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Posted to commits@beam.apache.org by dh...@apache.org on 2016/03/24 03:48:22 UTC
[58/67] incubator-beam git commit: Directory reorganization
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2eaa709c/examples/java/src/main/java/com/google/cloud/dataflow/examples/complete/TopWikipediaSessions.java
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diff --git a/examples/java/src/main/java/com/google/cloud/dataflow/examples/complete/TopWikipediaSessions.java b/examples/java/src/main/java/com/google/cloud/dataflow/examples/complete/TopWikipediaSessions.java
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+/*
+ * Copyright (C) 2015 Google Inc.
+ *
+ * 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.dataflow.examples.complete;
+
+import com.google.api.services.bigquery.model.TableRow;
+import com.google.cloud.dataflow.sdk.Pipeline;
+import com.google.cloud.dataflow.sdk.coders.TableRowJsonCoder;
+import com.google.cloud.dataflow.sdk.io.TextIO;
+import com.google.cloud.dataflow.sdk.options.DataflowPipelineOptions;
+import com.google.cloud.dataflow.sdk.options.Default;
+import com.google.cloud.dataflow.sdk.options.Description;
+import com.google.cloud.dataflow.sdk.options.PipelineOptions;
+import com.google.cloud.dataflow.sdk.options.PipelineOptionsFactory;
+import com.google.cloud.dataflow.sdk.options.Validation;
+import com.google.cloud.dataflow.sdk.transforms.Count;
+import com.google.cloud.dataflow.sdk.transforms.DoFn;
+import com.google.cloud.dataflow.sdk.transforms.DoFn.RequiresWindowAccess;
+import com.google.cloud.dataflow.sdk.transforms.PTransform;
+import com.google.cloud.dataflow.sdk.transforms.ParDo;
+import com.google.cloud.dataflow.sdk.transforms.SerializableComparator;
+import com.google.cloud.dataflow.sdk.transforms.Top;
+import com.google.cloud.dataflow.sdk.transforms.windowing.CalendarWindows;
+import com.google.cloud.dataflow.sdk.transforms.windowing.IntervalWindow;
+import com.google.cloud.dataflow.sdk.transforms.windowing.Sessions;
+import com.google.cloud.dataflow.sdk.transforms.windowing.Window;
+import com.google.cloud.dataflow.sdk.values.KV;
+import com.google.cloud.dataflow.sdk.values.PCollection;
+
+import org.joda.time.Duration;
+import org.joda.time.Instant;
+
+import java.util.List;
+
+/**
+ * An example that reads Wikipedia edit data from Cloud Storage and computes the user with
+ * the longest string of edits separated by no more than an hour within each month.
+ *
+ * <p>Concepts: Using Windowing to perform time-based aggregations of data.
+ *
+ * <p>It is not recommended to execute this pipeline locally, given the size of the default input
+ * data.
+ *
+ * <p>To execute this pipeline using the Dataflow service, specify pipeline configuration:
+ * <pre>{@code
+ * --project=YOUR_PROJECT_ID
+ * --stagingLocation=gs://YOUR_STAGING_DIRECTORY
+ * --runner=BlockingDataflowPipelineRunner
+ * }
+ * </pre>
+ * and an output prefix on GCS:
+ * <pre>{@code
+ * --output=gs://YOUR_OUTPUT_PREFIX
+ * }</pre>
+ *
+ * <p>The default input is {@code gs://dataflow-samples/wikipedia_edits/*.json} and can be
+ * overridden with {@code --input}.
+ *
+ * <p>The input for this example is large enough that it's a good place to enable (experimental)
+ * autoscaling:
+ * <pre>{@code
+ * --autoscalingAlgorithm=BASIC
+ * --maxNumWorkers=20
+ * }
+ * </pre>
+ * This will automatically scale the number of workers up over time until the job completes.
+ */
+public class TopWikipediaSessions {
+ private static final String EXPORTED_WIKI_TABLE = "gs://dataflow-samples/wikipedia_edits/*.json";
+
+ /**
+ * Extracts user and timestamp from a TableRow representing a Wikipedia edit.
+ */
+ static class ExtractUserAndTimestamp extends DoFn<TableRow, String> {
+ @Override
+ public void processElement(ProcessContext c) {
+ TableRow row = c.element();
+ int timestamp = (Integer) row.get("timestamp");
+ String userName = (String) row.get("contributor_username");
+ if (userName != null) {
+ // Sets the implicit timestamp field to be used in windowing.
+ c.outputWithTimestamp(userName, new Instant(timestamp * 1000L));
+ }
+ }
+ }
+
+ /**
+ * Computes the number of edits in each user session. A session is defined as
+ * a string of edits where each is separated from the next by less than an hour.
+ */
+ static class ComputeSessions
+ extends PTransform<PCollection<String>, PCollection<KV<String, Long>>> {
+ @Override
+ public PCollection<KV<String, Long>> apply(PCollection<String> actions) {
+ return actions
+ .apply(Window.<String>into(Sessions.withGapDuration(Duration.standardHours(1))))
+
+ .apply(Count.<String>perElement());
+ }
+ }
+
+ /**
+ * Computes the longest session ending in each month.
+ */
+ private static class TopPerMonth
+ extends PTransform<PCollection<KV<String, Long>>, PCollection<List<KV<String, Long>>>> {
+ @Override
+ public PCollection<List<KV<String, Long>>> apply(PCollection<KV<String, Long>> sessions) {
+ return sessions
+ .apply(Window.<KV<String, Long>>into(CalendarWindows.months(1)))
+
+ .apply(Top.of(1, new SerializableComparator<KV<String, Long>>() {
+ @Override
+ public int compare(KV<String, Long> o1, KV<String, Long> o2) {
+ return Long.compare(o1.getValue(), o2.getValue());
+ }
+ }).withoutDefaults());
+ }
+ }
+
+ static class SessionsToStringsDoFn extends DoFn<KV<String, Long>, KV<String, Long>>
+ implements RequiresWindowAccess {
+
+ @Override
+ public void processElement(ProcessContext c) {
+ c.output(KV.of(
+ c.element().getKey() + " : " + c.window(), c.element().getValue()));
+ }
+ }
+
+ static class FormatOutputDoFn extends DoFn<List<KV<String, Long>>, String>
+ implements RequiresWindowAccess {
+ @Override
+ public void processElement(ProcessContext c) {
+ for (KV<String, Long> item : c.element()) {
+ String session = item.getKey();
+ long count = item.getValue();
+ c.output(session + " : " + count + " : " + ((IntervalWindow) c.window()).start());
+ }
+ }
+ }
+
+ static class ComputeTopSessions extends PTransform<PCollection<TableRow>, PCollection<String>> {
+
+ private final double samplingThreshold;
+
+ public ComputeTopSessions(double samplingThreshold) {
+ this.samplingThreshold = samplingThreshold;
+ }
+
+ @Override
+ public PCollection<String> apply(PCollection<TableRow> input) {
+ return input
+ .apply(ParDo.of(new ExtractUserAndTimestamp()))
+
+ .apply(ParDo.named("SampleUsers").of(
+ new DoFn<String, String>() {
+ @Override
+ public void processElement(ProcessContext c) {
+ if (Math.abs(c.element().hashCode()) <= Integer.MAX_VALUE * samplingThreshold) {
+ c.output(c.element());
+ }
+ }
+ }))
+
+ .apply(new ComputeSessions())
+
+ .apply(ParDo.named("SessionsToStrings").of(new SessionsToStringsDoFn()))
+ .apply(new TopPerMonth())
+ .apply(ParDo.named("FormatOutput").of(new FormatOutputDoFn()));
+ }
+ }
+
+ /**
+ * Options supported by this class.
+ *
+ * <p>Inherits standard Dataflow configuration options.
+ */
+ private static interface Options extends PipelineOptions {
+ @Description(
+ "Input specified as a GCS path containing a BigQuery table exported as json")
+ @Default.String(EXPORTED_WIKI_TABLE)
+ String getInput();
+ void setInput(String value);
+
+ @Description("File to output results to")
+ @Validation.Required
+ String getOutput();
+ void setOutput(String value);
+ }
+
+ public static void main(String[] args) {
+ Options options = PipelineOptionsFactory.fromArgs(args)
+ .withValidation()
+ .as(Options.class);
+ DataflowPipelineOptions dataflowOptions = options.as(DataflowPipelineOptions.class);
+
+ Pipeline p = Pipeline.create(dataflowOptions);
+
+ double samplingThreshold = 0.1;
+
+ p.apply(TextIO.Read
+ .from(options.getInput())
+ .withCoder(TableRowJsonCoder.of()))
+ .apply(new ComputeTopSessions(samplingThreshold))
+ .apply(TextIO.Write.named("Write").withoutSharding().to(options.getOutput()));
+
+ p.run();
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2eaa709c/examples/java/src/main/java/com/google/cloud/dataflow/examples/complete/TrafficMaxLaneFlow.java
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diff --git a/examples/java/src/main/java/com/google/cloud/dataflow/examples/complete/TrafficMaxLaneFlow.java b/examples/java/src/main/java/com/google/cloud/dataflow/examples/complete/TrafficMaxLaneFlow.java
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+/*
+ * Copyright (C) 2015 Google Inc.
+ *
+ * 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.dataflow.examples.complete;
+
+import com.google.api.services.bigquery.model.TableFieldSchema;
+import com.google.api.services.bigquery.model.TableReference;
+import com.google.api.services.bigquery.model.TableRow;
+import com.google.api.services.bigquery.model.TableSchema;
+import com.google.cloud.dataflow.examples.common.DataflowExampleOptions;
+import com.google.cloud.dataflow.examples.common.DataflowExampleUtils;
+import com.google.cloud.dataflow.examples.common.ExampleBigQueryTableOptions;
+import com.google.cloud.dataflow.examples.common.ExamplePubsubTopicAndSubscriptionOptions;
+import com.google.cloud.dataflow.sdk.Pipeline;
+import com.google.cloud.dataflow.sdk.PipelineResult;
+import com.google.cloud.dataflow.sdk.coders.AvroCoder;
+import com.google.cloud.dataflow.sdk.coders.DefaultCoder;
+import com.google.cloud.dataflow.sdk.io.BigQueryIO;
+import com.google.cloud.dataflow.sdk.io.PubsubIO;
+import com.google.cloud.dataflow.sdk.io.TextIO;
+import com.google.cloud.dataflow.sdk.options.Default;
+import com.google.cloud.dataflow.sdk.options.Description;
+import com.google.cloud.dataflow.sdk.options.PipelineOptionsFactory;
+import com.google.cloud.dataflow.sdk.transforms.Combine;
+import com.google.cloud.dataflow.sdk.transforms.DoFn;
+import com.google.cloud.dataflow.sdk.transforms.PTransform;
+import com.google.cloud.dataflow.sdk.transforms.ParDo;
+import com.google.cloud.dataflow.sdk.transforms.SerializableFunction;
+import com.google.cloud.dataflow.sdk.transforms.windowing.SlidingWindows;
+import com.google.cloud.dataflow.sdk.transforms.windowing.Window;
+import com.google.cloud.dataflow.sdk.values.KV;
+import com.google.cloud.dataflow.sdk.values.PBegin;
+import com.google.cloud.dataflow.sdk.values.PCollection;
+import com.google.common.base.Strings;
+
+import org.apache.avro.reflect.Nullable;
+import org.joda.time.Duration;
+import org.joda.time.Instant;
+import org.joda.time.format.DateTimeFormat;
+import org.joda.time.format.DateTimeFormatter;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * A Dataflow Example that runs in both batch and streaming modes with traffic sensor data.
