The proto (java) -> bytes -> proto (python) sounds good. Have you tried moving your DoFn outside of your main module into a new module as per [1]. Other suggestions are to do the import in the function. Can you do the import once in the setup()[2] function? Have you considered using the cloud profiler[3] to see what is actually slow?
1: https://stackoverflow.com/questions/69436706/nameerror-name-beam-is-not-defined-in-lambda 2: https://github.com/apache/beam/blob/f9d5de34ae1dad251f5580073c0245a206224a69/sdks/python/apache_beam/transforms/core.py#L670 3: https://cloud.google.com/dataflow/docs/guides/profiling-a-pipeline#python On Fri, Jan 6, 2023 at 11:19 AM Lina Mårtensson <lina@camus.energy> wrote: > I am *so close* it seems. ;) > > I followed Luke's advice and am reading the proto > com.google.bigtable.v2.Row, then use a transform to convert that to bytes > in order to be able to send it across to Python. (I assume that's what I > should be doing with the proto?) > Once on the Python side, when running on Dataflow, I'm running into the > dreaded NameError. > save_main_session is True. > > Either > from google.cloud.bigtable_v2.types import Row > ... > class ParsePB(beam.DoFn): > def process(self, pb_bytes): > row = Row() > row.ParseFromString(pb_bytes) > > or > > from google.cloud.bigtable_v2.proto import data_pb2 as data_v2_pb2 > ... > class ParsePB(beam.DoFn): > def process(self, pb_bytes): > row = Row() > row.ParseFromString(pb_bytes) > > works in the DirectRunner (if I skip the Java connection and fake input > data), but not on Dataflow. > It works if I put the import in the process() function, although then > running the code is super slow. (I'm not sure why, but running an import on > every entry definitely sounds like it could cause that!) > > (I still have issues with the DirectRunner, as per my previous email.) > > Is there a good way to get around this? > > Thanks! > -Lina > > On Thu, Jan 5, 2023 at 4:49 PM Lina Mårtensson <lina@camus.energy> wrote: > >> Great, thanks! That was a huge improvement. >> >> >> On Thu, Jan 5, 2023 at 12:52 PM Luke Cwik <lc...@google.com> wrote: >> >>> By default Beam Java only uploads artifacts that have changed but it >>> looks like this is not the case for Beam Python and you need to explicitly >>> opt in with the --enable_artifact_caching flag[1]. >>> >>> It looks like this feature was added 1 year ago[2], should we make this >>> on by default? >>> >>> 1: >>> https://github.com/apache/beam/blob/3070160203c6734da0eb04b440e08b43f9fd33f3/sdks/python/apache_beam/options/pipeline_options.py#L794 >>> 2: https://github.com/apache/beam/pull/16229 >>> >>> >>> >>> On Thu, Jan 5, 2023 at 11:43 AM Lina Mårtensson <lina@camus.energy> >>> wrote: >>> >>>> Thanks! I have now successfully written a beautiful string of protobuf >>>> bytes into a file via Python. 🎉 >>>> >>>> Two issues though: >>>> 1. Robert said the Python direct runner would just work with this - but >>>> it's not working. After about half an hour of these messages repeated over >>>> and over again I interrupted the job: >>>> >>>> E0105 07:25:48.170601677 58210 fork_posix.cc:76] Other >>>> threads are currently calling into gRPC, skipping fork() handlers >>>> >>>> INFO:apache_beam.runners.portability.fn_api_runner.worker_handlers:b'2023/01/05 >>>> 06:57:10 Failed to obtain provisioning information: failed to dial server >>>> at localhost:41087\n\tcaused by:\ncontext deadline exceeded\n' >>>> 2. I (unsurprisingly) get back to the issue I had when I tested out the >>>> Spanner x-lang transform on Dataflow - the