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David Cavazos
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Fixed validation regular expressions (GoogleCloudPlatform#3332)
* Fixed validation regular expressions Updated the Pub/Sub regular expression to what Pub/Sub actually validates to. * Linked to resources for validation * Fixed lint warnings
1 parent 5ee8237 commit e72e7cb

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-64
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‎dataflow/flex-templates/streaming_beam_sql/Dockerfile‎

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FROM gcr.io/dataflow-templates-base/java11-template-launcher-base:latest
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# Define the Java command options required by Dataflow Flex Templates.
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ENV FLEX_TEMPLATE_JAVA_MAIN_CLASS="org.apache.beam.samples.StreamingBeamSQL"
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ENV FLEX_TEMPLATE_JAVA_MAIN_CLASS="org.apache.beam.samples.StreamingBeamSql"
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ENV FLEX_TEMPLATE_JAVA_CLASSPATH="/template/pipeline.jar"
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# Make sure to package as an uber-jar including all dependencies.

‎dataflow/flex-templates/streaming_beam_sql/README.md‎

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@@ -101,7 +101,7 @@ to transform the message data, and writes the results to a
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[BigQuery](https://cloud.google.com/bigquery) table.
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* [Dockerfile](Dockerfile)
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* [StreamingBeamSQL.java](src/main/java/org/apache/beam/samples/StreamingBeamSQL.java)
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* [StreamingBeamSql.java](src/main/java/org/apache/beam/samples/StreamingBeamSql.java)
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* [pom.xml](pom.xml)
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* [metadata.json](metadata.json)
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@@ -114,7 +114,7 @@ to transform the message data, and writes the results to a
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>
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> ```sh
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> mvn compile exec:java \
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> -Dexec.mainClass=org.apache.beam.samples.StreamingBeamSQL \
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> -Dexec.mainClass=org.apache.beam.samples.StreamingBeamSql \
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> -Dexec.args="\
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> --project=$PROJECT \
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> --inputSubscription=$SUBSCRIPTION \
@@ -189,6 +189,12 @@ necessary information to run the job, such as the SDK information and metadata.
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The [`metadata.json`](metadata.json) file contains additional information for
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the template such as the "name", "description", and input "parameters" field.
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We used
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[regular expressions](https://docs.microsoft.com/en-us/dotnet/standard/base-types/regular-expression-language-quick-reference)
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for validation on the input
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[Pub/Sub subscription](https://cloud.google.com/pubsub/docs/admin#resource_names)
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and [BigQuery table](https://cloud.google.com/bigquery/docs/tables#table_naming).
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The template file must be created in a Cloud Storage location,
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and is used to run a new Dataflow job.
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‎dataflow/flex-templates/streaming_beam_sql/metadata.json‎

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@@ -7,7 +7,7 @@
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"label": "Pub/Sub input subscription.",
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"helpText": "Pub/Sub subscription to read from.",
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"regexes": [
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"[-_.a-zA-Z0-9]+"
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"(?!goog)[a-zA-Z][-_.~+%a-zA-Z0-9]{2,}"
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]
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},
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{

‎dataflow/flex-templates/streaming_beam_sql/pom.xml‎

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xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
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<modelVersion>4.0.0</modelVersion>
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<parent>
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<groupId>com.google.cloud.samples</groupId>
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<artifactId>shared-configuration</artifactId>
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<version>1.0.17</version>
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</parent>
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<groupId>org.apache.beam.samples</groupId>
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<artifactId>streaming-beam-sql</artifactId>
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<version>1.0</version>

dataflow/flex-templates/streaming_beam_sql/src/main/java/org/apache/beam/samples/StreamingBeamSQL.java renamed to dataflow/flex-templates/streaming_beam_sql/src/main/java/org/apache/beam/samples/StreamingBeamSql.java

