Pub/Sub ke BigQuery dengan template UDF Python

Template Pub/Sub to BigQuery dengan UDF Python adalah pipeline streaming yang membaca pesan berformat JSON dari Pub/Sub dan menulisnya ke tabel BigQuery. Secara opsional, Anda dapat memberikan fungsi yang ditentukan pengguna (UDF) yang ditulis dalam Python untuk memproses pesan yang masuk.

Persyaratan pipeline

  • Tabel BigQuery harus ada dan memiliki skema.
  • Data pesan Pub/Sub harus menggunakan format JSON, atau Anda harus menyediakan UDF yang mengonversi data pesan ke JSON. Data JSON harus cocok dengan skema tabel BigQuery. Misalnya, jika payload JSON diformat sebagai {"k1":"v1", "k2":"v2"}, tabel BigQuery harus memiliki dua kolom string bernama k1 dan k2.
  • Tentukan parameter inputSubscription atau inputTopic, tetapi tidak keduanya.

Parameter template

Parameter Deskripsi
outputTableSpec Tabel BigQuery yang akan ditulis, diformat sebagai "PROJECT_ID:DATASET_NAME.TABLE_NAME".
inputSubscription Opsional: Langganan Pub/Sub yang akan dibaca, diformat sebagai "projects/PROJECT_ID/subscriptions/SUBCRIPTION_NAME".
inputTopic Opsional: Topik Pub/Sub yang akan dibaca, diformat sebagai "projects/PROJECT_ID/topics/TOPIC_NAME".
outputDeadletterTable Tabel BigQuery untuk pesan yang gagal mencapai tabel output, yang diformat sebagai "PROJECT_ID:DATASET_NAME.TABLE_NAME". Jika tidak ada, tabel akan dibuat saat pipeline berjalan. Jika parameter ini tidak ditentukan, nilai "OUTPUT_TABLE_SPEC_error_records" akan digunakan.
pythonExternalTextTransformGcsPath Opsional: URI Cloud Storage dari file kode Python yang menentukan fungsi yang ditentukan pengguna (UDF) yang ingin Anda gunakan. Misalnya, gs://my-bucket/my-udfs/my_file.py.
pythonExternalTextTransformFunctionName Opsional: Nama fungsi yang ditentukan pengguna (UDF) Python yang ingin Anda gunakan.
useStorageWriteApi Opsional: Jika true, pipeline akan menggunakan BigQuery Storage Write API. Nilai defaultnya adalah false. Untuk informasi selengkapnya, lihat Menggunakan Storage Write API.
useStorageWriteApiAtLeastOnce Opsional: Saat menggunakan Storage Write API, menentukan semantik tulis. Untuk menggunakan semantik setidaknya satu kali, tetapkan parameter ini ke true. Untuk menggunakan semantik tepat satu kali, tetapkan parameter ke false. Parameter ini hanya berlaku jika useStorageWriteApi adalah true. Nilai defaultnya adalah false.
numStorageWriteApiStreams Opsional: Saat menggunakan Storage Write API, menentukan jumlah aliran tulis. Jika useStorageWriteApi adalah true dan useStorageWriteApiAtLeastOnce adalah false, Anda harus menetapkan parameter ini.
storageWriteApiTriggeringFrequencySec Opsional: Saat menggunakan Storage Write API, tentukan frekuensi pemicuan, dalam detik. Jika useStorageWriteApi adalah true dan useStorageWriteApiAtLeastOnce adalah false, Anda harus menetapkan parameter ini.

Fungsi yang ditentukan pengguna (UDF)

Secara opsional, Anda dapat memperluas template ini dengan menulis fungsi yang ditentukan pengguna (UDF). Template memanggil UDF untuk setiap elemen input. Payload elemen diserialisasi sebagai string JSON. Untuk informasi selengkapnya, lihat Membuat fungsi yang ditentukan pengguna untuk template Dataflow.

