/*
* Copyright (C) 2019 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 com.google.api.services.bigquery.model.TableFieldSchema;
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.BigQueryToTFRecord.Options;
import com.google.cloud.teleport.templates.common.BigQueryConverters.BigQueryReadOptions;
import com.google.protobuf.ByteString;
import java.util.Iterator;
import java.util.Random;
import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericRecord;
import org.apache.avro.util.Utf8;
import org.apache.beam.sdk.Pipeline;
import org.apache.beam.sdk.PipelineResult;
import org.apache.beam.sdk.coders.ByteArrayCoder;
import org.apache.beam.sdk.io.FileIO;
import org.apache.beam.sdk.io.TFRecordIO;
import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
import org.apache.beam.sdk.io.gcp.bigquery.SchemaAndRecord;
import org.apache.beam.sdk.options.Default;
import org.apache.beam.sdk.options.PipelineOptionsFactory;
import org.apache.beam.sdk.options.ValueProvider;
import org.apache.beam.sdk.transforms.Partition;
import org.apache.beam.sdk.transforms.Reshuffle;
import org.apache.beam.sdk.values.PCollection;
import org.apache.beam.sdk.values.PCollectionList;
import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.annotations.VisibleForTesting;
import org.tensorflow.example.Example;
import org.tensorflow.example.Feature;
import org.tensorflow.example.Features;
/**
* Dataflow template which reads BigQuery data and writes it to GCS as a set of TFRecords. The
* source is a SQL query.
*
* <p>Check out <a
* href="https://github.com/GoogleCloudPlatform/DataflowTemplates/blob/main/v1/README_Cloud_BigQuery_to_GCS_TensorFlow_Records.md">README</a>
* for instructions on how to use or modify this template.
*/
@Template(
name = "Cloud_BigQuery_to_GCS_TensorFlow_Records",
category = TemplateCategory.BATCH,
displayName = "BigQuery to TensorFlow Records",
description =
"The BigQuery to Cloud Storage TFRecords template is a pipeline that reads data from a BigQuery query and writes it to a Cloud Storage bucket in TFRecord format. "
+ "You can specify the training, testing, and validation percentage splits. "
+ "By default, the split is 1 or 100% for the training set and 0 or 0% for testing and validation sets. "
+ "When setting the dataset split, the sum of training, testing, and validation needs to add up to 1 or 100% (for example, 0.6+0.2+0.2). "
+ "Dataflow automatically determines the optimal number of shards for each output dataset.",
optionsClass = Options.class,
optionsOrder = {BigQueryReadOptions.class, Options.class},
documentation =
"https://cloud.google.com/dataflow/docs/guides/templates/provided/bigquery-to-tfrecords",
contactInformation = "https://cloud.google.com/support",
requirements = {
"The BigQuery dataset and table must exist.",
"The output Cloud Storage bucket must exist before pipeline execution. Training, testing, and validation subdirectories don't need to preexist and are autogenerated."
})
public class BigQueryToTFRecord {
/**
* The {@link BigQueryToTFRecord#buildFeatureFromIterator(Class, Object, Feature.Builder)} method
* handles {@link GenericData.Array} that are passed into the {@link
* BigQueryToTFRecord#buildFeature} method creating a TensorFlow feature from the record.
*/
private static final String TRAIN = "train/";
private static final String TEST = "test/";
private static final String VAL = "val/";
private static void buildFeatureFromIterator(
Class<?> fieldType, Object field, Feature.Builder feature) {
ByteString byteString;
GenericData.Array f = (GenericData.Array) field;
if (fieldType == Long.class) {
Iterator<Long> longIterator = f.iterator();
while (longIterator.hasNext()) {
Long longValue = longIterator.next();
feature.getInt64ListBuilder().addValue(longValue);
}
} else if (fieldType == double.class) {
Iterator<Double> doubleIterator = f.iterator();
while (doubleIterator.hasNext()) {
double doubleValue = doubleIterator.next();
feature.getFloatListBuilder().addValue((float) doubleValue);
}
} else if (fieldType == String.class) {
Iterator<Utf8> stringIterator = f.iterator();
while (stringIterator.hasNext()) {
String stringValue = stringIterator.next().toString();
byteString = ByteString.copyFromUtf8(stringValue);
feature.getBytesListBuilder().addValue(byteString);
}
} else if (fieldType == boolean.class) {
Iterator<Boolean> booleanIterator = f.iterator();
while (booleanIterator.hasNext()) {
Boolean boolValue = booleanIterator.next();
int boolAsInt = boolValue ? 1 : 0;
feature.getInt64ListBuilder().addValue(boolAsInt);
}
}
}
/**
* The {@link BigQueryToTFRecord#buildFeature} method takes in an individual field and type
* corresponding to a column value from a SchemaAndRecord Object returned from a BigQueryIO.read()
* step. The method builds a TensorFlow Feature based on the type of the object- ie: STRING, TIME,
* INTEGER etc..
