> But eg lookiups to log table might use ti key attrs . Yeah - that would be the price to pay. Trade-offs, trade-offs everywhere.
Such queries over a multi-partition table would have to do full table scan - they will not be able to use indexes (unless the queries use partition key). So they would **work** but would be **slow** if the table gets too big. However I would argue that if someone would like to partition those tables, they will frequently and aggressively prune the old partitions (this is the reason why they want to have it) - which will effectively keep those tables "small" (relatively). And even if full table scan is used, if there is a question about task_id audit log, that would be fine. Actually - I strongly believe - and we had this discussion in the past - that the log table is quite a bit of abomination, because it's not a "true" audit log if it is kept in modifiable database, and anyone who want to make it a "true" audit log will have to effectively send those log entries to a "write-only" storage and query the audit logs there. And that nicely fits into "unlogged" pattern -> you could add a trigger in your DB to automatically send audit logs somewhere "write-only" and aggressively prune the old data (i.e. partitions). So - from the side of airflow that would mean that those tables are "partitioning friendly", but not to implement partitioning itself. Leaving "partitioning|" and "handling recovery" and "handling audit logs" up to the Deployment Manager. Which IMHO should happen anyway for "log" table and would be nice pattern to describe as a way to achieve "true" audit log. J,. On Mon, Dec 29, 2025 at 9:02 PM Daniel Standish via dev < [email protected]> wrote: > The problem with partitioning them is that when querying you would need to > know the partition. > But eg lookiups to log table might use ti key attrs . > > I would try just trimming log and job table in small batches frequently > using an appropriate index, before thinking about the complexity of > partitioning. > > > > > > On Mon, Dec 29, 2025 at 11:24 AM Jarek Potiuk <[email protected]> wrote: > > > I have a feeling that making those **two** tables partition-friendly > should > > be easy - and maybe that's all that we need. That would make it possible > > for you to use the partitioning, (where it would not be necessary for > > everyone). > > There might be a few ways of doing it without losing uniqueness. For > > example Job ID and audit log ID could follow certain conventions - and > > always start with a partition key) thus providing uniqueness we need. > > > > Those two tables are pretty specific and neither job id nor log id impact > > anything else in our data model. > > > > I think such a change to make those two tables "partition friendly" could > > be accepted, but we could only say it after seeing a POC. > > > > J. > > > > > > On Mon, Dec 29, 2025 at 8:01 PM <[email protected]> wrote: > > > > > I do think that the second option is best, it is also what we wanted to > > > do, the only reason we did not do that is because from our tests, sql > > > alchemy sometimes breaks as it expects certain constraints which are > not > > > there, mainly for update queries, select works well, if I am not > > mistaken, > > > there are 3 or 4 large tables, job being the largest (7 times larger > than > > > the second largest), the question is, will such a pull request be > > approved? > > > > > > As we do lose the unique constraint (as it becomes per partition), > though > > > it is a sequence that most likely won't repeat until the previous has > > been > > > deleted, but if not, we might query unrelated job or log data, and so > > > changes in the api server are also needed, creating the pr is not a > > > problem, the question is how beneficial will it be, as if it is done to > > > those problematic tables, it means that the preferred way to manage > > > retention is from the db, and can be an optional alembic script. > > > > > > I do not want to just push a fix that will add more complexity than the > > > benefit it will bring. > > > > > > If we do go the pr way, a separate discussion is probably needed to > > decide > > > how it should be done (most likely an additional airflow command to > turn > > on > > > or off the partitions and retention) > > > > > > Doing it as a custom solution has caused problems with sqlalchemy, and > we > > > do not want to do so as if later an alembic script relies on the > primary > > > key in some way, we will need to fix it manually and deal with the > > problems > > > it may cause when we update. > > > > > > > On 29 Dec 2025, at 21:36, Jarek Potiuk <[email protected]> wrote: > > > > > > > > Another option is that you ( could make ONLY for those two tables - > > > > partition-friendly. And do not partition anything else. I think > that > > > > **could** be possible - both have datetime fields that could be used > as > > > > partition keys - you would have to assess if you can do it as your > > > "custom" > > > > solution or whether it would require some changes to airflow models. > > But > > > I > > > > can't see foreign key problems if ONLY those two tables are > > partitioned, > > > so > > > > likely you could do it yourself in your DB. > > > > > > > > In this case - maybe "let's solve those tables that are problematic" > is > > > > easier to do than "let's apply partitioning to everything". > > > > > > > >> On Mon, Dec 29, 2025 at 7:31 PM Jarek Potiuk <[email protected]> > > wrote: > > > >> > > > >> Purely theoretically, you could change the log and job tables to be > > > >> unlogged - and thus avoid WAL for them. > > > >> > > > >> The drawback of this: > > > >> > > > >> * the data in those tables will be lost if you pull the plug or kill > > -9 > > > >> the primary postgres server > > > >> * the tables are not available (at all) in replicas - so in case of > a > > > >> fallback, you would have to have a manual data import/export for > those > > > >> tables on fail-over, rather than rely on replicas being "ready to > > > fallback > > > >> immediately". Or accept data loss. > > > >> * the data from those