Trasmettere flussi di messaggi da Pub/Sub utilizzando Dataflow

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
2. Install the Google Cloud CLI.

3. To initialize the gcloud CLI, run the following command:

   gcloud init

4. Create or select a Google Cloud project.

   • Create a Google Cloud project:

     gcloud projects create PROJECT_ID

     Replace PROJECT_ID with a name for the Google Cloud project you are creating.

   • Select the Google Cloud project that you created:

     gcloud config set project PROJECT_ID

     Replace PROJECT_ID with your Google Cloud project name.

5. Make sure that billing is enabled for your Google Cloud project.

6. Enable the Dataflow, Compute Engine, Cloud Logging, Cloud Storage, Google Cloud Storage JSON API, Pub/Sub, Resource Manager, and Cloud Scheduler APIs:

   gcloud services enable dataflow.googleapis.com  compute.googleapis.com  logging.googleapis.com  storage-component.googleapis.com  storage-api.googleapis.com  pubsub.googleapis.com  cloudresourcemanager.googleapis.com  cloudscheduler.googleapis.com

7. Set up authentication:

   1. Create the service account:

      gcloud iam service-accounts create SERVICE_ACCOUNT_NAME

      Replace SERVICE_ACCOUNT_NAME with a name for the service account.

   2. Grant roles to the service account. Run the following command once for each of the following IAM roles: roles/dataflow.worker, roles/storage.objectAdmin, roles/pubsub.admin:

      gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com" --role=ROLE

      Replace the following:

      • SERVICE_ACCOUNT_NAME: the name of the service account
      • PROJECT_ID: the project ID where you created the service account
      • ROLE: the role to grant

   3. Grant the required role to the principal that will attach the service account to other resources.

      gcloud iam service-accounts add-iam-policy-binding SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com --member="user:USER_EMAIL" --role=roles/iam.serviceAccountUser

      Replace the following:

      • SERVICE_ACCOUNT_NAME: the name of the service account
      • PROJECT_ID: the project ID where you created the service account
      • USER_EMAIL: the email address for a Google Account
8. Install the Google Cloud CLI.

9. To initialize the gcloud CLI, run the following command:

   gcloud init

10. Create or select a Google Cloud project.

    • Create a Google Cloud project:

      gcloud projects create PROJECT_ID

      Replace PROJECT_ID with a name for the Google Cloud project you are creating.

    • Select the Google Cloud project that you created:

      gcloud config set project PROJECT_ID

      Replace PROJECT_ID with your Google Cloud project name.

11. Make sure that billing is enabled for your Google Cloud project.

12. Enable the Dataflow, Compute Engine, Cloud Logging, Cloud Storage, Google Cloud Storage JSON API, Pub/Sub, Resource Manager, and Cloud Scheduler APIs:

    gcloud services enable dataflow.googleapis.com  compute.googleapis.com  logging.googleapis.com  storage-component.googleapis.com  storage-api.googleapis.com  pubsub.googleapis.com  cloudresourcemanager.googleapis.com  cloudscheduler.googleapis.com

13. Set up authentication:

    1. Create the service account:

       gcloud iam service-accounts create SERVICE_ACCOUNT_NAME

       Replace SERVICE_ACCOUNT_NAME with a name for the service account.

    2. Grant roles to the service account. Run the following command once for each of the following IAM roles: roles/dataflow.worker, roles/storage.objectAdmin, roles/pubsub.admin:

       gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com" --role=ROLE

       Replace the following:

       • SERVICE_ACCOUNT_NAME: the name of the service account
       • PROJECT_ID: the project ID where you created the service account
       • ROLE: the role to grant

    3. Grant the required role to the principal that will attach the service account to other resources.

       gcloud iam service-accounts add-iam-policy-binding SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com --member="user:USER_EMAIL" --role=roles/iam.serviceAccountUser

       Replace the following:

       • SERVICE_ACCOUNT_NAME: the name of the service account
       • PROJECT_ID: the project ID where you created the service account
       • USER_EMAIL: the email address for a Google Account

14. Create local authentication credentials for your user account:

    gcloud auth application-default login

Configura il progetto Pub/Sub

1. Crea variabili per il bucket, il progetto e la regione. I nomi dei bucket Cloud Storage devono essere univoci a livello globale. Seleziona una regione Dataflow vicino a dove esegui i comandi in questa guida introduttiva. Il valore della variabile REGION deve essere un nome di regione valido. Per saperne di più su regioni e località, consulta Località di Dataflow.

   BUCKET_NAME=BUCKET_NAME
   PROJECT_ID=$(gcloud config get-value project)
   TOPIC_ID=TOPIC_ID
   REGION=DATAFLOW_REGION
   SERVICE_ACCOUNT=SERVICE_ACCOUNT_NAME@PROJECT_ID.iam.gserviceaccount.com

2. Crea un bucket Cloud Storage di proprietà di questo progetto:

   gcloud storage buckets create gs://$BUCKET_NAME

3. Crea un argomento Pub/Sub in questo progetto:

   gcloud pubsub topics create $TOPIC_ID

4. Crea un job Cloud Scheduler in questo progetto. Il job pubblica un messaggio in un argomento Pub/Sub a intervalli di un minuto.

