Ben - the issues of "things aren't hung, there is a shell command running", aren't they general to all DoFn's? i.e. I don't see why the runner would need to know that a shell command is running, but not that, say, a heavy monolithic computation is running. What's the benefit to the runner in knowing that the DoFn contains a shell command?
By saying "making sure that all shell commands finish", I suppose you're referring to the possibility of leaks if the user initiates a shell command and forgets to wait for it? I think that should be solvable again without Beam intervention, by making a utility class for running shell commands which implements AutoCloseable, and document that you have to use it that way. Ken - I think the question here is: are we ok with a situation where the runner doesn't check or care whether the shell command can run, and the user accepts this risk and studies what commands will be available on the worker environment provided by the runner they use in production, before productionizing a pipeline with those commands. Upon some thought I think it's ok. Of course, this carries an obligation for runners to document their worker environment and its changes across versions. Though for many runners such documentation may be trivial: "whatever your YARN cluster has, the runner doesn't change it in any way" and it may be good enough for users. And for other runners, like Dataflow, such documentation may also be trivial: "no guarantees whatsoever, only what you stage in --filesToStage is available". I can also see Beam develop to a point where we'd want all runners to be able to run your DoFn in a user-specified Docker container, and manage those intelligently - but I think that's quite a while away and it doesn't have to block work on a utility for executing shell commands. Though it'd be nice if the utility was forward-compatible with that future world. On Tue, Dec 6, 2016 at 2:16 AM Jean-Baptiste Onofré <j...@nanthrax.net> wrote: > Hi Eugene, > > thanks for the extended questions. > > I think we have two levels of expectations here: > - end-user responsibility > - worker/runner responsibility > > 1/ From a end-user perspective, the end-user has to know that using a > system command (via ExecIO) and more generally speaking anything which > relay on worker resources (for instance a local filesystem directory > available only on a worker) can fail if the expected resource is not > present on all workers. So, basically, all workers should have the same > topology. It's what I'm assuming for the PR. > For example, I have my Spark cluster, using the same Mesos/Docker setup, > then the user knows that all nodes in the cluster will have the same > setup and so resources (it could be provided by DevOps for instance). > On the other hand, running on Dataflow is different because I don't > "control" the nodes (bootstrapping or resources), but in that case, the > user knows it (he knows the runner he's using). > > 2/ As you said, we can expect that runner can deal with some > requirements (expressed depending of the pipeline and the runner), and > the runner can know the workers which provide capabilities matching > those requirements. > Then, the end user is not more responsible: the runner will try to > define if the pipeline can be executed, and where a DoFn has to be run > (on which worker). > > For me, it's two different levels where 2 is smarter but 1 can also make > sense. > > WDYT ? > > Regards > JB > > On 12/05/2016 08:51 PM, Eugene Kirpichov wrote: > > Hi JB, > > > > Thanks for bringing this to the mailing list. I also think that this is > > useful in general (and that use cases for Beam are more than just classic > > bigdata), and that there are interesting questions here at different > levels > > about how to do it right. > > > > I suggest to start with the highest-level question [and discuss the > > particular API only after agreeing on this, possibly in a separate > thread]: > > how to deal with the fact that Beam gives no guarantees about the > > environment on workers, e.g. which commands are available, which shell or > > even OS is being used, etc. Particularly: > > > > - Obviously different runners will have a different environment, e.g. > > Dataflow workers are not going to have Hadoop commands available because > > they are not running on a Hadoop cluster. So, pipelines and transforms > > developed using this connector will be necessarily non-portable between > > different runners. Maybe this is ok? But we need to give users a clear > > expectation about this. How do we phrase this expectation and where do we > > put it in the docs? > > > > - I'm concerned that this puts additional compatibility requirements on > > runners - it becomes necessary for a runner to document the environment > of > > its workers (OS, shell, privileges, guaranteed-installed packages, access > > to other things on the host machine e.g. whether or not the worker runs > in > > its own container, etc.) and to keep it stable - otherwise transforms and > > pipelines with this connector will be non-portable between runner > versions > > either. > > > > Another way to deal with this is to give up and say "the environment on > the > > workers is outside the scope of Beam; consult your runner's documentation > > or use your best judgment as to what the environment will be, and use > this > > at your own risk". > > > > What do others think? > > > > On Mon, Dec 5, 2016 at 5:09 AM Jean-Baptiste Onofré <j...@nanthrax.net> > wrote: > > > > Hi beamers, > > > > Today, Beam is mainly focused on data processing. > > Since the beginning of the project, we are discussing about extending > > the use cases coverage via DSLs and extensions (like for machine > > learning), or via IO. > > > > Especially for the IO, we can see Beam use for data integration and data > > ingestion. > > > > In this area, I'm proposing a first IO: ExecIO: > > > > https://issues.apache.org/jira/browse/BEAM-1059 > > https://github.com/apache/incubator-beam/pull/1451 > > > > Actually, this IO is mainly an ExecFn that executes system commands > > (again, keep in mind we are discussing about data integration/ingestion > > and not data processing). > > > > For convenience, this ExecFn is wrapped in Read and Write (as a regular > IO). > > > > Clearly, this IO/Fn depends of the worker where it runs. But it's under > > the user responsibility. > > > > During the review, Eugene and I discussed about: > > - is it an IO or just a fn ? > > - is it OK to have worker specific IO ? > > > > IMHO, an IO makes lot of sense to me and it's very convenient for end > > users. They can do something like: > > > > PCollection<String> output = > > pipeline.apply(ExecIO.read().withCommand("/path/to/myscript.sh")); > > > > The pipeline will execute myscript and the output pipeline will contain > > command execution std out/err. > > > > On the other hand, they can do: > > > > pcollection.apply(ExecIO.write()); > > > > where PCollection contains the commands to execute. > > > > Generally speaking, end users can call ExecFn wherever they want in the > > pipeline steps: > > > > PCollection<String> output = pipeline.apply(ParDo.of(new > ExecIO.ExecFn())); > > > > The input collection contains the commands to execute, and the output > > collection contains the commands execution result std out/err. > > > > Generally speaking, I'm preparing several IOs more on the data > > integration/ingestion area than on "pure" classic big data processing. I > > think it would give a new "dimension" to Beam. > > > > Thoughts ? > > > > Regards > > JB > > -- > > Jean-Baptiste Onofré > > jbono...@apache.org > > http://blog.nanthrax.net > > Talend - http://www.talend.com > > > > -- > Jean-Baptiste Onofré > jbono...@apache.org > http://blog.nanthrax.net > Talend - http://www.talend.com >
