examples/java/src/main/java/org/apache/beam/examples/cookbook/FilterExamples.java

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 * to you under the Apache License, Version 2.0 (the
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package org.apache.beam.examples.cookbook;

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 java.util.ArrayList;
import java.util.List;
import java.util.logging.Logger;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
import org.apache.beam.sdk.options.Default;
import org.apache.beam.sdk.options.Description;
import org.apache.beam.sdk.options.PipelineOptions;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.Validation;
import org.apache.beam.sdk.transforms.DoFn;
import org.apache.beam.sdk.transforms.Mean;
import org.apache.beam.sdk.transforms.PTransform;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.transforms.View;
import org.apache.beam.sdk.values.PCollection;
import org.apache.beam.sdk.values.PCollectionView;

/**
 * This is an example that demonstrates several approaches to filtering, and use of the Mean
 * transform. It shows how to dynamically set parameters by defining and using new pipeline options,
 * and how to use a value derived by the pipeline.
 *
 * <p>Concepts: The Mean transform; Options configuration; using pipeline-derived data as a side
 * input; approaches to filtering, selection, and projection.
 *
 * <p>The example reads public samples of weather data from BigQuery. It performs a
 * projection on the data, finds the global mean of the temperature readings, filters on readings
 * for a single given month, and then outputs only data (for that month) that has a mean temp
 * smaller than the derived global mean.
*
 * <p>Note: Before running this example, you must create a BigQuery dataset to contain your output
 * table.
 *
 * <p>To execute this pipeline locally, specify the BigQuery table for the output:
 * <pre>{@code
 *   --output=YOUR_PROJECT_ID:DATASET_ID.TABLE_ID
 *   [--monthFilter=<month_number>]
 * }
 * </pre>
 * where optional parameter {@code --monthFilter} is set to a number 1-12.
 *
 * <p>To change the runner, specify:
 * <pre>{@code
 *   --runner=YOUR_SELECTED_RUNNER
 * }
 * </pre>
 * See examples/java/README.md for instructions about how to configure different runners.
 *
 * <p>The BigQuery input table defaults to {@code clouddataflow-readonly:samples.weather_stations}
 * and can be overridden with {@code --input}.
 */
public class FilterExamples {
  // Default to using a 1000 row subset of the public weather station table publicdata:samples.gsod.
  private static final String WEATHER_SAMPLES_TABLE =
      "clouddataflow-readonly:samples.weather_stations";
  static final Logger LOG = Logger.getLogger(FilterExamples.class.getName());
  static final int MONTH_TO_FILTER = 7;

  /**
   * Examines each row in the input table. Outputs only the subset of the cells this example
   * is interested in-- the mean_temp and year, month, and day-- as a bigquery table row.
   */
  static class ProjectionFn extends DoFn<TableRow, TableRow> {
    @ProcessElement
    public void processElement(ProcessContext c){
      TableRow row = c.element();
      // Grab year, month, day, mean_temp from the row
      Integer year = Integer.parseInt((String) row.get("year"));
      Integer month = Integer.parseInt((String) row.get("month"));
      Integer day = Integer.parseInt((String) row.get("day"));
      Double meanTemp = Double.parseDouble(row.get("mean_temp").toString());
      // Prepares the data for writing to BigQuery by building a TableRow object
      TableRow outRow = new TableRow()
          .set("year", year).set("month", month)
          .set("day", day).set("mean_temp", meanTemp);
      c.output(outRow);
    }
  }

  /**
   * Implements 'filter' functionality.
   *
   * <p>Examines each row in the input table. Outputs only rows from the month
   * monthFilter, which is passed in as a parameter during construction of this DoFn.
   */
  static class FilterSingleMonthDataFn extends DoFn<TableRow, TableRow> {
    Integer monthFilter;

    public FilterSingleMonthDataFn(Integer monthFilter) {
      this.monthFilter = monthFilter;
    }

    @ProcessElement
    public void processElement(ProcessContext c){
      TableRow row = c.element();
      Integer month;
      month = (Integer) row.get("month");
      if (month.equals(this.monthFilter)) {
        c.output(row);
      }
    }
  }