*/
private static Feature buildFeature(Object field, String type) {
Feature.Builder feature = Feature.newBuilder();
ByteString byteString;
switch (type) {
case "STRING":
case "TIME":
case "DATE":
if (field instanceof GenericData.Array) {
buildFeatureFromIterator(String.class, field, feature);
} else {
byteString = ByteString.copyFromUtf8(field.toString());
feature.getBytesListBuilder().addValue(byteString);
}
break;
case "BYTES":
byteString = ByteString.copyFrom((byte[]) field);
feature.getBytesListBuilder().addValue(byteString);
break;
case "INTEGER":
case "INT64":
case "TIMESTAMP":
if (field instanceof GenericData.Array) {
buildFeatureFromIterator(Long.class, field, feature);
} else {
feature.getInt64ListBuilder().addValue((long) field);
}
break;
case "FLOAT":
case "FLOAT64":
if (field instanceof GenericData.Array) {
buildFeatureFromIterator(double.class, field, feature);
} else {
feature.getFloatListBuilder().addValue((float) (double) field);
}
break;
case "BOOLEAN":
case "BOOL":
if (field instanceof GenericData.Array) {
buildFeatureFromIterator(boolean.class, field, feature);
} else {
int boolAsInt = (boolean) field ? 1 : 0;
feature.getInt64ListBuilder().addValue(boolAsInt);
}
break;
default:
throw new RuntimeException("Unsupported type: " + type);
}
return feature.build();
}
/**
* The {@link BigQueryToTFRecord#record2Example(SchemaAndRecord)} method uses takes in a
* SchemaAndRecord Object returned from a BigQueryIO.read() step and builds a TensorFlow Example
* from the record.
*/
@VisibleForTesting
protected static byte[] record2Example(SchemaAndRecord schemaAndRecord) {
Example.Builder example = Example.newBuilder();
Features.Builder features = example.getFeaturesBuilder();
GenericRecord record = schemaAndRecord.getRecord();
for (TableFieldSchema field : schemaAndRecord.getTableSchema().getFields()) {
Object fieldValue = record.get(field.getName());
if (fieldValue != null) {
Feature feature = buildFeature(fieldValue, field.getType());
features.putFeature(field.getName(), feature);
}
}
return example.build().toByteArray();
}
/**
* The {@link BigQueryToTFRecord#concatURI} method uses takes in a Cloud Storage URI and a
* subdirectory name and safely concatenates them. The resulting String is used as a sink for
* TFRecords.
*/
private static String concatURI(String dir, String folder) {
if (dir.endsWith("/")) {
return dir + folder;
} else {
return dir + "/" + folder;
}
}
/**
* The {@link BigQueryToTFRecord#applyTrainTestValSplit} method transforms the PCollection by
* randomly partitioning it into PCollections for each dataset.
*/
static PCollectionList<byte[]> applyTrainTestValSplit(
PCollection<byte[]> input,
ValueProvider<Float> trainingPercentage,
ValueProvider<Float> testingPercentage,
ValueProvider<Float> validationPercentage,
Random rand) {
return input.apply(
Partition.of(
3,
(Partition.PartitionFn<byte[]>)
(number, numPartitions) -> {
Float train = trainingPercentage.get();
Float test = testingPercentage.get();
Float validation = validationPercentage.get();
Double d = rand.nextDouble();
if (train + test + validation != 1) {
throw new RuntimeException(
String.format(
"Train %.2f, Test %.2f, Validation"
+ " %.2f percentages must add up to 100 percent",
train, test, validation));
}
if (d < train) {
return 0;
} else if (d >= train && d < train + test) {
return 1;
} else {
return 2;
}
}));
}
/** Run the pipeline. */
public static void main(String[] args) {
Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
run(options);
}
/**
* Runs the pipeline to completion with the specified options. This method does not wait until the
* pipeline is finished before returning. Invoke {@code result.waitUntilFinish()} on the result
* object to block until the pipeline is finished running if blocking programmatic execution is
* required.
