Tim Delaney wrote:
On 17 May 2012 07:33, Ethan Furman wrote:
Just hit a snag:
In cPython the deterministic garbage collection allows me a
particular optimization when retrieving records from a dbf file --
namely, by using weakrefs I can tell if the record is still in
memory and active, and if so not hit the disk to get the data; with
PyPy (and probably the others) this doesn't work because the record
may still be around even when it is no longer active because it
hasn't been garbage collected yet.
What is the distinguishing feature of an "active" record? What is the
problem if you get back a reference to an inactive record? And if there
is indeed a problem, don't you already have a race condition on CPython?
1. Record is active;
2. Get reference to record through weak ref;
3. Record becomes inactive;
4. Start trying to use the (now inactive) record.
A record is an interesting critter -- it is given life either from the
user or from the disk-bound data; its fields can then change, but those
changes are not reflected on disk until .write_record() is called; I do
this because I am frequently moving data from one table to another,
making changes to the old record contents before creating the new record
with the changes -- since I do not call .write_record() on the old
record those changes do not get backed up to disk.
With CPython as soon as a record goes out of scope it dies, and the next
time I try to access that record I will get the disk version, without
the temporary changes I had made earlier (this is good). However, with
PyPy (and others) not all records are destroyed before I try to access
them again, and I end up seeing the temp data instead of the disk data.
~Ethan~
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