GCP: What is the best option to setup a periodic Data pipeline from Spanner to Big Query

Question

Task : We have to setup a periodic sync of records from Spanner to Big Query. Our Spanner database has a relational table hierarchy.

Option Considered I was thinking of using Dataflow templates to setup this data pipeline.

• Option1 : Setup a job with Dataflow template 'Cloud Spanner to Cloud Storage Text' and then another with Dataflow template 'Cloud Storage Text to BigQuery'. Con : The first template works only on a single table and we have many tables to export.

• Option2 : Use 'Cloud Spanner to Cloud Storage Avro' template which exports the entire database. Con : I only need to export selected tables within a database and I don't see a template to import Avro into Big Query.

Questions : Please suggest what is the best option for setting up this pipeline

Answer 1

There is currently no off-the-shelf parameterized direct export from Cloud Spanner to BigQuery.

To meet your requirements, a custom dataflow job ( spanner dataflow connector , dataflow templates ) scheduled periodically ( 1 , 2 ) would be the best bet. Incremental exports would require implementing change tracking in you database which can be done with commit timestamps .

For a no-code solution, you would have to relax your requirements and bulk export all tables periodically to Cloud Storage and bulk import them periodically into BigQuery. You could use a combination of a periodic trigger of an export from Cloud Spanner to Cloud Storage and schedule a periodic import from Cloud Storage to BigQuery .

Answer 2

Use a single Dataflow pipeline to do it in one shot/pass. Here's an example I wrote using the Java SDK to help get you started. It reads from Spanner, transforms it to a BigQuery TableRow using a ParDo , and then writes to BigQuery at the end. Under the hood it's using GCS, but that's all abstracted away from you as a user.

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);
    }
}

Output logs:

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"}]}