Spesifikasi fungsi

UDF memiliki spesifikasi berikut:

  • Input: kolom data pesan Pub/Sub, yang diserialisasi sebagai string JSON.
  • Output: string JSON yang cocok dengan skema tabel tujuan BigQuery.
  • Menjalankan template

    1. Buka halaman Create job from template Dataflow.
    2. Buka Buat tugas dari template
    3. Di kolom Nama tugas, masukkan nama tugas yang unik.
    4. Opsional: Untuk Endpoint regional, pilih nilai dari menu drop-down. Region defaultnya adalah us-central1.

      Untuk mengetahui daftar region tempat Anda dapat menjalankan tugas Dataflow, lihat Lokasi Dataflow.

    5. Dari menu drop-down Dataflow template, pilih the Pub/Sub to BigQuery with Python UDF template.
    6. Di kolom parameter yang disediakan, masukkan nilai parameter Anda.
    7. Opsional: Untuk beralih dari pemrosesan tepat satu kali ke mode streaming setidaknya sekali, pilih Setidaknya Sekali.
    8. Klik Run job.

    Di shell atau terminal, jalankan template:

    gcloud dataflow flex-template run JOB_NAME \
        --template-file-gcs-location gs://dataflow-templates-REGION_NAME/VERSION/flex/PubSub_to_BigQuery_Xlang \
        --region REGION_NAME \
        --staging-location STAGING_LOCATION \
        --parameters \
    inputTopic=projects/PROJECT_ID/topics/TOPIC_NAME,\
    outputTableSpec=PROJECT_ID:DATASET.TABLE_NAME

    Ganti kode berikut:

    • JOB_NAME: nama tugas unik pilihan Anda
    • REGION_NAME: region tempat Anda ingin men-deploy tugas Dataflow—misalnya, us-central1
    • VERSION: versi template yang ingin Anda gunakan

      Anda dapat menggunakan nilai berikut:

    • STAGING_LOCATION: lokasi untuk melakukan staging file lokal (misalnya, gs://your-bucket/staging)
    • TOPIC_NAME: nama topik Pub/Sub Anda
    • DATASET: set data BigQuery Anda
    • TABLE_NAME: nama tabel BigQuery Anda

    Untuk menjalankan template menggunakan REST API, kirim permintaan POST HTTP. Untuk mengetahui informasi selengkapnya tentang API dan cakupan otorisasinya, lihat projects.templates.launch.

    POST https://dataflow.googleapis.com/v1b3/projects/PROJECT_ID/locations/LOCATION/flexTemplates:launch
    {
       "launch_parameter": {
          "jobName": "JOB_NAME",
          "parameters": {
           "inputTopic": "projects/PROJECT_ID/subscriptions/SUBSCRIPTION_NAME",
           "outputTableSpec": "PROJECT_ID:DATASET.TABLE_NAME"
          },
          "containerSpecGcsPath": "gs://dataflow-templates-LOCATION/VERSION/flex/PubSub_to_BigQuery_Xlang",
       }
    }

    Ganti kode berikut:

    • PROJECT_ID: ID project Google Cloud tempat Anda ingin menjalankan tugas Dataflow
    • JOB_NAME: nama tugas unik pilihan Anda
    • LOCATION: region tempat Anda ingin men-deploy tugas Dataflow—misalnya, us-central1
    • VERSION: versi template yang ingin Anda gunakan

      Anda dapat menggunakan nilai berikut:

    • STAGING_LOCATION: lokasi untuk melakukan staging file lokal (misalnya, gs://your-bucket/staging)
    • TOPIC_NAME: nama topik Pub/Sub Anda
    • DATASET: set data BigQuery Anda
    • TABLE_NAME: nama tabel BigQuery Anda
    Java
    /*
     * Copyright (C) 2018 Google LLC
     *
     * Licensed under the Apache License, Version 2.0 (the "License"); you may not
     * use this file except in compliance with the License. You may obtain a copy of
     * the License at
     *
     *   http://www.apache.org/licenses/LICENSE-2.0
     *
     * Unless required by applicable law or agreed to in writing, software
     * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
     * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
     * License for the specific language governing permissions and limitations under
     * the License.
     */
    package com.google.cloud.teleport.v2.templates;
    
