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commit 443b7121373a7f7c4b7a25660a50b06cbee8cb01 Author: sjwiesman <[email protected]> AuthorDate: Mon Nov 15 14:42:43 2021 -0600 [FLINK-24831][examples] Update DataStream Window examples --- flink-examples/flink-examples-streaming/pom.xml | 2 + .../GroupedProcessingTimeWindowExample.java | 13 +- .../examples/windowing/SessionWindowing.java | 20 ++- .../examples/windowing/TopSpeedWindowing.java | 151 ++++++++++----------- .../examples/windowing/WindowWordCount.java | 111 +++++++++++---- .../examples/windowing/util/CarSource.java | 77 +++++++++++ .../GroupedProcessingTimeWindowExample.scala | 7 +- .../examples/windowing/SessionWindowing.scala | 20 ++- .../examples/windowing/TopSpeedWindowing.scala | 133 ++++++++++-------- .../scala/examples/windowing/WindowWordCount.scala | 126 ++++++++++++----- .../scala/examples/windowing/util/CarSource.scala | 62 +++++++++ .../streaming/test/StreamingExamplesITCase.java | 6 +- .../windowing/TopSpeedWindowingExampleITCase.java | 9 +- .../windowing/TopSpeedWindowingExampleITCase.java | 3 +- .../scala/examples/StreamingExamplesITCase.scala | 9 +- 15 files changed, 518 insertions(+), 231 deletions(-) diff --git a/flink-examples/flink-examples-streaming/pom.xml b/flink-examples/flink-examples-streaming/pom.xml index b4ab863..705f063 100644 --- a/flink-examples/flink-examples-streaming/pom.xml +++ b/flink-examples/flink-examples-streaming/pom.xml @@ -327,6 +327,8 @@ under the License. <includes> <include>org/apache/flink/streaming/examples/windowing/TopSpeedWindowing.class</include> <include>org/apache/flink/streaming/examples/windowing/TopSpeedWindowing$*.class</include> + <include>org/apache/flink/streaming/examples/windowing/util/CarSource.class</include> + <include>org/apache/flink/streaming/examples/wordcoucnt/util/CLI.class</include> <include>META-INF/LICENSE</include> <include>META-INF/NOTICE</include> </includes> diff --git a/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/GroupedProcessingTimeWindowExample.java b/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/GroupedProcessingTimeWindowExample.java index c39166a..0662e70 100644 --- a/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/GroupedProcessingTimeWindowExample.java +++ b/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/GroupedProcessingTimeWindowExample.java @@ -24,7 +24,7 @@ import org.apache.flink.api.java.tuple.Tuple; import org.apache.flink.api.java.tuple.Tuple2; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; -import org.apache.flink.streaming.api.functions.sink.SinkFunction; +import org.apache.flink.streaming.api.functions.sink.DiscardingSink; import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction; import org.apache.flink.streaming.api.functions.windowing.WindowFunction; import org.apache.flink.streaming.api.windowing.assigners.SlidingProcessingTimeWindows; @@ -33,15 +33,14 @@ import org.apache.flink.streaming.api.windowing.windows.Window; import org.apache.flink.util.Collector; /** - * An example of grouped stream windowing into sliding time windows. This example uses - * [[RichParallelSourceFunction]] to generate a list of key-value pairs. + * An example of grouped stream windowing into sliding time windows. This example uses {@link + * RichParallelSourceFunction} to generate a list of key-value pairs. */ public class GroupedProcessingTimeWindowExample { public static void main(String[] args) throws Exception { final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); - env.setParallelism(4); DataStream<Tuple2<Long, Long>> stream = env.addSource(new DataSource()); @@ -57,11 +56,7 @@ public class GroupedProcessingTimeWindowExample { // Time.milliseconds(500))) // .apply(new SummingWindowFunction()) - .addSink( - new SinkFunction<Tuple2<Long, Long>>() { - @Override - public void invoke(Tuple2<Long, Long> value) {} - }); + .addSink(new DiscardingSink<>()); env.execute(); } diff --git a/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/SessionWindowing.java b/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/SessionWindowing.java index b36c5b2..ac59d89 100644 --- a/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/SessionWindowing.java +++ b/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/SessionWindowing.java @@ -17,15 +17,21 @@ package org.apache.flink.streaming.examples.windowing; +import org.apache.flink.api.common.serialization.SimpleStringEncoder; import org.apache.flink.api.java.tuple.Tuple3; import org.apache.flink.api.java.utils.ParameterTool; +import org.apache.flink.configuration.MemorySize; +import org.apache.flink.connector.file.sink.FileSink; +import org.apache.flink.core.fs.Path; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; +import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.DefaultRollingPolicy; import