Modello Testo Cloud Storage a BigQuery (flusso)

/*
 * 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 com.google.api.client.json.JsonFactory;
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.TextToBigQueryStreaming.TextToBigQueryStreamingOptions;
import com.google.cloud.teleport.v2.transforms.BigQueryConverters.FailsafeJsonToTableRow;
import com.google.cloud.teleport.v2.transforms.ErrorConverters.WriteStringMessageErrors;
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.RowToStringFailsafeElementFn;
import com.google.cloud.teleport.v2.utils.BigQueryIOUtils;
import com.google.cloud.teleport.v2.utils.GCSUtils;
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.io.IOException;
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.extensions.gcp.util.Transport;
import org.apache.beam.sdk.io.TextIO;
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.options.Default;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.ValueProvider.StaticValueProvider;
import org.apache.beam.sdk.transforms.Flatten;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.transforms.Watch.Growth;
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.TupleTag;
import org.apache.beam.sdk.values.TupleTagList;
import org.apache.commons.lang3.StringUtils;
import org.joda.time.Duration;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * The {@link TextToBigQueryStreaming} is a streaming version of {@link TextIOToBigQuery} pipeline
 * that reads text files, applies a JavaScript UDF and writes the output to BigQuery. The pipeline
 * continuously polls for new files, reads them row-by-row and processes each record into BigQuery.
 * The polling interval is set at 10 seconds.
 *
 * <p>Check out <a
 * href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v2/googlecloud-to-googlecloud/README_Stream_GCS_Text_to_BigQuery_Flex.md">README</a>
 * for instructions on how to use or modify this template.
 */
@MultiTemplate({
  @Template(
      name = "Stream_GCS_Text_to_BigQuery_Flex",
      category = TemplateCategory.STREAMING,
      displayName = "Cloud Storage Text to BigQuery (Stream)",
      description = {
        "The Text Files on Cloud Storage to BigQuery pipeline is a streaming pipeline that allows you to stream text files stored in Cloud Storage, transform them using a JavaScript User Defined Function (UDF) that you provide, and append the result to BigQuery.\n",
        "The pipeline runs indefinitely and needs to be terminated manually via a\n"
            + "    <a href=\"https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline#cancel\">cancel</a> and not a\n"
            + "    <a href=\"https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline#drain\">drain</a>, due to its use of the\n"
            + "    <code>Watch</code> transform, which is a splittable <code>DoFn</code> that does not support\n"
            + "    draining."
      },
      skipOptions = {
        "pythonExternalTextTransfromGcsPath",
        "pythonExternalTextTransformFunctionName"
      },
      optionsClass = TextToBigQueryStreamingOptions.class,
      flexContainerName = "text-to-bigquery-streaming",
      documentation =
          "https://cloud.google.com/dataflow/docs/guides/templates/provided/text-to-bigquery-stream",
      contactInformation = "https://cloud.google.com/support",
      requirements = {
        "Create a JSON file that describes the schema of your output table in BigQuery.\n"
            + "    <p>\n"
            + "      Ensure that there is a top-level JSON array titled <code>fields</code> and that its\n"
            + "      contents follow the pattern <code>{\"name\": \"COLUMN_NAME\", \"type\": \"DATA_TYPE\"}</code>.\n"
            + "      For example:\n"
            + "    </p>\n"
            + "<pre class=\"prettyprint lang-json\">\n"
            + "{\n"
            + "  \"fields\": [\n"
            + "    {\n"
            + "      \"name\": \"location\",\n"
            + "      \"type\": \"STRING\"\n"
            + "    },\n"
            + "    {\n"
            + "      \"name\": \"name\",\n"
            + "      \"type\": \"STRING\"\n"
            + "    },\n"
            + "    {\n"
            + "      \"name\": \"age\",\n"
            + "      \"type\": \"STRING\"\n"
            + "    },\n"
            + "    {\n"
            + "      \"name\": \"color\",\n"
            + "      \"type\": \"STRING\",\n"
            + "      \"mode\": \"REQUIRED\"\n"
            + "    },\n"
            + "    {\n"
            + "      \"name\": \"coffee\",\n"
            + "      \"type\": \"STRING\",\n"
            + "      \"mode\": \"REQUIRED\"\n"
            + "    }\n"
            + "  ]\n"
            + "}\n"
            + "</pre>",
        "Create a JavaScript (<code>.js</code>) file with your UDF function that supplies the logic\n"