    import static com.google.cloud.teleport.v2.templates.TextToBigQueryStreaming.wrapBigQueryInsertError;
    
    import com.google.api.services.bigquery.model.TableRow;
    import com.google.cloud.teleport.metadata.MultiTemplate;
    import com.google.cloud.teleport.metadata.Template;
    import com.google.cloud.teleport.metadata.TemplateCategory;
    import com.google.cloud.teleport.metadata.TemplateParameter;
    import com.google.cloud.teleport.v2.coders.FailsafeElementCoder;
    import com.google.cloud.teleport.v2.common.UncaughtExceptionLogger;
    import com.google.cloud.teleport.v2.options.BigQueryStorageApiStreamingOptions;
    import com.google.cloud.teleport.v2.templates.PubSubToBigQuery.Options;
    import com.google.cloud.teleport.v2.transforms.BigQueryConverters.FailsafeJsonToTableRow;
    import com.google.cloud.teleport.v2.transforms.ErrorConverters;
    import com.google.cloud.teleport.v2.transforms.JavascriptTextTransformer.FailsafeJavascriptUdf;
    import com.google.cloud.teleport.v2.transforms.PythonExternalTextTransformer;
    import com.google.cloud.teleport.v2.transforms.PythonExternalTextTransformer.PythonExternalTextTransformerOptions;
    import com.google.cloud.teleport.v2.transforms.PythonExternalTextTransformer.RowToPubSubFailsafeElementFn;
    import com.google.cloud.teleport.v2.utils.BigQueryIOUtils;
    import com.google.cloud.teleport.v2.utils.ResourceUtils;
    import com.google.cloud.teleport.v2.values.FailsafeElement;
    import com.google.common.base.Strings;
    import com.google.common.collect.ImmutableList;
    import java.nio.charset.StandardCharsets;
    import org.apache.beam.runners.dataflow.options.DataflowPipelineWorkerPoolOptions;
    import org.apache.beam.sdk.Pipeline;
    import org.apache.beam.sdk.PipelineResult;
    import org.apache.beam.sdk.coders.CoderRegistry;
    import org.apache.beam.sdk.coders.StringUtf8Coder;
    import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
    import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.Write.CreateDisposition;
    import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.Write.WriteDisposition;
    import org.apache.beam.sdk.io.gcp.bigquery.BigQueryInsertError;
    import org.apache.beam.sdk.io.gcp.bigquery.InsertRetryPolicy;
    import org.apache.beam.sdk.io.gcp.bigquery.WriteResult;
    import org.apache.beam.sdk.io.gcp.pubsub.PubsubIO;
    import org.apache.beam.sdk.io.gcp.pubsub.PubsubMessage;
    import org.apache.beam.sdk.io.gcp.pubsub.PubsubMessageWithAttributesCoder;
    import org.apache.beam.sdk.options.Default;
    import org.apache.beam.sdk.options.PipelineOptions;
    import org.apache.beam.sdk.options.PipelineOptionsFactory;
    import org.apache.beam.sdk.transforms.DoFn;
    import org.apache.beam.sdk.transforms.Flatten;
    import org.apache.beam.sdk.transforms.MapElements;
    import org.apache.beam.sdk.transforms.PTransform;
    import org.apache.beam.sdk.transforms.ParDo;
    import org.apache.beam.sdk.values.PCollection;
    import org.apache.beam.sdk.values.PCollectionList;
    import org.apache.beam.sdk.values.PCollectionTuple;
    import org.apache.beam.sdk.values.Row;
    import org.apache.beam.sdk.values.TupleTag;
    import org.apache.beam.sdk.values.TupleTagList;
    import org.slf4j.Logger;
    import org.slf4j.LoggerFactory;
    