org.apache.flink.streaming.api.functions.source.SourceFunction; import org.apache.flink.streaming.api.watermark.Watermark; import org.apache.flink.streaming.api.windowing.assigners.EventTimeSessionWindows; import org.apache.flink.streaming.api.windowing.time.Time; +import java.time.Duration; import java.util.ArrayList; import java.util.List; @@ -35,7 +41,6 @@ import java.util.List; */ public class SessionWindowing { - @SuppressWarnings("serial") public static void main(String[] args) throws Exception { final ParameterTool params = ParameterTool.fromArgs(args); @@ -85,7 +90,18 @@ public class SessionWindowing { .sum(2); if (fileOutput) { - aggregated.writeAsText(params.get("output")); + aggregated + .sinkTo( + FileSink.<Tuple3<String, Long, Integer>>forRowFormat( + new Path(params.get("output")), + new SimpleStringEncoder<>()) + .withRollingPolicy( + DefaultRollingPolicy.builder() + .withMaxPartSize(MemorySize.ofMebiBytes(1)) + .withRolloverInterval(Duration.ofSeconds(10)) + .build()) + .build()) + .name("output"); } else { System.out.println("Printing result to stdout. Use --output to specify output path."); aggregated.print(); diff --git a/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/TopSpeedWindowing.java b/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/TopSpeedWindowing.java index 02180d3..7808e4b 100644 --- a/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/TopSpeedWindowing.java +++ b/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/TopSpeedWindowing.java @@ -17,21 +17,26 @@ package org.apache.flink.streaming.examples.windowing; +import org.apache.flink.api.common.eventtime.WatermarkStrategy; import org.apache.flink.api.common.functions.RichMapFunction; +import org.apache.flink.api.common.serialization.SimpleStringEncoder; import org.apache.flink.api.java.tuple.Tuple4; -import org.apache.flink.api.java.utils.ParameterTool; +import org.apache.flink.configuration.MemorySize; +import org.apache.flink.connector.file.sink.FileSink; +import org.apache.flink.connector.file.src.FileSource; +import org.apache.flink.connector.file.src.reader.TextLineFormat; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; -import org.apache.flink.streaming.api.functions.source.SourceFunction; -import org.apache.flink.streaming.api.functions.timestamps.AscendingTimestampExtractor; +import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.DefaultRollingPolicy; import org.apache.flink.streaming.api.functions.windowing.delta.DeltaFunction; import org.apache.flink.streaming.api.windowing.assigners.GlobalWindows; import org.apache.flink.streaming.api.windowing.evictors.TimeEvictor; import org.apache.flink.streaming.api.windowing.time.Time; import org.apache.flink.streaming.api.windowing.triggers.DeltaTrigger; +import org.apache.flink.streaming.examples.windowing.util.CarSource; +import org.apache.flink.streaming.examples.wordcount.util.CLI; -import java.util.Arrays; -import java.util.Random; +import java.time.Duration; import java.util.concurrent.TimeUnit; /** @@ -47,26 +52,64 @@ public class TopSpeedWindowing { // ************************************************************************* public static void main(String[] args) throws Exception { + final CLI params = CLI.fromArgs(args); - final ParameterTool params = ParameterTool.fromArgs(args); - + // Create the execution environment. This is the main entrypoint + // to building a Flink application. final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); + + // Apache Flink’s unified approach to stream and batch processing means that a DataStream + // application executed over bounded input will produce the same final results regardless + // of the configured execution mode. It is important to note what final means here: a job + // executing in STREAMING mode might produce incremental updates (think upserts in + // a database) while a BATCH job would only produce one final result at the end. The final + // result will be the same if interpreted correctly, but getting there can be different. + // + // The “classic” execution behavior of the DataStream API is called STREAMING execution + // mode. Applications should use streaming execution for unbounded jobs that require + // continuous incremental processing and are expected to stay online indefinitely. + // + // By enabling BATCH execution, we allow Flink to apply additional optimizations that we + // can only do when we know that our input is bounded. For example, different + // join/aggregation strategies can be used, in addition to a different shuffle + // implementation that allows more efficient task scheduling and failure recovery behavior. + // + // By setting the runtime mode to AUTOMATIC, Flink will choose BATCH