Hi Eugene,
I like your ShellCommands.execute().withCommand("foo") !
And you listed valid points and usages, especially around the
input/output of the command.
My question is where do we put such ShellCommands extension ? As a
module under IO ? As a new extensions module ?
Regards
JB
On 12/07/2016 06:24 PM, Eugene Kirpichov wrote:
Branched off into a separate thread.
How about ShellCommands.execute().withCommand("foo")? This is what it is -
it executes shell commands :)
Say, if I want to just execute a command for the sake of its side effect,
but I'm not interested in its output - it would feel odd to describe that
as either "reading" from the command or "writing" to it. Likewise, when I
execute commands in bash, I'm not thinking of it as reading or writing to
them.
Though, there are various modes of interaction with shell commands; some of
them could be called "reading" or "writing" I guess - or both!
- The command itself can be specified at pipeline construction time, or
fully dynamic (elements of a PCollection are themselves commands), or be
constructed from a fixed command and a variable set of arguments coming
from the PCollection (one-by-one or xargs-style, cramming as many arguments
as fit into the command line limit).
- We may also be writing elements of the PCollection to standard input of
the command - one-by-one, or in arbitrarily sized batches.
- We may be reading the command's stdout, its stderr, and its error code.
I think these options call for a more flexible naming and set of APIs than
read and write. And more flexible than a single DoFn, too (which is
something I hadn't thought of before - this connector definitely has room
for doing some interesting things).
On Wed, Dec 7, 2016 at 1:32 AM Jean-Baptiste Onofré <j...@nanthrax.net> wrote:
By the way, just to elaborate a bit why I provided as an IO:
1. From an user experience perspective, I think we have to provide
convenient way to write pipeline. Any syntax simplifying this is valuable.
I think it's easier to write:
pipeline.apply(ExecIO.read().withCommand("foo"))
than:
pipeline.apply(Create.of("foo")).apply(ParDo.of(new ExecFn());
2. For me (maybe I'm wrong ;)), an IO is an extension dedicated for
"connector": reading/writing from/to a data source. So, even without the
IO "wrapping" (by wrapping, I mean the Read and Write), I think Exec
extension should be in IO as it's a source/write of data.
Regards
JB
On 12/07/2016 08:37 AM, Robert Bradshaw wrote:
I don't mean to derail the tricky environment questions, but I'm not
seeing why this is bundled as an IO rather than a plain DoFn (which
can be applied to a PCollection of one or more commands, yielding
their outputs). Especially for the case of a Read, which in this case
is not splittable (initially or dynamically) and always produces a
single element--feels much more like a Map to me.
On Tue, Dec 6, 2016 at 3:26 PM, Eugene Kirpichov
<kirpic...@google.com.invalid> wrote:
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
--
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