    /**
     * The {@link PubSubToBigQuery} pipeline is a streaming pipeline which ingests data in JSON format
     * from Cloud Pub/Sub, executes a UDF, and outputs the resulting records to BigQuery. Any errors
     * which occur in the transformation of the data or execution of the UDF will be output to a
     * separate errors table in BigQuery. The errors table will be created if it does not exist prior to
     * execution. Both output and error tables are specified by the user as template parameters.
     *
     * <p><b>Pipeline Requirements</b>
     *
     * <ul>
     *   <li>The Pub/Sub topic exists.
     *   <li>The BigQuery output table exists.
     * </ul>
     *
     * <p>Check out <a
     * href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v2/googlecloud-to-googlecloud/README_PubSub_to_BigQuery_Flex.md">README</a>
     * for instructions on how to use or modify this template.
     */
    @MultiTemplate({
      @Template(
          name = "PubSub_to_BigQuery_Flex",
          category = TemplateCategory.STREAMING,
          displayName = "Pub/Sub to BigQuery",
          description =
              "The Pub/Sub to BigQuery template is a streaming pipeline that reads JSON-formatted messages from a Pub/Sub topic or subscription, and writes them to a BigQuery table. "
                  + "You can use the template as a quick solution to move Pub/Sub data to BigQuery. "
                  + "The template reads JSON-formatted messages from Pub/Sub and converts them to BigQuery elements.",
          optionsClass = Options.class,
          skipOptions = {
            "pythonExternalTextTransformGcsPath",
            "pythonExternalTextTransformFunctionName",
          },
          flexContainerName = "pubsub-to-bigquery",
          documentation =
              "https://cloud.google.com/dataflow/docs/guides/templates/provided/pubsub-to-bigquery",
          contactInformation = "https://cloud.google.com/support",
          requirements = {
            "The <a href=\"https://cloud.google.com/pubsub/docs/reference/rest/v1/PubsubMessage\">`data` field</a> of Pub/Sub messages must use the JSON format, described in this <a href=\"https://developers.google.com/api-client-library/java/google-http-java-client/json\">JSON guide</a>. For example, messages with values in the `data` field formatted as `{\"k1\":\"v1\", \"k2\":\"v2\"}` can be inserted into a BigQuery table with two columns, named `k1` and `k2`, with a string data type.",
            "The output table must exist prior to running the pipeline. The table schema must match the input JSON objects."
          },
          streaming = true,
          supportsAtLeastOnce = true,
          supportsExactlyOnce = true),
      @Template(
          name = "PubSub_to_BigQuery_Xlang",
          category = TemplateCategory.STREAMING,
          displayName = "Pub/Sub to BigQuery with Python UDFs",
          type = Template.TemplateType.XLANG,
          description =
              "The Pub/Sub to BigQuery template is a streaming pipeline that reads JSON-formatted messages from a Pub/Sub topic or subscription, and writes them to a BigQuery table. "
                  + "You can use the template as a quick solution to move Pub/Sub data to BigQuery. "
                  + "The template reads JSON-formatted messages from Pub/Sub and converts them to BigQuery elements.",
          optionsClass = Options.class,
          skipOptions = {
            "javascriptTextTransformGcsPath",
            "javascriptTextTransformFunctionName",
            "javascriptTextTransformReloadIntervalMinutes"
          },
          flexContainerName = "pubsub-to-bigquery-xlang",
          documentation =
              "https://cloud.google.com/dataflow/docs/guides/templates/provided/pubsub-to-bigquery",
          contactInformation = "https://cloud.google.com/support",
          requirements = {
            "The <a href=\"https://cloud.google.com/pubsub/docs/reference/rest/v1/PubsubMessage\">`data` field</a> of Pub/Sub messages must use the JSON format, described in this <a href=\"https://developers.google.com/api-client-library/java/google-http-java-client/json\">JSON guide</a>. For example, messages with values in the `data` field formatted as `{\"k1\":\"v1\", \"k2\":\"v2\"}` can be inserted into a BigQuery table with two columns, named `k1` and `k2`, with a string data type.",
            "The output table must exist prior to running the pipeline. The table schema must match the input JSON objects."
          },
          streaming = true,
          supportsAtLeastOnce = true,
          supportsExactlyOnce = true)
    })
    public class PubSubToBigQuery {
    
