從 Cloud Storage 到 BigQuery 的資料遮蓋/代碼化 (使用 Cloud DLP) 範本

/*
 * 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.templates;

import com.google.api.services.bigquery.model.TableCell;
import com.google.api.services.bigquery.model.TableFieldSchema;
import com.google.api.services.bigquery.model.TableRow;
import com.google.api.services.bigquery.model.TableSchema;
import com.google.cloud.dlp.v2.DlpServiceClient;
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.templates.DLPTextToBigQueryStreaming.TokenizePipelineOptions;
import com.google.privacy.dlp.v2.ContentItem;
import com.google.privacy.dlp.v2.DeidentifyContentRequest;
import com.google.privacy.dlp.v2.DeidentifyContentRequest.Builder;
import com.google.privacy.dlp.v2.DeidentifyContentResponse;
import com.google.privacy.dlp.v2.FieldId;
import com.google.privacy.dlp.v2.ProjectName;
import com.google.privacy.dlp.v2.Table;
import com.google.privacy.dlp.v2.Value;
import java.io.BufferedReader;
import java.io.IOException;
import java.nio.channels.Channels;
import java.nio.channels.ReadableByteChannel;
import java.nio.charset.StandardCharsets;
import java.sql.SQLException;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.Map;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.regex.Pattern;
import java.util.stream.Collectors;
import org.apache.beam.runners.dataflow.options.DataflowPipelineOptions;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.PipelineResult;
import org.apache.beam.sdk.coders.KvCoder;
import org.apache.beam.sdk.coders.StringUtf8Coder;
import org.apache.beam.sdk.io.Compression;
import org.apache.beam.sdk.io.FileIO;
import org.apache.beam.sdk.io.FileIO.ReadableFile;
import org.apache.beam.sdk.io.ReadableFileCoder;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
import org.apache.beam.sdk.io.gcp.bigquery.DynamicDestinations;
import org.apache.beam.sdk.io.gcp.bigquery.InsertRetryPolicy;
import org.apache.beam.sdk.io.gcp.bigquery.TableDestination;
import org.apache.beam.sdk.io.range.OffsetRange;
import org.apache.beam.sdk.metrics.Distribution;
import org.apache.beam.sdk.metrics.Metrics;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.Validation.Required;
import org.apache.beam.sdk.options.ValueProvider;
import org.apache.beam.sdk.options.ValueProvider.NestedValueProvider;
import org.apache.beam.sdk.transforms.DoFn;
import org.apache.beam.sdk.transforms.GroupByKey;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.transforms.Watch;
import org.apache.beam.sdk.transforms.WithKeys;
import org.apache.beam.sdk.transforms.splittabledofn.OffsetRangeTracker;
import org.apache.beam.sdk.transforms.splittabledofn.RestrictionTracker;
import org.apache.beam.sdk.transforms.windowing.AfterProcessingTime;
import org.apache.beam.sdk.transforms.windowing.FixedWindows;
import org.apache.beam.sdk.transforms.windowing.Repeatedly;
import org.apache.beam.sdk.transforms.windowing.Window;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.PCollection;
import org.apache.beam.sdk.values.PCollectionView;
import org.apache.beam.sdk.values.ValueInSingleWindow;
import org.apache.commons.csv.CSVFormat;
import org.apache.commons.csv.CSVRecord;
import org.joda.time.Duration;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * The {@link DLPTextToBigQueryStreaming} is a streaming pipeline that reads CSV files from a
 * storage location (e.g. Google Cloud Storage), uses Cloud DLP API to inspect, classify, and mask
 * sensitive information (e.g. PII Data like passport or SIN number) and at the end stores
 * obfuscated data in BigQuery (Dynamic Table Creation) to be used for various purposes. e.g. data
 * analytics, ML model. Cloud DLP inspection and masking can be configured by the user and can make
 * use of over 90 built in detectors and masking techniques like tokenization, secure hashing, date
 * shifting, partial masking, and more.
 *
 * <p><b>Pipeline Requirements</b>
 *
 * <ul>
 *   <li>DLP Templates exist (e.g. deidentifyTemplate, InspectTemplate)
 *   <li>The BigQuery Dataset exists
 * </ul>
 *
 * <p>Check out <a
 * href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v1/README_Stream_DLP_GCS_Text_to_BigQuery.md">README</a>
 * for instructions on how to use or modify this template.
 */
@Template(
    name = "Stream_DLP_GCS_Text_to_BigQuery",
    category = TemplateCategory.STREAMING,
    displayName = "Data Masking/Tokenization from Cloud Storage to BigQuery (using Cloud DLP)",
    description = {
      "The Data Masking/Tokenization from Cloud Storage to BigQuery template uses <a href=\"https://cloud.google.com/dlp/docs\">Sensitive Data Protection</a> and creates a streaming pipeline that does the following steps:\n"
          + "1. Reads CSV files from a Cloud Storage bucket.\n"
          + "2. Calls the Cloud Data Loss Prevention API (part of Sensitive Data Protection) for de-identification.\n"
          + "3. Writes the de-identified data into the specified BigQuery table.",
      "The template supports using both a Sensitive Data Protection <a href=\"https://cloud.google.com/dlp/docs/creating-templates\">inspection template</a> and a Sensitive Data Protection <a href=\"https://cloud.google.com/dlp/docs/creating-templates-deid\">de-identification template</a>. As a result, the template supports both of the following tasks:\n"
          + "- Inspect for potentially sensitive information and de-identify the data.\n"
          + "- De-identify structured data where columns are specified to be de-identified and no inspection is needed.",
      "Note: This template does not support a regional path for de-identification template location. Only a global path is supported."
    },
    optionsClass = TokenizePipelineOptions.class,
    documentation =
        "https://cloud.google.com/dataflow/docs/guides/templates/provided/dlp-text-to-bigquery",
    contactInformation = "https://cloud.google.com/support",
    preview = true,
    requirements = {
      "The input data to tokenize must exist.",
      "The Sensitive Data Protection templates must exist (for example, DeidentifyTemplate and InspectTemplate). For more details, see <a href=\"https://cloud.google.com/dlp/docs/concepts-templates\">Sensitive Data Protection templates</a>.",
      "The BigQuery dataset must exist."
    },
    streaming = true,
    hidden = true)
public class DLPTextToBigQueryStreaming {