   Se non esiste un'app App Engine per il progetto, questo passaggio ne creerà una.

   gcloud scheduler jobs create pubsub publisher-job --schedule="* * * * *" \
       --topic=$TOPIC_ID --message-body="Hello!" --location=$REGION

   Avvia il job.

   gcloud scheduler jobs run publisher-job --location=$REGION

5. Utilizza i comandi seguenti per clonare il repository di Quickstart e vai alla directory del codice campione:

   Java

   git clone https://github.com/GoogleCloudPlatform/java-docs-samples.git
   cd java-docs-samples/pubsub/streaming-analytics

   Python

   git clone https://github.com/GoogleCloudPlatform/python-docs-samples.git
   cd python-docs-samples/pubsub/streaming-analytics
   pip install -r requirements.txt  # Install Apache Beam dependencies

Inserire flussi di messaggi da Pub/Sub in Cloud Storage

Esempio di codice

Questo codice campione utilizza Dataflow per:

• Leggi i messaggi Pub/Sub.
• Raggruppa (o raggruppa) i messaggi in intervalli di dimensioni fisse in base ai timestamp di pubblicazione.

• Scrivere i messaggi in ogni finestra in file in Cloud Storage.

import java.io.IOException;
import org.apache.beam.examples.common.WriteOneFilePerWindow;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.gcp.pubsub.PubsubIO;
import org.apache.beam.sdk.options.Default;
import org.apache.beam.sdk.options.Description;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.StreamingOptions;
import org.apache.beam.sdk.options.Validation.Required;
import org.apache.beam.sdk.transforms.windowing.FixedWindows;
import org.apache.beam.sdk.transforms.windowing.Window;
import org.joda.time.Duration;

public class PubSubToGcs {
  /*
   * Define your own configuration options. Add your own arguments to be processed
   * by the command-line parser, and specify default values for them.
   */
  public interface PubSubToGcsOptions extends StreamingOptions {
    @Description("The Cloud Pub/Sub topic to read from.")
    @Required
    String getInputTopic();

    void setInputTopic(String value);

    @Description("Output file's window size in number of minutes.")
    @Default.Integer(1)
    Integer getWindowSize();

    void setWindowSize(Integer value);

    @Description("Path of the output file including its filename prefix.")
    @Required
    String getOutput();

    void setOutput(String value);
  }

  public static void main(String[] args) throws IOException {
    // The maximum number of shards when writing output.
    int numShards = 1;

    PubSubToGcsOptions options =
        PipelineOptionsFactory.fromArgs(args).withValidation().as(PubSubToGcsOptions.class);

    options.setStreaming(true);

    Pipeline pipeline = Pipeline.create(options);

    pipeline
        // 1) Read string messages from a Pub/Sub topic.
        .apply("Read PubSub Messages", PubsubIO.readStrings().fromTopic(options.getInputTopic()))
        // 2) Group the messages into fixed-sized minute intervals.
        .apply(Window.into(FixedWindows.of(Duration.standardMinutes(options.getWindowSize()))))
        // 3) Write one file to GCS for every window of messages.
        .apply("Write Files to GCS", new WriteOneFilePerWindow(options.getOutput(), numShards));

    // Execute the pipeline and wait until it finishes running.
    pipeline.run().waitUntilFinish();
  }
}

import argparse
from datetime import datetime
import logging
import random

from apache_beam import (
    DoFn,
    GroupByKey,
    io,
    ParDo,
    Pipeline,
    PTransform,
    WindowInto,
    WithKeys,
)
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.transforms.window import FixedWindows


class GroupMessagesByFixedWindows(PTransform):
    """A composite transform that groups Pub/Sub messages based on publish time
    and outputs a list of tuples, each containing a message and its publish time.
    """

    def __init__(self, window_size, num_shards=5):
        # Set window size to 60 seconds.
        self.window_size = int(window_size * 60)
        self.num_shards = num_shards

    def expand(self, pcoll):
        return (
            pcoll
            # Bind window info to each element using element timestamp (or publish time).
            | "Window into fixed intervals"
            >> WindowInto(FixedWindows(self.window_size))
            | "Add timestamp to windowed elements" >> ParDo(AddTimestamp())
            # Assign a random key to each windowed element based on the number of shards.
            | "Add key" >> WithKeys(lambda _: random.randint(0, self.num_shards - 1))
            # Group windowed elements by key. All the elements in the same window must fit
            # memory for this. If not, you need to use `beam.util.BatchElements`.
            | "Group by key" >> GroupByKey()
        )


class AddTimestamp(DoFn):
    def process(self, element, publish_time=DoFn.TimestampParam):
        """Processes each windowed element by extracting the message body and its
        publish time into a tuple.
        """
        yield (
            element.decode("utf-8"),
            datetime.utcfromtimestamp(float(publish_time)).strftime(
                "%Y-%m-%d %H:%M:%S.%f"
            ),
        )


class WriteToGCS(DoFn):
    def __init__(self, output_path):
        self.output_path = output_path

    def process(self, key_value, window=DoFn.WindowParam):
        """Write messages in a batch to Google Cloud Storage."""