      /** The log to output status messages to. */
      private static final Logger LOG = LoggerFactory.getLogger(PubSubToBigQuery.class);
    
      /** The tag for the main output for the UDF. */
      public static final TupleTag<FailsafeElement<PubsubMessage, String>> UDF_OUT =
          new TupleTag<FailsafeElement<PubsubMessage, String>>() {};
    
      /** The tag for the main output of the json transformation. */
      public static final TupleTag<TableRow> TRANSFORM_OUT = new TupleTag<TableRow>() {};
    
      /** The tag for the dead-letter output of the udf. */
      public static final TupleTag<FailsafeElement<PubsubMessage, String>> UDF_DEADLETTER_OUT =
          new TupleTag<FailsafeElement<PubsubMessage, String>>() {};
    
      /** The tag for the dead-letter output of the json to table row transform. */
      public static final TupleTag<FailsafeElement<PubsubMessage, String>> TRANSFORM_DEADLETTER_OUT =
          new TupleTag<FailsafeElement<PubsubMessage, String>>() {};
    
      /** The default suffix for error tables if dead letter table is not specified. */
      public static final String DEFAULT_DEADLETTER_TABLE_SUFFIX = "_error_records";
    
      /** Pubsub message/string coder for pipeline. */
      public static final FailsafeElementCoder<PubsubMessage, String> CODER =
          FailsafeElementCoder.of(PubsubMessageWithAttributesCoder.of(), StringUtf8Coder.of());
    
      /** String/String Coder for FailsafeElement. */
      public static final FailsafeElementCoder<String, String> FAILSAFE_ELEMENT_CODER =
          FailsafeElementCoder.of(StringUtf8Coder.of(), StringUtf8Coder.of());
    
      /**
       * The {@link Options} class provides the custom execution options passed by the executor at the
       * command-line.
       */
      public interface Options
          extends PipelineOptions,
              BigQueryStorageApiStreamingOptions,
              PythonExternalTextTransformerOptions,
              DataflowPipelineWorkerPoolOptions {
        @TemplateParameter.BigQueryTable(
            order = 1,
            groupName = "Target",
            description = "BigQuery output table",
            helpText =
                "The BigQuery table to write to, formatted as `PROJECT_ID:DATASET_NAME.TABLE_NAME`.")
        String getOutputTableSpec();
    
        void setOutputTableSpec(String value);
    
        @TemplateParameter.PubsubTopic(
            order = 2,
            groupName = "Source",
            optional = true,
            description = "Input Pub/Sub topic",
            helpText =
                "The Pub/Sub topic to read from, formatted as `projects/<PROJECT_ID>/topics/<TOPIC_NAME>`.")
        String getInputTopic();
    
        void setInputTopic(String value);
    
        @TemplateParameter.PubsubSubscription(
            order = 3,
            groupName = "Source",
            optional = true,
            description = "Pub/Sub input subscription",
            helpText =
                "The Pub/Sub subscription to read from, "
                    + "formatted as `projects/<PROJECT_ID>/subscriptions/<SUBCRIPTION_NAME>`.")
        String getInputSubscription();
    
        void setInputSubscription(String value);
    
        @TemplateParameter.BigQueryTable(
            order = 4,
            optional = true,
            description =
                "Table for messages failed to reach the output table (i.e., Deadletter table)",
            helpText =
                "The BigQuery table to use for messages that failed to reach the output table, "
                    + "formatted as `PROJECT_ID:DATASET_NAME.TABLE_NAME`. If the table "
                    + "doesn't exist, it is created when the pipeline runs. "
                    + "If this parameter is not specified, "
                    + "the value `OUTPUT_TABLE_SPEC_error_records` is used instead.")
        String getOutputDeadletterTable();
    
        void setOutputDeadletterTable(String value);
    