tables will not be present in backups > > > >> > > > >> I am not 100% sure, but I believe loss of data in both tables is not > > > >> really catastrophic for Airflow, so maybe it's acceptable risk (but > > > likely > > > >> you should do a disaster-recovery test to see what happens and how > to > > > >> recover in case, indeed, someone pulls the plug on your postgres > > server. > > > >> > > > >> J, > > > >> > > > >> > > > >> > > > >> > > > >>> On Mon, Dec 29, 2025 at 7:16 PM Natanel <[email protected]> > > > wrote: > > > >>> > > > >>> Yes, the problem is the manual deletions, we have tried it, it > > > resulted in > > > >>> the same exact issue, as the scheduled db procedures which clean up > > the > > > >>> rows marked as deleted actually get deleted, and so it takes up > > > storage, > > > >>> yet it does not solve the WAL problem, the problematic table is > > > actually > > > >>> not task_instance, it is relatively small, the log and job tables > are > > > the > > > >>> biggest tables (the problem with them is the primary key change > > > required), > > > >>> by a multiple of 10 (or more, cluster dependant). > > > >>> > > > >>> The smaller batches might solve the issue, however, it seems to > just > > > delay > > > >>> the problem a little rather than solve it, as deleting data with a > > > delete > > > >>> query (especially a lot of data) is not a very light operation, and > > so > > > I > > > >>> think that this is the main issue. > > > >>> > > > >>> It would be nice if we could use partitions instead, as it is a > > lighter > > > >>> operation, and does not require us to maintain a query and manage > our > > > db, > > > >>> I > > > >>> have thought about changing the models, most of the changes are > > > relatively > > > >>> simple, for some it is just removing the foreign key and relying on > > ORM > > > >>> level constraints, for others, it requires adding a pre query to > have > > > the > > > >>> same constrains but I do not like that idea, maybe there is another > > > way to > > > >>> make airflow "partition-friendly"? > > > >>> > > > >>> I can't think of a nice way to do so, maybe it does not exist, as > the > > > db > > > >>> clean is as simple as a delete query gets, yet when there is a lot > of > > > >>> data, > > > >>> it is all duplicated in WALs. > > > >>> > > > >>> On Mon, Dec 29, 2025, 19:40 Daniel Standish via dev < > > > >>> [email protected]> > > > >>> wrote: > > > >>> > > > >>>> Have you looked at doing manual deletions? I.e. writing your own > > sql? > > > >>>> > > > >>>> The db clean command is probably not "optimal" for all scenarios. > > > >>>> > > > >>>> So for example, if the main problem table for you is > task_instance, > > > you > > > >>>> could periodically delete TI records in smaller batches using some > > > >>>> appropriate index (whether it exists now or you add it). Then > maybe > > > you > > > >>>> would not stress the db as hard. > > > >>>> > > > >>>> Airflow isn't designed to use partitions so, you may not get good > > > >>> results > > > >>>> with that approach. > > > >>>> > > > >>>> > > > >>>> > > > >>>> On Mon, Dec 29, 2025 at 7:32 AM Natanel <[email protected]> > > > >>> wrote: > > > >>>> > > > >>>>> Hello everyone, after having issues with the 'airflow db clean' > > > >>> command, > > > >>>>> where due to the amount of dags and tasks that are running every > > day > > > >>> in > > > >>>> our > > > >>>>> deployments, we get a lot of new data every day, which is stored > in > > > >>> the > > > >>>>> database, and when we delete the data, due to the way PGSQL > works, > > > the > > > >>>>> WAL's get replicated to both the archive storage and the main > data > > > >>>> storage > > > >>>>> of the db instance, which in turn, causes a significant jump in > cpu > > > >>>> usage, > > > >>>>> ram usage and disk usage, whenever we run the command, which > causes > > > >>> all > > > >>>>> kinds of issues, we even had it once fill up the db storage, and > > > >>> causing > > > >>>>> the database to be unresponsive, forcing us to move to our backup > > > >>>> database, > > > >>>>> after we haven't ran the command for a few months due to human > > error. > > > >>>>> > > > >>>>> As of now, I know that this is the accepted and widely used way > of > > > >>>> managing > > > >>>>> the airflow database's size, however, we noticed that it may > cause > > > >>> issues > > > >>>>> in certain cases, just like in our case, where if the db has not > > been > > > >>>>> cleaned up for a while, cleaning it can be problematic. > > > >>>>> > > > >>>>> We decided to try and partition the table, and use pgsql's built > in > > > >>>>> retention of partitions, which does not issue a DELETE query, and > > is > > > >>>>> lighter and faster, while being simpler to use, however, we have > > > >>>>> encountered issues due to having Foreign Key constraints in some > > > >>> tables, > > > >>>>> having to duplicate such keys and other than forcing code changes > > (as > > > >>> the > > > >>>>> foreign key must include the partitioned key, as the partitioned > > key > > > >>> must > > > >>>>> be part of the primary key), while also having the issue of > > > sqlalchemy > > > >>>>> breaking once we change the primary key, with the addition of the > > > >>>>> constraints on the primary key breaking. > > > >>>>> > > > >>>>> And in Mysql, due to the foreign keys, it is not possible to > > > partition > > > >>>>> tables which include them, as it is not supported yet (according > to > > > >>> this > > > >>>>> < > > > >>>>> > > > >>>> > > > >>> > > > > > > https://dev.mysql.com/doc/refman/8.4/en/partitioning-limitations-storage-engines.html > > > >>>>>> > > > >>>>> ). > > > >>>>> > > > >>>>> Has anyone else tried to use the databases built in partition > > > >>> retention > > > >>>>> system instead of the 'airflow db clean' command? > > > >>>>> > > > >>>>> Thanks, Natanel. > > > >>>>> > > > >>>> > > > >>> > > > >> > > > > > > --------------------------------------------------------------------- > > > To unsubscribe, e-mail: [email protected] > > > For additional commands, e-mail: [email protected] > > > > > > > > >
