Используйте один конвейер Dataflow, чтобы сделать это за один выстрел / проход.Вот пример, который я написал, используя Java SDK, чтобы помочь вам начать.Он читает из Spanner, преобразует его в BigQuery TableRow, используя ParDo, а затем записывает в BigQuery в конце.Под капотом он использует GCS, но это все отвлечено от вас как пользователя.
package org.polleyg;
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.spanner.Struct;
import org.apache.beam.runners.dataflow.options.DataflowPipelineOptions;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
import org.apache.beam.sdk.io.gcp.spanner.SpannerIO;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.transforms.DoFn;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.values.PCollection;
import java.util.ArrayList;
import java.util.List;
import static org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.Write.CreateDisposition.CREATE_IF_NEEDED;
import static org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.Write.WriteDisposition.WRITE_TRUNCATE;
/**
* Do some randomness
*/
public class TemplatePipeline {
public static void main(String[] args) {
PipelineOptionsFactory.register(DataflowPipelineOptions.class);
DataflowPipelineOptions options = PipelineOptionsFactory.fromArgs(args).withValidation().as(DataflowPipelineOptions.class);
Pipeline pipeline = Pipeline.create(options);
PCollection<Struct> records = pipeline.apply("read_from_spanner",
SpannerIO.read()
.withInstanceId("spanner-to-dataflow-to-bq")
.withDatabaseId("the-dude")
.withQuery("SELECT * FROM Singers"));
records.apply("convert-2-bq-row", ParDo.of(new DoFn<Struct, TableRow>() {
@ProcessElement
public void processElement(ProcessContext c) throws Exception {
TableRow row = new TableRow();
row.set("id", c.element().getLong("SingerId"));
row.set("first", c.element().getString("FirstName"));
row.set("last", c.element().getString("LastName"));
c.output(row);
}
})).apply("write-to-bq", BigQueryIO.writeTableRows()
.to(String.format("%s:spanner_to_bigquery.singers", options.getProject()))
.withCreateDisposition(CREATE_IF_NEEDED)
.withWriteDisposition(WRITE_TRUNCATE)
.withSchema(getTableSchema()));
pipeline.run();
}
private static TableSchema getTableSchema() {
List<TableFieldSchema> fields = new ArrayList<>();
fields.add(new TableFieldSchema().setName("id").setType("INTEGER"));
fields.add(new TableFieldSchema().setName("first").setType("STRING"));
fields.add(new TableFieldSchema().setName("last").setType("STRING"));
return new TableSchema().setFields(fields);
}
}
Выходные журналы:
00:10:54,011 0 [direct-runner-worker] INFO org.apache.beam.sdk.io.gcp.bigquery.BatchLoads - Writing BigQuery temporary files to gs://spanner-dataflow-bq/tmp/BigQueryWriteTemp/beam_load_templatepipelinegrahampolley0531141053eff9d0d4_3dd2ba3a1c0347cf860241ddcd310a12/ before loading them.
00:10:59,332 5321 [direct-runner-worker] INFO org.apache.beam.sdk.io.gcp.bigquery.TableRowWriter - Opening TableRowWriter to gs://spanner-dataflow-bq/tmp/BigQueryWriteTemp/beam_load_templatepipelinegrahampolley0531141053eff9d0d4_3dd2ba3a1c0347cf860241ddcd310a12/c374d44a-a7db-407e-aaa4-fe6aa5f6a9ef.
00:11:01,178 7167 [direct-runner-worker] INFO org.apache.beam.sdk.io.gcp.bigquery.WriteTables - Loading 1 files into {datasetId=spanner_to_bigquery, projectId=grey-sort-challenge, tableId=singers} using job {jobId=beam_load_templatepipelinegrahampolley0531141053eff9d0d4_3dd2ba3a1c0347cf860241ddcd310a12_b4b4722df4326c6f5a93d7824981dc73_00001_00000-0, location=australia-southeast1, projectId=grey-sort-challenge}, attempt 0
00:11:02,495 8484 [direct-runner-worker] INFO org.apache.beam.sdk.io.gcp.bigquery.BigQueryServicesImpl - Started BigQuery job: {jobId=beam_load_templatepipelinegrahampolley0531141053eff9d0d4_3dd2ba3a1c0347cf860241ddcd310a12_b4b4722df4326c6f5a93d7824981dc73_00001_00000-0, location=australia-southeast1, projectId=grey-sort-challenge}.
bq show -j --format=prettyjson --project_id=grey-sort-challenge beam_load_templatepipelinegrahampolley0531141053eff9d0d4_3dd2ba3a1c0347cf860241ddcd310a12_b4b4722df4326c6f5a93d7824981dc73_00001_00000-0
00:11:02,495 8484 [direct-runner-worker] INFO org.apache.beam.sdk.io.gcp.bigquery.WriteTables - Load job {jobId=beam_load_templatepipelinegrahampolley0531141053eff9d0d4_3dd2ba3a1c0347cf860241ddcd310a12_b4b4722df4326c6f5a93d7824981dc73_00001_00000-0, location=australia-southeast1, projectId=grey-sort-challenge} started
00:11:03,183 9172 [direct-runner-worker] INFO org.apache.beam.sdk.io.gcp.bigquery.BigQueryServicesImpl - Still waiting for BigQuery job beam_load_templatepipelinegrahampolley0531141053eff9d0d4_3dd2ba3a1c0347cf860241ddcd310a12_b4b4722df4326c6f5a93d7824981dc73_00001_00000-0, currently in status {"state":"RUNNING"}
bq show -j --format=prettyjson --project_id=grey-sort-challenge beam_load_templatepipelinegrahampolley0531141053eff9d0d4_3dd2ba3a1c0347cf860241ddcd310a12_b4b4722df4326c6f5a93d7824981dc73_00001_00000-0
00:11:05,043 11032 [direct-runner-worker] INFO org.apache.beam.sdk.io.gcp.bigquery.BigQueryServicesImpl - BigQuery job {jobId=beam_load_templatepipelinegrahampolley0531141053eff9d0d4_3dd2ba3a1c0347cf860241ddcd310a12_b4b4722df4326c6f5a93d7824981dc73_00001_00000-0, location=australia-southeast1, projectId=grey-sort-challenge} completed in state DONE
00:11:05,044 11033 [direct-runner-worker] INFO org.apache.beam.sdk.io.gcp.bigquery.WriteTables - Load job {jobId=beam_load_templatepipelinegrahampolley0531141053eff9d0d4_3dd2ba3a1c0347cf860241ddcd310a12_b4b4722df4326c6f5a93d7824981dc73_00001_00000-0, location=australia-southeast1, projectId=grey-sort-challenge} succeeded. Statistics: {"completionRatio":1.0,"creationTime":"1559311861461","endTime":"1559311863323","load":{"badRecords":"0","inputFileBytes":"81","inputFiles":"1","outputBytes":"45","outputRows":"2"},"startTime":"1559311862043","totalSlotMs":"218","reservationUsage":[{"name":"default-pipeline","slotMs":"218"}]}