+ * You can configure the running mode by setting {@literal --streaming} to true or false.
+ *
+ * <p>Concepts: The batch and streaming runners, sliding windows, Google Cloud Pub/Sub
+ * topic injection, use of the AvroCoder to encode a custom class, and custom Combine transforms.
+ *
+ * <p>This example analyzes traffic sensor data using SlidingWindows. For each window,
+ * it finds the lane that had the highest flow recorded, for each sensor station. It writes
+ * those max values along with auxiliary info to a BigQuery table.
+ *
+ * <p>In batch mode, the pipeline reads traffic sensor data from {@literal --inputFile}.
+ *
+ * <p>In streaming mode, the pipeline reads the data from a Pub/Sub topic.
+ * By default, the example will run a separate pipeline to inject the data from the default
+ * {@literal --inputFile} to the Pub/Sub {@literal --pubsubTopic}. It will make it available for
+ * the streaming pipeline to process. You may override the default {@literal --inputFile} with the
+ * file of your choosing. You may also set {@literal --inputFile} to an empty string, which will
+ * disable the automatic Pub/Sub injection, and allow you to use separate tool to control the input
+ * to this example. An example code, which publishes traffic sensor data to a Pub/Sub topic,
+ * is provided in
+ * <a href="https://github.com/GoogleCloudPlatform/cloud-pubsub-samples-python/tree/master/gce-cmdline-publisher"></a>.
+ *
+ * <p>The example is configured to use the default Pub/Sub topic and the default BigQuery table
+ * from the example common package (there are no defaults for a general Dataflow pipeline).
+ * You can override them by using the {@literal --pubsubTopic}, {@literal --bigQueryDataset}, and
+ * {@literal --bigQueryTable} options. If the Pub/Sub topic or the BigQuery table do not exist,
+ * the example will try to create them.
+ *
+ * <p>The example will try to cancel the pipelines on the signal to terminate the process (CTRL-C)
+ * and then exits.
+ */
+public class TrafficMaxLaneFlow {
+
+ private static final String PUBSUB_TIMESTAMP_LABEL_KEY = "timestamp_ms";
+ private static final Integer VALID_INPUTS = 4999;
+
+ static final int WINDOW_DURATION = 60; // Default sliding window duration in minutes
+ static final int WINDOW_SLIDE_EVERY = 5; // Default window 'slide every' setting in minutes
+
+ /**
+ * This class holds information about each lane in a station reading, along with some general
+ * information from the reading.
+ */
+ @DefaultCoder(AvroCoder.class)
+ static class LaneInfo {
+ @Nullable String stationId;
+ @Nullable String lane;
+ @Nullable String direction;
+ @Nullable String freeway;
+ @Nullable String recordedTimestamp;
+ @Nullable Integer laneFlow;
+ @Nullable Integer totalFlow;
+ @Nullable Double laneAO;
+ @Nullable Double laneAS;
+
+ public LaneInfo() {}
+
+ public LaneInfo(String stationId, String lane, String direction, String freeway,
+ String timestamp, Integer laneFlow, Double laneAO,
+ Double laneAS, Integer totalFlow) {
+ this.stationId = stationId;
+ this.lane = lane;
+ this.direction = direction;
+ this.freeway = freeway;
+ this.recordedTimestamp = timestamp;
+ this.laneFlow = laneFlow;
+ this.laneAO = laneAO;
+ this.laneAS = laneAS;
+ this.totalFlow = totalFlow;
+ }
+
+ public String getStationId() {
+ return this.stationId;
+ }
+ public String getLane() {
+ return this.lane;
+ }
+ public String getDirection() {
+ return this.direction;
+ }
+ public String getFreeway() {
+ return this.freeway;
+ }
+ public String getRecordedTimestamp() {
+ return this.recordedTimestamp;
+ }
+ public Integer getLaneFlow() {
+ return this.laneFlow;
+ }
+ public Double getLaneAO() {
+ return this.laneAO;
+ }
+ public Double getLaneAS() {
+ return this.laneAS;
+ }
+ public Integer getTotalFlow() {
+ return this.totalFlow;
+ }
+ }
+
+ /**
+ * Extract the timestamp field from the input string, and use it as the element timestamp.
+ */
+ static class ExtractTimestamps extends DoFn<String, String> {
+ private static final DateTimeFormatter dateTimeFormat =
+ DateTimeFormat.forPattern("MM/dd/yyyy HH:mm:ss");
+
+ @Override
+ public void processElement(DoFn<String, String>.ProcessContext c) throws Exception {
+ String[] items = c.element().split(",");
+ if (items.length > 0) {
+ try {
+ String timestamp = items[0];
+ c.outputWithTimestamp(c.element(), new Instant(dateTimeFormat.parseMillis(timestamp)));
+ } catch (IllegalArgumentException e) {
+ // Skip the invalid input.
+ }
+ }
+ }
+ }
+
+ /**
+ * Extract flow information for each of the 8 lanes in a reading, and output as separate tuples.
+ * This will let us determine which lane has the max flow for that station over the span of the
+ * window, and output not only the max flow from that calculation, but other associated
+ * information. The number of lanes for which data is present depends upon which freeway the data
+ * point comes from.
+ */
+ static class ExtractFlowInfoFn extends DoFn<String, KV<String, LaneInfo>> {
+
+ @Override
+ public void processElement(ProcessContext c) {
+ String[] items = c.element().split(",");
+ if (items.length < 48) {
+ // Skip the invalid input.
+ return;
+ }
+ // extract the sensor information for the lanes from the input string fields.
+ String timestamp = items[0];
+ String stationId = items[1];
+ String freeway = items[2];
+ String direction = items[3];
+ Integer totalFlow = tryIntParse(items[7]);
+ for (int i = 1; i <= 8; ++i) {
+ Integer laneFlow = tryIntParse(items[6 + 5 * i]);
+ Double laneAvgOccupancy = tryDoubleParse(items[7 + 5 * i]);
+ Double laneAvgSpeed = tryDoubleParse(items[8 + 5 * i]);
+ if (laneFlow == null || laneAvgOccupancy == null || laneAvgSpeed == null) {
+ return;
+ }
+ LaneInfo laneInfo = new LaneInfo(stationId, "lane" + i, direction, freeway, timestamp,
+ laneFlow, laneAvgOccupancy, laneAvgSpeed, totalFlow);
+ c.output(KV.of(stationId, laneInfo));
+ }
+ }
+ }
+
+ /**
+ * A custom 'combine function' used with the Combine.perKey transform. Used to find the max lane
+ * flow over all the data points in the Window. Extracts the lane flow from the input string and
+ * determines whether it's the max seen so far. We're using a custom combiner instead of the Max
+ * transform because we want to retain the additional information we've associated with the flow
+ * value.
+ */
+ public static class MaxFlow implements SerializableFunction<Iterable<LaneInfo>, LaneInfo> {
+ @Override
+ public LaneInfo apply(Iterable<LaneInfo> input) {
+ Integer max = 0;
+ LaneInfo maxInfo = new LaneInfo();
+ for (LaneInfo item : input) {
+ Integer flow = item.getLaneFlow();
+ if (flow != null && (flow >= max)) {
+ max = flow;
+ maxInfo = item;
+ }
+ }
+ return maxInfo;
+ }
+ }
+
+ /**
+ * Format the results of the Max Lane flow calculation to a TableRow, to save to BigQuery.
+ * Add the timestamp from the window context.
+ */
+ static class FormatMaxesFn extends DoFn<KV<String, LaneInfo>, TableRow> {
+ @Override
+ public void processElement(ProcessContext c) {
+
+ LaneInfo laneInfo = c.element().getValue();
+ TableRow row = new TableRow()
+ .set("station_id", c.element().getKey())
+ .set("direction", laneInfo.getDirection())
+ .set("freeway", laneInfo.getFreeway())
+ .set("lane_max_flow", laneInfo.getLaneFlow())
+ .set("lane", laneInfo.getLane())
+ .set("avg_occ", laneInfo.getLaneAO())
+ .set("avg_speed", laneInfo.getLaneAS())
+ .set("total_flow", laneInfo.getTotalFlow())
+ .set("recorded_timestamp", laneInfo.getRecordedTimestamp())
+ .set("window_timestamp", c.timestamp().toString());
+ c.output(row);
+ }
+
+ /** Defines the BigQuery schema used for the output. */
+ static TableSchema getSchema() {
+ List<TableFieldSchema> fields = new ArrayList<>();
+ fields.add(new TableFieldSchema().setName("station_id").setType("STRING"));
+ fields.add(new TableFieldSchema().setName("direction").setType("STRING"));
+ fields.add(new TableFieldSchema().setName("freeway").setType("STRING"));
+ fields.add(new TableFieldSchema().setName("lane_max_flow").setType("INTEGER"));
+ fields.add(new TableFieldSchema().setName("lane").setType("STRING"));
+ fields.add(new TableFieldSchema().setName("avg_occ").setType("FLOAT"));
+ fields.add(new TableFieldSchema().setName("avg_speed").setType("FLOAT"));
+ fields.add(new TableFieldSchema().setName("total_flow").setType("INTEGER"));
+ fields.add(new TableFieldSchema().setName("window_timestamp").setType("TIMESTAMP"));
+ fields.add(new TableFieldSchema().setName("recorded_timestamp").setType("STRING"));
+ TableSchema schema = new TableSchema().setFields(fields);
+ return schema;
+ }
+ }
+
+ /**
+ * This PTransform extracts lane info, calculates the max lane flow found for a given station (for
+ * the current Window) using a custom 'combiner', and formats the results for BigQuery.
+ */
+ static class MaxLaneFlow
+ extends PTransform<PCollection<KV<String, LaneInfo>>, PCollection<TableRow>> {
+ @Override
+ public PCollection<TableRow> apply(PCollection<KV<String, LaneInfo>> flowInfo) {
+ // stationId, LaneInfo => stationId + max lane flow info
+ PCollection<KV<String, LaneInfo>> flowMaxes =
+ flowInfo.apply(Combine.<String, LaneInfo>perKey(
+ new MaxFlow()));
+
+ // <stationId, max lane flow info>... => row...