        @TemplateParameter.Boolean(
            order = 5,
            optional = true,
            parentName = "useStorageWriteApi",
            parentTriggerValues = {"true"},
            description = "Use at at-least-once semantics in BigQuery Storage Write API",
            helpText =
                "When using the Storage Write API, specifies the write semantics. "
                    + "To use at-least-once semantics (https://beam.apache.org/documentation/io/built-in/google-bigquery/#at-least-once-semantics)"
                    + ", set this parameter to true. "
                    + "To use exactly-once semantics, set the parameter to `false`. "
                    + "This parameter applies only when `useStorageWriteApi` is `true`. "
                    + "The default value is `false`.")
        @Default.Boolean(false)
        @Override
        Boolean getUseStorageWriteApiAtLeastOnce();
    
        void setUseStorageWriteApiAtLeastOnce(Boolean value);
      }
    
      /**
       * The main entry-point for pipeline execution. This method will start the pipeline but will not
       * wait for it's execution to finish. If blocking execution is required, use the {@link
       * PubSubToBigQuery#run(Options)} method to start the pipeline and invoke {@code
       * result.waitUntilFinish()} on the {@link PipelineResult}.
       *
       * @param args The command-line args passed by the executor.
       */
      public static void main(String[] args) {
        UncaughtExceptionLogger.register();
    
        Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
        BigQueryIOUtils.validateBQStorageApiOptionsStreaming(options);
        //    options.setWorkerDiskType(
        //
        // "compute.googleapis.com/projects/cloud-teleport-testing/zones/us-central1-a/diskTypes/t2a-test");
    
        run(options);
      }
    
      /**
       * Runs the pipeline to completion with the specified options. This method does not wait until the
       * pipeline is finished before returning. Invoke {@code result.waitUntilFinish()} on the result
       * object to block until the pipeline is finished running if blocking programmatic execution is
       * required.
       *
       * @param options The execution options.
       * @return The pipeline result.
       */
      public static PipelineResult run(Options options) {
    
        boolean useInputSubscription = !Strings.isNullOrEmpty(options.getInputSubscription());
        boolean useInputTopic = !Strings.isNullOrEmpty(options.getInputTopic());
        if (useInputSubscription == useInputTopic) {
          throw new IllegalArgumentException(
              "Either input topic or input subscription must be provided, but not both.");
        }
    
        Pipeline pipeline = Pipeline.create(options);
    
        CoderRegistry coderRegistry = pipeline.getCoderRegistry();
        coderRegistry.registerCoderForType(CODER.getEncodedTypeDescriptor(), CODER);
    
        /*
         * Steps:
         *  1) Read messages in from Pub/Sub
         *  2) Transform the PubsubMessages into TableRows
         *     - Transform message payload via UDF
         *     - Convert UDF result to TableRow objects
         *  3) Write successful records out to BigQuery
         *  4) Write failed records out to BigQuery
         */
    
        /*
         * Step #1: Read messages in from Pub/Sub
         * Either from a Subscription or Topic
         */
    
        PCollection<PubsubMessage> messages = null;
        if (useInputSubscription) {
          messages =
              pipeline.apply(
                  "ReadPubSubSubscription",
                  PubsubIO.readMessagesWithAttributes()
                      .fromSubscription(options.getInputSubscription()));
        } else {
          messages =
              pipeline.apply(
                  "ReadPubSubTopic",
                  PubsubIO.readMessagesWithAttributes().fromTopic(options.getInputTopic()));
        }
    
        PCollectionTuple convertedTableRows =
            messages
                /*
                 * Step #2: Transform the PubsubMessages into TableRows
                 */
                .apply("ConvertMessageToTableRow", new PubsubMessageToTableRow(options));
    
        /*
         * Step #3: Write the successful records out to BigQuery
         */
        WriteResult writeResult =
            convertedTableRows
                .get(TRANSFORM_OUT)
                .apply(
                    "WriteSuccessfulRecords",
                    BigQueryIO.writeTableRows()
                        .withoutValidation()
                        .withCreateDisposition(CreateDisposition.CREATE_NEVER)
                        .withWriteDisposition(WriteDisposition.WRITE_APPEND)
                        .withExtendedErrorInfo()
                        .withFailedInsertRetryPolicy(InsertRetryPolicy.retryTransientErrors())
                        .to(options.getOutputTableSpec()));
    