            + "    to transform the lines of text. Note that your function must return a JSON string.\n"
            + "    <p>For example, this function splits each line of a CSV file and returns a JSON string after\n"
            + "      transforming the values.</p>\n"
            + "<pre class=\"prettyprint\" suppresswarning>\n"
            + "function transform(line) {\n"
            + "var values = line.split(',');\n"
            + "\n"
            + "var obj = new Object();\n"
            + "obj.location = values[0];\n"
            + "obj.name = values[1];\n"
            + "obj.age = values[2];\n"
            + "obj.color = values[3];\n"
            + "obj.coffee = values[4];\n"
            + "var jsonString = JSON.stringify(obj);\n"
            + "\n"
            + "return jsonString;\n"
            + "}\n"
            + "</pre>"
      },
      streaming = true,
      supportsAtLeastOnce = true),
  @Template(
      name = "Stream_GCS_Text_to_BigQuery_Xlang",
      category = TemplateCategory.STREAMING,
      displayName = "Cloud Storage Text to BigQuery (Stream) with Python UDF",
      type = Template.TemplateType.XLANG,
      description = {
        "The Text Files on Cloud Storage to BigQuery pipeline is a streaming pipeline that allows you to stream text files stored in Cloud Storage, transform them using a Python User Defined Function (UDF) that you provide, and append the result to BigQuery.\n",
        "The pipeline runs indefinitely and needs to be terminated manually via a\n"
            + "    <a href=\"https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline#cancel\">cancel</a> and not a\n"
            + "    <a href=\"https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline#drain\">drain</a>, due to its use of the\n"
            + "    <code>Watch</code> transform, which is a splittable <code>DoFn</code> that does not support\n"
            + "    draining."
      },
      skipOptions = {
        "javascriptTextTransformGcsPath",
        "javascriptTextTransformFunctionName",
        "javascriptTextTransformReloadIntervalMinutes"
      },
      optionsClass = TextToBigQueryStreamingOptions.class,
      flexContainerName = "text-to-bigquery-streaming-xlang",
      documentation =
          "https://cloud.google.com/dataflow/docs/guides/templates/provided/text-to-bigquery-stream",
      contactInformation = "https://cloud.google.com/support",
      requirements = {
        "Create a JSON file that describes the schema of your output table in BigQuery.\n"
            + "    <p>\n"
            + "      Ensure that there is a top-level JSON array titled <code>fields</code> and that its\n"
            + "      contents follow the pattern <code>{\"name\": \"COLUMN_NAME\", \"type\": \"DATA_TYPE\"}</code>.\n"
            + "      For example:\n"
            + "    </p>\n"
            + "<pre class=\"prettyprint lang-json\">\n"
            + "{\n"
            + "  \"fields\": [\n"
            + "    {\n"
            + "      \"name\": \"location\",\n"
            + "      \"type\": \"STRING\"\n"
            + "    },\n"
            + "    {\n"
            + "      \"name\": \"name\",\n"
            + "      \"type\": \"STRING\"\n"
            + "    },\n"
            + "    {\n"
            + "      \"name\": \"age\",\n"
            + "      \"type\": \"STRING\"\n"
            + "    },\n"
            + "    {\n"
            + "      \"name\": \"color\",\n"
            + "      \"type\": \"STRING\",\n"
            + "      \"mode\": \"REQUIRED\"\n"
            + "    },\n"
            + "    {\n"
            + "      \"name\": \"coffee\",\n"
            + "      \"type\": \"STRING\",\n"
            + "      \"mode\": \"REQUIRED\"\n"
            + "    }\n"
            + "  ]\n"
            + "}\n"
            + "</pre>",
        "Create a Python (<code>.js</code>) file with your UDF function that supplies the logic\n"
            + "    to transform the lines of text. Note that your function must return a JSON string.\n"
            + "    <p>For example, this function splits each line of a CSV file and returns a JSON string after\n"
            + "      transforming the values.</p>\n"
            + "<pre class=\"prettyprint\" suppresswarning>\n"
            + "import json\n"
            + "def transform(line): \n"
            + "  values = line.split(',')\n"
            + "\n"
            + "  obj = {\n"
            + "     'location' : values[0],\n"
            + "     'name' : values[1],\n"
            + "     'age' : values[2],\n"
            + "     'color' : values[3],\n"
            + "     'coffee' : values[4]\n"
            + "  }\n"
            + "  jsonString = JSON.dumps(obj);\n"
            + "\n"
            + "  return jsonString;\n"
            + "\n"
            + "</pre>"
      },
      streaming = true,
      supportsAtLeastOnce = true)
})
public class TextToBigQueryStreaming {