  public static final Logger LOG = LoggerFactory.getLogger(DLPTextToBigQueryStreaming.class);

  /** Default interval for polling files in GCS. */
  private static final Duration DEFAULT_POLL_INTERVAL = Duration.standardSeconds(30);

  /** Expected only CSV file in GCS bucket. */
  private static final String ALLOWED_FILE_EXTENSION = String.valueOf("csv");

  /** Regular expression that matches valid BQ table IDs. */
  private static final Pattern TABLE_REGEXP = Pattern.compile("[-\\w$@]{1,1024}");

  /** Default batch size if value not provided in execution. */
  private static final Integer DEFAULT_BATCH_SIZE = 100;

  /** Regular expression that matches valid BQ column name . */
  private static final Pattern COLUMN_NAME_REGEXP = Pattern.compile("^[A-Za-z_]+[A-Za-z_0-9]*$");

  /** Default window interval to create side inputs for header records. */
  private static final Duration WINDOW_INTERVAL = Duration.standardSeconds(30);

  /**
   * Main entry point for executing the pipeline. This will run the pipeline asynchronously. If
   * blocking execution is required, use the {@link
   * DLPTextToBigQueryStreaming#run(TokenizePipelineOptions)} 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) {

    TokenizePipelineOptions options =
        PipelineOptionsFactory.fromArgs(args).withValidation().as(TokenizePipelineOptions.class);
    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(TokenizePipelineOptions options) {
    // Create the pipeline
    Pipeline p = Pipeline.create(options);
    /*
     * Steps:
     *   1) Read from the text source continuously based on default interval e.g. 30 seconds
     *       - Setup a window for 30 secs to capture the list of files emitted.
     *       - Group by file name as key and ReadableFile as a value.
     *   2) Output each readable file for content processing.
     *   3) Split file contents based on batch size for parallel processing.
     *   4) Process each split as a DLP table content request to invoke API.
     *   5) Convert DLP Table Rows to BQ Table Row.
     *   6) Create dynamic table and insert successfully converted records into BQ.
     */

