/*
* Copyright (C) 2020 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License"); you may not
* use this file except in compliance with the License. You may obtain a copy of
* the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations under
* the License.
*/
package com.google.cloud.teleport.v2.templates;
import static com.google.cloud.teleport.v2.transforms.StatefulRowCleaner.RowCleanerDeadLetterQueueSanitizer;
import com.google.api.services.bigquery.model.TableRow;
import com.google.cloud.bigquery.TableId;
import com.google.cloud.teleport.metadata.Template;
import com.google.cloud.teleport.metadata.TemplateCategory;
import com.google.cloud.teleport.metadata.TemplateParameter;
import com.google.cloud.teleport.metadata.TemplateParameter.TemplateEnumOption;
import com.google.cloud.teleport.v2.cdc.dlq.DeadLetterQueueManager;
import com.google.cloud.teleport.v2.cdc.dlq.StringDeadLetterQueueSanitizer;
import com.google.cloud.teleport.v2.cdc.mappers.BigQueryDefaultSchemas;
import com.google.cloud.teleport.v2.cdc.mappers.DataStreamMapper;
import com.google.cloud.teleport.v2.cdc.mappers.MergeInfoMapper;
import com.google.cloud.teleport.v2.cdc.merge.BigQueryMerger;
import com.google.cloud.teleport.v2.cdc.merge.MergeConfiguration;
import com.google.cloud.teleport.v2.cdc.sources.DataStreamIO;
import com.google.cloud.teleport.v2.coders.FailsafeElementCoder;
import com.google.cloud.teleport.v2.common.UncaughtExceptionLogger;
import com.google.cloud.teleport.v2.options.BigQueryStorageApiStreamingOptions;
import com.google.cloud.teleport.v2.templates.DataStreamToBigQuery.Options;
import com.google.cloud.teleport.v2.transforms.DLQWriteTransform;
import com.google.cloud.teleport.v2.transforms.StatefulRowCleaner;
import com.google.cloud.teleport.v2.transforms.UDFTextTransformer.InputUDFOptions;
import com.google.cloud.teleport.v2.transforms.UDFTextTransformer.InputUDFToTableRow;
import com.google.cloud.teleport.v2.utils.BigQueryIOUtils;
import com.google.cloud.teleport.v2.values.FailsafeElement;
import com.google.common.base.Splitter;
import java.util.HashSet;
import java.util.Set;
import java.util.regex.Pattern;
import org.apache.beam.runners.dataflow.options.DataflowPipelineOptions;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.PipelineResult;
import org.apache.beam.sdk.coders.StringUtf8Coder;
import org.apache.beam.sdk.extensions.gcp.options.GcpOptions;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.Write.CreateDisposition;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.Write.WriteDisposition;
import org.apache.beam.sdk.io.gcp.bigquery.InsertRetryPolicy;
import org.apache.beam.sdk.io.gcp.bigquery.WriteResult;
import org.apache.beam.sdk.options.Default;
import org.apache.beam.sdk.options.PipelineOptions;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.StreamingOptions;
import org.apache.beam.sdk.transforms.DoFn;
import org.apache.beam.sdk.transforms.Flatten;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.transforms.Reshuffle;
import org.apache.beam.sdk.values.KV;
import org.apache.beam.sdk.values.PCollection;
import org.apache.beam.sdk.values.PCollectionList;
import org.apache.beam.sdk.values.PCollectionTuple;
import org.apache.beam.sdk.values.TupleTag;
import org.joda.time.Duration;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* This pipeline ingests DataStream data from GCS. The data is then cleaned and validated against a
* BigQuery Table. If new columns or tables appear, they are automatically added to BigQuery. The
* data is then inserted into BigQuery staging tables and Merged into a final replica table.
*
* <p>NOTE: Future versions are planned to support: Pub/Sub, GCS, or Kafka as per DataStream
*
* <p>Check out <a
* href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v2/datastream-to-bigquery/README_Cloud_Datastream_to_BigQuery.md">README</a>
* for instructions on how to use or modify this template.
*/
@Template(
name = "Cloud_Datastream_to_BigQuery",
category = TemplateCategory.STREAMING,
displayName = "Datastream to BigQuery",
description = {
"The Datastream to BigQuery template is a streaming pipeline that reads <a href=\"https://cloud.google.com/datastream/docs\">Datastream</a> data and replicates it into BigQuery. "
+ "The template reads data from Cloud Storage using Pub/Sub notifications and replicates it into a time partitioned BigQuery staging table. "
+ "Following replication, the template executes a MERGE in BigQuery to upsert all change data capture (CDC) changes into a replica of the source table.\n",
"The template handles creating and updating the BigQuery tables managed by the replication. "
+ "When data definition language (DDL) is required, a callback to Datastream extracts the source table schema and translates it into BigQuery data types. Supported operations include the following:\n"
+ "- New tables are created as data is inserted.\n"
+ "- New columns are added to BigQuery tables with null initial values.\n"
+ "- Dropped columns are ignored in BigQuery and future values are null.\n"
+ "- Renamed columns are added to BigQuery as new columns.\n"
+ "- Type changes are not propagated to BigQuery."
