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This creates an end-to-end test that checks for proper billing pipeline IO writes. The only remaining test would be to add a test for the Bigquery query, but see b/70839142 for why I've deemed that more work than worthwhile. ------------- Created by MOE: https://github.com/google/moe MOE_MIGRATED_REVID=179706561
145 lines
6.1 KiB
Java
145 lines
6.1 KiB
Java
// Copyright 2017 The Nomulus Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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package google.registry.beam;
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import google.registry.beam.BillingEvent.InvoiceGroupingKey;
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import google.registry.beam.BillingEvent.InvoiceGroupingKey.InvoiceGroupingKeyCoder;
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import google.registry.config.RegistryConfig.Config;
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import javax.inject.Inject;
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import org.apache.beam.runners.dataflow.DataflowRunner;
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import org.apache.beam.runners.dataflow.options.DataflowPipelineOptions;
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import org.apache.beam.sdk.Pipeline;
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import org.apache.beam.sdk.coders.SerializableCoder;
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import org.apache.beam.sdk.io.DefaultFilenamePolicy.Params;
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import org.apache.beam.sdk.io.FileBasedSink;
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import org.apache.beam.sdk.io.TextIO;
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import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
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import org.apache.beam.sdk.options.Description;
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import org.apache.beam.sdk.options.PipelineOptionsFactory;
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import org.apache.beam.sdk.options.ValueProvider;
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import org.apache.beam.sdk.transforms.Count;
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import org.apache.beam.sdk.transforms.MapElements;
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import org.apache.beam.sdk.transforms.PTransform;
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import org.apache.beam.sdk.values.KV;
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import org.apache.beam.sdk.values.PCollection;
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import org.apache.beam.sdk.values.TypeDescriptor;
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import org.apache.beam.sdk.values.TypeDescriptors;
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/**
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* Definition of a Dataflow pipeline template, which generates a given month's invoices.
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*
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* <p>To stage this template on GCS, run the {@link
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* google.registry.tools.DeployInvoicingPipelineCommand} Nomulus command.
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*
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* <p>Then, you can run the staged template via the API client library, gCloud or a raw REST call.
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* For an example using the API client library, see {@link
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* google.registry.billing.GenerateInvoicesAction}.
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*
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* @see <a href="https://cloud.google.com/dataflow/docs/templates/overview">Dataflow Templates</a>
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*/
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public class InvoicingPipeline {
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@Inject @Config("projectId") String projectId;
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@Inject @Config("apacheBeamBucketUrl") String beamBucket;
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@Inject InvoicingPipeline() {}
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/** Custom options for running the invoicing pipeline. */
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interface InvoicingPipelineOptions extends DataflowPipelineOptions {
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/** Returns the yearMonth we're generating invoices for, in yyyy-MM format. */
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@Description("The yearMonth we generate invoices for, in yyyy-MM format.")
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ValueProvider<String> getYearMonth();
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/** Sets the yearMonth we generate invoices for. */
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void setYearMonth(ValueProvider<String> value);
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}
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/** Deploys the invoicing pipeline as a template on GCS, for a given projectID and GCS bucket. */
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public void deploy() {
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InvoicingPipelineOptions options = PipelineOptionsFactory.as(InvoicingPipelineOptions.class);
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options.setProject(projectId);
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options.setRunner(DataflowRunner.class);
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options.setStagingLocation(beamBucket + "/staging");
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options.setTemplateLocation(beamBucket + "/templates/invoicing");
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Pipeline p = Pipeline.create(options);
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PCollection<BillingEvent> billingEvents =
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p.apply(
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"Read BillingEvents from Bigquery",
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BigQueryIO.read(BillingEvent::parseFromRecord)
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.fromQuery(InvoicingUtils.makeQueryProvider(options.getYearMonth(), projectId))
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.withCoder(SerializableCoder.of(BillingEvent.class))
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.usingStandardSql()
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.withoutValidation());
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applyTerminalTransforms(billingEvents);
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p.run();
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}
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/**
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* Applies output transforms to the {@code BillingEvent} source collection.
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*
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* <p>This is factored out purely to facilitate testing.
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*/
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void applyTerminalTransforms(PCollection<BillingEvent> billingEvents) {
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billingEvents.apply(
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"Write events to separate CSVs keyed by registrarId_tld pair", writeDetailReports());
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billingEvents
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.apply("Generate overall invoice rows", new GenerateInvoiceRows())
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.apply("Write overall invoice to CSV", writeInvoice());
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}
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/** Transform that converts a {@code BillingEvent} into an invoice CSV row. */
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private static class GenerateInvoiceRows
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extends PTransform<PCollection<BillingEvent>, PCollection<String>> {
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@Override
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public PCollection<String> expand(PCollection<BillingEvent> input) {
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return input
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.apply(
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"Map to invoicing key",
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MapElements.into(TypeDescriptor.of(InvoiceGroupingKey.class))
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.via(BillingEvent::getInvoiceGroupingKey))
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.setCoder(new InvoiceGroupingKeyCoder())
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.apply("Count occurrences", Count.perElement())
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.apply(
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"Format as CSVs",
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MapElements.into(TypeDescriptors.strings())
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.via((KV<InvoiceGroupingKey, Long> kv) -> kv.getKey().toCsv(kv.getValue())));
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}
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}
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/** Returns an IO transform that writes the overall invoice to a single CSV file. */
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private TextIO.Write writeInvoice() {
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return TextIO.write()
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.to(beamBucket + "/results/overall_invoice")
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.withHeader(InvoiceGroupingKey.invoiceHeader())
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.withoutSharding()
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.withSuffix(".csv");
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}
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/** Returns an IO transform that writes detail reports to registrar-tld keyed CSV files. */
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private TextIO.TypedWrite<BillingEvent, Params> writeDetailReports() {
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return TextIO.<BillingEvent>writeCustomType()
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.to(
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InvoicingUtils.makeDestinationFunction(beamBucket + "/results"),
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InvoicingUtils.makeEmptyDestinationParams(beamBucket + "/results"))
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.withFormatFunction(BillingEvent::toCsv)
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.withoutSharding()
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.withTempDirectory(FileBasedSink.convertToFileResourceIfPossible(beamBucket + "/temporary"))
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.withHeader(BillingEvent.getHeader())
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.withSuffix(".csv");
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}
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}
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