Template Cloud Storage ke Elasticsearch

/*
 * Copyright (C) 2021 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.elasticsearch.templates;

import static org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.Preconditions.checkArgument;

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.v2.coders.FailsafeElementCoder;
import com.google.cloud.teleport.v2.common.UncaughtExceptionLogger;
import com.google.cloud.teleport.v2.elasticsearch.options.GCSToElasticsearchOptions;
import com.google.cloud.teleport.v2.elasticsearch.transforms.WriteToElasticsearch;
import com.google.cloud.teleport.v2.transforms.CsvConverters;
import com.google.cloud.teleport.v2.transforms.ErrorConverters.WriteStringMessageErrors;
import com.google.cloud.teleport.v2.utils.SchemaUtils;
import com.google.cloud.teleport.v2.values.FailsafeElement;
import com.google.common.base.Strings;
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.NullableCoder;
import org.apache.beam.sdk.coders.StringUtf8Coder;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.transforms.WithTimestamps;
import org.apache.beam.sdk.values.PCollectionTuple;
import org.apache.beam.sdk.values.TupleTag;
import org.apache.beam.sdk.values.TypeDescriptors;
import org.joda.time.Instant;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * The {@link GCSToElasticsearch} pipeline exports data from one or more CSV files in Cloud Storage
 * to Elasticsearch.
 *
 * <p>Check out <a
 * href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v2/googlecloud-to-elasticsearch/README_GCS_to_Elasticsearch.md">README</a>
 * for instructions on how to use or modify this template.
 */
@MultiTemplate({
  @Template(
      name = "GCS_to_Elasticsearch",
      category = TemplateCategory.BATCH,
      displayName = "Cloud Storage to Elasticsearch",
      description = {
        "The Cloud Storage to Elasticsearch template is a batch pipeline that reads data from CSV files stored in a Cloud Storage bucket and writes the data into Elasticsearch as JSON documents.",
        "If the CSV files contain headers, set the <code>containsHeaders</code> template parameter to <code>true</code>.\n"
            + "Otherwise, create a JSON schema file that describes the data. Specify the Cloud Storage URI of the schema file in the jsonSchemaPath template parameter. "
            + "The following example shows a JSON schema:\n"
            + "<code>[{\"name\":\"id\", \"type\":\"text\"}, {\"name\":\"age\", \"type\":\"integer\"}]</code>\n"
            + "Alternatively, you can provide a Javascript user-defined function (UDF) that parses the CSV text and outputs Elasticsearch documents."
      },
      optionsClass = GCSToElasticsearchOptions.class,
      skipOptions = {
        "javascriptTextTransformReloadIntervalMinutes",
        "pythonExternalTextTransformGcsPath",
        "pythonExternalTextTransformFunctionName"
      },
      flexContainerName = "gcs-to-elasticsearch",
      documentation =
          "https://cloud.google.com/dataflow/docs/guides/templates/provided/cloud-storage-to-elasticsearch",
      contactInformation = "https://cloud.google.com/support",
      preview = true,
      requirements = {
        "The Cloud Storage bucket must exist.",
        "A Elasticsearch host on a Google Cloud instance or on Elasticsearch Cloud that is accessible from Dataflow must exist.",
        "A BigQuery table for error output must exist."
      }),
  @Template(
      name = "GCS_to_Elasticsearch_Xlang",
      category = TemplateCategory.BATCH,
      displayName = "Cloud Storage to Elasticsearch with Python UDFs",
      type = Template.TemplateType.XLANG,
      description = {
        "The Cloud Storage to Elasticsearch template is a batch pipeline that reads data from CSV files stored in a Cloud Storage bucket and writes the data into Elasticsearch as JSON documents.",
        "If the CSV files contain headers, set the <code>containsHeaders</code> template parameter to <code>true</code>.\n"
            + "Otherwise, create a JSON schema file that describes the data. Specify the Cloud Storage URI of the schema file in the jsonSchemaPath template parameter. "
            + "The following example shows a JSON schema:\n"
            + "<code>[{\"name\":\"id\", \"type\":\"text\"}, {\"name\":\"age\", \"type\":\"integer\"}]</code>\n"
            + "Alternatively, you can provide a Python user-defined function (UDF) that parses the CSV text and outputs Elasticsearch documents."
      },
      optionsClass = GCSToElasticsearchOptions.class,
      skipOptions = {
        "javascriptTextTransformGcsPath",
        "javascriptTextTransformFunctionName",
        "javascriptTextTransformReloadIntervalMinutes"
      },
      flexContainerName = "gcs-to-elasticsearch-xlang",
      documentation =
          "https://cloud.google.com/dataflow/docs/guides/templates/provided/cloud-storage-to-elasticsearch",
      contactInformation = "https://cloud.google.com/support",
      preview = true,
      requirements = {
        "The Cloud Storage bucket must exist.",
        "A Elasticsearch host on a Google Cloud instance or on Elasticsearch Cloud that is accessible from Dataflow must exist.",
        "A BigQuery table for error output must exist."
      })
})
public class GCSToElasticsearch {

  /** The tag for the headers of the CSV if required. */
  static final TupleTag<String> CSV_HEADERS = new TupleTag<String>() {};

  /** The tag for the lines of the CSV. */
  static final TupleTag<String> CSV_LINES = new TupleTag<String>() {};

