Membaca dari beberapa topik Kafka ke Dataflow

Menunjukkan cara membuat pipeline Dataflow yang membaca dari beberapa topik Kafka dan menjalankan logika bisnis yang berbeda berdasarkan nama topik.

Mempelajari lebih lanjut

Untuk dokumentasi mendetail yang menyertakan contoh kode ini, lihat artikel berikut:

• Membaca dari Apache Kafka ke Dataflow

Contoh kode

Java

Untuk melakukan autentikasi ke Dataflow, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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

Untuk melakukan autentikasi ke Dataflow, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, lihat Menyiapkan autentikasi untuk lingkungan pengembangan lokal.

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))

Langkah berikutnya

Untuk menelusuri dan memfilter contoh kode untuk produk Google Cloud lainnya, lihat Google Cloud browser contoh.