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 MÃ¥rtensson <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 MÃ¥rtensson <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 MÃ¥rtensson 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 MÃ¥rtensson <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. >>>>>> >>>>>