        ts_format = "%H:%M"
        window_start = window.start.to_utc_datetime().strftime(ts_format)
        window_end = window.end.to_utc_datetime().strftime(ts_format)
        shard_id, batch = key_value
        filename = "-".join([self.output_path, window_start, window_end, str(shard_id)])

        with io.gcsio.GcsIO().open(filename=filename, mode="w") as f:
            for message_body, publish_time in batch:
                f.write(f"{message_body},{publish_time}\n".encode())


def run(input_topic, output_path, window_size=1.0, num_shards=5, pipeline_args=None):
    # Set `save_main_session` to True so DoFns can access globally imported modules.
    pipeline_options = PipelineOptions(
        pipeline_args, streaming=True, save_main_session=True
    )

    with Pipeline(options=pipeline_options) as pipeline:
        (
            pipeline
            # Because `timestamp_attribute` is unspecified in `ReadFromPubSub`, Beam
            # binds the publish time returned by the Pub/Sub server for each message
            # to the element's timestamp parameter, accessible via `DoFn.TimestampParam`.
            # https://beam.apache.org/releases/pydoc/current/apache_beam.io.gcp.pubsub.html#apache_beam.io.gcp.pubsub.ReadFromPubSub
            | "Read from Pub/Sub" >> io.ReadFromPubSub(topic=input_topic)
            | "Window into" >> GroupMessagesByFixedWindows(window_size, num_shards)
            | "Write to GCS" >> ParDo(WriteToGCS(output_path))
        )


if __name__ == "__main__":
    logging.getLogger().setLevel(logging.INFO)

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--input_topic",
        help="The Cloud Pub/Sub topic to read from."
        '"projects/<PROJECT_ID>/topics/<TOPIC_ID>".',
    )
    parser.add_argument(
        "--window_size",
        type=float,
        default=1.0,
        help="Output file's window size in minutes.",
    )
    parser.add_argument(
        "--output_path",
        help="Path of the output GCS file including the prefix.",
    )
    parser.add_argument(
        "--num_shards",
        type=int,
        default=5,
        help="Number of shards to use when writing windowed elements to GCS.",
    )
    known_args, pipeline_args = parser.parse_known_args()

    run(
        known_args.input_topic,
        known_args.output_path,
        known_args.window_size,
        known_args.num_shards,
        pipeline_args,
    )

Avvia la pipeline

Per avviare la pipeline, esegui il seguente comando:

mvn compile exec:java \
  -Dexec.mainClass=com.examples.pubsub.streaming.PubSubToGcs \
  -Dexec.cleanupDaemonThreads=false \
  -Dexec.args=" \
    --project=$PROJECT_ID \
    --region=$REGION \
    --inputTopic=projects/$PROJECT_ID/topics/$TOPIC_ID \
    --output=gs://$BUCKET_NAME/samples/output \
    --gcpTempLocation=gs://$BUCKET_NAME/temp \
    --runner=DataflowRunner \
    --windowSize=2 \
    --serviceAccount=$SERVICE_ACCOUNT"

python PubSubToGCS.py \
  --project=$PROJECT_ID \
  --region=$REGION \
  --input_topic=projects/$PROJECT_ID/topics/$TOPIC_ID \
  --output_path=gs://$BUCKET_NAME/samples/output \
  --runner=DataflowRunner \
  --window_size=2 \
  --num_shards=2 \
  --temp_location=gs://$BUCKET_NAME/temp \
  --service_account_email=$SERVICE_ACCOUNT

Il comando precedente viene eseguito localmente e avvia un job Dataflow eseguito nel cloud. Quando il comando restituisce JOB_MESSAGE_DETAILED: Workers have started successfully, esci dal programma locale utilizzando Ctrl+C.

Monitorare l'avanzamento dei job e delle pipeline

Puoi osservare l'avanzamento del job nella console Dataflow.

Vai alla console Dataflow

Apri la visualizzazione dei dettagli del job per vedere:

• Struttura del job
• Log job
• Metriche della fase

Potresti dover attendere qualche minuto prima di vedere i file di output in Cloud Storage.

In alternativa, utilizza la riga di comando riportata di seguito per controllare quali file sono stati scritti.

gcloud storage ls gs://${BUCKET_NAME}/samples/

L'output dovrebbe essere simile al seguente:

gs://{$BUCKET_NAME}/samples/output-22:30-22:32-0-of-1
gs://{$BUCKET_NAME}/samples/output-22:32-22:34-0-of-1
gs://{$BUCKET_NAME}/samples/output-22:34-22:36-0-of-1
gs://{$BUCKET_NAME}/samples/output-22:36-22:38-0-of-1

gs://{$BUCKET_NAME}/samples/output-22:30-22:32-0
gs://{$BUCKET_NAME}/samples/output-22:30-22:32-1
gs://{$BUCKET_NAME}/samples/output-22:32-22:34-0
gs://{$BUCKET_NAME}/samples/output-22:32-22:34-1