On Thu, Feb 4, 2021 at 12:38 PM Kyle Weaver <kcwea...@google.com> wrote:
> So, an external transform is uniquely identified by its URN. An external >> transform identified by a URN may refer to an arbitrary composite which may >> have sub-transforms that refer to different environments. I think with the >> above proposal we'll lose this flexibility. >> What we need is a way to override environments (or properties of >> environments) that results in the final pipeline proto. Once we modify such >> environments in the proto it will be reflected to all transforms that >> utilize such environments. > > > As far as I can tell we currently only register a single environment for > the entire transform (and it's always the default). Am I missing something? > https://github.com/apache/beam/blob/0cfa80fd919d141a2061393ec5c12521c7d7af0b/sdks/java/expansion-service/src/main/java/org/apache/beam/sdk/expansion/service/ExpansionService.java#L447-L449 > > Anyway, I don't see how sub-transforms require overrides. We should be > able to propagate environment options to sub-transforms to achieve the same > purpose. > The discussion of resource hints at https://lists.apache.org/thread.html/ra40286b66a03a1d9f4086c9e1ecdeb9f299836d2d0361c3e3fe7c382%40%3Cdev.beam.apache.org%3E actually may tie into this as well. I would assume a localised request for, say, high memory should be propagated down to cross-language pipelines. It is possible that other customizations (such as making sure specific dependencies are available, or filesystems mounted) would fit here too. This gets into the distinction of customizing what kind of environment one wants to have (which could be generally applicable) vs. an absolute designation of a particular environment (e.g. a docker image). Note that what we're running into in part is that "pipeline options" are the wrong level of granularity for specifying characteristics of an environment, as there is not a single environment to parameterize (or, possibly, even one per language). If I call ExpansionRequset(MyFancyTransform,environment_config=docker_path) and MyFancyTransform is composed of two environments, to which does docker_path apply? What about PTransforms that use ExternalTransforms under the hood (e.g does some pre-processing and then calls SQL, or calls Kafka followed by some Python-level post-processing)? 'sdk_harness_container_image_overrides' is such a property (which >> unfortunately only works for Dataflow today). Also this only works for >> Docker URLs. Maybe we can extend this property to all runners or introduce >> a new property that works for all types of environments ? > > > In my original email, I wrote that sdk_harness_container_image_overrides > is no more flexible than having a single option per SDK, since the default > container images for all external transforms in each SDK are expected to be > the same. For example, in the case of a pipeline with two external > transforms that both use the same default container image, > sdk_harness_container_image_overrides does not let the user give those two > transforms different containers. > > From a design standpoint, I feel find-replace is hacky and backwards. It's > cleaner to specify what kind of environment we want directly in > the ExpansionRequest. That way all of the environment creation logic > belongs inside the expansion service. > While Environments logically belong with Transforms, it is the expansion service's job to attach the right environments to the transforms that it vends. The caller should not need any visibility into the environment(s) that an expansion service uses, which is an implementation detail that the expansion service is free to change at any time. (In fact, whether it is (partially or fully) implemented as an external transform is an implementation detail that the end user should not need to care about or depend on.) I personally think pattern matching and substitution by runners (maybe more sophisticated than regexp on container names) is a reasonable way to approach customization of environments. For example, suppose I construct a pipeline that uses both Python and Java transforms. (I could do this from Go, Java, or Python). If I want to run this locally (e.g. on the Python FnAPI runner), I would prefer that the python bits be run in-process but would have to shell out (maybe via docker, maybe something cheaper) for the java bits. On the other hand, if I want to run this same pipeline (ideally, the same model proto, such that we don't have runner-dependent construction) on Flink, I might want the java bits to be inlined and the Python bits to be in a separate process. On Dataflow, both would live in containers. To do this, the Python runner would say "hey, I know that Python environment" and just swap it out for in-process, and vice versa. (For isolation/other reasons, one may want the option to force everything to be docker, but that's more of a "don't make substitutions" option than manually providing environment configs.) On the other hand, as we go the route of custom containers, especially expansion services that might vend custom containers, I think we need a way to push down *properties* of environments (such as resource hints) through the expansion service that may influence the environments that get attached and returned. It would be helpful for me to have concrete usecases of why a user wants to customize the container used by some transform they did not write, which could possibly inform the best course(s) of action here. > > > On Wed, Feb 3, 2021 at 5:07 PM Chamikara Jayalath <chamik...@google.com> > wrote: > >> >> >> On Wed, Feb 3, 2021 at 12:34 PM Kyle Weaver <kcwea...