        /*
         * Step 3 Contd.
         * Elements that failed inserts into BigQuery are extracted and converted to FailsafeElement
         */
        PCollection<FailsafeElement<String, String>> failedInserts =
            BigQueryIOUtils.writeResultToBigQueryInsertErrors(writeResult, options)
                .apply(
                    "WrapInsertionErrors",
                    MapElements.into(FAILSAFE_ELEMENT_CODER.getEncodedTypeDescriptor())
                        .via((BigQueryInsertError e) -> wrapBigQueryInsertError(e)))
                .setCoder(FAILSAFE_ELEMENT_CODER);
    
        /*
         * Step #4: Write records that failed table row transformation
         * or conversion out to BigQuery deadletter table.
         */
        PCollectionList.of(
                ImmutableList.of(
                    convertedTableRows.get(UDF_DEADLETTER_OUT),
                    convertedTableRows.get(TRANSFORM_DEADLETTER_OUT)))
            .apply("Flatten", Flatten.pCollections())
            .apply(
                "WriteFailedRecords",
                ErrorConverters.WritePubsubMessageErrors.newBuilder()
                    .setErrorRecordsTable(
                        !Strings.isNullOrEmpty(options.getOutputDeadletterTable())
                            ? options.getOutputDeadletterTable()
                            : options.getOutputTableSpec() + DEFAULT_DEADLETTER_TABLE_SUFFIX)
                    .setErrorRecordsTableSchema(ResourceUtils.getDeadletterTableSchemaJson())
                    .build());
    
        // 5) Insert records that failed insert into deadletter table
        failedInserts.apply(
            "WriteFailedRecords",
            ErrorConverters.WriteStringMessageErrors.newBuilder()
                .setErrorRecordsTable(
                    !Strings.isNullOrEmpty(options.getOutputDeadletterTable())
                        ? options.getOutputDeadletterTable()
                        : options.getOutputTableSpec() + DEFAULT_DEADLETTER_TABLE_SUFFIX)
                .setErrorRecordsTableSchema(ResourceUtils.getDeadletterTableSchemaJson())
                .build());
    
        return pipeline.run();
      }
    
      /**
       * The {@link PubsubMessageToTableRow} class is a {@link PTransform} which transforms incoming
       * {@link PubsubMessage} objects into {@link TableRow} objects for insertion into BigQuery while
       * applying an optional UDF to the input. The executions of the UDF and transformation to {@link
       * TableRow} objects is done in a fail-safe way by wrapping the element with it's original payload
       * inside the {@link FailsafeElement} class. The {@link PubsubMessageToTableRow} transform will
       * output a {@link PCollectionTuple} which contains all output and dead-letter {@link
       * PCollection}.
       *
       * <p>The {@link PCollectionTuple} output will contain the following {@link PCollection}:
       *
       * <ul>
       *   <li>{@link PubSubToBigQuery#UDF_OUT} - Contains all {@link FailsafeElement} records
       *       successfully processed by the optional UDF.
       *   <li>{@link PubSubToBigQuery#UDF_DEADLETTER_OUT} - Contains all {@link FailsafeElement}
       *       records which failed processing during the UDF execution.
       *   <li>{@link PubSubToBigQuery#TRANSFORM_OUT} - Contains all records successfully converted from
       *       JSON to {@link TableRow} objects.
       *   <li>{@link PubSubToBigQuery#TRANSFORM_DEADLETTER_OUT} - Contains all {@link FailsafeElement}
       *       records which couldn't be converted to table rows.
       * </ul>
       */
      static class PubsubMessageToTableRow
          extends PTransform<PCollection<PubsubMessage>, PCollectionTuple> {
    
        private final Options options;
    
        PubsubMessageToTableRow(Options options) {
          this.options = options;
        }
    