    PCollection<KV<String, Iterable<ReadableFile>>> csvFiles =
        p
            /*
             * 1) Read from the text source continuously based on default interval e.g. 300 seconds
             *     - Setup a window for 30 secs to capture the list of files emitted.
             *     - Group by file name as key and ReadableFile as a value.
             */
            .apply(
                "Poll Input Files",
                FileIO.match()
                    .filepattern(options.getInputFilePattern())
                    .continuously(DEFAULT_POLL_INTERVAL, Watch.Growth.never()))
            .apply("Find Pattern Match", FileIO.readMatches().withCompression(Compression.AUTO))
            .apply("Add File Name as Key", WithKeys.of(file -> getFileName(file)))
            .setCoder(KvCoder.of(StringUtf8Coder.of(), ReadableFileCoder.of()))
            .apply(
                "Fixed Window(30 Sec)",
                Window.<KV<String, ReadableFile>>into(FixedWindows.of(WINDOW_INTERVAL))
                    .triggering(
                        Repeatedly.forever(
                            AfterProcessingTime.pastFirstElementInPane()
                                .plusDelayOf(Duration.ZERO)))
                    .discardingFiredPanes()
                    .withAllowedLateness(Duration.ZERO))
            .apply(GroupByKey.create());

    PCollection<KV<String, TableRow>> bqDataMap =
        csvFiles

            // 2) Output each readable file for content processing.
            .apply(
                "File Handler",
                ParDo.of(
                    new DoFn<KV<String, Iterable<ReadableFile>>, KV<String, ReadableFile>>() {
                      @ProcessElement
                      public void processElement(ProcessContext c) {
                        String fileKey = c.element().getKey();
                        c.element()
                            .getValue()
                            .forEach(
                                file -> {
                                  c.output(KV.of(fileKey, file));
                                });
                      }
                    }))

            // 3) Split file contents based on batch size for parallel processing.
            .apply(
                "Process File Contents",
                ParDo.of(
                    new CSVReader(
                        NestedValueProvider.of(
                            options.getBatchSize(),
                            batchSize -> {
                              if (batchSize != null) {
                                return batchSize;
                              } else {
                                return DEFAULT_BATCH_SIZE;
                              }
                            }))))

            // 4) Create a DLP Table content request and invoke DLP API for each processing
            .apply(
                "DLP-Tokenization",
                ParDo.of(
                    new DLPTokenizationDoFn(
                        options.getDlpProjectId(),
                        options.getDeidentifyTemplateName(),
                        options.getInspectTemplateName())))

            // 5) Convert DLP Table Rows to BQ Table Row
            .apply("Process Tokenized Data", ParDo.of(new TableRowProcessorDoFn()));

    // 6) Create dynamic table and insert successfully converted records into BQ.
    bqDataMap.apply(
        "Write To BQ",
        BigQueryIO.<KV<String, TableRow>>write()
            .to(new BQDestination(options.getDatasetName(), options.getDlpProjectId()))
            .withFormatFunction(
                element -> {
                  return element.getValue();
                })
            .withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_APPEND)
            .withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_IF_NEEDED)
            .withoutValidation()
            .withFailedInsertRetryPolicy(InsertRetryPolicy.retryTransientErrors()));

    return p.run();
  }

  /**
   * The {@link TokenizePipelineOptions} interface provides the custom execution options passed by
   * the executor at the command-line.
   */
  public interface TokenizePipelineOptions extends DataflowPipelineOptions {

    @TemplateParameter.GcsReadFile(
        order = 1,
        groupName = "Source",
        description = "Input Cloud Storage File(s)",
        helpText = "The CSV files to read input data records from. Wildcards are also accepted.",
        example = "gs://mybucket/my_csv_filename.csv or gs://mybucket/file-*.csv")
    ValueProvider<String> getInputFilePattern();

    void setInputFilePattern(ValueProvider<String> value);

    @TemplateParameter.Text(
        order = 2,
        groupName = "Source",
        regexes = {
          "^projects\\/[^\\n\\r\\/]+(\\/locations\\/[^\\n\\r\\/]+)?\\/deidentifyTemplates\\/[^\\n\\r\\/]+$"
        },
        description = "Cloud DLP deidentify template name",
        helpText =
            "The Sensitive Data Protection de-identification template to use for API requests, specified with the pattern `projects/<PROJECT_ID>/deidentifyTemplates/<TEMPLATE_ID>`.",
        example =
            "projects/your-project-id/locations/global/deidentifyTemplates/generated_template_id")
    @Required
    ValueProvider<String> getDeidentifyTemplateName();

    void setDeidentifyTemplateName(ValueProvider<String> value);