  private static final Logger LOG = LoggerFactory.getLogger(TextToBigQueryStreaming.class);

  /** The tag for the main output for the UDF. */
  private static final TupleTag<FailsafeElement<String, String>> UDF_OUT =
      new TupleTag<FailsafeElement<String, String>>() {};

  /** The tag for the dead-letter output of the udf. */
  private static final TupleTag<FailsafeElement<String, String>> UDF_DEADLETTER_OUT =
      new TupleTag<FailsafeElement<String, String>>() {};

  /** The tag for the main output of the json transformation. */
  private static final TupleTag<TableRow> TRANSFORM_OUT = new TupleTag<TableRow>() {};

  /** The tag for the dead-letter output of the json to table row transform. */
  private static final TupleTag<FailsafeElement<String, String>> TRANSFORM_DEADLETTER_OUT =
      new TupleTag<FailsafeElement<String, String>>() {};

  /** The default suffix for error tables if dead letter table is not specified. */
  private static final String DEFAULT_DEADLETTER_TABLE_SUFFIX = "_error_records";

  /** Default interval for polling files in GCS. */
  private static final Duration DEFAULT_POLL_INTERVAL = Duration.standardSeconds(10);

  /** Coder for FailsafeElement. */
  private static final FailsafeElementCoder<String, String> FAILSAFE_ELEMENT_CODER =
      FailsafeElementCoder.of(StringUtf8Coder.of(), StringUtf8Coder.of());

  private static final JsonFactory JSON_FACTORY = Transport.getJsonFactory();

  /**
   * Main entry point for executing the pipeline. This will run the pipeline asynchronously. If
   * blocking execution is required, use the {@link
   * TextToBigQueryStreaming#run(TextToBigQueryStreamingOptions)} method to start the pipeline and
   * invoke {@code result.waitUntilFinish()} on the {@link PipelineResult}
   *
   * @param args The command-line arguments to the pipeline.
   */
  public static void main(String[] args) {
    UncaughtExceptionLogger.register();

    // Parse the user options passed from the command-line
    TextToBigQueryStreamingOptions options =
        PipelineOptionsFactory.fromArgs(args)
            .withValidation()
            .as(TextToBigQueryStreamingOptions.class);
    BigQueryIOUtils.validateBQStorageApiOptionsStreaming(options);

    run(options);
  }

  /**
   * Runs the pipeline with the supplied options.
   *
   * @param options The execution parameters to the pipeline.
   * @return The result of the pipeline execution.
   */
  public static PipelineResult run(TextToBigQueryStreamingOptions options) {

    // Create the pipeline
    Pipeline pipeline = Pipeline.create(options);

