/*
* Copyright (C) 2018 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.templates;
import static com.google.cloud.teleport.util.ValueProviderUtils.eitherOrValueProvider;
import com.google.cloud.spanner.Options.RpcPriority;
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.templates.SpannerToText.SpannerToTextOptions;
import com.google.cloud.teleport.templates.common.SpannerConverters;
import com.google.cloud.teleport.templates.common.SpannerConverters.CreateTransactionFnWithTimestamp;
import com.google.cloud.teleport.templates.common.SpannerConverters.SpannerReadOptions;
import com.google.cloud.teleport.templates.common.TextConverters.FilesystemWriteOptions;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.io.FileSystems;
import org.apache.beam.sdk.io.TextIO;
import org.apache.beam.sdk.io.fs.ResourceId;
import org.apache.beam.sdk.io.gcp.spanner.LocalSpannerIO;
import org.apache.beam.sdk.io.gcp.spanner.ReadOperation;
import org.apache.beam.sdk.io.gcp.spanner.SpannerConfig;
import org.apache.beam.sdk.io.gcp.spanner.Transaction;
import org.apache.beam.sdk.options.PipelineOptions;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.ValueProvider;
import org.apache.beam.sdk.transforms.Create;
import org.apache.beam.sdk.transforms.MapElements;
import org.apache.beam.sdk.transforms.PTransform;
import org.apache.beam.sdk.transforms.ParDo;
import org.apache.beam.sdk.transforms.SerializableFunction;
import org.apache.beam.sdk.transforms.View;
import org.apache.beam.sdk.values.PBegin;
import org.apache.beam.sdk.values.PCollection;
import org.apache.beam.sdk.values.PCollectionView;
import org.apache.beam.sdk.values.TypeDescriptors;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* Dataflow template which copies a Spanner table to a Text sink. It exports a Spanner table using
* <a href="https://cloud.google.com/spanner/docs/reads#read_data_in_parallel">Batch API</a>, which
* creates multiple workers in parallel for better performance. The result is written to a CSV file
* in Google Cloud Storage. The table schema file is saved in json format along with the exported
* table.
*
* <p>Schema file sample: { "id":"INT64", "name":"STRING(MAX)" }
*
* <p>A sample run:
*
* <pre>
* mvn compile exec:java \
* -Dexec.mainClass=com.google.cloud.teleport.templates.SpannerToText \
* -Dexec.args="--runner=DataflowRunner \
* --spannerProjectId=projectId \
* --gcpTempLocation=gs://gsTmpLocation \
* --spannerInstanceId=instanceId \
* --spannerDatabaseId=databaseId \
* --spannerTable=table_name \
* --spannerSnapshotTime=snapshot_time \
* --textWritePrefix=gcsOutputPath"
* </pre>
*/
@Template(
name = "Spanner_to_GCS_Text",
category = TemplateCategory.BATCH,
displayName = "Cloud Spanner to Text Files on Cloud Storage",
description =
"A pipeline which reads in Cloud Spanner table and writes it to Cloud Storage as CSV text files.",
optionsClass = SpannerToTextOptions.class,
contactInformation = "https://cloud.google.com/support")
public class SpannerToText {
private static final Logger LOG = LoggerFactory.getLogger(SpannerToText.class);
/** Custom PipelineOptions. */
public interface SpannerToTextOptions
extends PipelineOptions, SpannerReadOptions, FilesystemWriteOptions {
@TemplateParameter.GcsWriteFolder(
order = 1,
optional = true,
description = "Cloud Storage temp directory for storing CSV files",
helpText = "The Cloud Storage path where the temporary CSV files can be stored.",
example = "gs://your-bucket/your-path")
ValueProvider<String> getCsvTempDirectory();
@SuppressWarnings("unused")
void setCsvTempDirectory(ValueProvider<String> value);
@TemplateParameter.Enum(
order = 2,
enumOptions = {"LOW", "MEDIUM", "HIGH"},
optional = true,
description = "Priority for Spanner RPC invocations",
helpText =
"The request priority for Cloud Spanner calls. The value must be one of: [HIGH,MEDIUM,LOW].")
ValueProvider<RpcPriority> getSpannerPriority();
void setSpannerPriority(ValueProvider<RpcPriority> value);
}
/**
* Runs a pipeline which reads in Records from Spanner, and writes the CSV to TextIO sink.
*
* @param args arguments to the pipeline
*/
public static void main(String[] args) {
LOG.info("Starting pipeline setup");
PipelineOptionsFactory.register(SpannerToTextOptions.class);
SpannerToTextOptions options =
PipelineOptionsFactory.fromArgs(args).withValidation().as(SpannerToTextOptions.class);
FileSystems.setDefaultPipelineOptions(options);
Pipeline pipeline = Pipeline.create(options);
SpannerConfig spannerConfig =
SpannerConfig.create()
.withHost(options.getSpannerHost())
.withProjectId(options.getSpannerProjectId())
.withInstanceId(options.getSpannerInstanceId())
.withDatabaseId(options.getSpannerDatabaseId())
.withRpcPriority(options.getSpannerPriority());
PTransform<PBegin, PCollection<ReadOperation>> spannerExport =
SpannerConverters.ExportTransformFactory.create(
options.getSpannerTable(),
spannerConfig,
options.getTextWritePrefix(),
options.getSpannerSnapshotTime());
/* CreateTransaction and CreateTransactionFn classes in LocalSpannerIO
* only take a timestamp object for exact staleness which works when
* parameters are provided during template compile time. They do not work with
* a Timestamp valueProvider which can take parameters at runtime. Hence a new
* ParDo class CreateTransactionFnWithTimestamp had to be created for this
* purpose.
*/
PCollectionView<Transaction> tx =
pipeline
.apply("Setup for Transaction", Create.of(1))
.apply(
"Create transaction",
ParDo.of(
new CreateTransactionFnWithTimestamp(
spannerConfig, options.getSpannerSnapshotTime())))
.apply("As PCollectionView", View.asSingleton());
PCollection<String> csv =
pipeline
.apply("Create export", spannerExport)
// We need to use LocalSpannerIO.readAll() instead of LocalSpannerIO.read()
// because ValueProvider parameters such as table name required for
// LocalSpannerIO.read() can be read only inside DoFn but LocalSpannerIO.read() is of
// type PTransform<PBegin, Struct>, which prevents prepending it with DoFn that reads
// these parameters at the pipeline execution time.
.apply(
"Read all records",
LocalSpannerIO.readAll().withTransaction(tx).withSpannerConfig(spannerConfig))
.apply(
"Struct To Csv",
MapElements.into(TypeDescriptors.strings())
.via(struct -> (new SpannerConverters.StructCsvPrinter()).print(struct)));
ValueProvider<ResourceId> tempDirectoryResource =
ValueProvider.NestedValueProvider.of(
eitherOrValueProvider(options.getCsvTempDirectory(), options.getTextWritePrefix()),
(SerializableFunction<String, ResourceId>) s -> FileSystems.matchNewResource(s, true));
csv.apply(
"Write to storage",
TextIO.write()
.to(options.getTextWritePrefix())
.withSuffix(".csv")
.withTempDirectory(tempDirectoryResource));
pipeline.run();
LOG.info("Completed pipeline setup");
}
}