    @TemplateParameter.Text(
        order = 3,
        groupName = "DLP Configuration",
        optional = true,
        regexes = {
          "^projects\\/[^\\n\\r\\/]+(\\/locations\\/[^\\n\\r\\/]+)?\\/inspectTemplates\\/[^\\n\\r\\/]+$"
        },
        description = "Cloud DLP inspect template name",
        helpText =
            "The Sensitive Data Protection inspection template to use for API requests, specified"
                + " with the pattern `projects/<PROJECT_ID>/identifyTemplates/<TEMPLATE_ID>`.",
        example =
            "projects/your-project-id/locations/global/inspectTemplates/generated_template_id")
    ValueProvider<String> getInspectTemplateName();

    void setInspectTemplateName(ValueProvider<String> value);

    @TemplateParameter.Integer(
        order = 4,
        groupName = "DLP Configuration",
        optional = true,
        description = "Batch size",
        helpText =
            "The chunking or batch size to use for sending data to inspect and detokenize. For a CSV file, the value of `batchSize` is the number of rows in a batch."
                + " Determine the batch size based on the size of the records and the sizing of the file."
                + " The DLP API has a payload size limit of 524 KB per API call.")
    @Required
    ValueProvider<Integer> getBatchSize();

    void setBatchSize(ValueProvider<Integer> value);

    @TemplateParameter.Text(
        order = 5,
        groupName = "Target",
        regexes = {"^[^.]*$"},
        description = "BigQuery Dataset",
        helpText =
            "The BigQuery dataset to use when sending tokenized results. The dataset must exist prior to execution.")
    ValueProvider<String> getDatasetName();

    void setDatasetName(ValueProvider<String> value);

    @TemplateParameter.ProjectId(
        order = 6,
        groupName = "DLP Configuration",
        description = "Cloud DLP project ID",
        helpText =
            "The ID for the Google Cloud project that owns the DLP API resource. This project"
                + " can be the same project that owns the Sensitive Data Protection templates, or it"
                + " can be a separate project.")
    ValueProvider<String> getDlpProjectId();

    void setDlpProjectId(ValueProvider<String> value);
  }

  /**
   * The {@link CSVReader} class uses experimental Split DoFn to split each csv file contents in
   * chunks and process it in non-monolithic fashion. For example: if a CSV file has 100 rows and
   * batch size is set to 15, then initial restrictions for the SDF will be 1 to 7 and split
   * restriction will be {{1-2},{2-3}..{7-8}} for parallel executions.
   */
  static class CSVReader extends DoFn<KV<String, ReadableFile>, KV<String, Table>> {

    private ValueProvider<Integer> batchSize;
    private PCollectionView<List<KV<String, List<String>>>> headerMap;

    /** This counter is used to track number of lines processed against batch size. */
    private Integer lineCount;

    public CSVReader(ValueProvider<Integer> batchSize) {
      lineCount = 1;
      this.batchSize = batchSize;
    }

    @ProcessElement
    public void processElement(ProcessContext c, RestrictionTracker<OffsetRange, Long> tracker)
        throws IOException {
      for (long i = tracker.currentRestriction().getFrom(); tracker.tryClaim(i); ++i) {
        String fileKey = c.element().getKey();
        try (BufferedReader br = getReader(c.element().getValue())) {
          List<Table.Row> rows = new ArrayList<>();
          Table dlpTable = null;
          /** finding out EOL for this restriction so that we know the SOL */
          int endOfLine = (int) (i * batchSize.get().intValue());
          int startOfLine = (endOfLine - batchSize.get().intValue());

          // getting the DLP table headers
          Iterator<CSVRecord> csvRows = CSVFormat.DEFAULT.parse(br).iterator();
          if (!csvRows.hasNext()) {
            LOG.info("File `" + c.element().getKey() + "` is empty");
            continue;
          }
          List<FieldId> dlpTableHeaders = toDlpTableHeaders(csvRows.next());

          /** skipping all the rows that's not part of this restriction */
          for (int line = 0; line < startOfLine; line++) {
            if (csvRows.hasNext()) {
              csvRows.next();
            }
          }
          /** looping through buffered reader and creating DLP Table Rows equals to batch */
          while (csvRows.hasNext() && lineCount <= batchSize.get()) {