    // Register the coder for pipeline
    FailsafeElementCoder<String, String> coder =
        FailsafeElementCoder.of(StringUtf8Coder.of(), StringUtf8Coder.of());

    CoderRegistry coderRegistry = pipeline.getCoderRegistry();
    coderRegistry.registerCoderForType(coder.getEncodedTypeDescriptor(), coder);

    // Determine if we are using Python UDFs or JS UDFs based on the provided options.
    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.");
    }

    /*
     * Steps:
     *  1) Read from the text source continuously.
     *  2) Convert to FailsafeElement.
     *  3) Apply Javascript udf transformation.
     *    - Tag records that were successfully transformed and those
     *      that failed transformation.
     *  4) Convert records to TableRow.
     *    - Tag records that were successfully converted and those
     *      that failed conversion.
     *  5) Insert successfully converted records into BigQuery.
     *    - Errors encountered while streaming will be sent to deadletter table.
     *  6) Insert records that failed into deadletter table.
     */

    PCollection<String> sourceRead =
        pipeline.apply(
            TextIO.read()
                .from(options.getInputFilePattern())
                .watchForNewFiles(DEFAULT_POLL_INTERVAL, Growth.never()));
    PCollectionTuple transformedOutput;
    if (usePythonUdf) {
      transformedOutput =
          sourceRead
              .apply(
                  "MapToRecord",
                  PythonExternalTextTransformer.FailsafeRowPythonExternalUdf
                      .stringMappingFunction())
              .setRowSchema(PythonExternalTextTransformer.FailsafeRowPythonExternalUdf.ROW_SCHEMA)
              .apply(
                  "InvokeUDF",
                  PythonExternalTextTransformer.FailsafePythonExternalUdf.newBuilder()
                      .setFileSystemPath(options.getPythonExternalTextTransformGcsPath())
                      .setFunctionName(options.getPythonExternalTextTransformFunctionName())
                      .build())
              .apply(
                  ParDo.of(new RowToStringFailsafeElementFn(UDF_OUT, UDF_DEADLETTER_OUT))
                      .withOutputTags(UDF_OUT, TupleTagList.of(UDF_DEADLETTER_OUT)));

    } else {
      transformedOutput =
          pipeline

              // 1) Read from the text source continuously.
              .apply(
                  "ReadFromSource",
                  TextIO.read()
                      .from(options.getInputFilePattern())
                      .watchForNewFiles(DEFAULT_POLL_INTERVAL, Growth.never()))

              // 2) Convert to FailsafeElement.
              .apply(
                  "ConvertToFailsafeElement",
                  MapElements.into(FAILSAFE_ELEMENT_CODER.getEncodedTypeDescriptor())
                      .via(input -> FailsafeElement.of(input, input)))

              // 3) Apply Javascript udf transformation.
              .apply(
                  "ApplyUDFTransformation",
                  FailsafeJavascriptUdf.<String>newBuilder()
                      .setFileSystemPath(options.getJavascriptTextTransformGcsPath())
                      .setFunctionName(options.getJavascriptTextTransformFunctionName())
                      .setReloadIntervalMinutes(
                          options.getJavascriptTextTransformReloadIntervalMinutes())
                      .setSuccessTag(UDF_OUT)
                      .setFailureTag(UDF_DEADLETTER_OUT)
                      .build());
    }

    PCollectionTuple convertedTableRows =
        transformedOutput

            // 4) Convert records to TableRow.
            .get(UDF_OUT)
            .apply(
                "ConvertJSONToTableRow",
                FailsafeJsonToTableRow.<String>newBuilder()
                    .setSuccessTag(TRANSFORM_OUT)
                    .setFailureTag(TRANSFORM_DEADLETTER_OUT)
                    .build());