},
optionsClass = Options.class,
flexContainerName = "datastream-to-bigquery",
documentation =
"https://cloud.google.com/dataflow/docs/guides/templates/provided/datastream-to-bigquery",
contactInformation = "https://cloud.google.com/support",
requirements = {
"A Datastream stream that is ready to or already replicating data.",
"<a href=\"https://cloud.google.com/storage/docs/reporting-changes\">Cloud Storage Pub/Sub notifications</a> are enabled for the Datastream data.",
"BigQuery destination datasets are created and the Compute Engine Service Account has been granted admin access to them.",
"A primary key is necessary in the source table for the destination replica table to be created.",
"A MySQL or Oracle source database. PostgreSQL databases are not supported."
})
public class DataStreamToBigQuery {
private static final Logger LOG = LoggerFactory.getLogger(DataStreamToBigQuery.class);
private static final String AVRO_SUFFIX = "avro";
private static final String JSON_SUFFIX = "json";
/** The tag for the main output of the json transformation. */
public static final TupleTag<TableRow> TRANSFORM_OUT = new TupleTag<TableRow>() {};
/** String/String Coder for FailsafeElement. */
public static final FailsafeElementCoder<String, String> FAILSAFE_ELEMENT_CODER =
FailsafeElementCoder.of(StringUtf8Coder.of(), StringUtf8Coder.of());
/** The tag for the dead-letter output of the json to table row transform. */
public static final TupleTag<FailsafeElement<String, String>> TRANSFORM_DEADLETTER_OUT =
new TupleTag<FailsafeElement<String, String>>() {};
/**
* Options supported by the pipeline.
*
* <p>Inherits standard configuration options.
*/
public interface Options
extends PipelineOptions,
StreamingOptions,
InputUDFOptions,
BigQueryStorageApiStreamingOptions {
@TemplateParameter.Text(
order = 1,
description = "File location for Datastream file output in Cloud Storage.",
helpText =
"This is the file location for Datastream file output in Cloud Storage, in the "
+ "format: gs://${BUCKET}/${ROOT_PATH}/.")
String getInputFilePattern();
void setInputFilePattern(String value);
@TemplateParameter.Enum(
order = 2,
enumOptions = {@TemplateEnumOption("avro"), @TemplateEnumOption("json")},
description = "Datastream output file format (avro/json).",
helpText =
"The format of the output files produced by Datastream. Value can be 'avro' or 'json'.")
@Default.String("avro")
String getInputFileFormat();
void setInputFileFormat(String value);
@TemplateParameter.PubsubSubscription(
order = 3,
description = "The Pub/Sub subscription on the Cloud Storage bucket.",
helpText =
"The Pub/Sub subscription used by Cloud Storage to notify Dataflow of new files"
+ " available for processing, in the format: "
+ "projects/{PROJECT_NAME}/subscriptions/{SUBSCRIPTION_NAME}")
String getGcsPubSubSubscription();
void setGcsPubSubSubscription(String value);
@TemplateParameter.Text(
order = 4,
optional = true,
description = "Name or template for the stream to poll for schema information.",
helpText =
"This is the name or template for the stream to poll for schema information. Default is"
+ " {_metadata_stream}. The default value is enough under most conditions.")
String getStreamName();
void setStreamName(String value);
@TemplateParameter.DateTime(
order = 5,
optional = true,
description =
"The starting DateTime used to fetch from Cloud Storage "
+ "(https://tools.ietf.org/html/rfc3339).",
helpText =
"The starting DateTime used to fetch from Cloud Storage "
+ "(https://tools.ietf.org/html/rfc3339).")
@Default.String("1970-01-01T00:00:00.00Z")
String getRfcStartDateTime();
void setRfcStartDateTime(String value);
@TemplateParameter.Integer(
order = 6,
optional = true,
description = "File read concurrency",
helpText = "The number of concurrent DataStream files to read. Default is 10.")
@Default.Integer(10)
Integer getFileReadConcurrency();
void setFileReadConcurrency(Integer value);
@TemplateParameter.ProjectId(
order = 7,
optional = true,
description = "Project Id for BigQuery datasets.",
helpText =
"Project for BigQuery datasets to output data into. The default for this parameter "
+ "is the project where the Dataflow pipeline is running.")