overhead for starting a job is >>>> unbearably slow, the time mainly spent in transferring the expansion >>>> service jar (115 MB) + my jar (105 MB) with my new code and its >>>> dependencies: >>>> >>>> INFO:apache_beam.runners.dataflow.internal.apiclient:Starting GCS >>>> upload to >>>> gs://hce-mimo-inbox/beam_temp/beamapp-builder-0105191153-992959-3fhktuyb.1672945913.993243/beam-sdks-java-io-google-cloud-platform-expansion-service-2.39.0-uBMB6BRMpxmYFg1PPu1yUxeoyeyX_lYX1NX0LVL7ZcM.jar... >>>> >>>> INFO:apache_beam.runners.dataflow.internal.apiclient:Completed GCS >>>> upload to >>>> gs://hce-mimo-inbox/beam_temp/beamapp-builder-0105191153-992959-3fhktuyb.1672945913.993243/beam-sdks-java-io-google-cloud-platform-expansion-service-2.39.0-uBMB6BRMpxmYFg1PPu1yUxeoyeyX_lYX1NX0LVL7ZcM.jar >>>> in 321 seconds. >>>> >>>> INFO:apache_beam.runners.dataflow.internal.apiclient:Starting GCS >>>> upload to >>>> gs://hce-mimo-inbox/beam_temp/beamapp-builder-0105191153-992959-3fhktuyb.1672945913.993243/java_bigtable_deploy-Ed1r7YOeLKLTmg2RGNktkym9sVYciCiielpk61r6CJ4.jar... >>>> >>>> INFO:apache_beam.runners.dataflow.internal.apiclient:Completed GCS >>>> upload to >>>> gs://hce-mimo-inbox/beam_temp/beamapp-builder-0105191153-992959-3fhktuyb.1672945913.993243/java_bigtable_deploy-Ed1r7YOeLKLTmg2RGNktkym9sVYciCiielpk61r6CJ4.jar >>>> in 295 seconds. >>>> I have a total of 13 minutes until any workers have started on >>>> Dataflow, then another 4.5 minutes once the job actually does anything >>>> (which eventually is to read a whopping 3 cells from Bigtable ;). >>>> >>>> How could this be improved? >>>> For one, it seems to me like the upload of >>>> sdks:java:io:google-cloud-platform:expansion-service:shadowJar from my >>>> computer shouldn't be necessary - shouldn't Dataflow have that >>>> already/could it be fetched by Dataflow rather than having to upload it >>>> over slow internet? >>>> And what about my own jar - it's not bound to change very often, so >>>> would it be possible to upload somewhere and then fetch it from there? >>>> >>>> Thanks! >>>> -Lina >>>> >>>> On Tue, Jan 3, 2023 at 1:23 PM Luke Cwik <lc...@google.com> wrote: >>>> >>>>> I would suggest using BigtableIO which also returns a >>>>> protobuf com.google.bigtable.v2.Row. This should allow you to replicate >>>>> what SpannerIO is doing. >>>>> >>>>> Alternatively you could provide a way to convert the HBase result into >>>>> a Beam row by specifying a converter and a schema for it and then you >>>>> could >>>>> use the already well known Beam Schema type: >>>>> >>>>> https://github.com/apache/beam/blob/0b8f0b4db7a0de4977e30bcfeb50b5c14c7c1572/model/pipeline/src/main/proto/org/apache/beam/model/pipeline/v1/beam_runner_api.proto#L1068 >>>>> >>>>> Otherwise you'll have to register the HBase result coder with a well >>>>> known name so that the runner API coder URN is something that you know and >>>>> then on the Python side you would need a coder for that URN as well allow >>>>> you to understand the bytes being sent across from the Java portion of the >>>>> pipeline. >>>>> >>>>> On Fri, Dec 30, 2022 at 12:59 AM Lina Mårtensson <lina@camus.energy> >>>>> wrote: >>>>> >>>>>> And next issue... I'm getting KeyError: 'beam:coders:javasdk:0.1' which >>>>>> I learned >>>>>> <https://cwiki.apache.org/confluence/display/BEAM/Multi-language+Pipelines+Tips> >>>>>> is because the transform is trying to return something that there isn't >>>>>> a standard >>>>>> Beam coder for >>>>>> <https://github.com/apache/beam/blob/05428866cdbf1ea8e4c1789dd40327673fd39451/model/pipeline/src/main/proto/beam_runner_api.proto#L784> >>>>>> . >>>>>> Makes