+ PCollection<TableRow> results = flowMaxes.apply(
+ ParDo.of(new FormatMaxesFn()));
+
+ return results;
+ }
+ }
+
+ static class ReadFileAndExtractTimestamps extends PTransform<PBegin, PCollection<String>> {
+ private final String inputFile;
+
+ public ReadFileAndExtractTimestamps(String inputFile) {
+ this.inputFile = inputFile;
+ }
+
+ @Override
+ public PCollection<String> apply(PBegin begin) {
+ return begin
+ .apply(TextIO.Read.from(inputFile))
+ .apply(ParDo.of(new ExtractTimestamps()));
+ }
+ }
+
+ /**
+ * Options supported by {@link TrafficMaxLaneFlow}.
+ *
+ * <p>Inherits standard configuration options.
+ */
+ private interface TrafficMaxLaneFlowOptions extends DataflowExampleOptions,
+ ExamplePubsubTopicAndSubscriptionOptions, ExampleBigQueryTableOptions {
+ @Description("Input file to inject to Pub/Sub topic")
+ @Default.String("gs://dataflow-samples/traffic_sensor/"
+ + "Freeways-5Minaa2010-01-01_to_2010-02-15_test2.csv")
+ String getInputFile();
+ void setInputFile(String value);
+
+ @Description("Numeric value of sliding window duration, in minutes")
+ @Default.Integer(WINDOW_DURATION)
+ Integer getWindowDuration();
+ void setWindowDuration(Integer value);
+
+ @Description("Numeric value of window 'slide every' setting, in minutes")
+ @Default.Integer(WINDOW_SLIDE_EVERY)
+ Integer getWindowSlideEvery();
+ void setWindowSlideEvery(Integer value);
+
+ @Description("Whether to run the pipeline with unbounded input")
+ @Default.Boolean(false)
+ boolean isUnbounded();
+ void setUnbounded(boolean value);
+ }
+
+ /**
+ * Sets up and starts streaming pipeline.
+ *
+ * @throws IOException if there is a problem setting up resources
+ */
+ public static void main(String[] args) throws IOException {
+ TrafficMaxLaneFlowOptions options = PipelineOptionsFactory.fromArgs(args)
+ .withValidation()
+ .as(TrafficMaxLaneFlowOptions.class);
+ options.setBigQuerySchema(FormatMaxesFn.getSchema());
+ // Using DataflowExampleUtils to set up required resources.
+ DataflowExampleUtils dataflowUtils = new DataflowExampleUtils(options, options.isUnbounded());
+
+ Pipeline pipeline = Pipeline.create(options);
+ TableReference tableRef = new TableReference();
+ tableRef.setProjectId(options.getProject());
+ tableRef.setDatasetId(options.getBigQueryDataset());
+ tableRef.setTableId(options.getBigQueryTable());
+
+ PCollection<String> input;
+ if (options.isUnbounded()) {
+ // Read unbounded PubSubIO.
+ input = pipeline.apply(PubsubIO.Read
+ .timestampLabel(PUBSUB_TIMESTAMP_LABEL_KEY)
+ .subscription(options.getPubsubSubscription()));
+ } else {
+ // Read bounded PubSubIO.
+ input = pipeline.apply(PubsubIO.Read
+ .timestampLabel(PUBSUB_TIMESTAMP_LABEL_KEY)
+ .subscription(options.getPubsubSubscription()).maxNumRecords(VALID_INPUTS));
+
+ // To read bounded TextIO files, use:
+ // input = pipeline.apply(new ReadFileAndExtractTimestamps(options.getInputFile()));
+ }
+ input
+ // row... => <station route, station speed> ...
+ .apply(ParDo.of(new ExtractFlowInfoFn()))
+ // map the incoming data stream into sliding windows. The default window duration values
+ // work well if you're running the accompanying Pub/Sub generator script with the
+ // --replay flag, which simulates pauses in the sensor data publication. You may want to
+ // adjust them otherwise.
+ .apply(Window.<KV<String, LaneInfo>>into(SlidingWindows.of(
+ Duration.standardMinutes(options.getWindowDuration())).
+ every(Duration.standardMinutes(options.getWindowSlideEvery()))))
+ .apply(new MaxLaneFlow())
+ .apply(BigQueryIO.Write.to(tableRef)
+ .withSchema(FormatMaxesFn.getSchema()));
+
+ // Inject the data into the Pub/Sub topic with a Dataflow batch pipeline.
+ if (!Strings.isNullOrEmpty(options.getInputFile())
+ && !Strings.isNullOrEmpty(options.getPubsubTopic())) {
+ dataflowUtils.runInjectorPipeline(
+ new ReadFileAndExtractTimestamps(options.getInputFile()),
+ options.getPubsubTopic(),
+ PUBSUB_TIMESTAMP_LABEL_KEY);
+ }
+
+ // Run the pipeline.
+ PipelineResult result = pipeline.run();
+
+ // dataflowUtils will try to cancel the pipeline and the injector before the program exists.
+ dataflowUtils.waitToFinish(result);
+ }
+
+ private static Integer tryIntParse(String number) {
+ try {
+ return Integer.parseInt(number);
+ } catch (NumberFormatException e) {
+ return null;
+ }
+ }
+
+ private static Double tryDoubleParse(String number) {
+ try {
+ return Double.parseDouble(number);
+ } catch (NumberFormatException e) {
+ return null;
+ }
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2eaa709c/examples/java/src/main/java/com/google/cloud/dataflow/examples/complete/TrafficRoutes.java
----------------------------------------------------------------------
diff --git a/examples/java/src/main/java/com/google/cloud/dataflow/examples/complete/TrafficRoutes.java b/examples/java/src/main/java/com/google/cloud/dataflow/examples/complete/TrafficRoutes.java
new file mode 100644
index 0000000..e3e88c2
--- /dev/null
+++ b/examples/java/src/main/java/com/google/cloud/dataflow/examples/complete/TrafficRoutes.java
@@ -0,0 +1,459 @@
+/*
+ * Copyright (C) 2015 Google Inc.
+ *
+ * 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.dataflow.examples.complete;
+
+import com.google.api.services.bigquery.model.TableFieldSchema;
+import com.google.api.services.bigquery.model.TableReference;
+import com.google.api.services.bigquery.model.TableRow;
+import com.google.api.services.bigquery.model.TableSchema;
+import com.google.cloud.dataflow.examples.common.DataflowExampleOptions;
+import com.google.cloud.dataflow.examples.common.DataflowExampleUtils;
+import com.google.cloud.dataflow.examples.common.ExampleBigQueryTableOptions;
+import com.google.cloud.dataflow.examples.common.ExamplePubsubTopicAndSubscriptionOptions;
+import com.google.cloud.dataflow.sdk.Pipeline;
+import com.google.cloud.dataflow.sdk.PipelineResult;
+import com.google.cloud.dataflow.sdk.coders.AvroCoder;
+import com.google.cloud.dataflow.sdk.coders.DefaultCoder;
+import com.google.cloud.dataflow.sdk.io.BigQueryIO;
+import com.google.cloud.dataflow.sdk.io.PubsubIO;
+import com.google.cloud.dataflow.sdk.io.TextIO;
+import com.google.cloud.dataflow.sdk.options.Default;
+import com.google.cloud.dataflow.sdk.options.Description;
+import com.google.cloud.dataflow.sdk.options.PipelineOptionsFactory;
+import com.google.cloud.dataflow.sdk.transforms.DoFn;
+import com.google.cloud.dataflow.sdk.transforms.GroupByKey;
+import com.google.cloud.dataflow.sdk.transforms.PTransform;
+import com.google.cloud.dataflow.sdk.transforms.ParDo;
+import com.google.cloud.dataflow.sdk.transforms.windowing.SlidingWindows;
+import com.google.cloud.dataflow.sdk.transforms.windowing.Window;
+import com.google.cloud.dataflow.sdk.values.KV;
+import com.google.cloud.dataflow.sdk.values.PBegin;
+import com.google.cloud.dataflow.sdk.values.PCollection;
+import com.google.common.base.Strings;
+import com.google.common.collect.Lists;
+
+import org.apache.avro.reflect.Nullable;
+import org.joda.time.Duration;
+import org.joda.time.Instant;
+import org.joda.time.format.DateTimeFormat;
+import org.joda.time.format.DateTimeFormatter;
+
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.HashMap;
+import java.util.Hashtable;
+import java.util.List;
+import java.util.Map;
+
+/**
+ * A Dataflow Example that runs in both batch and streaming modes with traffic sensor data.
+ * You can configure the running mode by setting {@literal --streaming} to true or false.
+ *
+ * <p>Concepts: The batch and streaming runners, GroupByKey, sliding windows, and
+ * Google Cloud Pub/Sub topic injection.
+ *
+ * <p>This example analyzes traffic sensor data using SlidingWindows. For each window,
+ * it calculates the average speed over the window for some small set of predefined 'routes',
+ * and looks for 'slowdowns' in those routes. It writes its results to a BigQuery table.
+ *
+ * <p>In batch mode, the pipeline reads traffic sensor data from {@literal --inputFile}.
+ *
+ * <p>In streaming mode, the pipeline reads the data from a Pub/Sub topic.
+ * By default, the example will run a separate pipeline to inject the data from the default
+ * {@literal --inputFile} to the Pub/Sub {@literal --pubsubTopic}. It will make it available for
+ * the streaming pipeline to process. You may override the default {@literal --inputFile} with the
+ * file of your choosing. You may also set {@literal --inputFile} to an empty string, which will
+ * disable the automatic Pub/Sub injection, and allow you to use separate tool to control the input
+ * to this example. An example code, which publishes traffic sensor data to a Pub/Sub topic,
+ * is provided in
+ * <a href="https://github.com/GoogleCloudPlatform/cloud-pubsub-samples-python/tree/master/gce-cmdline-publisher"></a>.
+ *
+ * <p>The example is configured to use the default Pub/Sub topic and the default BigQuery table
+ * from the example common package (there are no defaults for a general Dataflow pipeline).
+ * You can override them by using the {@literal --pubsubTopic}, {@literal --bigQueryDataset}, and
+ * {@literal --bigQueryTable} options. If the Pub/Sub topic or the BigQuery table do not exist,
+ * the example will try to create them.
+ *
+ * <p>The example will try to cancel the pipelines on the signal to terminate the process (CTRL-C)
+ * and then exits.
