Hi, Phil,
Well, when I read about "commands" modifying the model and stored in
a log
for future replay it sounds a lot like Prevayler with the luxury of
automatic "transaction command object" creation based on parameters
extracted via a concern.
Well, I think that the level of discussion here (migration) is very
different from Prevayler in that Prevayler's only concern is to be
able to backup its state in case of system failure. Prevayler doesn't
even care about persistence (as we know it). So, I'm not sure how much
of a lesson we can learn from it. That said, though, making
transactions serializable so they can be stored like Prevayler does is
indeed a neat little trick.
With Prevayler, you are supposed to create snapshots of your model
in order
to avoid replaying all the commands. Those snapshots could be
considered a
cache(command(t)) of your model to optimise replaying at t+1.
I think "you can" is more appropriate than "you are supposed to". :-)
But yes, good explanation.
One problem I had with Prevayler is "snapshot generation". It takes
a lot of
resources (O(model's value size)). I partly solved it by
partitioning my
model in smaller snapshots. But I still had a problem with "dead" old
data... like last year "order lines", they were still present in the
snapshot ! So I had to remove "old values", and it was a mess.
Normally, you shouldn't need to do that. The _only_ thing that is
present in the Prevayler snapshot is what you have in your live data
model. The state of your system is serialized "as is". So, if you had
strange things in your snapshot, it's not because of Prevayler's
holding on to it, it's because that data is still around in one of
your objects.
That data may still be in your journal files (log of all
transactions), but not the snapshot (log of the system's state at time
t).
Anyway, I think I understand your line of thinking better. Thank you
for clarifying.
Cheers,
=David
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