*
* @param options The execution options.
* @return The pipeline result.
*/
public static PipelineResult run(Options options) {
Random rand = new Random(100); // set random seed
Pipeline pipeline = Pipeline.create(options);
PCollection<byte[]> bigQueryToExamples =
pipeline
.apply(
"RecordToExample",
BigQueryIO.read(BigQueryToTFRecord::record2Example)
.fromQuery(options.getReadQuery())
.withCoder(ByteArrayCoder.of())
.withTemplateCompatibility()
.withoutValidation()
.usingStandardSql()
.withMethod(BigQueryIO.TypedRead.Method.DIRECT_READ)
// Enable BigQuery Storage API
)
.apply("ReshuffleResults", Reshuffle.viaRandomKey());
PCollectionList<byte[]> partitionedExamples =
applyTrainTestValSplit(
bigQueryToExamples,
options.getTrainingPercentage(),
options.getTestingPercentage(),
options.getValidationPercentage(),
rand);
partitionedExamples
.get(0)
.apply(
"WriteTFTrainingRecord",
FileIO.<byte[]>write()
.via(TFRecordIO.sink())
.to(
ValueProvider.NestedValueProvider.of(
options.getOutputDirectory(), dir -> concatURI(dir, TRAIN)))
.withNumShards(0)
.withSuffix(options.getOutputSuffix()));
partitionedExamples
.get(1)
.apply(
"WriteTFTestingRecord",
FileIO.<byte[]>write()
.via(TFRecordIO.sink())
.to(
ValueProvider.NestedValueProvider.of(
options.getOutputDirectory(), dir -> concatURI(dir, TEST)))
.withNumShards(0)
.withSuffix(options.getOutputSuffix()));
partitionedExamples
.get(2)
.apply(
"WriteTFValidationRecord",
FileIO.<byte[]>write()
.via(TFRecordIO.sink())
.to(
ValueProvider.NestedValueProvider.of(
options.getOutputDirectory(), dir -> concatURI(dir, VAL)))
.withNumShards(0)
.withSuffix(options.getOutputSuffix()));
return pipeline.run();
}
/** Define command line arguments. */
public interface Options extends BigQueryReadOptions {
@TemplateParameter.GcsWriteFolder(
order = 1,
description = "Output Cloud Storage directory.",
helpText =
"The top-level Cloud Storage path prefix to use when writing the training, testing, and validation TFRecord files. Subdirectories for resulting training, testing, and validation TFRecord files are automatically generated from `outputDirectory`. For example, `gs://mybucket/output/train`",
example = "gs://mybucket/output")
ValueProvider<String> getOutputDirectory();
void setOutputDirectory(ValueProvider<String> outputDirectory);
@TemplateParameter.Text(
order = 2,
optional = true,
regexes = {"^[A-Za-z_0-9.]*"},
description = "The output suffix for TFRecord files",
helpText =
"The file suffix for the training, testing, and validation TFRecord files that are written. The default value is `.tfrecord`.")
@Default.String(".tfrecord")
ValueProvider<String> getOutputSuffix();
void setOutputSuffix(ValueProvider<String> outputSuffix);
@TemplateParameter.Float(
order = 3,
optional = true,
description = "Percentage of data to be in the training set ",
helpText =
"The percentage of query data allocated to training TFRecord files. The default value is 1, or 100%.")
@Default.Float(1)
ValueProvider<Float> getTrainingPercentage();
void setTrainingPercentage(ValueProvider<Float> trainingPercentage);
@TemplateParameter.Float(
order = 4,
optional = true,
description = "Percentage of data to be in the testing set ",
helpText =
"The percentage of query data allocated to testing TFRecord files. The default value is 0, or 0%.")
@Default.Float(0)
ValueProvider<Float> getTestingPercentage();
void setTestingPercentage(ValueProvider<Float> testingPercentage);
@TemplateParameter.Float(
order = 5,
optional = true,
description = "Percentage of data to be in the validation set ",
helpText =
"The percentage of query data allocated to validation TFRecord files. The default value is 0, or 0%.")
@Default.Float(0)
ValueProvider<Float> getValidationPercentage();
void setValidationPercentage(ValueProvider<Float> validationPercentage);
}
}