            CSVRecord csvRow = csvRows.next();
            rows.add(convertCsvRowToTableRow(csvRow));
            lineCount += 1;
          }
          /** creating DLP table and output for next transformation */
          dlpTable = Table.newBuilder().addAllHeaders(dlpTableHeaders).addAllRows(rows).build();
          c.output(KV.of(fileKey, dlpTable));

          LOG.debug(
              "Current Restriction From: {}, Current Restriction To: {},"
                  + " StartofLine: {}, End Of Line {}, BatchData {}",
              tracker.currentRestriction().getFrom(),
              tracker.currentRestriction().getTo(),
              startOfLine,
              endOfLine,
              dlpTable.getRowsCount());
        }
      }
    }

    private static List<FieldId> toDlpTableHeaders(CSVRecord headerRow) {
      List<FieldId> result = new ArrayList<>();
      for (String header : headerRow) {
        result.add(FieldId.newBuilder().setName(header).build());
      }
      return result;
    }

    /**
     * SDF needs to define a @GetInitialRestriction method that can create a restriction describing
     * the complete work for a given element. For our case this would be the total number of rows
     * for each CSV file. We will calculate the number of split required based on total number of
     * rows and batch size provided.
     *
     * @throws IOException
     */
    @GetInitialRestriction
    public OffsetRange getInitialRestriction(@Element KV<String, ReadableFile> csvFile)
        throws IOException {

      int rowCount = 0;
      int totalSplit = 0;
      try (BufferedReader br = getReader(csvFile.getValue())) {
        /** assume first row is header */
        int checkRowCount = (int) br.lines().count() - 1;
        rowCount = (checkRowCount < 1) ? 1 : checkRowCount;
        totalSplit = rowCount / batchSize.get().intValue();
        int remaining = rowCount % batchSize.get().intValue();
        /**
         * Adjusting the total number of split based on remaining rows. For example: batch size of
         * 15 for 100 rows will have total 7 splits. As it's a range last split will have offset
         * range {7,8}
         */
        if (remaining > 0) {
          totalSplit = totalSplit + 2;

        } else {
          totalSplit = totalSplit + 1;
        }
      }

      LOG.debug("Initial Restriction range from 1 to: {}", totalSplit);
      return new OffsetRange(1, totalSplit);
    }

    /**
     * SDF needs to define a @SplitRestriction method that can split the initial restriction to a
     * number of smaller restrictions. For example: a initial restriction of (x, N) as input and
     * produces pairs (x, 0), (x, 1), …, (x, N-1) as output.
     */
    @SplitRestriction
    public void splitRestriction(
        @Element KV<String, ReadableFile> csvFile,
        @Restriction OffsetRange range,
        OutputReceiver<OffsetRange> out) {
      /** split the initial restriction by 1 */
      for (final OffsetRange p : range.split(1, 1)) {
        out.output(p);
      }
    }

    @NewTracker
    public OffsetRangeTracker newTracker(@Restriction OffsetRange range) {
      return new OffsetRangeTracker(new OffsetRange(range.getFrom(), range.getTo()));
    }

    private TaTableoRowonvertCsvRowToTableRow(CSVRecord csvRow) {
      /** convert from CSV row to DLP Table Row */
      Iterator<String> valueIterator = csvRow.iterator();
      TaTableoRowuilder tableRowBuilder = TaTableow.newBuilder();
      while (valueIterator.hasNext()) {
        String value = valueIterator.next();
        if (value != null) {
          tableRowBuilder.adaddValuesaValueewBuilder().sesetStringValuealue.toString()).build());
        } else {
          tableRowBuilder.adaddValuesaValueewBuilder().sesetStringValue").build());
        }
      }

      return tableRowBuilder.build();
    }

    private List<String> getHeaders(List<KV<String, List<String>>> headerMap, String fileKey) {
      return headerMap.stream()
          .filter(map -> map.getKey().equalsIgnoreCase(fileKey))
          .findFirst()
          .map(e -> e.getValue())
          .orElse(null);
    }
  }