String getOutputProjectId();
void setOutputProjectId(String projectId);
@TemplateParameter.Text(
order = 8,
description = "Name or template for the dataset to contain staging tables.",
helpText =
"This is the name for the dataset to contain staging tables. This parameter supports "
+ "templates (e.g. {_metadata_dataset}_log or my_dataset_log). Normally, this "
+ "parameter is a dataset name.")
@Default.String("{_metadata_dataset}")
String getOutputStagingDatasetTemplate();
void setOutputStagingDatasetTemplate(String value);
@TemplateParameter.Text(
order = 9,
optional = true,
description = "Template for the name of staging tables.",
helpText =
"This is the template for the name of staging tables (e.g. {_metadata_table}). "
+ "Default is {_metadata_table}_log.")
@Default.String("{_metadata_table}_log")
String getOutputStagingTableNameTemplate();
void setOutputStagingTableNameTemplate(String value);
@TemplateParameter.Text(
order = 10,
description = "Template for the dataset to contain replica tables.",
helpText =
"This is the name for the dataset to contain replica tables. This parameter supports"
+ " templates (e.g. {_metadata_dataset} or my_dataset). Normally, this parameter is"
+ " a dataset name.")
@Default.String("{_metadata_dataset}")
String getOutputDatasetTemplate();
void setOutputDatasetTemplate(String value);
@TemplateParameter.Text(
order = 11,
optional = true,
description = "Template for the name of replica tables.",
helpText =
"This is the template for the name of replica tables (e.g. {_metadata_table}). "
+ "Default is {_metadata_table}.")
@Default.String("{_metadata_table}")
String getOutputTableNameTemplate();
void setOutputTableNameTemplate(String value);
@TemplateParameter.Text(
order = 12,
optional = true,
description = "Fields to be ignored",
helpText = "Fields to ignore in BigQuery (comma separator).",
example = "_metadata_stream,_metadata_schema")
@Default.String(
"_metadata_stream,_metadata_schema,_metadata_table,_metadata_source,"
+ "_metadata_tx_id,_metadata_dlq_reconsumed,_metadata_primary_keys,"
+ "_metadata_error,_metadata_retry_count")
String getIgnoreFields();
void setIgnoreFields(String value);
@TemplateParameter.Text(
order = 13,
optional = true,
description = "The number of minutes between merges for a given table",
helpText = "The number of minutes between merges for a given table.")
@Default.Integer(5)
Integer getMergeFrequencyMinutes();
void setMergeFrequencyMinutes(Integer value);
@TemplateParameter.Text(
order = 14,
description = "Dead letter queue directory.",
helpText =
"This is the file path for Dataflow to write the dead letter queue output. This "
+ "path should not be in the same path as the Datastream file output.")
@Default.String("")
String getDeadLetterQueueDirectory();
void setDeadLetterQueueDirectory(String value);
@TemplateParameter.Text(
order = 15,
optional = true,
description = "The number of minutes between DLQ Retries.",
helpText = "The number of minutes between DLQ Retries.")
@Default.Integer(10)
Integer getDlqRetryMinutes();
void setDlqRetryMinutes(Integer value);
@TemplateParameter.Text(
order = 16,
optional = true,
description = "Datastream API Root URL (only required for testing)",
helpText = "Datastream API Root URL")
@Default.String("https://datastream.googleapis.com/")
String getDataStreamRootUrl();
void setDataStreamRootUrl(String value);
@TemplateParameter.Boolean(
order = 17,
optional = true,
description = "A switch to disable MERGE queries for the job.",
helpText = "A switch to disable MERGE queries for the job.")
@Default.Boolean(true)
Boolean getApplyMerge();
void setApplyMerge(Boolean value);
@TemplateParameter.Integer(
order = 18,
optional = true,
description = "Concurrent queries for merge.",
helpText =
"The number of concurrent BigQuery MERGE queries. Only effective when applyMerge is set"
+ " to true. Default is 30.")
@Default.Integer(MergeConfiguration.DEFAULT_MERGE_CONCURRENCY)
Integer getMergeConcurrency();
void setMergeConcurrency(Integer value);
@TemplateParameter.Integer(
order = 19,
optional = true,
description = "Partition retention days.",
helpText =
"The number of days to use for partition retention when running BigQuery merges."
+ " Default is 1.")
@Default.Integer(MergeConfiguration.DEFAULT_PARTITION_RETENTION_DAYS)
Integer getPartitionRetentionDays();
void setPartitionRetentionDays(Integer value);
}
/**
* Main entry point for executing the pipeline.
*
* @param args The command-line arguments to the pipeline.