        @Override
        public PCollectionTuple expand(PCollection<PubsubMessage> input) {
          boolean useJavascriptUdf =
              !Strings.isNullOrEmpty(options.getJavascriptTextTransformGcsPath());
          boolean usePythonUdf =
              !Strings.isNullOrEmpty(options.getPythonExternalTextTransformGcsPath());
          if (useJavascriptUdf && usePythonUdf) {
            throw new IllegalArgumentException(
                "Either javascript or Python gcs path must be provided, but not both.");
          }
          PCollectionTuple udfOut;
          if (usePythonUdf) {
            PCollection<Row> udfRowsOut =
                input
                    // Map the incoming messages into FailsafeElements so we can recover from failures
                    // across multiple transforms.
                    .apply(
                        "MapToRecord",
                        PythonExternalTextTransformer.FailsafeRowPythonExternalUdf
                            .pubSubMappingFunction())
                    .setRowSchema(PythonExternalTextTransformer.FailsafeRowPythonExternalUdf.ROW_SCHEMA)
                    .apply(
                        "InvokeUDF",
                        PythonExternalTextTransformer.FailsafePythonExternalUdf.newBuilder()
                            .setFileSystemPath(options.getPythonExternalTextTransformGcsPath())
                            .setFunctionName(options.getPythonExternalTextTransformFunctionName())
                            .build());
            udfOut =
                udfRowsOut.apply(
                    "MapRowsToFailsafeElements",
                    ParDo.of(new RowToPubSubFailsafeElementFn(UDF_OUT, UDF_DEADLETTER_OUT))
                        .withOutputTags(UDF_OUT, TupleTagList.of(UDF_DEADLETTER_OUT)));
          } else {
            udfOut =
                input
                    // Map the incoming messages into FailsafeElements so we can recover from failures
                    // across multiple transforms.
                    .apply("MapToRecord", ParDo.of(new PubsubMessageToFailsafeElementFn()))
                    .apply(
                        "InvokeUDF",
                        FailsafeJavascriptUdf.<PubsubMessage>newBuilder()
                            .setFileSystemPath(options.getJavascriptTextTransformGcsPath())
                            .setFunctionName(options.getJavascriptTextTransformFunctionName())
                            .setReloadIntervalMinutes(
                                options.getJavascriptTextTransformReloadIntervalMinutes())
                            .setSuccessTag(UDF_OUT)
                            .setFailureTag(UDF_DEADLETTER_OUT)
                            .build());
          }
    
          // Convert the records which were successfully processed by the UDF into TableRow objects.
          PCollectionTuple jsonToTableRowOut =
              udfOut
                  .get(UDF_OUT)
                  .apply(
                      "JsonToTableRow",
                      FailsafeJsonToTableRow.<PubsubMessage>newBuilder()
                          .setSuccessTag(TRANSFORM_OUT)
                          .setFailureTag(TRANSFORM_DEADLETTER_OUT)
                          .build());
    
          // Re-wrap the PCollections so we can return a single PCollectionTuple
          return PCollectionTuple.of(UDF_OUT, udfOut.get(UDF_OUT))
              .and(UDF_DEADLETTER_OUT, udfOut.get(UDF_DEADLETTER_OUT))
              .and(TRANSFORM_OUT, jsonToTableRowOut.get(TRANSFORM_OUT))
              .and(TRANSFORM_DEADLETTER_OUT, jsonToTableRowOut.get(TRANSFORM_DEADLETTER_OUT));
        }
      }
    
      /**
       * The {@link PubsubMessageToFailsafeElementFn} wraps an incoming {@link PubsubMessage} with the
       * {@link FailsafeElement} class so errors can be recovered from and the original message can be
       * output to a error records table.
       */
      static class PubsubMessageToFailsafeElementFn
          extends DoFn<PubsubMessage, FailsafeElement<PubsubMessage, String>> {
        @ProcessElement
        public void processElement(ProcessContext context) {
          PubsubMessage message = context.element();
          context.output(
              FailsafeElement.of(message, new String(message.getPayload(), StandardCharsets.UTF_8)));
        }
      }
    }
    

    Langkah berikutnya