+ */
+
+public class TrafficRoutes {
+
+ private static final String PUBSUB_TIMESTAMP_LABEL_KEY = "timestamp_ms";
+ private static final Integer VALID_INPUTS = 4999;
+
+ // Instantiate some small predefined San Diego routes to analyze
+ static Map<String, String> sdStations = buildStationInfo();
+ static final int WINDOW_DURATION = 3; // Default sliding window duration in minutes
+ static final int WINDOW_SLIDE_EVERY = 1; // Default window 'slide every' setting in minutes
+
+ /**
+ * This class holds information about a station reading's average speed.
+ */
+ @DefaultCoder(AvroCoder.class)
+ static class StationSpeed implements Comparable<StationSpeed> {
+ @Nullable String stationId;
+ @Nullable Double avgSpeed;
+ @Nullable Long timestamp;
+
+ public StationSpeed() {}
+
+ public StationSpeed(String stationId, Double avgSpeed, Long timestamp) {
+ this.stationId = stationId;
+ this.avgSpeed = avgSpeed;
+ this.timestamp = timestamp;
+ }
+
+ public String getStationId() {
+ return this.stationId;
+ }
+ public Double getAvgSpeed() {
+ return this.avgSpeed;
+ }
+
+ @Override
+ public int compareTo(StationSpeed other) {
+ return Long.compare(this.timestamp, other.timestamp);
+ }
+ }
+
+ /**
+ * This class holds information about a route's speed/slowdown.
+ */
+ @DefaultCoder(AvroCoder.class)
+ static class RouteInfo {
+ @Nullable String route;
+ @Nullable Double avgSpeed;
+ @Nullable Boolean slowdownEvent;
+
+
+ public RouteInfo() {}
+
+ public RouteInfo(String route, Double avgSpeed, Boolean slowdownEvent) {
+ this.route = route;
+ this.avgSpeed = avgSpeed;
+ this.slowdownEvent = slowdownEvent;
+ }
+
+ public String getRoute() {
+ return this.route;
+ }
+ public Double getAvgSpeed() {
+ return this.avgSpeed;
+ }
+ public Boolean getSlowdownEvent() {
+ return this.slowdownEvent;
+ }
+ }
+
+ /**
+ * Extract the timestamp field from the input string, and use it as the element timestamp.
+ */
+ static class ExtractTimestamps extends DoFn<String, String> {
+ private static final DateTimeFormatter dateTimeFormat =
+ DateTimeFormat.forPattern("MM/dd/yyyy HH:mm:ss");
+
+ @Override
+ public void processElement(DoFn<String, String>.ProcessContext c) throws Exception {
+ String[] items = c.element().split(",");
+ String timestamp = tryParseTimestamp(items);
+ if (timestamp != null) {
+ try {
+ c.outputWithTimestamp(c.element(), new Instant(dateTimeFormat.parseMillis(timestamp)));
+ } catch (IllegalArgumentException e) {
+ // Skip the invalid input.
+ }
+ }
+ }
+ }
+
+ /**
+ * Filter out readings for the stations along predefined 'routes', and output
+ * (station, speed info) keyed on route.
+ */
+ static class ExtractStationSpeedFn extends DoFn<String, KV<String, StationSpeed>> {
+
+ @Override
+ public void processElement(ProcessContext c) {
+ String[] items = c.element().split(",");
+ String stationType = tryParseStationType(items);
+ // For this analysis, use only 'main line' station types
+ if (stationType != null && stationType.equals("ML")) {
+ Double avgSpeed = tryParseAvgSpeed(items);
+ String stationId = tryParseStationId(items);
+ // For this simple example, filter out everything but some hardwired routes.
+ if (avgSpeed != null && stationId != null && sdStations.containsKey(stationId)) {
+ StationSpeed stationSpeed =
+ new StationSpeed(stationId, avgSpeed, c.timestamp().getMillis());
+ // The tuple key is the 'route' name stored in the 'sdStations' hash.
+ KV<String, StationSpeed> outputValue = KV.of(sdStations.get(stationId), stationSpeed);
+ c.output(outputValue);
+ }
+ }
+ }
+ }
+
+ /**
+ * For a given route, track average speed for the window. Calculate whether
+ * traffic is currently slowing down, via a predefined threshold. If a supermajority of
+ * speeds in this sliding window are less than the previous reading we call this a 'slowdown'.
+ * Note: these calculations are for example purposes only, and are unrealistic and oversimplified.
+ */
+ static class GatherStats
+ extends DoFn<KV<String, Iterable<StationSpeed>>, KV<String, RouteInfo>> {
+ @Override
+ public void processElement(ProcessContext c) throws IOException {
+ String route = c.element().getKey();
+ double speedSum = 0.0;
+ int speedCount = 0;
+ int speedups = 0;
+ int slowdowns = 0;
+ List<StationSpeed> infoList = Lists.newArrayList(c.element().getValue());
+ // StationSpeeds sort by embedded timestamp.
+ Collections.sort(infoList);
+ Map<String, Double> prevSpeeds = new HashMap<>();
+ // For all stations in the route, sum (non-null) speeds. Keep a count of the non-null speeds.
+ for (StationSpeed item : infoList) {
+ Double speed = item.getAvgSpeed();
+ if (speed != null) {
+ speedSum += speed;
+ speedCount++;
+ Double lastSpeed = prevSpeeds.get(item.getStationId());
+ if (lastSpeed != null) {
+ if (lastSpeed < speed) {
+ speedups += 1;
+ } else {
+ slowdowns += 1;
+ }
+ }
+ prevSpeeds.put(item.getStationId(), speed);
+ }
+ }
+ if (speedCount == 0) {
+ // No average to compute.
+ return;
+ }
+ double speedAvg = speedSum / speedCount;
+ boolean slowdownEvent = slowdowns >= 2 * speedups;
+ RouteInfo routeInfo = new RouteInfo(route, speedAvg, slowdownEvent);
+ c.output(KV.of(route, routeInfo));
+ }
+ }
+
+ /**
+ * Format the results of the slowdown calculations to a TableRow, to save to BigQuery.
+ */
+ static class FormatStatsFn extends DoFn<KV<String, RouteInfo>, TableRow> {
+ @Override
+ public void processElement(ProcessContext c) {
+ RouteInfo routeInfo = c.element().getValue();
+ TableRow row = new TableRow()
+ .set("avg_speed", routeInfo.getAvgSpeed())
+ .set("slowdown_event", routeInfo.getSlowdownEvent())
+ .set("route", c.element().getKey())
+ .set("window_timestamp", c.timestamp().toString());
+ c.output(row);
+ }
+
+ /**
+ * Defines the BigQuery schema used for the output.
+ */
+ static TableSchema getSchema() {
+ List<TableFieldSchema> fields = new ArrayList<>();
+ fields.add(new TableFieldSchema().setName("route").setType("STRING"));
+ fields.add(new TableFieldSchema().setName("avg_speed").setType("FLOAT"));
+ fields.add(new TableFieldSchema().setName("slowdown_event").setType("BOOLEAN"));
+ fields.add(new TableFieldSchema().setName("window_timestamp").setType("TIMESTAMP"));
+ TableSchema schema = new TableSchema().setFields(fields);
+ return schema;
+ }
+ }
+
+ /**
+ * This PTransform extracts speed info from traffic station readings.
+ * It groups the readings by 'route' and analyzes traffic slowdown for that route.
+ * Lastly, it formats the results for BigQuery.
+ */
+ static class TrackSpeed extends
+ PTransform<PCollection<KV<String, StationSpeed>>, PCollection<TableRow>> {
+ @Override
+ public PCollection<TableRow> apply(PCollection<KV<String, StationSpeed>> stationSpeed) {
+ // Apply a GroupByKey transform to collect a list of all station
+ // readings for a given route.
+ PCollection<KV<String, Iterable<StationSpeed>>> timeGroup = stationSpeed.apply(
+ GroupByKey.<String, StationSpeed>create());
+
+ // Analyze 'slowdown' over the route readings.
+ PCollection<KV<String, RouteInfo>> stats = timeGroup.apply(ParDo.of(new GatherStats()));
+
+ // Format the results for writing to BigQuery
+ PCollection<TableRow> results = stats.apply(
+ ParDo.of(new FormatStatsFn()));
+
+ return results;
+ }
+ }
+
+ static class ReadFileAndExtractTimestamps extends PTransform<PBegin, PCollection<String>> {
+ private final String inputFile;
+
+ public ReadFileAndExtractTimestamps(String inputFile) {
+ this.inputFile = inputFile;
+ }
+
+ @Override
+ public PCollection<String> apply(PBegin begin) {
+ return begin
+ .apply(TextIO.Read.from(inputFile))
+ .apply(ParDo.of(new ExtractTimestamps()));
+ }
+ }
+
+ /**
+ * Options supported by {@link TrafficRoutes}.
+ *
+ * <p>Inherits standard configuration options.
+ */
+ private interface TrafficRoutesOptions extends DataflowExampleOptions,
+ ExamplePubsubTopicAndSubscriptionOptions, ExampleBigQueryTableOptions {
+ @Description("Input file to inject to Pub/Sub topic")
+ @Default.String("gs://dataflow-samples/traffic_sensor/"
+ + "Freeways-5Minaa2010-01-01_to_2010-02-15_test2.csv")
+ String getInputFile();
+ void setInputFile(String value);
+
+ @Description("Numeric value of sliding window duration, in minutes")
+ @Default.Integer(WINDOW_DURATION)
+ Integer getWindowDuration();
+ void setWindowDuration(Integer value);
+
+ @Description("Numeric value of window 'slide every' setting, in minutes")
+ @Default.Integer(WINDOW_SLIDE_EVERY)
+ Integer getWindowSlideEvery();
+ void setWindowSlideEvery(Integer value);
+
+ @Description("Whether to run the pipeline with unbounded input")
+ @Default.Boolean(false)
+ boolean isUnbounded();
+ void setUnbounded(boolean value);
+ }
+
+ /**
+ * Sets up and starts streaming pipeline.
+ *
+ * @throws IOException if there is a problem setting up resources
+ */
+ public static void main(String[] args) throws IOException {
+ TrafficRoutesOptions options = PipelineOptionsFactory.fromArgs(args)
+ .withValidation()
+ .as(TrafficRoutesOptions.class);
+
+ options.setBigQuerySchema(FormatStatsFn.getSchema());
+ // Using DataflowExampleUtils to set up required resources.
+ DataflowExampleUtils dataflowUtils = new DataflowExampleUtils(options, options.isUnbounded());
+
+ Pipeline pipeline = Pipeline.create(options);
+ TableReference tableRef = new TableReference();
+ tableRef.setProjectId(options.getProject());
+ tableRef.setDatasetId(options.getBigQueryDataset());
+ tableRef.setTableId(options.getBigQueryTable());
+
+ PCollection<String> input;
+ if (options.isUnbounded()) {
+ // Read unbounded PubSubIO.
+ input = pipeline.apply(PubsubIO.Read
+ .timestampLabel(PUBSUB_TIMESTAMP_LABEL_KEY)
+ .subscription(options.getPubsubSubscription()));
+ } else {
+ // Read bounded PubSubIO.