    WriteResult writeResult =
        convertedTableRows

            // 5) Insert successfully converted records into BigQuery.
            .get(TRANSFORM_OUT)
            .apply(
                "InsertIntoBigQuery",
                BigQueryIO.writeTableRows()
                    .withJsonSchema(GCSUtils.getGcsFileAsString(options.getJSONPath()))
                    .to(options.getOutputTable())
                    .withExtendedErrorInfo()
                    .withoutValidation()
                    .withCreateDisposition(CreateDisposition.CREATE_IF_NEEDED)
                    .withWriteDisposition(WriteDisposition.WRITE_APPEND)
                    .withFailedInsertRetryPolicy(InsertRetryPolicy.retryTransientErrors())
                    .withCustomGcsTempLocation(
                        StaticValueProvider.of(options.getBigQueryLoadingTemporaryDirectory())));

    // 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(TextToBigQueryStreaming::wrapBigQueryInsertError));

    // 6) Insert records that failed transformation or conversion into deadletter table
    PCollectionList.of(
            ImmutableList.of(
                transformedOutput.get(UDF_DEADLETTER_OUT),
                convertedTableRows.get(TRANSFORM_DEADLETTER_OUT),
                failedInserts))
        .apply("Flatten", Flatten.pCollections())
        .apply(
            "WriteFailedRecords",
            WriteStringMessageErrors.newBuilder()
                .setErrorRecordsTable(
                    StringUtils.isNotEmpty(options.getOutputDeadletterTable())
                        ? options.getOutputDeadletterTable()
                        : options.getOutputTable() + DEFAULT_DEADLETTER_TABLE_SUFFIX)
                .setErrorRecordsTableSchema(ResourceUtils.getDeadletterTableSchemaJson())
                .build());

    return pipeline.run();
  }

  /**
   * Method to wrap a {@link BigQueryInsertError} into a {@link FailsafeElement}.
   *
   * @param insertError BigQueryInsert error.
   * @return FailsafeElement object.
   * @throws IOException
   */
  static FailsafeElement<String, String> wrapBigQueryInsertError(BigQueryInsertError insertError) {

    FailsafeElement<String, String> failsafeElement;
    try {

      String rowPayload = JSON_FACTORY.toString(insertError.getRow());
      String errorMessage = JSON_FACTORY.toString(insertError.getError());

      failsafeElement = FailsafeElement.of(rowPayload, rowPayload);
      failsafeElement.setErrorMessage(errorMessage);

    } catch (IOException e) {
      throw new RuntimeException(e);
    }

    return failsafeElement;
  }

  /**
   * The {@link TextToBigQueryStreamingOptions} class provides the custom execution options passed
   * by the executor at the command-line.
   */
  public interface TextToBigQueryStreamingOptions
      extends TextIOToBigQuery.Options, BigQueryStorageApiStreamingOptions {
    @TemplateParameter.BigQueryTable(
        order = 1,
        optional = true,
        description = "The dead-letter table name to output failed messages to BigQuery",
        helpText =
            "Table for messages that failed to reach the output table. If a table doesn't exist, it is created during "
                + "pipeline execution. If not specified, `<outputTableSpec>_error_records` is used.",
        example = "<PROJECT_ID>:<DATASET_NAME>.<TABLE_NAME>")
    String getOutputDeadletterTable();

    void setOutputDeadletterTable(String value);

    // Hide the UseStorageWriteApiAtLeastOnce in the UI, because it will automatically be turned
    // on when pipeline is running on ALO mode and using the Storage Write API
    @TemplateParameter.Boolean(
        order = 2,
        optional = true,
        parentName = "useStorageWriteApi",
        parentTriggerValues = {"true"},
        description = "Use at at-least-once semantics in BigQuery Storage Write API",
        helpText =
            "This parameter takes effect only if `Use BigQuery Storage Write API` is enabled. If"
                + " enabled the at-least-once semantics will be used for Storage Write API, otherwise"
                + " exactly-once semantics will be used.",
        hiddenUi = true)
    @Default.Boolean(false)
    @Override
    Boolean getUseStorageWriteApiAtLeastOnce();

    void setUseStorageWriteApiAtLeastOnce(Boolean value);
  }
}