*/
public static void main(String[] args) {
UncaughtExceptionLogger.register();
LOG.info("Starting Input Files to BigQuery");
Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
options.setStreaming(true);
options.setEnableStreamingEngine(true);
validateOptions(options);
run(options);
}
private static void validateOptions(Options options) {
String outputDataset = options.getOutputDatasetTemplate();
String outputStagingDs = options.getOutputStagingDatasetTemplate();
String outputTable = options.getOutputTableNameTemplate();
String outputStagingTb = options.getOutputStagingTableNameTemplate();
if (outputDataset.equals(outputStagingDs) && outputTable.equals(outputStagingTb)) {
throw new IllegalArgumentException(
"Can not have equal templates for output tables and staging tables.");
}
String inputFileFormat = options.getInputFileFormat();
if (!(inputFileFormat.equals(AVRO_SUFFIX) || inputFileFormat.equals(JSON_SUFFIX))) {
throw new IllegalArgumentException(
"Input file format must be one of: avro, json or left empty - found " + inputFileFormat);
}
BigQueryIOUtils.validateBQStorageApiOptionsStreaming(options);
}
/**
* 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.getJavascriptTextTransformReloadIntervalMinutes(),
options.getPythonTextTransformGcsPath(),
options.getPythonTextTransformFunctionName(),
options.getRuntimeRetries(),
FAILSAFE_ELEMENT_CODER);
StatefulRowCleaner statefulCleaner = StatefulRowCleaner.of();
/*
* 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);
PCollectionTuple cleanedRows =
tableRowRecords
.get(failsafeTableRowTransformer.transformOut)
.apply("UDF to TableRow/Oracle Cleaner", statefulCleaner);
PCollection<TableRow> shuffledTableRows =
cleanedRows
.get(statefulCleaner.successTag)
.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() // takes effect only when Storage Write API is off
.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()
.withProjectId(bigqueryProjectId)
.withMergeWindowDuration(
Duration.standardMinutes(options.getMergeFrequencyMinutes()))
.withMergeConcurrency(options.getMergeConcurrency())
.withPartitionRetention(options.getPartitionRetentionDays())));
}
/*
* Stage 4: Write Failures to GCS Dead Letter Queue
*/
PCollection<String> udfDlqJson =
PCollectionList.of(tableRowRecords.get(failsafeTableRowTransformer.udfDeadletterOut))
.and(tableRowRecords.get(failsafeTableRowTransformer.transformDeadletterOut))
.apply("Transform Failures/Flatten", Flatten.pCollections())
.apply(
"Transform Failures/Sanitize",
MapElements.via(new StringDeadLetterQueueSanitizer()));
PCollection<String> rowCleanerJson =
cleanedRows
.get(statefulCleaner.failureTag)
.apply(
"Transform Failures/Oracle Cleaner Failures",
MapElements.via(new RowCleanerDeadLetterQueueSanitizer()));
PCollection<String> bqWriteDlqJson =
BigQueryIOUtils.writeResultToBigQueryInsertErrors(writeResult, options)
.apply("BigQuery Failures", MapElements.via(new BigQueryDeadLetterQueueSanitizer()));
PCollectionList.of(udfDlqJson)
.and(rowCleanerJson)
.and(bqWriteDlqJson)
.apply("Write To DLQ/Flatten", Flatten.pCollections())
.apply(
"Write To DLQ/Writer",
DLQWriteTransform.WriteDLQ.newBuilder()
.withDlqDirectory(dlqDirectory)
.withTmpDirectory(tempDlqDir)
.setIncludePaneInfo(true)
.build());
// Execute the pipeline and return the result.
return pipeline.run();
}
private static Set<String> getFieldsToIgnore(String fields) {
return new HashSet<>(Splitter.on(Pattern.compile("\\s*,\\s*")).splitToList(fields));
}
private static TableRow removeTableRowFields(TableRow tableRow, Set<String> ignoreFields) {
LOG.debug("BigQuery Writes: {}", tableRow);
TableRow cleanTableRow = tableRow.clone();
Set<String> rowKeys = tableRow.keySet();
for (String rowKey : rowKeys) {
if (ignoreFields.contains(rowKey)) {
cleanTableRow.remove(rowKey);
}
}
return cleanTableRow;
}
private static String getBigQueryProjectId(Options options) {
return options.getOutputProjectId() == null
? options.as(GcpOptions.class).getProject()
: options.getOutputProjectId();
}
private static DeadLetterQueueManager buildDlqManager(Options options) {
String tempLocation =
options.as(DataflowPipelineOptions.class).getTempLocation().endsWith("/")
? options.as(DataflowPipelineOptions.class).getTempLocation()
: options.as(DataflowPipelineOptions.class).getTempLocation() + "/";
String dlqDirectory =
options.getDeadLetterQueueDirectory().isEmpty()
? tempLocation + "dlq/"
: options.getDeadLetterQueueDirectory();
LOG.info("Dead-letter queue directory: {}", dlqDirectory);
return DeadLetterQueueManager.create(dlqDirectory);
}
}