+ input = pipeline.apply(PubsubIO.Read
+ .timestampLabel(PUBSUB_TIMESTAMP_LABEL_KEY)
+ .subscription(options.getPubsubSubscription()).maxNumRecords(VALID_INPUTS));
+
+ // To read bounded TextIO files, use:
+ // input = pipeline.apply(TextIO.Read.from(options.getInputFile()))
+ // .apply(ParDo.of(new ExtractTimestamps()));
+ }
+ input
+ // row... => <station route, station speed> ...
+ .apply(ParDo.of(new ExtractStationSpeedFn()))
+ // map the incoming data stream into sliding windows.
+ // The default window duration values work well if you're running the accompanying Pub/Sub
+ // generator script without the --replay flag, so that there are no simulated pauses in
+ // the sensor data publication. You may want to adjust the values otherwise.
+ .apply(Window.<KV<String, StationSpeed>>into(SlidingWindows.of(
+ Duration.standardMinutes(options.getWindowDuration())).
+ every(Duration.standardMinutes(options.getWindowSlideEvery()))))
+ .apply(new TrackSpeed())
+ .apply(BigQueryIO.Write.to(tableRef)
+ .withSchema(FormatStatsFn.getSchema()));
+
+ // Inject the data into the Pub/Sub topic with a Dataflow batch pipeline.
+ if (!Strings.isNullOrEmpty(options.getInputFile())
+ && !Strings.isNullOrEmpty(options.getPubsubTopic())) {
+ dataflowUtils.runInjectorPipeline(
+ new ReadFileAndExtractTimestamps(options.getInputFile()),
+ options.getPubsubTopic(),
+ PUBSUB_TIMESTAMP_LABEL_KEY);
+ }
+
+ // Run the pipeline.
+ PipelineResult result = pipeline.run();
+
+ // dataflowUtils will try to cancel the pipeline and the injector before the program exists.
+ dataflowUtils.waitToFinish(result);
+ }
+
+ private static Double tryParseAvgSpeed(String[] inputItems) {
+ try {
+ return Double.parseDouble(tryParseString(inputItems, 9));
+ } catch (NumberFormatException e) {
+ return null;
+ } catch (NullPointerException e) {
+ return null;
+ }
+ }
+
+ private static String tryParseStationType(String[] inputItems) {
+ return tryParseString(inputItems, 4);
+ }
+
+ private static String tryParseStationId(String[] inputItems) {
+ return tryParseString(inputItems, 1);
+ }
+
+ private static String tryParseTimestamp(String[] inputItems) {
+ return tryParseString(inputItems, 0);
+ }
+
+ private static String tryParseString(String[] inputItems, int index) {
+ return inputItems.length >= index ? inputItems[index] : null;
+ }
+
+ /**
+ * Define some small hard-wired San Diego 'routes' to track based on sensor station ID.
+ */
+ private static Map<String, String> buildStationInfo() {
+ Map<String, String> stations = new Hashtable<String, String>();
+ stations.put("1108413", "SDRoute1"); // from freeway 805 S
+ stations.put("1108699", "SDRoute2"); // from freeway 78 E
+ stations.put("1108702", "SDRoute2");
+ return stations;
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2eaa709c/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/BigQueryTornadoes.java
----------------------------------------------------------------------
diff --git a/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/BigQueryTornadoes.java b/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/BigQueryTornadoes.java
new file mode 100644
index 0000000..503bcad
--- /dev/null
+++ b/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/BigQueryTornadoes.java
@@ -0,0 +1,179 @@
+/*
+ * Copyright (C) 2015 Google Inc.
+ *
+ * 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.dataflow.examples.cookbook;
+
+import com.google.api.services.bigquery.model.TableFieldSchema;
+import com.google.api.services.bigquery.model.TableRow;
+import com.google.api.services.bigquery.model.TableSchema;
+import com.google.cloud.dataflow.sdk.Pipeline;
+import com.google.cloud.dataflow.sdk.io.BigQueryIO;
+import com.google.cloud.dataflow.sdk.options.Default;
+import com.google.cloud.dataflow.sdk.options.Description;
+import com.google.cloud.dataflow.sdk.options.PipelineOptions;
+import com.google.cloud.dataflow.sdk.options.PipelineOptionsFactory;
+import com.google.cloud.dataflow.sdk.options.Validation;
+import com.google.cloud.dataflow.sdk.transforms.Count;
+import com.google.cloud.dataflow.sdk.transforms.DoFn;
+import com.google.cloud.dataflow.sdk.transforms.PTransform;
+import com.google.cloud.dataflow.sdk.transforms.ParDo;
+import com.google.cloud.dataflow.sdk.values.KV;
+import com.google.cloud.dataflow.sdk.values.PCollection;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * An example that reads the public samples of weather data from BigQuery, counts the number of
+ * tornadoes that occur in each month, and writes the results to BigQuery.
+ *
+ * <p>Concepts: Reading/writing BigQuery; counting a PCollection; user-defined PTransforms
+ *
+ * <p>Note: Before running this example, you must create a BigQuery dataset to contain your output
+ * table.
+ *
+ * <p>To execute this pipeline locally, specify general pipeline configuration:
+ * <pre>{@code
+ * --project=YOUR_PROJECT_ID
+ * }
+ * </pre>
+ * and the BigQuery table for the output, with the form
+ * <pre>{@code
+ * --output=YOUR_PROJECT_ID:DATASET_ID.TABLE_ID
+ * }</pre>
+ *
+ * <p>To execute this pipeline using the Dataflow service, specify pipeline configuration:
+ * <pre>{@code
+ * --project=YOUR_PROJECT_ID
+ * --stagingLocation=gs://YOUR_STAGING_DIRECTORY
+ * --runner=BlockingDataflowPipelineRunner
+ * }
+ * </pre>
+ * and the BigQuery table for the output:
+ * <pre>{@code
+ * --output=YOUR_PROJECT_ID:DATASET_ID.TABLE_ID
+ * }</pre>
+ *
+ * <p>The BigQuery input table defaults to {@code clouddataflow-readonly:samples.weather_stations}
+ * and can be overridden with {@code --input}.
+ */
+public class BigQueryTornadoes {
+ // Default to using a 1000 row subset of the public weather station table publicdata:samples.gsod.
+ private static final String WEATHER_SAMPLES_TABLE =
+ "clouddataflow-readonly:samples.weather_stations";
+
+ /**
+ * Examines each row in the input table. If a tornado was recorded
+ * in that sample, the month in which it occurred is output.
+ */
+ static class ExtractTornadoesFn extends DoFn<TableRow, Integer> {
+ @Override
+ public void processElement(ProcessContext c){
+ TableRow row = c.element();
+ if ((Boolean) row.get("tornado")) {
+ c.output(Integer.parseInt((String) row.get("month")));
+ }
+ }
+ }
+
+ /**
+ * Prepares the data for writing to BigQuery by building a TableRow object containing an
+ * integer representation of month and the number of tornadoes that occurred in each month.
+ */
+ static class FormatCountsFn extends DoFn<KV<Integer, Long>, TableRow> {
+ @Override
+ public void processElement(ProcessContext c) {
+ TableRow row = new TableRow()
+ .set("month", c.element().getKey())
+ .set("tornado_count", c.element().getValue());
+ c.output(row);
+ }
+ }
+
+ /**
+ * Takes rows from a table and generates a table of counts.
+ *
+ * <p>The input schema is described by
+ * https://developers.google.com/bigquery/docs/dataset-gsod .
+ * The output contains the total number of tornadoes found in each month in
+ * the following schema:
+ * <ul>
+ * <li>month: integer</li>
+ * <li>tornado_count: integer</li>
+ * </ul>
+ */
+ static class CountTornadoes
+ extends PTransform<PCollection<TableRow>, PCollection<TableRow>> {
+ @Override
+ public PCollection<TableRow> apply(PCollection<TableRow> rows) {
+
+ // row... => month...
+ PCollection<Integer> tornadoes = rows.apply(
+ ParDo.of(new ExtractTornadoesFn()));
+
+ // month... => <month,count>...
+ PCollection<KV<Integer, Long>> tornadoCounts =
+ tornadoes.apply(Count.<Integer>perElement());
+
+ // <month,count>... => row...
+ PCollection<TableRow> results = tornadoCounts.apply(
+ ParDo.of(new FormatCountsFn()));
+
+ return results;
+ }
+ }
+
+ /**
+ * Options supported by {@link BigQueryTornadoes}.
+ *
+ * <p>Inherits standard configuration options.
+ */
+ private static interface Options extends PipelineOptions {
+ @Description("Table to read from, specified as "
+ + "<project_id>:<dataset_id>.<table_id>")
+ @Default.String(WEATHER_SAMPLES_TABLE)
+ String getInput();
+ void setInput(String value);
+
+ @Description("BigQuery table to write to, specified as "
+ + "<project_id>:<dataset_id>.<table_id>. The dataset must already exist.")
+ @Validation.Required
+ String getOutput();
+ void setOutput(String value);
+ }
+
+ public static void main(String[] args) {
+ Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
+
+ Pipeline p = Pipeline.create(options);
+
+ // Build the table schema for the output table.
+ List<TableFieldSchema> fields = new ArrayList<>();
+ fields.add(new TableFieldSchema().setName("month").setType("INTEGER"));
+ fields.add(new TableFieldSchema().setName("tornado_count").setType("INTEGER"));
+ TableSchema schema = new TableSchema().setFields(fields);
+
+ p.apply(BigQueryIO.Read.from(options.getInput()))
+ .apply(new CountTornadoes())
+ .apply(BigQueryIO.Write
+ .to(options.getOutput())
+ .withSchema(schema)
+ .withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_IF_NEEDED)
+ .withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_TRUNCATE));
+
+ p.run();
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2eaa709c/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/CombinePerKeyExamples.java
----------------------------------------------------------------------
diff --git a/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/CombinePerKeyExamples.java b/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/CombinePerKeyExamples.java
new file mode 100644
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--- /dev/null
+++ b/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/CombinePerKeyExamples.java
@@ -0,0 +1,223 @@
+/*
+ * Copyright (C) 2015 Google Inc.