  /**
   * Examines each row (weather reading) in the input table. Output the temperature
   * reading for that row ('mean_temp').
   */
  static class ExtractTempFn extends DoFn<TableRow, Double> {
    @ProcessElement
    public void processElement(ProcessContext c){
      TableRow row = c.element();
      Double meanTemp = Double.parseDouble(row.get("mean_temp").toString());
      c.output(meanTemp);
    }
  }



  /**
   * Finds the global mean of the mean_temp for each day/record, and outputs
   * only data that has a mean temp larger than this global mean.
   */
  static class BelowGlobalMean
      extends PTransform<PCollection<TableRow>, PCollection<TableRow>> {
    Integer monthFilter;

    public BelowGlobalMean(Integer monthFilter) {
      this.monthFilter = monthFilter;
    }


    @Override
    public PCollection<TableRow> expand(PCollection<TableRow> rows) {

      // Extract the mean_temp from each row.
      PCollection<Double> meanTemps = rows.apply(
          ParDo.of(new ExtractTempFn()));

      // Find the global mean, of all the mean_temp readings in the weather data,
      // and prepare this singleton PCollectionView for use as a side input.
      final PCollectionView<Double> globalMeanTemp =
          meanTemps.apply(Mean.<Double>globally())
               .apply(View.<Double>asSingleton());

      // Rows filtered to remove all but a single month
      PCollection<TableRow> monthFilteredRows = rows
          .apply(ParDo.of(new FilterSingleMonthDataFn(monthFilter)));

      // Then, use the global mean as a side input, to further filter the weather data.
      // By using a side input to pass in the filtering criteria, we can use a value
      // that is computed earlier in pipeline execution.
      // We'll only output readings with temperatures below this mean.
      PCollection<TableRow> filteredRows = monthFilteredRows
          .apply("ParseAndFilter", ParDo
              .of(new DoFn<TableRow, TableRow>() {
                @ProcessElement
                public void processElement(ProcessContext c) {
                  Double meanTemp = Double.parseDouble(c.element().get("mean_temp").toString());
                  Double gTemp = c.sideInput(globalMeanTemp);
                  if (meanTemp < gTemp) {
                    c.output(c.element());
                  }
                }
              }).withSideInputs(globalMeanTemp));

      return filteredRows;
    }
  }


  /**
   * Options supported by {@link FilterExamples}.
   *
   * <p>Inherits standard configuration options.
   */
  private interface Options extends PipelineOptions {
    @Description("Table to read from, specified as "
        + "<project_id>:<dataset_id>.<table_id>")
    @Default.String(WEATHER_SAMPLES_TABLE)
    String getInput();
    void setInput(String value);

    @Description("Table to write to, specified as "
        + "<project_id>:<dataset_id>.<table_id>. "
        + "The dataset_id must already exist")
    @Validation.Required
    String getOutput();
    void setOutput(String value);

    @Description("Numeric value of month to filter on")
    @Default.Integer(MONTH_TO_FILTER)
    Integer getMonthFilter();
    void setMonthFilter(Integer value);
  }

  /**
   * Helper method to build the table schema for the output table.
   */
  private static TableSchema buildWeatherSchemaProjection() {
    List<TableFieldSchema> fields = new ArrayList<>();
    fields.add(new TableFieldSchema().setName("year").setType("INTEGER"));
    fields.add(new TableFieldSchema().setName("month").setType("INTEGER"));
    fields.add(new TableFieldSchema().setName("day").setType("INTEGER"));
    fields.add(new TableFieldSchema().setName("mean_temp").setType("FLOAT"));
    TableSchema schema = new TableSchema().setFields(fields);
    return schema;
  }

  public static void main(String[] args)
      throws Exception {

    Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
    Pipeline p = Pipeline.create(options);

    TableSchema schema = buildWeatherSchemaProjection();

    p.apply(BigQueryIO.read().from(options.getInput()))
     .apply(ParDo.of(new ProjectionFn()))
     .apply(new BelowGlobalMean(options.getMonthFilter()))
     .apply(BigQueryIO.writeTableRows()
        .to(options.getOutput())
        .withSchema(schema)
        .withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_IF_NEEDED)
        .withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_TRUNCATE));

    p.run().waitUntilFinish();
  }
}