  /**
   * The {@link DLPTokenizationDoFn} class executes tokenization request by calling DLP api. It uses
   * DLP table as a content item as CSV file contains fully structured data. DLP templates (e.g.
   * de-identify, inspect) need to exist before this pipeline runs. As response from the API is
   * received, this DoFn outputs KV of new table with table id as key.
   */
  static class DLPTokenizationDoFn extends DoFn<KV<String, Table>, KV<String, Table>> {
    private ValueProvider<String> dlpProjectId;
    private DlDlpServiceClientlpServiceClient;
    private ValueProvider<String> deIdentifyTemplateName;
    private ValueProvider<String> inspectTemplateName;
    private boolean inspectTemplateExist;
    private Builder requestBuilder;
    private final Distribution numberOfRowsTokenized =
        Metrics.distribution(DLPTokenizationDoFn.class, "numberOfRowsTokenizedDistro");
    private final Distribution numberOfBytesTokenized =
        Metrics.distribution(DLPTokenizationDoFn.class, "numberOfBytesTokenizedDistro");

    public DLPTokenizationDoFn(
        ValueProvider<String> dlpProjectId,
        ValueProvider<String> deIdentifyTemplateName,
        ValueProvider<String> inspectTemplateName) {
      this.dlpProjectId = dlpProjectId;
      this.dlpServiceClient = null;
      this.deIdentifyTemplateName = deIdentifyTemplateName;
      this.inspectTemplateName = inspectTemplateName;
      this.inspectTemplateExist = false;
    }

    @Setup
    public void setup() {
      if (this.inspectTemplateName.isAccessible()) {
        if (this.inspectTemplateName.get() != null) {
          this.inspectTemplateExist = true;
        }
      }
      if (this.deIdentifyTemplateName.isAccessible()) {
        if (this.deIdentifyTemplateName.get() != null) {
          this.requestBuilder =
              DeDeidentifyContentRequestewBuilder()
                  .setParent(PrProjectNamef(this.dlpProjectId.get()).toString())
                  .sesetDeidentifyTemplateNamehis.deIdentifyTemplateName.get());
          if (this.inspectTemplateExist) {
            this.requestBuilder.setInspectTemplateName(this.inspectTemplateName.get());
          }
        }
      }
    }

    @StartBundle
    public void startBundle() throws SQLException {

      try {
        this.dlpServiceClient = DlDlpServiceClientreate();

      } catch (IOException e) {
        LOG.error("Failed to create DLP Service Client", e.getMessage());
        throw new RuntimeException(e);
      }
    }

    @FinishBundle
    public void finishBundle() throws Exception {
      if (this.dlpServiceClient != null) {
        this.dlpServiceClient.clclose;
      }
    }

    @ProcessElement
    public void processElement(ProcessContext c) {
      String key = c.element().getKey();
      TaTableonEncryptedData = c.element().getValue();
      CoContentItemableItem = CoContentItemewBuilder().setTable(nonEncryptedData).build();
      this.requestBuilder.setItem(tableItem);
      DeDeidentifyContentResponseesponse =
          dldlpServiceClient.deidentifyContent(this.requestBuilder.build())      TaTableokenizedData = reresponse.getItem()etTable();
      numberOfRowsTokenized.update(totokenizedData.getRowsList()ize());
      numberOfBytesTokenized.update(tokenizedData.toByteArray().length);
      c.output(KV.of(key, tokenizedData));
    }
  }

  /**
   * The {@link TableRowProcessorDoFn} class process tokenized DLP tables and convert them to
   * BigQuery Table Row.
   */
  public static class TableRowProcessorDoFn extends DoFn<KV<String, Table>, KV<String, TableRow>> {

    @ProcessElement
    public void processElement(ProcessContext c) {

      TaTableokenizedData = c.element().getValue();
      List<String> headers =
          totokenizedData.getHeadersList()tream()
              .map(fid -> fid.getName())
              .collect(Collectors.toList());
      List<Table.RoR>owutputRows = totokenizedData.getRowsList()      if (outputRows.size() > 0) {
        for (TaTableoRowutputRow : outputRows) {
          if (outputRow.getValuesCount() != headers.size()) {
            throw new IllegalArgumentException(
                "CSV file's header count must exactly match with data element count");
          }
          c.output(
              KV.of(
                  c.element().getKey(),
                  createBqRow(outputRow, headers.toArray(new String[headers.size()]))));
        }
      }
    }