+ *
+ * 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.dataflow.examples.cookbook;
+
+import com.google.api.services.bigquery.model.TableFieldSchema;
+import com.google.api.services.bigquery.model.TableRow;
+import com.google.api.services.bigquery.model.TableSchema;
+import com.google.cloud.dataflow.sdk.Pipeline;
+import com.google.cloud.dataflow.sdk.io.BigQueryIO;
+import com.google.cloud.dataflow.sdk.options.Default;
+import com.google.cloud.dataflow.sdk.options.Description;
+import com.google.cloud.dataflow.sdk.options.PipelineOptions;
+import com.google.cloud.dataflow.sdk.options.PipelineOptionsFactory;
+import com.google.cloud.dataflow.sdk.options.Validation;
+import com.google.cloud.dataflow.sdk.transforms.Aggregator;
+import com.google.cloud.dataflow.sdk.transforms.Combine;
+import com.google.cloud.dataflow.sdk.transforms.DoFn;
+import com.google.cloud.dataflow.sdk.transforms.PTransform;
+import com.google.cloud.dataflow.sdk.transforms.ParDo;
+import com.google.cloud.dataflow.sdk.transforms.SerializableFunction;
+import com.google.cloud.dataflow.sdk.transforms.Sum;
+import com.google.cloud.dataflow.sdk.values.KV;
+import com.google.cloud.dataflow.sdk.values.PCollection;
+
+import java.util.ArrayList;
+import java.util.List;
+
+/**
+ * An example that reads the public 'Shakespeare' data, and for each word in
+ * the dataset that is over a given length, generates a string containing the
+ * list of play names in which that word appears, and saves this information
+ * to a bigquery table.
+ *
+ * <p>Concepts: the Combine.perKey transform, which lets you combine the values in a
+ * key-grouped Collection, and how to use an Aggregator to track information in the
+ * Monitoring UI.
+ *
+ * <p>Note: Before running this example, you must create a BigQuery dataset to contain your output
+ * table.
+ *
+ * <p>To execute this pipeline locally, specify general pipeline configuration:
+ * <pre>{@code
+ * --project=YOUR_PROJECT_ID
+ * }
+ * </pre>
+ * and the BigQuery table for the output:
+ * <pre>{@code
+ * --output=YOUR_PROJECT_ID:DATASET_ID.TABLE_ID
+ * }</pre>
+ *
+ * <p>To execute this pipeline using the Dataflow service, specify pipeline configuration:
+ * <pre>{@code
+ * --project=YOUR_PROJECT_ID
+ * --stagingLocation=gs://<STAGING DIRECTORY>
+ * --runner=BlockingDataflowPipelineRunner
+ * }
+ * </pre>
+ * and the BigQuery table for the output:
+ * <pre>{@code
+ * --output=YOUR_PROJECT_ID:DATASET_ID.TABLE_ID
+ * }</pre>
+ *
+ * <p>The BigQuery input table defaults to {@code publicdata:samples.shakespeare} and can
+ * be overridden with {@code --input}.
+ */
+public class CombinePerKeyExamples {
+ // Use the shakespeare public BigQuery sample
+ private static final String SHAKESPEARE_TABLE =
+ "publicdata:samples.shakespeare";
+ // We'll track words >= this word length across all plays in the table.
+ private static final int MIN_WORD_LENGTH = 9;
+
+ /**
+ * Examines each row in the input table. If the word is greater than or equal to MIN_WORD_LENGTH,
+ * outputs word, play_name.
+ */
+ static class ExtractLargeWordsFn extends DoFn<TableRow, KV<String, String>> {
+ private final Aggregator<Long, Long> smallerWords =
+ createAggregator("smallerWords", new Sum.SumLongFn());
+
+ @Override
+ public void processElement(ProcessContext c){
+ TableRow row = c.element();
+ String playName = (String) row.get("corpus");
+ String word = (String) row.get("word");
+ if (word.length() >= MIN_WORD_LENGTH) {
+ c.output(KV.of(word, playName));
+ } else {
+ // Track how many smaller words we're not including. This information will be
+ // visible in the Monitoring UI.
+ smallerWords.addValue(1L);
+ }
+ }
+ }
+
+
+ /**
+ * Prepares the data for writing to BigQuery by building a TableRow object
+ * containing a word with a string listing the plays in which it appeared.
+ */
+ static class FormatShakespeareOutputFn extends DoFn<KV<String, String>, TableRow> {
+ @Override
+ public void processElement(ProcessContext c) {
+ TableRow row = new TableRow()
+ .set("word", c.element().getKey())
+ .set("all_plays", c.element().getValue());
+ c.output(row);
+ }
+ }
+
+ /**
+ * Reads the public 'Shakespeare' data, and for each word in the dataset
+ * over a given length, generates a string containing the list of play names
+ * in which that word appears. It does this via the Combine.perKey
+ * transform, with the ConcatWords combine function.
+ *
+ * <p>Combine.perKey is similar to a GroupByKey followed by a ParDo, but
+ * has more restricted semantics that allow it to be executed more
+ * efficiently. These records are then formatted as BQ table rows.
+ */
+ static class PlaysForWord
+ extends PTransform<PCollection<TableRow>, PCollection<TableRow>> {
+ @Override
+ public PCollection<TableRow> apply(PCollection<TableRow> rows) {
+
+ // row... => <word, play_name> ...
+ PCollection<KV<String, String>> words = rows.apply(
+ ParDo.of(new ExtractLargeWordsFn()));
+
+ // word, play_name => word, all_plays ...
+ PCollection<KV<String, String>> wordAllPlays =
+ words.apply(Combine.<String, String>perKey(
+ new ConcatWords()));
+
+ // <word, all_plays>... => row...
+ PCollection<TableRow> results = wordAllPlays.apply(
+ ParDo.of(new FormatShakespeareOutputFn()));
+
+ return results;
+ }
+ }
+
+ /**
+ * A 'combine function' used with the Combine.perKey transform. Builds a
+ * comma-separated string of all input items. So, it will build a string
+ * containing all the different Shakespeare plays in which the given input
+ * word has appeared.
+ */
+ public static class ConcatWords implements SerializableFunction<Iterable<String>, String> {
+ @Override
+ public String apply(Iterable<String> input) {
+ StringBuilder all = new StringBuilder();
+ for (String item : input) {
+ if (!item.isEmpty()) {
+ if (all.length() == 0) {
+ all.append(item);
+ } else {
+ all.append(",");
+ all.append(item);
+ }
+ }
+ }
+ return all.toString();
+ }
+ }
+
+ /**
+ * Options supported by {@link CombinePerKeyExamples}.
+ *
+ * <p>Inherits standard configuration options.
+ */
+ private static interface Options extends PipelineOptions {
+ @Description("Table to read from, specified as "
+ + "<project_id>:<dataset_id>.<table_id>")
+ @Default.String(SHAKESPEARE_TABLE)
+ String getInput();
+ void setInput(String value);
+
+ @Description("Table to write to, specified as "
+ + "<project_id>:<dataset_id>.<table_id>. "
+ + "The dataset_id must already exist")
+ @Validation.Required
+ String getOutput();
+ void setOutput(String value);
+ }
+
+ public static void main(String[] args)
+ throws Exception {
+
+ Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
+ Pipeline p = Pipeline.create(options);
+
+ // Build the table schema for the output table.
+ List<TableFieldSchema> fields = new ArrayList<>();
+ fields.add(new TableFieldSchema().setName("word").setType("STRING"));
+ fields.add(new TableFieldSchema().setName("all_plays").setType("STRING"));
+ TableSchema schema = new TableSchema().setFields(fields);
+
+ p.apply(BigQueryIO.Read.from(options.getInput()))
+ .apply(new PlaysForWord())
+ .apply(BigQueryIO.Write
+ .to(options.getOutput())
+ .withSchema(schema)
+ .withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_IF_NEEDED)
+ .withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_TRUNCATE));
+
+ p.run();
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2eaa709c/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/DatastoreWordCount.java
----------------------------------------------------------------------
diff --git a/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/DatastoreWordCount.java b/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/DatastoreWordCount.java
new file mode 100644
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--- /dev/null
+++ b/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/DatastoreWordCount.java
@@ -0,0 +1,269 @@
+/*
+ * Copyright (C) 2015 Google Inc.
+ *
+ * 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.dataflow.examples.cookbook;
+
+import static com.google.api.services.datastore.client.DatastoreHelper.getPropertyMap;
+import static com.google.api.services.datastore.client.DatastoreHelper.getString;
+import static com.google.api.services.datastore.client.DatastoreHelper.makeFilter;
+import static com.google.api.services.datastore.client.DatastoreHelper.makeKey;
+import static com.google.api.services.datastore.client.DatastoreHelper.makeValue;
+
+import com.google.api.services.datastore.DatastoreV1.Entity;
+import com.google.api.services.datastore.DatastoreV1.Key;
+import com.google.api.services.datastore.DatastoreV1.Property;
+import com.google.api.services.datastore.DatastoreV1.PropertyFilter;
+import com.google.api.services.datastore.DatastoreV1.Query;
+import com.google.api.services.datastore.DatastoreV1.Value;
+import com.google.cloud.dataflow.examples.WordCount;
+import com.google.cloud.dataflow.sdk.Pipeline;
+import com.google.cloud.dataflow.sdk.io.DatastoreIO;
+import com.google.cloud.dataflow.sdk.io.Read;
+import com.google.cloud.dataflow.sdk.io.TextIO;
+import com.google.cloud.dataflow.sdk.options.Default;
+import com.google.cloud.dataflow.sdk.options.Description;
+import com.google.cloud.dataflow.sdk.options.PipelineOptions;
+import com.google.cloud.dataflow.sdk.options.PipelineOptionsFactory;
+import com.google.cloud.dataflow.sdk.options.Validation;
+import com.google.cloud.dataflow.sdk.transforms.DoFn;
+import com.google.cloud.dataflow.sdk.transforms.MapElements;
+import com.google.cloud.dataflow.sdk.transforms.ParDo;
+
+import java.util.Map;
+import java.util.UUID;
+
+import javax.annotation.Nullable;
+
+/**
+ * A WordCount example using DatastoreIO.
+ *
+ * <p>This example shows how to use DatastoreIO to read from Datastore and
+ * write the results to Cloud Storage. Note that this example will write
+ * data to Datastore, which may incur charge for Datastore operations.
+ *
+ * <p>To run this example, users need to use gcloud to get credential for Datastore:
+ * <pre>{@code
+ * $ gcloud auth login
+ * }</pre>
+ *
+ * <p>To run this pipeline locally, the following options must be provided:
+ * <pre>{@code
+ * --project=YOUR_PROJECT_ID
+ * --dataset=YOUR_DATASET_ID
+ * --output=[YOUR_LOCAL_FILE | gs://YOUR_OUTPUT_PATH]
+ * }</pre>
+ *
+ * <p>To run this example using Dataflow service, you must additionally
+ * provide either {@literal --stagingLocation} or {@literal --tempLocation}, and
+ * select one of the Dataflow pipeline runners, eg
+ * {@literal --runner=BlockingDataflowPipelineRunner}.
