Ler vários tópicos do Kafka no Dataflow

Mostra como criar um pipeline do Dataflow que lê de vários tópicos do Kafka e executa diferentes lógicas de negócios com base no nome do tópico.

Mais informações

Para ver a documentação detalhada que inclui este exemplo de código, consulte:

Exemplo de código

Java

Para autenticar no Dataflow, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local. 

import java.util.List;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.PipelineResult;
import org.apache.beam.sdk.io.TextIO;
import org.apache.beam.sdk.io.kafka.KafkaIO;
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.transforms.Filter;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.values.TypeDescriptors;
import org.apache.kafka.common.serialization.LongDeserializer;
import org.apache.kafka.common.serialization.StringDeserializer;
import org.joda.time.Duration;
import org.joda.time.Instant;

public class KafkaReadTopics {

  public static Pipeline createPipeline(Options options) {
    String topic1 = options.getTopic1();
    String topic2 = options.getTopic2();

    // Build the pipeline.
    var pipeline = Pipeline.create(options);
    var allTopics = pipeline
        .apply(KafkaIO.<Long, String>read()
            .withTopics(List.of(topic1, topic2))
            .withBootstrapServers(options.getBootstrapServer())
            .withKeyDeserializer(LongDeserializer.class)
            .withValueDeserializer(StringDeserializer.class)
            .withMaxReadTime(Duration.standardSeconds(10))
            .withStartReadTime(Instant.EPOCH)
        );

    // Create separate pipeline branches for each topic.
    // The first branch filters on topic1.
    allTopics
        .apply(Filter.by(record -> record.getTopic().equals(topic1)))
        .apply(MapElements
            .into(TypeDescriptors.strings())
            .via(record -> record.getKV().getValue()))
        .apply(TextIO.write()
            .to(topic1)
            .withSuffix(".txt")
            .withNumShards(1)
        );

    // The second branch filters on topic2.
    allTopics
        .apply(Filter.by(record -> record.getTopic().equals(topic2)))
        .apply(MapElements
            .into(TypeDescriptors.strings())
            .via(record -> record.getKV().getValue()))
        .apply(TextIO.write()
            .to(topic2)
            .withSuffix(".txt")
            .withNumShards(1)
        );
    return pipeline;
  }
}

Python

Para autenticar no Dataflow, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local. 

import argparse

import apache_beam as beam

from apache_beam.io.kafka import ReadFromKafka
from apache_beam.io.textio import WriteToText
from apache_beam.options.pipeline_options import PipelineOptions


def read_from_kafka() -> None:
    # Parse the pipeline options passed into the application. Example:
    #   --bootstrap_server=$BOOTSTRAP_SERVER --output=$STORAGE_BUCKET --streaming
    # For more information, see
    # https://beam.apache.org/documentation/programming-guide/#configuring-pipeline-options
    class MyOptions(PipelineOptions):
        @staticmethod
        def _add_argparse_args(parser: argparse.ArgumentParser) -> None:
            parser.add_argument('--bootstrap_server')
            parser.add_argument('--output')

    options = MyOptions()
    with beam.Pipeline(options=options) as pipeline:
        # Read from two Kafka topics.
        all_topics = pipeline | ReadFromKafka(consumer_config={
                "bootstrap.servers": options.bootstrap_server
            },
            topics=["topic1", "topic2"],
            with_metadata=True,
            max_num_records=10,
            start_read_time=0
        )

        # Filter messages from one topic into one branch of the pipeline.
        (all_topics
            | beam.Filter(lambda message: message.topic == 'topic1')
            | beam.Map(lambda message: message.value.decode('utf-8'))
            | "Write topic1" >> WriteToText(
                file_path_prefix=options.output + '/topic1/output',
                file_name_suffix='.txt',
                num_shards=1))

        # Filter messages from the other topic.
        (all_topics
            | beam.Filter(lambda message: message.topic == 'topic2')
            | beam.Map(lambda message: message.value.decode('utf-8'))
            | "Write topic2" >> WriteToText(
                file_path_prefix=options.output + '/topic2/output',
                file_name_suffix='.txt',
                num_shards=1))

A seguir

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