    private static TableRow createBqRow(TaTableoRowokenizedValue, String[] headers) {
      TableRow bqRow = new TableRow();
      AtomicInteger headerIndex = new AtomicInteger(0);
      List<TableCell> cells = new ArrayList<>();
      tokenizedValue
          .getValuesList()
          .forEach(
              value -> {
                String checkedHeaderName =
                    checkHeaderName(headers[headerIndex.getAndIncrement()].toString());
                bqRow.set(checkedHeaderName, value.getStringValue());
                cells.add(new TableCell().set(checkedHeaderName, value.getStringValue()));
              });
      bqRow.setF(cells);
      return bqRow;
    }
  }

  /**
   * The {@link BQDestination} class creates BigQuery table destination and table schema based on
   * the CSV file processed in earlier transformations. Table id is same as filename Table schema is
   * same as file header columns.
   */
  public static class BQDestination
      extends DynamicDestinations<KV<String, TableRow>, KV<String, TableRow>> {

    private ValueProvider<String> datasetName;
    private ValueProvider<String> projectId;

    public BQDestination(ValueProvider<String> datasetName, ValueProvider<String> projectId) {
      this.datasetName = datasetName;
      this.projectId = projectId;
    }

    @Override
    public KV<String, TableRow> getDestination(ValueInSingleWindow<KV<String, TableRow>> element) {
      String key = element.getValue().getKey();
      String tableName = String.format("%s:%s.%s", projectId.get(), datasetName.get(), key);
      LOG.debug("Table Name {}", tableName);
      return KV.of(tableName, element.getValue().getValue());
    }

    @Override
    public TableDestination getTable(KV<String, TableRow> destination) {
      TableDestination dest =
          new TableDestination(destination.getKey(), "pii-tokenized output data from dataflow");
      LOG.debug("Table Destination {}", dest.getTableSpec());
      return dest;
    }

    @Override
    public TableSchema getSchema(KV<String, TableRow> destination) {

      TableRow bqRow = destination.getValue();
      TableSchema schema = new TableSchema();
      List<TableFieldSchema> fields = new ArrayList<TableFieldSchema>();
      List<TableCell> cells = bqRow.getF();
      for (int i = 0; i < cells.size(); i++) {
        Map<String, Object> object = cells.get(i);
        String header = object.keySet().iterator().next();
        /** currently all BQ data types are set to String */
        fields.add(new TableFieldSchema().setName(checkHeaderName(header)).setType("STRING"));
      }

      schema.sesetFieldsields);
      return schema;
    }
  }

  private static String getFileName(ReadableFile file) {
    String csvFileName = file.getMetadata().resourceId().getFilename().toString();
    /** taking out .csv extension from file name e.g fileName.csv->fileName */
    String[] fileKey = csvFileName.split("\\.", 2);

    if (!fileKey[1].equals(ALLOWED_FILE_EXTENSION) || !TABLE_REGEXP.matcher(fileKey[0]).matches()) {
      throw new RuntimeException(
          "[Filename must contain a CSV extension "
              + " BQ table name must contain only letters, numbers, or underscores ["
              + fileKey[1]
              + "], ["
              + fileKey[0]
              + "]");
    }
    /** returning file name without extension */
    return fileKey[0];
  }

  private static BufferedReader getReader(ReadableFile csvFile) {
    BufferedReader br = null;
    ReadableByteChannel channel = null;
    /** read the file and create buffered reader */
    try {
      channel = csvFile.openSeekable();

    } catch (IOException e) {
      LOG.error("Failed to Read File {}", e.getMessage());
      throw new RuntimeException(e);
    }

    if (channel != null) {

      br = new BufferedReader(Channels.newReader(channel, StandardCharsets.UTF_8.name()));
    }

    return br;
  }

  private static String checkHeaderName(String name) {
    /** some checks to make sure BQ column names don't fail e.g. special characters */
    String checkedHeader = name.replaceAll("\\s", "_");
    checkedHeader = checkedHeader.replaceAll("'", "");
    checkedHeader = checkedHeader.replaceAll("/", "");
    if (!COLUMN_NAME_REGEXP.matcher(checkedHeader).matches()) {
      throw new IllegalArgumentException("Column name can't be matched to a valid format " + name);
    }
    return checkedHeader;
  }
}