+ *
+ * <p><b>Note:</b> this example creates entities with <i>Ancestor keys</i> to ensure that all
+ * entities created are in the same entity group. Similarly, the query used to read from the Cloud
+ * Datastore uses an <i>Ancestor filter</i>. Ancestors are used to ensure strongly consistent
+ * results in Cloud Datastore. For more information, see the Cloud Datastore documentation on
+ * <a href="https://cloud.google.com/datastore/docs/concepts/structuring_for_strong_consistency">
+ * Structing Data for Strong Consistency</a>.
+ */
+public class DatastoreWordCount {
+
+ /**
+ * A DoFn that gets the content of an entity (one line in a
+ * Shakespeare play) and converts it to a string.
+ */
+ static class GetContentFn extends DoFn<Entity, String> {
+ @Override
+ public void processElement(ProcessContext c) {
+ Map<String, Value> props = getPropertyMap(c.element());
+ Value value = props.get("content");
+ if (value != null) {
+ c.output(getString(value));
+ }
+ }
+ }
+
+ /**
+ * A helper function to create the ancestor key for all created and queried entities.
+ *
+ * <p>We use ancestor keys and ancestor queries for strong consistency. See
+ * {@link DatastoreWordCount} javadoc for more information.
+ */
+ static Key makeAncestorKey(@Nullable String namespace, String kind) {
+ Key.Builder keyBuilder = makeKey(kind, "root");
+ if (namespace != null) {
+ keyBuilder.getPartitionIdBuilder().setNamespace(namespace);
+ }
+ return keyBuilder.build();
+ }
+
+ /**
+ * A DoFn that creates entity for every line in Shakespeare.
+ */
+ static class CreateEntityFn extends DoFn<String, Entity> {
+ private final String namespace;
+ private final String kind;
+ private final Key ancestorKey;
+
+ CreateEntityFn(String namespace, String kind) {
+ this.namespace = namespace;
+ this.kind = kind;
+
+ // Build the ancestor key for all created entities once, including the namespace.
+ ancestorKey = makeAncestorKey(namespace, kind);
+ }
+
+ public Entity makeEntity(String content) {
+ Entity.Builder entityBuilder = Entity.newBuilder();
+
+ // All created entities have the same ancestor Key.
+ Key.Builder keyBuilder = makeKey(ancestorKey, kind, UUID.randomUUID().toString());
+ // NOTE: Namespace is not inherited between keys created with DatastoreHelper.makeKey, so
+ // we must set the namespace on keyBuilder. TODO: Once partitionId inheritance is added,
+ // we can simplify this code.
+ if (namespace != null) {
+ keyBuilder.getPartitionIdBuilder().setNamespace(namespace);
+ }
+
+ entityBuilder.setKey(keyBuilder.build());
+ entityBuilder.addProperty(Property.newBuilder().setName("content")
+ .setValue(Value.newBuilder().setStringValue(content)));
+ return entityBuilder.build();
+ }
+
+ @Override
+ public void processElement(ProcessContext c) {
+ c.output(makeEntity(c.element()));
+ }
+ }
+
+ /**
+ * Options supported by {@link DatastoreWordCount}.
+ *
+ * <p>Inherits standard configuration options.
+ */
+ public static interface Options extends PipelineOptions {
+ @Description("Path of the file to read from and store to Datastore")
+ @Default.String("gs://dataflow-samples/shakespeare/kinglear.txt")
+ String getInput();
+ void setInput(String value);
+
+ @Description("Path of the file to write to")
+ @Validation.Required
+ String getOutput();
+ void setOutput(String value);
+
+ @Description("Dataset ID to read from datastore")
+ @Validation.Required
+ String getDataset();
+ void setDataset(String value);
+
+ @Description("Dataset entity kind")
+ @Default.String("shakespeare-demo")
+ String getKind();
+ void setKind(String value);
+
+ @Description("Dataset namespace")
+ String getNamespace();
+ void setNamespace(@Nullable String value);
+
+ @Description("Read an existing dataset, do not write first")
+ boolean isReadOnly();
+ void setReadOnly(boolean value);
+
+ @Description("Number of output shards")
+ @Default.Integer(0) // If the system should choose automatically.
+ int getNumShards();
+ void setNumShards(int value);
+ }
+
+ /**
+ * An example that creates a pipeline to populate DatastoreIO from a
+ * text input. Forces use of DirectPipelineRunner for local execution mode.
+ */
+ public static void writeDataToDatastore(Options options) {
+ Pipeline p = Pipeline.create(options);
+ p.apply(TextIO.Read.named("ReadLines").from(options.getInput()))
+ .apply(ParDo.of(new CreateEntityFn(options.getNamespace(), options.getKind())))
+ .apply(DatastoreIO.writeTo(options.getDataset()));
+
+ p.run();
+ }
+
+ /**
+ * Build a Cloud Datastore ancestor query for the specified {@link Options#getNamespace} and
+ * {@link Options#getKind}.
+ *
+ * <p>We use ancestor keys and ancestor queries for strong consistency. See
+ * {@link DatastoreWordCount} javadoc for more information.
+ *
+ * @see <a href="https://cloud.google.com/datastore/docs/concepts/queries#Datastore_Ancestor_filters">Ancestor filters</a>
+ */
+ static Query makeAncestorKindQuery(Options options) {
+ Query.Builder q = Query.newBuilder();
+ q.addKindBuilder().setName(options.getKind());
+ q.setFilter(makeFilter(
+ "__key__",
+ PropertyFilter.Operator.HAS_ANCESTOR,
+ makeValue(makeAncestorKey(options.getNamespace(), options.getKind()))));
+ return q.build();
+ }
+
+ /**
+ * An example that creates a pipeline to do DatastoreIO.Read from Datastore.
+ */
+ public static void readDataFromDatastore(Options options) {
+ Query query = makeAncestorKindQuery(options);
+
+ // For Datastore sources, the read namespace can be set on the entire query.
+ DatastoreIO.Source source = DatastoreIO.source()
+ .withDataset(options.getDataset())
+ .withQuery(query)
+ .withNamespace(options.getNamespace());
+
+ Pipeline p = Pipeline.create(options);
+ p.apply("ReadShakespeareFromDatastore", Read.from(source))
+ .apply("StringifyEntity", ParDo.of(new GetContentFn()))
+ .apply("CountWords", new WordCount.CountWords())
+ .apply("PrintWordCount", MapElements.via(new WordCount.FormatAsTextFn()))
+ .apply("WriteLines", TextIO.Write.to(options.getOutput())
+ .withNumShards(options.getNumShards()));
+ p.run();
+ }
+
+ /**
+ * An example to demo how to use {@link DatastoreIO}. The runner here is
+ * customizable, which means users could pass either {@code DirectPipelineRunner}
+ * or {@code DataflowPipelineRunner} in the pipeline options.
+ */
+ public static void main(String args[]) {
+ // The options are used in two places, for Dataflow service, and
+ // building DatastoreIO.Read object
+ Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
+
+ if (!options.isReadOnly()) {
+ // First example: write data to Datastore for reading later.
+ //
+ // NOTE: this write does not delete any existing Entities in the Datastore, so if run
+ // multiple times with the same output dataset, there may be duplicate entries. The
+ // Datastore Query tool in the Google Developers Console can be used to inspect or erase all
+ // entries with a particular namespace and/or kind.
+ DatastoreWordCount.writeDataToDatastore(options);
+ }
+
+ // Second example: do parallel read from Datastore.
+ DatastoreWordCount.readDataFromDatastore(options);
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2eaa709c/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/DeDupExample.java
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diff --git a/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/DeDupExample.java b/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/DeDupExample.java
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@@ -0,0 +1,100 @@
+/*
+ * Copyright (C) 2015 Google Inc.
+ *
+ * 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.dataflow.examples.cookbook;
+
+import com.google.cloud.dataflow.sdk.Pipeline;
+import com.google.cloud.dataflow.sdk.io.TextIO;
+import com.google.cloud.dataflow.sdk.options.DataflowPipelineOptions;
+import com.google.cloud.dataflow.sdk.options.Default;
+import com.google.cloud.dataflow.sdk.options.DefaultValueFactory;
+import com.google.cloud.dataflow.sdk.options.Description;
+import com.google.cloud.dataflow.sdk.options.PipelineOptions;
+import com.google.cloud.dataflow.sdk.options.PipelineOptionsFactory;
+import com.google.cloud.dataflow.sdk.transforms.RemoveDuplicates;
+import com.google.cloud.dataflow.sdk.util.gcsfs.GcsPath;
+
+/**
+ * This example uses as input Shakespeare's plays as plaintext files, and will remove any
+ * duplicate lines across all the files. (The output does not preserve any input order).
+ *
+ * <p>Concepts: the RemoveDuplicates transform, and how to wire transforms together.
+ * Demonstrates {@link com.google.cloud.dataflow.sdk.io.TextIO.Read}/
+ * {@link RemoveDuplicates}/{@link com.google.cloud.dataflow.sdk.io.TextIO.Write}.
+ *
+ * <p>To execute this pipeline locally, specify general pipeline configuration:
+ * --project=YOUR_PROJECT_ID
+ * and a local output file or output prefix on GCS:
+ * --output=[YOUR_LOCAL_FILE | gs://YOUR_OUTPUT_PREFIX]
+ *
+ * <p>To execute this pipeline using the Dataflow service, specify pipeline configuration:
+ * --project=YOUR_PROJECT_ID
+ * --stagingLocation=gs://YOUR_STAGING_DIRECTORY
+ * --runner=BlockingDataflowPipelineRunner
+ * and an output prefix on GCS:
+ * --output=gs://YOUR_OUTPUT_PREFIX
+ *
+ * <p>The input defaults to {@code gs://dataflow-samples/shakespeare/*} and can be
+ * overridden with {@code --input}.
+ */
+public class DeDupExample {
+
+ /**
+ * Options supported by {@link DeDupExample}.
+ *
+ * <p>Inherits standard configuration options.
+ */
+ private static interface Options extends PipelineOptions {
+ @Description("Path to the directory or GCS prefix containing files to read from")
+ @Default.String("gs://dataflow-samples/shakespeare/*")
+ String getInput();
+ void setInput(String value);
+
+ @Description("Path of the file to write to")
+ @Default.InstanceFactory(OutputFactory.class)
+ String getOutput();
+ void setOutput(String value);
+
+ /** Returns gs://${STAGING_LOCATION}/"deduped.txt". */
+ public static class OutputFactory implements DefaultValueFactory<String> {
+ @Override
+ public String create(PipelineOptions options) {
+ DataflowPipelineOptions dataflowOptions = options.as(DataflowPipelineOptions.class);
+ if (dataflowOptions.getStagingLocation() != null) {
+ return GcsPath.fromUri(dataflowOptions.getStagingLocation())
+ .resolve("deduped.txt").toString();
+ } else {
+ throw new IllegalArgumentException("Must specify --output or --stagingLocation");
+ }
+ }
+ }
+ }
+
+
+ public static void main(String[] args)
+ throws Exception {
+
+ Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
+ Pipeline p = Pipeline.create(options);
+
+ p.apply(TextIO.Read.named("ReadLines").from(options.getInput()))
+ .apply(RemoveDuplicates.<String>create())
+ .apply(TextIO.Write.named("DedupedShakespeare")
+ .to(options.getOutput()));
+
+ p.run();
+ }
+}
http://git-wip-us.apache.org/repos/asf/incubator-beam/blob/2eaa709c/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/FilterExamples.java
----------------------------------------------------------------------
diff --git a/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/FilterExamples.java b/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/FilterExamples.java
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--- /dev/null
+++ b/examples/java/src/main/java/com/google/cloud/dataflow/examples/cookbook/FilterExamples.java
@@ -0,0 +1,266 @@
+/*
+ * Copyright (C) 2015 Google Inc.
