This AIP looks really great, thanks to all involved with it! I do have a question about it that I hoping someone can answer. I’ve noticed with the kubernetes executor that when a DAG runs and there is say a syntax error with it or a misconfiguration of some sort that ends up in the task attributes, clearing the task doesn’t start completely fresh with fresh copy of the DAG and thus task attributes. Would we expect the same behavior with this AIP or would it in someway help to alleviate that issue?
On Wed, Feb 27, 2019 at 1:53 PM James Meickle <jmeic...@quantopian.com.invalid> wrote: > On the topic of using Docker, I highly recommend looking at Argo Workflows > and some of their sample code: https://github.com/argoproj/argo > > tl;dr is that it's a workflow management tool where DAGs are expressed as > YAML manifests, and tasks are just containers run on Kubernetes. > > I think that there's a lot of value in Airflow's use of Python rather than > a YAML-based DSL. But I do think that containers are the future, and I'm > hopeful that Airflow develops in the direction of focusing on being a > principled Python framework for managing tasks/data executed in containers, > and the resulting execution state. > > > On Tue, Feb 26, 2019 at 8:55 PM Maxime Beauchemin < > maximebeauche...@gmail.com> wrote: > > > Related thoughts: > > > > * on the topic of serialization, let's be clear whether we're talking > about > > unidirectional serialization and *not* deserialization back to the > object. > > This works for making the web server stateless, but isn't a solution > around > > how DAG definition get shipped around on the cluster (which would be nice > > to have from a system standpoint, but we'd have to break lots of dynamic > > features, things like callbacks and attaching complex objects to DAGs, > ...) > > > > * docker as "serialization" is interesting, I looked into "pex" format in > > the past. It's pretty cool to think of DAGs as micro docker application > > that get shipped around and executed. The challenge with this is that it > > makes it hard to control Airflow's core. Upgrading Airflow becomes [also] > > about upgrading the DAG docker images. We had similar concerns with > "pex". > > The data platform team looses their handle on the core, or has to get in > > the docker building business, which is atypical. For an upgrade, you'd > have > > to ask/force the people who own the DAG dockers to upgrade their images, > > else they won't run or something. Contract could be like "we'll only run > > your Airflow-docker-dag container if it's in a certain version range" or > > something like that. I think it's a cool idea. It gets intricate for the > > stateless web server though, it's a bit of a mind bender :) You could ask > > the docker to render the page (isn't that crazy?!) or ask the docker for > a > > serialized version of the DAG that allows you to render the page (similar > > to point 1). > > > > * About storing in the db, for efficiency, the pk should be the SHA of > the > > deterministic serialized DAG. Only store a new entry if the DAG has > > changed, and stamp the DagRun to a FK of that serialized DAG table. If > > people have shapeshifting DAG within DagRuns we just do best effort, show > > them the last one or something like that > > > > * everyone hates pickles (including me), but it really almost works, > might > > be worth revisiting, or at least I think it's good for me to list out the > > blockers: > > * JinjaTemplate objects are not serializable for some odd obscure > > reason, I think the community can solve that easily, if someone wants a > > full brain dump on this I can share what I know > > * Size: as you pickle something, someone might have attached things > > that recurse into hundreds of GBs-size pickle. Like some > > on_failure_callback may bring in the whole Slack api library. That can be > > solved or mitigated in different ways. At some point I thought I'd have a > > DAG.validate() method that makes sure that the DAG can be pickled, and > > serialized to a reasonable size pickle. I also think we'd have to make > sure > > operators are defined as more "abstract" otherwise the pickle includes > > things like the whole pyhive lib and all sorts of other deps. It could be > > possible to limit what gets attached to the pickle (whitelist classes), > and > > dehydrate objects during serialization / and rehydrate them on the other > > size (assuming classes are on the worker too). If that sounds crazy to > you, > > it's because it is. > > > > * the other crazy idea is thinking of git repo (the code itself) as the > > serialized DAG. There are git filesystem in userspace [fuse] that allow > > dynamically accessing the git history like it's just a folder, as in > > `REPO/{ANY_GIT_REF}/dags/mydag.py` . Beautifully hacky. A company with a > > blue logo with a big F on it that I used to work at did that. Talking > about > > embracing config-as-code! The DagRun can just stamp the git SHA it's > > running with. > > > > Sorry about the confusion, config as code gets tricky around the corners. > > But it's all worth it, right? Right!? :) > > > > On Tue, Feb 26, 2019 at 3:09 AM Kevin Yang <yrql...@gmail.com> wrote: > > > > > My bad, I was misunderstanding a bit and mixing up two issues. I was > > > thinking about the multiple runs for one DagRun issue( e.g. after we > > clear > > > the DagRun). > > > > > > This is an orthogonal issue. So the current implementation can work in > > the > > > long term plan. > > > > > > Cheers, > > > Kevin Y > > > > > > On Tue, Feb 26, 2019 at 2:34 AM Ash Berlin-Taylor <a...@apache.org> > > wrote: > > > > > > > > > > > > On 26 Feb 2019, at 09:37, Kevin Yang <yrql...@gmail.com> wrote: > > > > > > > > > > Now since we're already trying to have multiple graphs for one > > > > > execution_date, maybe we should just have multiple DagRun. > > > > > > > > I thought that there is exactly 1 graph for a DAG run - dag_run has a > > > > "graph_id" column > > > > > > -- Kyle Hamlin