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/*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. 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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*/
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package org.apache.beam.samples;
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// Copyright 2020 Google Inc.
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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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20-
import java.util.Arrays;
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package org.apache.beam.samples;
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import com.google.api.services.bigquery.model.TableFieldSchema;
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import com.google.api.services.bigquery.model.TableRow;
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import com.google.api.services.bigquery.model.TableSchema;
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import com.google.gson.Gson;
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import com.google.pubsub.v1.ProjectSubscriptionName;
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import java.util.Arrays;
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import org.apache.avro.reflect.Nullable;
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import org.apache.beam.sdk.Pipeline;
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import org.apache.beam.sdk.coders.AvroCoder;
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import org.apache.beam.sdk.coders.DefaultCoder;
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import org.apache.beam.sdk.extensions.gcp.options.GcpOptions;
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import org.apache.beam.sdk.extensions.sql.SqlTransform;
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import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
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import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.Write.CreateDisposition;
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import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO.Write.WriteDisposition;
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import org.apache.beam.sdk.io.gcp.bigquery.BigQueryIO;
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import org.apache.beam.sdk.io.gcp.pubsub.PubsubIO;
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import org.apache.beam.sdk.options.Default;
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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.StreamingOptions;
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import org.apache.beam.sdk.options.Validation;
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import org.apache.beam.sdk.schemas.Schema;
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import org.apache.beam.sdk.transforms.Create;
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import org.apache.beam.sdk.transforms.MapElements;
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import org.apache.beam.sdk.transforms.WithTimestamps;
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import org.apache.beam.sdk.transforms.windowing.FixedWindows;
@@ -57,34 +51,40 @@
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* An Apache Beam streaming pipeline that reads JSON encoded messages fromPub/Sub,
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* uses Beam SQL to transform the message data, and writes the results to a BigQuery.
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*/
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public class StreamingBeamSQL {
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private static final Logger LOG = LoggerFactory.getLogger(StreamingBeamSQL.class);
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public class StreamingBeamSql {
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private static final Logger LOG = LoggerFactory.getLogger(StreamingBeamSql.class);
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private static final Gson GSON = new Gson();
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public interface Options extends StreamingOptions {
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@Description("Pub/Sub subscription to read from.")
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@Validation.Required
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String getInputSubscription();
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void setInputSubscription(String value);
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70-
@Description("BigQuery table to write to, in the form 'project:dataset.table' or 'dataset.table'.")
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@Description("BigQuery table to write to, in the form "
66+
+ "'project:dataset.table' or 'dataset.table'.")
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@Default.String("beam_samples.streaming_beam_sql")
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String getOutputTable();
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void setOutputTable(String value);
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}
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@DefaultCoder(AvroCoder.class)
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private static class PageReviewMessage {
78-
@Nullable String url;
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@Nullable String review;
75+
@Nullable
76+
String url;
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@Nullable
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String review;
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}
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public static void main(final String[] args) {
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Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
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options.setStreaming(true);
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var project = options.as(GcpOptions.class).getProject();
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var subscription = ProjectSubscriptionName.of(project, options.getInputSubscription()).toString();
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var subscription = ProjectSubscriptionName
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.of(project, options.getInputSubscription()).toString();
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var schema = Schema.builder()
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.addStringField("url")
@@ -96,47 +96,50 @@ public static void main(final String[] args) {
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pipeline
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// Read, parse, and validate messages from Pub/Sub.
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.apply("Read messages from Pub/Sub", PubsubIO.readStrings().fromSubscription(subscription))
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.apply("Parse JSON into SQL rows", MapElements.into(TypeDescriptor.of(Row.class)).via(message -> {
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// This is a good place to add error handling.
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// The first transform should act as a validation layer to make sure
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// that any data coming to the processing pipeline must be valid.
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// See `MapElements.MapWithFailures` for more details.
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LOG.info("message: {}", message);
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var msg = GSON.fromJson(message, PageReviewMessage.class);
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return Row.withSchema(schema).addValues(
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msg.url, // row url
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msg.review.equals("positive") ? 1.0 : 0.0, // row page_score
109-
new Instant() // row processing_time
110-
).build();
111-
})).setRowSchema(schema) // make sure to set the row schema for the PCollection
99+
.apply("Parse JSON into SQL rows", MapElements.into(TypeDescriptor.of(Row.class))
100+
.via(message -> {
101+
// This is a good place to add error handling.
102+
// The first transform should act as a validation layer to make sure
103+
// that any data coming to the processing pipeline must be valid.
104+
// See `MapElements.MapWithFailures` for more details.
105+
LOG.info("message: {}", message);
106+
var msg = GSON.fromJson(message, PageReviewMessage.class);
107+
return Row.withSchema(schema).addValues(
108+
msg.url, // row url
109+
msg.review.equals("positive") ? 1.0 : 0.0, // row page_score
110+
new Instant() // row processing_time
111+
).build();
112+
})).setRowSchema(schema) // make sure to set the row schema for the PCollection
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113114
// Add timestamps and bundle elements into windows.
114-
.apply("Add processing time", WithTimestamps.of((row) -> row.getDateTime("processing_time").toInstant()))
115+
.apply("Add processing time", WithTimestamps
116+
.of((row) -> row.getDateTime("processing_time").toInstant()))
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.apply("Fixed-size windows", Window.into(FixedWindows.of(Duration.standardMinutes(1))))
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117119
// Apply a SQL query for every window of elements.
118120
.apply("Run Beam SQL query", SqlTransform.query(
119-
"SELECT " +
120-
" url, " +
121-
" COUNT(page_score) AS num_reviews, " +
122-
" AVG(page_score) AS score, " +
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" MIN(processing_time) AS first_date, " +
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" MAX(processing_time) AS last_date " +
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"FROM PCOLLECTION " +
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"GROUP BY url"
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"SELECT "
122+
+ " url, "
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+ " COUNT(page_score) AS num_reviews, "
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+ " AVG(page_score) AS score, "
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+ " MIN(processing_time) AS first_date, "
126+
+ " MAX(processing_time) AS last_date "
127+
+ "FROM PCOLLECTION "
128+
+ "GROUP BY url"
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))
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// Convert the SQL Rows into BigQuery TableRows and write them to BigQuery.
130-
.apply("Convert to BigQuery TableRow", MapElements.into(TypeDescriptor.of(TableRow.class)).via(row -> {
131-
LOG.info("rating summary: {} {} ({} reviews)", row.getDouble("score"), row.getString("url"),
132-
row.getInt64("num_reviews"));
133-
return new TableRow()
134-
.set("url", row.getString("url"))
135-
.set("num_reviews", row.getInt64("num_reviews"))
136-
.set("score", row.getDouble("score"))
137-
.set("first_date", row.getDateTime("first_date").toInstant().toString())
138-
.set("last_date", row.getDateTime("last_date").toInstant().toString());
139-
}))
132+
.apply("Convert to BigQuery TableRow", MapElements.into(TypeDescriptor.of(TableRow.class))
133+
.via(row -> {
134+
LOG.info("rating summary: {} {} ({} reviews)", row.getDouble("score"),
135+
row.getString("url"), row.getInt64("num_reviews"));
136+
return new TableRow()
137+
.set("url", row.getString("url"))
138+
.set("num_reviews", row.getInt64("num_reviews"))
139+
.set("score", row.getDouble("score"))
140+
.set("first_date", row.getDateTime("first_date").toInstant().toString())
141+
.set("last_date", row.getDateTime("last_date").toInstant().toString());
142+
}))
140143
.apply("Write to BigQuery", BigQueryIO.writeTableRows()
141144
.to(options.getOutputTable())
142145
.withSchema(new TableSchema().setFields(Arrays.asList(

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