+ *
+ * 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.dataflow.examples.cookbook;
+
+import com.google.api.services.bigquery.model.TableFieldSchema;
+import com.google.api.services.bigquery.model.TableRow;
+import com.google.api.services.bigquery.model.TableSchema;
+import com.google.cloud.dataflow.sdk.Pipeline;
+import com.google.cloud.dataflow.sdk.io.BigQueryIO;
+import com.google.cloud.dataflow.sdk.options.Default;
+import com.google.cloud.dataflow.sdk.options.Description;
+import com.google.cloud.dataflow.sdk.options.PipelineOptions;
+import com.google.cloud.dataflow.sdk.options.PipelineOptionsFactory;
+import com.google.cloud.dataflow.sdk.options.Validation;
+import com.google.cloud.dataflow.sdk.transforms.DoFn;
+import com.google.cloud.dataflow.sdk.transforms.Mean;
+import com.google.cloud.dataflow.sdk.transforms.PTransform;
+import com.google.cloud.dataflow.sdk.transforms.ParDo;
+import com.google.cloud.dataflow.sdk.transforms.View;
+import com.google.cloud.dataflow.sdk.values.PCollection;
+import com.google.cloud.dataflow.sdk.values.PCollectionView;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.logging.Logger;
+
+/**
+ * This is an example that demonstrates several approaches to filtering, and use of the Mean
+ * transform. It shows how to dynamically set parameters by defining and using new pipeline options,
+ * and how to use a value derived by the pipeline.
+ *
+ * <p>Concepts: The Mean transform; Options configuration; using pipeline-derived data as a side
+ * input; approaches to filtering, selection, and projection.
+ *
+ * <p>The example reads public samples of weather data from BigQuery. It performs a
+ * projection on the data, finds the global mean of the temperature readings, filters on readings
+ * for a single given month, and then outputs only data (for that month) that has a mean temp
+ * smaller than the derived global mean.
+*
+ * <p>Note: Before running this example, you must create a BigQuery dataset to contain your output
+ * table.
+ *
+ * <p>To execute this pipeline locally, specify general pipeline configuration:
+ * <pre>{@code
+ * --project=YOUR_PROJECT_ID
+ * }
+ * </pre>
+ * and the BigQuery table for the output:
+ * <pre>{@code
+ * --output=YOUR_PROJECT_ID:DATASET_ID.TABLE_ID
+ * [--monthFilter=<month_number>]
+ * }
+ * </pre>
+ * where optional parameter {@code --monthFilter} is set to a number 1-12.
+ *
+ * <p>To execute this pipeline using the Dataflow service, specify pipeline configuration:
+ * <pre>{@code
+ * --project=YOUR_PROJECT_ID
+ * --stagingLocation=gs://YOUR_STAGING_DIRECTORY
+ * --runner=BlockingDataflowPipelineRunner
+ * }
+ * </pre>
+ * and the BigQuery table for the output:
+ * <pre>{@code
+ * --output=YOUR_PROJECT_ID:DATASET_ID.TABLE_ID
+ * [--monthFilter=<month_number>]
+ * }
+ * </pre>
+ * where optional parameter {@code --monthFilter} is set to a number 1-12.
+ *
+ * <p>The BigQuery input table defaults to {@code clouddataflow-readonly:samples.weather_stations}
+ * and can be overridden with {@code --input}.
+ */
+public class FilterExamples {
+ // Default to using a 1000 row subset of the public weather station table publicdata:samples.gsod.
+ private static final String WEATHER_SAMPLES_TABLE =
+ "clouddataflow-readonly:samples.weather_stations";
+ static final Logger LOG = Logger.getLogger(FilterExamples.class.getName());
+ static final int MONTH_TO_FILTER = 7;
+
+ /**
+ * Examines each row in the input table. Outputs only the subset of the cells this example
+ * is interested in-- the mean_temp and year, month, and day-- as a bigquery table row.
+ */
+ static class ProjectionFn extends DoFn<TableRow, TableRow> {
+ @Override
+ public void processElement(ProcessContext c){
+ TableRow row = c.element();
+ // Grab year, month, day, mean_temp from the row
+ Integer year = Integer.parseInt((String) row.get("year"));
+ Integer month = Integer.parseInt((String) row.get("month"));
+ Integer day = Integer.parseInt((String) row.get("day"));
+ Double meanTemp = Double.parseDouble(row.get("mean_temp").toString());
+ // Prepares the data for writing to BigQuery by building a TableRow object
+ TableRow outRow = new TableRow()
+ .set("year", year).set("month", month)
+ .set("day", day).set("mean_temp", meanTemp);
+ c.output(outRow);
+ }
+ }
+
+ /**
+ * Implements 'filter' functionality.
+ *
+ * <p>Examines each row in the input table. Outputs only rows from the month
+ * monthFilter, which is passed in as a parameter during construction of this DoFn.
+ */
+ static class FilterSingleMonthDataFn extends DoFn<TableRow, TableRow> {
+ Integer monthFilter;
+
+ public FilterSingleMonthDataFn(Integer monthFilter) {
+ this.monthFilter = monthFilter;
+ }
+
+ @Override
+ public void processElement(ProcessContext c){
+ TableRow row = c.element();
+ Integer month;
+ month = (Integer) row.get("month");
+ if (month.equals(this.monthFilter)) {
+ c.output(row);
+ }
+ }
+ }
+
+ /**
+ * Examines each row (weather reading) in the input table. Output the temperature
+ * reading for that row ('mean_temp').
+ */
+ static class ExtractTempFn extends DoFn<TableRow, Double> {
+ @Override
+ public void processElement(ProcessContext c){
+ TableRow row = c.element();
+ Double meanTemp = Double.parseDouble(row.get("mean_temp").toString());
+ c.output(meanTemp);
+ }
+ }
+
+
+
+ /*
+ * Finds the global mean of the mean_temp for each day/record, and outputs
+ * only data that has a mean temp larger than this global mean.
+ **/
+ static class BelowGlobalMean
+ extends PTransform<PCollection<TableRow>, PCollection<TableRow>> {
+ Integer monthFilter;
+
+ public BelowGlobalMean(Integer monthFilter) {
+ this.monthFilter = monthFilter;
+ }
+
+
+ @Override
+ public PCollection<TableRow> apply(PCollection<TableRow> rows) {
+
+ // Extract the mean_temp from each row.
+ PCollection<Double> meanTemps = rows.apply(
+ ParDo.of(new ExtractTempFn()));
+
+ // Find the global mean, of all the mean_temp readings in the weather data,
+ // and prepare this singleton PCollectionView for use as a side input.
+ final PCollectionView<Double> globalMeanTemp =
+ meanTemps.apply(Mean.<Double>globally())
+ .apply(View.<Double>asSingleton());
+
+ // Rows filtered to remove all but a single month
+ PCollection<TableRow> monthFilteredRows = rows
+ .apply(ParDo.of(new FilterSingleMonthDataFn(monthFilter)));
+
+ // Then, use the global mean as a side input, to further filter the weather data.
+ // By using a side input to pass in the filtering criteria, we can use a value
+ // that is computed earlier in pipeline execution.
+ // We'll only output readings with temperatures below this mean.
+ PCollection<TableRow> filteredRows = monthFilteredRows
+ .apply(ParDo
+ .named("ParseAndFilter")
+ .withSideInputs(globalMeanTemp)
+ .of(new DoFn<TableRow, TableRow>() {
+ @Override
+ public void processElement(ProcessContext c) {
+ Double meanTemp = Double.parseDouble(c.element().get("mean_temp").toString());
+ Double gTemp = c.sideInput(globalMeanTemp);
+ if (meanTemp < gTemp) {
+ c.output(c.element());
+ }
+ }
+ }));
+
+ return filteredRows;
+ }
+ }
+
+
+ /**
+ * Options supported by {@link FilterExamples}.
+ *
+ * <p>Inherits standard configuration options.
+ */
+ private static interface Options extends PipelineOptions {
+ @Description("Table to read from, specified as "
+ + "<project_id>:<dataset_id>.<table_id>")
+ @Default.String(WEATHER_SAMPLES_TABLE)
+ String getInput();
+ void setInput(String value);
+
+ @Description("Table to write to, specified as "
+ + "<project_id>:<dataset_id>.<table_id>. "
+ + "The dataset_id must already exist")
+ @Validation.Required
+ String getOutput();
+ void setOutput(String value);
+
+ @Description("Numeric value of month to filter on")
+ @Default.Integer(MONTH_TO_FILTER)
+ Integer getMonthFilter();
+ void setMonthFilter(Integer value);
+ }
+
+ /**
+ * Helper method to build the table schema for the output table.
+ */
+ private static TableSchema buildWeatherSchemaProjection() {
+ List<TableFieldSchema> fields = new ArrayList<>();
+ fields.add(new TableFieldSchema().setName("year").setType("INTEGER"));
+ fields.add(new TableFieldSchema().setName("month").setType("INTEGER"));
+ fields.add(new TableFieldSchema().setName("day").setType("INTEGER"));
+ fields.add(new TableFieldSchema().setName("mean_temp").setType("FLOAT"));
+ TableSchema schema = new TableSchema().setFields(fields);
+ return schema;
+ }
+
+ public static void main(String[] args)
+ throws Exception {
+
+ Options options = PipelineOptionsFactory.fromArgs(args).withValidation().as(Options.class);
+ Pipeline p = Pipeline.create(options);
+
+ TableSchema schema = buildWeatherSchemaProjection();
+
+ p.apply(BigQueryIO.Read.from(options.getInput()))
+ .apply(ParDo.of(new ProjectionFn()))
+ .apply(new BelowGlobalMean(options.getMonthFilter()))
+ .apply(BigQueryIO.Write
+ .to(options.getOutput())
+ .withSchema(schema)
+ .withCreateDisposition(BigQueryIO.Write.CreateDisposition.CREATE_IF_NEEDED)
+ .withWriteDisposition(BigQueryIO.Write.WriteDisposition.WRITE_TRUNCATE));
+
+ p.run();
+ }
+}