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* distributed with this work for additional information
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* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
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*
* http://www.apache.org/licenses/LICENSE-2.0
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* Unless required by applicable law or agreed to in writing, software
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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 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.Count;
import org.apache.beam.sdk.transforms.DoFn;
import org.apache.beam.sdk.transforms.PTransform;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.PCollection;
/**
* An example that reads the public samples of weather data from BigQuery, counts the number of
* tornadoes that occur in each month, and writes the results to BigQuery.
*
* <p>Concepts: Reading/writing BigQuery; counting a PCollection; user-defined PTransforms
*
* <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 with the form:
* <pre>{@code
* --output=YOUR_PROJECT_ID:DATASET_ID.TABLE_ID
* }</pre>
*
* <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 BigQueryTornadoes {
// 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";
/**
* Examines each row in the input table. If a tornado was recorded
* in that sample, the month in which it occurred is output.
*/
static class ExtractTornadoesFn extends DoFn<TableRow, Integer> {
@ProcessElement
public void processElement(ProcessContext c){
TableRow row = c.element();
if ((Boolean) row.get("tornado")) {
c.output(Integer.parseInt((String) row.get("month")));
}
}
}
/**
* Prepares the data for writing to BigQuery by building a TableRow object containing an
* integer representation of month and the number of tornadoes that occurred in each month.
*/
static class FormatCountsFn extends DoFn<KV<Integer, Long>, TableRow> {
@ProcessElement
public void processElement(ProcessContext c) {
TableRow row = new TableRow()
.set("month", c.element().getKey())
.set("tornado_count", c.element().getValue());
c.output(row);
}
}
/**
* Takes rows from a table and generates a table of counts.
*
* <p>The input schema is described by
* https://developers.google.com/bigquery/docs/dataset-gsod .
* The output contains the total number of tornadoes found in each month in
* the following schema:
* <ul>
* <li>month: integer</li>
* <li>tornado_count: integer</li>
* </ul>
*/
static class CountTornadoes
extends PTransform<PCollection<TableRow>, PCollection<TableRow>> {
@Override
public PCollection<TableRow> expand(PCollection<TableRow> rows) {
// row... => month...
PCollection<Integer> tornadoes = rows.apply(
ParDo.of(new ExtractTornadoesFn()));
// month... => <month,count>...
PCollection<KV<Integer, Long>> tornadoCounts =
tornadoes.apply(Count.<Integer>perElement());
// <month,count>... => row...
PCollection<TableRow> results = tornadoCounts.apply(
ParDo.of(new FormatCountsFn()));
return results;
}
}
/**
* Options supported by {@link BigQueryTornadoes}.
*
* <p>Inherits standard configuration options.
*/
public 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("BigQuery table to write to, specified as "
+ "<project_id>:<dataset_id>.<table_id>. The dataset must already exist.")
@Validation.Required
String getOutput();
void setOutput(String value);
}
public static void main(String[] args) {
Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
Pipeline p = Pipeline.create(options);
// Build the table schema for the output table.
List<TableFieldSchema> fields = new ArrayList<>();
fields.add(new TableFieldSchema().setName("month").setType("INTEGER"));
fields.add(new TableFieldSchema().setName("tornado_count").setType("INTEGER"));
TableSchema schema = new TableSchema().setFields(fields);
p.apply(BigQueryIO.read().from(options.getInput()))
.apply(new CountTornadoes())
.apply(BigQueryIO.writeTableRows()
.to(options.getOutput())
.withSchema(schema)
.withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_IF_NEEDED)
.withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_TRUNCATE));
p.run().waitUntilFinish();
}
}