use of com.google.cloud.teleport.v2.cdc.mappers.DataStreamMapper in project DataflowTemplates by GoogleCloudPlatform.
the class DataStreamToBigQuery method run.
/**
* Runs the pipeline with the supplied options.
*
* @param options The execution parameters to the pipeline.
* @return The result of the pipeline execution.
*/
public static PipelineResult run(Options options) {
/*
* Stages:
* 1) Ingest and Normalize Data to FailsafeElement with JSON Strings
* 2) Write JSON Strings to TableRow Collection
* - Optionally apply a UDF
* 3) BigQuery Output of TableRow Data
* a) Map New Columns & Write to Staging Tables
* b) Map New Columns & Merge Staging to Target Table
* 4) Write Failures to GCS Dead Letter Queue
*/
Pipeline pipeline = Pipeline.create(options);
DeadLetterQueueManager dlqManager = buildDlqManager(options);
String bigqueryProjectId = getBigQueryProjectId(options);
String dlqDirectory = dlqManager.getRetryDlqDirectoryWithDateTime();
String tempDlqDir = dlqManager.getRetryDlqDirectory() + "tmp/";
InputUDFToTableRow<String> failsafeTableRowTransformer = new InputUDFToTableRow<String>(options.getJavascriptTextTransformGcsPath(), options.getJavascriptTextTransformFunctionName(), options.getPythonTextTransformGcsPath(), options.getPythonTextTransformFunctionName(), options.getRuntimeRetries(), FAILSAFE_ELEMENT_CODER);
/*
* Stage 1: Ingest and Normalize Data to FailsafeElement with JSON Strings
* a) Read DataStream data from GCS into JSON String FailsafeElements (datastreamJsonRecords)
* b) Reconsume Dead Letter Queue data from GCS into JSON String FailsafeElements
* (dlqJsonRecords)
* c) Flatten DataStream and DLQ Streams (jsonRecords)
*/
PCollection<FailsafeElement<String, String>> datastreamJsonRecords = pipeline.apply(new DataStreamIO(options.getStreamName(), options.getInputFilePattern(), options.getInputFileFormat(), options.getGcsPubSubSubscription(), options.getRfcStartDateTime()).withFileReadConcurrency(options.getFileReadConcurrency()));
// Elements sent to the Dead Letter Queue are to be reconsumed.
// A DLQManager is to be created using PipelineOptions, and it is in charge
// of building pieces of the DLQ.
PCollection<FailsafeElement<String, String>> dlqJsonRecords = pipeline.apply("DLQ Consumer/reader", dlqManager.dlqReconsumer(options.getDlqRetryMinutes())).apply("DLQ Consumer/cleaner", ParDo.of(new DoFn<String, FailsafeElement<String, String>>() {
@ProcessElement
public void process(@Element String input, OutputReceiver<FailsafeElement<String, String>> receiver) {
receiver.output(FailsafeElement.of(input, input));
}
})).setCoder(FAILSAFE_ELEMENT_CODER);
PCollection<FailsafeElement<String, String>> jsonRecords = PCollectionList.of(datastreamJsonRecords).and(dlqJsonRecords).apply("Merge Datastream & DLQ", Flatten.pCollections());
/*
* Stage 2: Write JSON Strings to TableRow PCollectionTuple
* a) Optionally apply a Javascript or Python UDF
* b) Convert JSON String FailsafeElements to TableRow's (tableRowRecords)
*/
PCollectionTuple tableRowRecords = jsonRecords.apply("UDF to TableRow/udf", failsafeTableRowTransformer);
PCollection<TableRow> shuffledTableRows = tableRowRecords.get(failsafeTableRowTransformer.transformOut).apply("UDF to TableRow/ReShuffle", Reshuffle.<TableRow>viaRandomKey().withNumBuckets(100));
/*
* Stage 3: BigQuery Output of TableRow Data
* a) Map New Columns & Write to Staging Tables (writeResult)
* b) Map New Columns & Merge Staging to Target Table (null)
*
* failsafe: writeResult.getFailedInsertsWithErr()
*/
// TODO(beam 2.23): InsertRetryPolicy should be CDC compliant
Set<String> fieldsToIgnore = getFieldsToIgnore(options.getIgnoreFields());
WriteResult writeResult = shuffledTableRows.apply("Map to Staging Tables", new DataStreamMapper(options.as(GcpOptions.class), options.getOutputProjectId(), options.getOutputStagingDatasetTemplate(), options.getOutputStagingTableNameTemplate()).withDataStreamRootUrl(options.getDataStreamRootUrl()).withDefaultSchema(BigQueryDefaultSchemas.DATASTREAM_METADATA_SCHEMA).withDayPartitioning(true).withIgnoreFields(fieldsToIgnore)).apply("Write Successful Records", BigQueryIO.<KV<TableId, TableRow>>write().to(new BigQueryDynamicConverters().bigQueryDynamicDestination()).withFormatFunction(element -> removeTableRowFields(element.getValue(), fieldsToIgnore)).withFormatRecordOnFailureFunction(element -> element.getValue()).withoutValidation().ignoreInsertIds().withCreateDisposition(CreateDisposition.CREATE_NEVER).withWriteDisposition(WriteDisposition.WRITE_APPEND).withExtendedErrorInfo().withMethod(BigQueryIO.Write.Method.STREAMING_INSERTS).withFailedInsertRetryPolicy(InsertRetryPolicy.retryTransientErrors()));
if (options.getApplyMerge()) {
shuffledTableRows.apply("Map To Replica Tables", new DataStreamMapper(options.as(GcpOptions.class), options.getOutputProjectId(), options.getOutputDatasetTemplate(), options.getOutputTableNameTemplate()).withDataStreamRootUrl(options.getDataStreamRootUrl()).withDefaultSchema(BigQueryDefaultSchemas.DATASTREAM_METADATA_SCHEMA).withIgnoreFields(fieldsToIgnore)).apply("BigQuery Merge/Build MergeInfo", new MergeInfoMapper(bigqueryProjectId, options.getOutputStagingDatasetTemplate(), options.getOutputStagingTableNameTemplate(), options.getOutputDatasetTemplate(), options.getOutputTableNameTemplate())).apply("BigQuery Merge/Merge into Replica Tables", BigQueryMerger.of(MergeConfiguration.bigQueryConfiguration().withMergeWindowDuration(Duration.standardMinutes(options.getMergeFrequencyMinutes()))));
}
/*
* Stage 4: Write Failures to GCS Dead Letter Queue
*/
PCollection<String> udfDlqJson = PCollectionList.of(tableRowRecords.get(failsafeTableRowTransformer.udfDeadletterOut)).and(tableRowRecords.get(failsafeTableRowTransformer.transformDeadletterOut)).apply("UDF Failures/Flatten", Flatten.pCollections()).apply("UDF Failures/Sanitize", MapElements.via(new StringDeadLetterQueueSanitizer()));
PCollection<String> bqWriteDlqJson = writeResult.getFailedInsertsWithErr().apply("BigQuery Failures", MapElements.via(new BigQueryDeadLetterQueueSanitizer()));
PCollectionList.of(udfDlqJson).and(bqWriteDlqJson).apply("Write To DLQ/Flatten", Flatten.pCollections()).apply("Write To DLQ/Writer", DLQWriteTransform.WriteDLQ.newBuilder().withDlqDirectory(dlqDirectory).withTmpDirectory(tempDlqDir).build());
// Execute the pipeline and return the result.
return pipeline.run();
}
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