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

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

  /* Logger for class. */
  private static final Logger LOG = LoggerFactory.getLogger(GCSToElasticsearch.class);

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

  /**
   * Main entry point for pipeline execution.
   *
   * @param args Command line arguments to the pipeline.
   */
  public static void main(String[] args) {
    UncaughtExceptionLogger.register();

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

    run(options);
  }

  /**
   * Runs the pipeline to completion with the specified options.
   *
   * @param options The execution options.
   * @return The pipeline result.
   */
  private static PipelineResult run(GCSToElasticsearchOptions options) {
    // Create the pipeline
    Pipeline pipeline = Pipeline.create(options);

    // Register the coder for pipeline
    CoderRegistry coderRegistry = pipeline.getCoderRegistry();
    coderRegistry.registerCoderForType(
        FAILSAFE_ELEMENT_CODER.getEncodedTypeDescriptor(), FAILSAFE_ELEMENT_CODER);

    // Throw error if containsHeaders is true and a schema or Udf is also set.
    if (options.getContainsHeaders()) {
      checkArgument(
          options.getJavascriptTextTransformGcsPath() == null
              && options.getJsonSchemaPath() == null
              && options.getPythonExternalTextTransformGcsPath() == null,
          "Cannot parse file containing headers with UDF or Json schema.");
    }

    // Throw error if only one retry configuration parameter is set.
    checkArgument(
        (options.getMaxRetryAttempts() == null && options.getMaxRetryDuration() == null)
            || (options.getMaxRetryAttempts() != null && options.getMaxRetryDuration() != null),
        "To specify retry configuration both max attempts and max duration must be set.");

    // Throw error if both Javascript UDF and Python UDF are set. We can only apply one or the
    // other.
    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 records from CSV(s) via {@link CsvConverters.ReadCsv}.
     *        2) Convert lines to JSON strings via {@link CsvConverters.LineToFailsafeJson}.
     *        3a) Write JSON strings as documents to Elasticsearch via {@link ElasticsearchIO}.
     *        3b) Write elements that failed processing to {@link org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO}.
     */
    PCollectionTuple readCsvLines =
        pipeline
            /*
             * Step 1: Read CSV file(s) from Cloud Storage using {@link CsvConverters.ReadCsv}.
             */
            .apply(
            "ReadCsv",
            CsvConverters.ReadCsv.newBuilder()
                .setCsvFormat(options.getCsvFormat())
                .setDelimiter(options.getDelimiter())
                .setHasHeaders(options.getContainsHeaders())
                .setInputFileSpec(options.getInputFileSpec())
                .setHeaderTag(CSV_HEADERS)
                .setLineTag(CSV_LINES)
                .setFileEncoding(options.getCsvFileEncoding())
                .build());
    /*
     * Step 2: Convert lines to Elasticsearch document.
     */
    CsvConverters.LineToFailsafeJson.Builder lineToFailsafeJsonBuilder =
        CsvConverters.LineToFailsafeJson.newBuilder()
            .setDelimiter(options.getDelimiter())
            .setJsonSchemaPath(options.getJsonSchemaPath())
            .setHeaderTag(CSV_HEADERS)
            .setLineTag(CSV_LINES)
            .setUdfOutputTag(PROCESSING_OUT)
            .setUdfDeadletterTag(PROCESSING_DEADLETTER_OUT);
    if (options.getPythonExternalTextTransformGcsPath() != null) {
      lineToFailsafeJsonBuilder
          .setPythonUdfFileSystemPath(options.getPythonExternalTextTransformGcsPath())
          .setPythonUdfFunctionName(options.getPythonExternalTextTransformFunctionName());
    } else {
      lineToFailsafeJsonBuilder
          .setJavascriptUdfFileSystemPath(options.getJavascriptTextTransformGcsPath())
          .setJavascriptUdfFunctionName(options.getJavascriptTextTransformFunctionName());
    }
    PCollectionTuple convertedCsvLines =
        readCsvLines.apply("ConvertLine", lineToFailsafeJsonBuilder.build());
    /*
     * Step 3a: Write elements that were successfully processed to Elasticsearch using {@link WriteToElasticsearch}.
     */
    convertedCsvLines
        .get(PROCESSING_OUT)
        .apply(
            "GetJsonDocuments",
            MapElements.into(TypeDescriptors.strings()).via(FailsafeElement::getPayload))
        .apply(
            "WriteToElasticsearch",
            WriteToElasticsearch.newBuilder()
                .setUserAgent("dataflow-gcs-to-elasticsearch-template/v2")
                .setOptions(options.as(GCSToElasticsearchOptions.class))
                .build());

    /*
     * Step 3b: Write elements that failed processing to deadletter table via {@link BigQueryIO}.
     */
    convertedCsvLines
        .get(PROCESSING_DEADLETTER_OUT)
        .apply(
            "AddTimestamps",
            WithTimestamps.of((FailsafeElement<String, String> failures) -> new Instant()))
        .apply(
            "WriteFailedElementsToBigQuery",
            WriteStringMessageErrors.newBuilder()
                .setErrorRecordsTable(options.getDeadletterTable())
                .setErrorRecordsTableSchema(SchemaUtils.DEADLETTER_SCHEMA)
                .build());

    return pipeline.run();
  }
}