On 17/04/2018 6:27 PM, Adrien Cossa wrote:
Hi,
On 04/14/2018 02:38 AM, Mike Dewhirst wrote:
Does it actually stop users reading? If the entire migration happens
in a single transaction, the database (Postgres anyway) should remain
accessible until the moment it is committed.
Maybe you could announce a maintenance operation which will only
interrupt certain actions for a few minutes?
I am not sure if I understand well how that works. If the migration is
atomic, is is true that:
- the users can read normally between the beginning of the migration
and the beginning of the transaction commit, and they will get the old
data
- the users trying to read during the transaction commit will have to
wait for it to finish, and they will get the migrated data
- the users who tries to write at anytime between the beginning of the
migration and the end of the transaction commit will have to wait for
it to finish, and they might overwrite the migrated data
If it works like that, I think that the solution I was thinking about
is good enough for my needs:
- process the queryset by chunks. Note that if you want to use
prefetch_related, you can't use a queryset iterator. So I get the
successive chunks by filtering with PK ranges ... I have benchmarked a
bit and a good value for the chunk size seems to be 500, it's not
slower then any other value and keeps the memory usage down. If I had
a lot of RAM, I could also raise this value to 5000 or 15000 without
really slowing the process.
- I have actually two separate atomic migrations (in case something
goes wrong, it is better then one non atomic migration with two atomic
operations): one to process the objects that are not modifiable by the
users (because they are in a certain status etc.) and a second to
process the objects that could be modified by the users. The second
migration concerns only 4-5% of the total objects, so it should be
much faster. As I use the PKs to fetch the objects, I have to fix the
desired chunk size of 500 in order to get some chunks of (approx.) the
same size with 500 * total_objects_count / filtered_queryset_count.
There remains the problem of users that would try to write during the
second migration: their changes will be written indeed to the old
model, but not taken in account by the new models (remember I want to
split one model in two smaller ones). So maybe I should append here to
the second migration all the operations that are responsible for
deleting the old model? This way, people trying to write will get an
error - which is the best we can do here. Am I right?
It would be nice to avoid errors. That is why I suggested announcing
that you intend to take the system offline for a short period. It takes
off all the pressure and you can choose the simplest mechanism.
Users will get a benefit from the migration or you wouldn't be doing it.
Therefore they should be happy to accept a little downtime. You might
have to do a bit of selling :)
I might consider making production readonly, dumping the database,
loading it up on a fast machine with heaps of RAM and a SSD for the
migration then dumping and reloading on the production machine.
That way you can leave it online read-only and take it offline only for
the relatively brief reload after the off-site migration. A bit of
practice and timing will indicate whether that method has legs. Or wings!
Thanks for your help!
Cheers,
Adrien
Adrien Cossa <co...@init.at> wrote:
Hi everybody!
I would like to know what options exist when you have a huge
migration that will obviously not run on your productive server.
I have spitted a model in two smaller ones and wrote then a migration
to populate these new models. The number of original objects is
around 250,000 and I have also a few references to update. In the
end, the migration lasted more than 30 mn on my machine (16 GB RAM
and it was swapping a lot) and it failed on another machine because
the RAM was out (the process was using then about 13 GB). On the
productive server we have even less RAM so to run the migration as it
is is really out of question.
I have tried to use all the Django mechanisms that I know to optimize
the queries: select_related, prefetch_related, bulk_create,
QuerySet.update... Now, the migration I am talking about use
bulk_create(batch_size=None) and process the whole queryset at once.
Before that, as the migration was not so long lasting because I had 2
references less to update, I tried other values for batch_size and
also I processed the queryset as pages of a few hundreds or thousands
objects. The results were not better then batch_size=None and "all at
once", that's why I finally used "basic settings" (and the migration
was lasting about 5 minutes). I will have to reintroduce some tweaks
because the extra updates of the two relations I mentioned is making
here a big difference.
I am wondering if someone already found him/herself in a similar
situation, and with what solution you finally came to.
If the migration lasts very long, it's not a problem by itself but I
don't want to lock the database for 15 mn. The fact is that I don't
know what is happening during the migration process, what is locked
by what? I will split the migration in "pages" to use less RAM
anyway, but I was also thinking of migrating in two different steps
*or* files, in order to process separately the objects that are not
editable (basically most of them, that we keep for history but they
are read-only) and the others (which should be much faster and thus
people working will not be blocked for long). Does it make sense?
Some other ideas?
Thanks a lot!
Adrien
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