@google.com> wrote: >> >>> Hi Beamers, >>> >>> Recently we’ve had some requests on user@ and Slack for instructions on >>> how to use custom-built containers in cross-language pipelines (typically >>> calling Java transforms from a predominantly Python pipeline). Currently, >>> it seems like there is no way to change the container used by a >>> cross-language transform except by modifying and rebuilding the expansion >>> service. The SDK does not pass pipeline options to the expansion service >>> (BEAM-9449 [1]). Fixing BEAM-9449 does not solve everything, however. Even >>> if pipeline options are passed, the existing set of pipeline options still >>> limits the amount of control we have over environments. Here are the >>> existing pipeline options that I’m aware of: >>> >>> Python [2] and Go [3] have these: >>> >>> - >>> >>> environment_type (DOCKER, PROCESS, LOOPBACK) >>> - >>> >>> environment_config (This one is confusingly overloaded. It’s a >>> string that means different things depending on environment_type. For >>> DOCKER, it is the Docker image URL. For PROCESS it is a JSON blob. For >>> EXTERNAL, it is the external service address.) >>> >>> >>> Whereas Java [4] has defaultEnvironmentType and >>> defaultEnvironmentConfig, which are named differently but otherwise act the >>> same as the above. >>> >>> I was unsatisfied with environment_config for a number of reasons. >>> First, having a single overloaded option that can mean entirely different >>> things depending on context is poor design. Second, in PROCESS mode, >>> requiring the user to type in a JSON blob for environment_config is not >>> especially human-friendly (though it has also been argued that JSON makes >>> complex arguments like this easier to parse). Finally, we must overload >>> this string further to introduce new environment-specific options, such as >>> a mounted Docker volume (BEAM-5440 [5]). >>> >> >> Agree. >> >> >>> >>> To address these problems, I added a new option called >>> “environment_options” (BEAM-10671 [6]). (This option has been implemented >>> in the Python SDK, but not the other SDKs yet.) Environment_options, >>> similar to the “experiments” option, takes a list of strings, for example >>> “--environment_option=docker_container_image=my_beam_sdk:latest”. It could >>> be argued we should have made “docker_container_image” etc. top-level >>> options instead, but this “catch-all” design makes what I am about to >>> propose a lot easier. >>> >>> The solution proposed in PR #11638 [7] set a flag to include >>> unrecognized pipeline options during serialization, since otherwise >>> unrecognized options are dropped. In a Python pipeline, this will allow us >>> to set environment_config and default_environment_config to separate >>> values, for Python and Java containers, respectively. However, this still >>> limits us to one container image for all Python and Go transforms, and one >>> container image for all Java transforms. As more cross-language transforms >>> are implemented, sooner or later someone will want to have different Java >>> SDK containers for different external transforms. >>> >>> (I should also mention the sdk_harness_container_image_overrides >>> pipeline option [8], which is currently only supported by the Dataflow >>> runner. It lets us basically perform a find/replace on container image >>> strings. This is not significantly more flexible than having a single >>> option per SDK, since the default container images for all external >>> transforms in each SDK are expected to be the same.) >>> >>> Environments logically belong with transforms, and that’s how it works >>> in the Runner API [9]. The problem now is that from the user’s perspective, >>> the environment is bound to the expansion service. After addressing >>> BEAM-9449, the problem will be that one or two environments at most are >>> bound to the pipeline. Ideally, though, users should have fully granular >>> control over environments at the transform level. >>> >>> All this context for a very simple proposal: we should have all >>> ExternalTransform subclasses take optional environment_type and >>> environment_options fields in their constructors. As with their >>> corresponding pipeline options, these options would default to DOCKER and >>> none, respectively. Then we could overwrite the environment_type and >>> environment_options in the pipeline options passed to the expansion service >>> with these values. (Alternatively, we could pass environment_type and >>> environment_options to the expansion service individually to avoid having >>> to overwrite their original values, but their original values should be >>> irrelevant to the expansion service anyway.) >>> >>> What do you think? >>> >> >> So, an external transform is uniquely identified by its URN. An external >> transform identified by a URN may refer to an arbitrary composite which may >> have sub-transforms that refer to different environments. I think with the >> above proposal we'll lose this flexibility. >> What we need is a way to override environments (or properties of >> environments) that results in the final pipeline proto. Once we modify such >> environments in the proto it will be reflected to all transforms that >> utilize such environments. >> >> 'sdk_harness_container_image_overrides' is such a property (which >> unfortunately only works for Dataflow today). Also this only works for >> Docker URLs. Maybe we can extend this property to all runners or introduce >> a new property that works for all types of environments ? >> >> Thanks, >> Cham >> >> >>> >>> [1] https://issues.apache.org/jira/browse/BEAM-9449 >>> >>> [2] >>> https://github.com/apache/beam/blob/f2c9b6e1aa5d38385f4c168107c85d4fe7f0f259/sdks/python/apache_beam/options/pipeline_options.py#L1097-L1115 >>> >>> [3] >>> https://github.com/apache/beam/blob/b56b61a9a6401271f14746000ecc38b17aab753d/sdks/go/pkg/beam/options/jobopts/options.go#L41-L53 >>> >>> [4] >>> https://github.com/apache/beam/blob/b56b61a9a6401271f14746000ecc38b17aab753d/sdks/java/core/src/main/java/org/apache/beam/sdk/options/PortablePipelineOptions.java#L53-L71 >>> >>> [5] https://issues.apache.org/jira/browse/BEAM-5440 >>> >>> [6] https://issues.apache.org/jira/browse/BEAM-10671 >>> >>> [7] https://github.com/apache/beam/pull/11638 >>> >>> [8] >>> https://github.com/apache/beam/blob/f2c9b6e1aa5d38385f4c168107c85d4fe7f0f259/sdks/python/apache_beam/options/pipeline_options.py#L840-L850 >>> >>> [9] >>> https://github.com/apache/beam/blob/b56b61a9a6401271f14746000ecc38b17aab753d/model/pipeline/src/main/proto/beam_runner_api.proto#L194 >>> >>>
