> > sure it is a file size problem? Is it possible that the object simply
> > contains unpicklable references?
>
> > try store it on the filesystem in the
> > os.path.join(request.folder,'private') folder.
>
> > > 937984 Bytes is the size of the file when I pickle a large object to a
> > > file. This is protocol 0. When I attempted to use protocol 2, it
> > > failed.
>
> > > I recently realized that the session itself is pickled, so I tried to
> > > simply add the object to the session to see what would happen. Same
> > > result.
>
> > > I'm dealing with large hierarchical data and would prefer to keep the
> > > data in the session. However, it looks like this might not be
> > > feasible for large amounts of data. What about increasing the memory
> > > block for web2py?
>
> > > I'm thinking my other option would be to keep it in cache.ram and in
> > > the database. What do you think?
>
> > > On Jul 22, 5:37 pm, mdipierro <mdipie...@cs.depaul.edu> wrote:
>
> > > > How big is the pickled file?
>
> > > > On Jul 22, 5:08 pm, "topher.baron" <topher.ba...@gmail.com> wrote:
>
> > > > > web2py community,
>
> > > > > I'm currently implementing a web application on localhost running OS
> > > > > 10.6.4. When I cPickle.dump a large object to the /tmp directory,
> > > > > web2py crashes. The same operations work with smaller objects.
>
> > > > > Does this mean I need to increase the heap? If so, how do I do this?
> > > > > If not, any suggestions?
>
> > > > > Thanks in advance.