sense, but... how do I fix this? The documentation talks >>>>>> about how to do this for the input, but not for the output. >>>>>> >>>>>> Comparing to Spanner, it looks like Spanner returns a protobuf, which >>>>>> I'm guessing somehow gets converted to bytes... But CloudBigtableIO >>>>>> <https://github.com/googleapis/java-bigtable-hbase/blob/main/bigtable-dataflow-parent/bigtable-hbase-beam/src/main/java/com/google/cloud/bigtable/beam/CloudBigtableIO.java> >>>>>> returns org.apache.hadoop.hbase.client.Result. >>>>>> >>>>>> My buildExternal method looks like follows: >>>>>> >>>>>> @Override >>>>>> >>>>>> public PTransform<PBegin, PCollection<Result>> buildExternal( >>>>>> >>>>>> BigtableReadBuilder.Configuration configuration) { >>>>>> >>>>>> >>>>>> return Read.from(CloudBigtableIO.read( >>>>>> >>>>>> new CloudBigtableScanConfiguration.Builder() >>>>>> >>>>>> >>>>>> .withProjectId(configuration.projectId) >>>>>> >>>>>> >>>>>> .withInstanceId(configuration.instanceId) >>>>>> >>>>>> >>>>>> .withTableId(configuration.tableId) >>>>>> >>>>>> .build() >>>>>> >>>>>> )); >>>>>> >>>>>> >>>>>> I also got a warning, which I *believe* is unrelated (but also an >>>>>> issue): >>>>>> >>>>>> INFO:apache_beam.utils.subprocess_server:b"WARNING: Configuration >>>>>> class >>>>>> 'energy.camus.beam.BigtableRegistrar$BigtableReadBuilder$Configuration' >>>>>> has >>>>>> no schema registered. Attempting to construct with setter approach." >>>>>> >>>>>> INFO:apache_beam.utils.subprocess_server:b'Dec 30, 2022 7:46:14 AM >>>>>> org.apache.beam.sdk.expansion.service.ExpansionService$ExternalTransformRegistrarLoader >>>>>> payloadToConfig' >>>>>> What is this schema and what should it look like? >>>>>> >>>>>> Thanks! >>>>>> -Lina >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> On Fri, Dec 30, 2022 at 12:28 AM Lina Mårtensson <lina@camus.energy> >>>>>> wrote: >>>>>> >>>>>>> Thanks! This was really helpful. It took a while to figure out the >>>>>>> details - a section in the docs on what's required of these jars for >>>>>>> non-Java users would be a great addition. >>>>>>> >>>>>>> But once I did, the Bazel config was actually quite straightforward >>>>>>> and makes sense. >>>>>>> I pasted the first section from here >>>>>>> <https://github.com/bazelbuild/rules_jvm_external/blob/master/README.md#usage> >>>>>>> into >>>>>>> my WORKSPACE file and changed the artifacts to the ones I needed. (How >>>>>>> to >>>>>>> find the right ones remains confusing.) >>>>>>> >>>>>>> After that I updated my BUILD rules and Blaze had easy and >>>>>>> straightforward configs for it, all I needed was this: >>>>>>> >>>>>>> # From >>>>>>> https://github.com/google/bazel-common/blob/master/third_party/java/auto/BUILD >>>>>>> . >>>>>>> >>>>>>> # The auto service is what registers our Registrar class, and it >>>>>>> needs to be a plugin which >>>>>>> >>>>>>> # makes it run at compile-time. >>>>>>> >>>>>>> java_plugin( >>>>>>> >>>>>>> name = "auto_service_processor", >>>>>>> >>>>>>> processor_class = >>>>>>> "com.google.auto.service.processor.AutoServiceProcessor", >>>>>>> >>>>>>> deps = [ >>>>>>> >>>>>>> "@maven//:com_google_auto_service_auto_service", >>>>>>> >>>>>>> "@maven//:com_google_auto_service_auto_service_annotations", >>>>>>> >>>>>>> "@maven//:org_apache_beam_beam_vendor_guava_26_0_jre", >>>>>>> >>>>>>> ], >>>>>>> >>>>>>> ) >>>>>>> >>>>>>> >>>>>>> java_binary( >>>>>>> >>>>>>> name = "java_hbase", >>>>>>> >>>>>>> main_class = "energy.camus.beam.BigtableRegistrar", >>>>>>> >>>>>>> plugins = [":auto_service_processor"], >>>>>>> >>>>>>> srcs = ["src/main/java/energy/camus/beam/BigtableRegistrar.java" >>>>>>> ], >>>>>>> >>>>>>> deps = [ >>>>>>> >>>>>>> "@maven//:com_google_auto_service_auto_service", >>>>>>> >>>>>>> "@maven//:com_google_auto_service_auto_service_annotations", >>>>>>> >>>>>>> >>>>>>> "@maven//:com_google_cloud_bigtable_bigtable_hbase_beam", >>>>>>> >>>>>>> >>>>>>> "@maven//:org_apache_beam_beam_sdks_java_core", >>>>>>> >>>>>>> "@maven//:org_apache_beam_beam_vendor_guava_26_0_jre", >>>>>>> >>>>>>> "@maven//:org_apache_hbase_hbase_shaded_client", >>>>>>> >>>>>>> ], >>>>>>> >>>>>>> ) >>>>>>> >>>>>>> >>>>>>> On Thu, Dec 29, 2022 at 2:43 PM Luke Cwik <lc...@google.com> wrote: >>>>>>> >>>>>>>> AutoService relies on Java's compiler annotation processor. >>>>>>>> https://github.com/google/auto/tree/main/service#getting-started >>>>>>>> shows that you need to configure Java's compiler to use the annotation >>>>>>>> processors within AutoService. >>>>>>>> >>>>>>>> I saw this public gist that seemed to enable using the AutoService >>>>>>>> annotation processor with Bazel >>>>>>>> https://gist.github.com/jart/5333824b94cd706499a7bfa1e086ee00 >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> On Thu, Dec 29, 2022 at 2:27 PM Lina Mårtensson via dev < >>>>>>>> dev@beam.apache.org> wrote: >>>>>>>> >>>>>>>>> That's good news about the direct runner, thanks! >>>>>>>>> >>>>>>>>> On Thu, Dec 29, 2022 at 2:02 PM Robert Bradshaw < >>>>>>>>> rober...@google.com> wrote: >>>>>>>>> >>>>>>>>>> On Thu, Jul 28, 2022 at 5:37 PM Chamikara Jayalath via dev >>>>>>>>>> <dev@beam.apache.org> wrote: >>>>>>>>>> > >>>>>>>>>> > On Thu, Jul 28, 2022 at 4:51 PM Lina Mårtensson >>>>>>>>>> <lina@camus.energy> wrote: >>>>>>>>>> >> >>>>>>>>>> >> Thanks for the detailed answers! >>>>>>>>>> >> >>>>>>>>>> >> I totally get the points about development & maintenance cost, >>>>>>>>>> and, >>>>>>>>>> >> from a user perspective, about getting the performance right. >>>>>>>>>> >> >>>>>>>>>> >> I decided to try out the Spanner connector to get a sense of >>>>>>>>>> how well >>>>>>>>>> >> the x-language approach works in our world, since that's an >>>>>>>>>> existing >>>>>>>>>> >> x-language connector. >>>>>>>>>> >> Overall, it works and with minimal intervention as you say - >>>>>>>>>> it is >>>>>>>>>> >> very slow, though. >>>>>>>>>> >> I'm a little confused about "portable runners" - if I >>>>>>>>>> understand this >>>>>>>>>> >> correctly, this means we couldn't run with the DirectRunner >>>>>>>>>> anymore if >>>>>>>>>> >> using an x-language connector? (At least it didn't work when I >>>>>>>>>> tried >>>>>>>>>> >> it.) >>>>>>>>>> > >>>>>>>>>> > >>>>>>>>>> > You'll have to use the portable DirectRunner - >>>>>>>>>> https://github.com/apache/beam/tree/master/sdks/python/apache_beam/runners/portability >>>>>>>>>> > >>>>>>>>>> > Job service for this can be started using following command: >>>>>>>>>> > python >>>>>>>>>> apache_beam/runners/portability/local_job_service_main.py -p <port> >>>>>>>>>> >>>>>>>>>> Note that the Python direct runner is already a portable runner, >>>>>>>>>> so >>>>>>>>>> you shouldn't have to do anything special (like start up a >>>>>>>>>> separate >>>>>>>>>> job service and pass extra options) to run locally. Just use the >>>>>>>>>> cross-language transforms as you would any normal Python >>>>>>>>>> transform. >>>>>>>>>> >>>>>>>>>> The goal is to make this as smooth and transparent as possible; >>>>>>>>>> please >>>>>>>>>> keep coming back to us if you find rough edges. >>>>>>>>>> >>>>>>>>>