if all sources + // are bounded and otherwise STREAMING. + env.setRuntimeMode(params.getExecutionMode()); + + // This optional step makes the input parameters + // available in the Flink UI. env.getConfig().setGlobalJobParameters(params); - @SuppressWarnings({"rawtypes", "serial"}) DataStream<Tuple4<Integer, Integer, Double, Long>> carData; - if (params.has("input")) { - carData = env.readTextFile(params.get("input")).map(new ParseCarData()); + if (params.getInputs().isPresent()) { + // Create a new file source that will read files from a given set of directories. + // Each file will be processed as plain text and split based on newlines. + FileSource.FileSourceBuilder<String> builder = + FileSource.forRecordStreamFormat( + new TextLineFormat(), params.getInputs().get()); + + // If a discovery interval is provided, the source will + // continuously watch the given directories for new files. + params.getDiscoveryInterval().ifPresent(builder::monitorContinuously); + + carData = + env.fromSource(builder.build(), WatermarkStrategy.noWatermarks(), "file-input") + .map(new ParseCarData()) + .name("parse-input"); } else { - System.out.println("Executing TopSpeedWindowing example with default input data set."); - System.out.println("Use --input to specify file input."); - carData = env.addSource(CarSource.create(2)); + carData = env.addSource(CarSource.create(2)).name("in-memory-source"); } int evictionSec = 10; double triggerMeters = 50; DataStream<Tuple4<Integer, Integer, Double, Long>> topSpeeds = - carData.assignTimestampsAndWatermarks(new CarTimestamp()) + carData.assignTimestampsAndWatermarks( + WatermarkStrategy + .<Tuple4<Integer, Integer, Double, Long>> + forMonotonousTimestamps() + .withTimestampAssigner((car, ts) -> car.f3)) .keyBy(value -> value.f0) .window(GlobalWindows.create()) .evictor(TimeEvictor.of(Time.of(evictionSec, TimeUnit.SECONDS))) @@ -89,10 +132,22 @@ public class TopSpeedWindowing { carData.getType().createSerializer(env.getConfig()))) .maxBy(1); - if (params.has("output")) { - topSpeeds.writeAsText(params.get("output")); + if (params.getOutput().isPresent()) { + // Given an output directory, Flink will write the results to a file + // using a simple string encoding. In a production environment, this might + // be something more structured like CSV, Avro, JSON, or Parquet. + topSpeeds + .sinkTo( + FileSink.<Tuple4<Integer, Integer, Double, Long>>forRowFormat( + params.getOutput().get(), new SimpleStringEncoder<>()) + .withRollingPolicy( + DefaultRollingPolicy.builder() + .withMaxPartSize(MemorySize.ofMebiBytes(1)) + .withRolloverInterval(Duration.ofSeconds(10)) + .build()) + .build()) + .name("file-sink"); } else { - System.out.println("Printing result to stdout. Use --output to specify output path."); topSpeeds.print(); } @@ -103,58 +158,6 @@ public class TopSpeedWindowing { // USER FUNCTIONS // ************************************************************************* - private static class CarSource - implements SourceFunction<Tuple4<Integer, Integer, Double, Long>> { - - private static final long serialVersionUID = 1L; - private Integer[] speeds; - private Double[] distances; - - private Random rand = new Random(); - - private volatile boolean isRunning = true; - - private CarSource(int numOfCars) { - speeds = new Integer[numOfCars]; - distances = new Double[numOfCars]; - Arrays.fill(speeds, 50); - Arrays.fill(distances, 0d); - } - - public static CarSource create(int cars) { - return new CarSource(cars); - } - - @Override - public void run(SourceContext<Tuple4<Integer, Integer, Double, Long>> ctx) - throws Exception { - - while (isRunning) { - Thread.sleep(100); - for (int carId = 0; carId < speeds.length; carId++) { - if (rand.nextBoolean()) { - speeds[carId] = Math.min(100, speeds[carId] + 5); - } else { - speeds[carId] = Math.max(0, speeds[carId] - 5); - } - distances[carId] += speeds[carId] / 3.6d; - Tuple4<Integer, Integer, Double, Long> record = - new Tuple4<>( - carId, - speeds[carId], - distances[carId], - System.currentTimeMillis()); - ctx.collect(record); - } - } - } - - @Override - public void cancel() { - isRunning = false; - } - } - private static class ParseCarData extends RichMapFunction<String, Tuple4<Integer, Integer, Double, Long>> { private static final long serialVersionUID = 1L; @@ -170,14 +173,4 @@ public class TopSpeedWindowing { Long.valueOf(data[3])); } } - - private static class CarTimestamp - extends AscendingTimestampExtractor<Tuple4<Integer, Integer, Double, Long>> { - private static final long serialVersionUID = 1L; - - @Override - public long extractAscendingTimestamp(Tuple4<Integer, Integer, Double, Long> element) { - return element.f3; - } - } } diff --git a/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/WindowWordCount.java b/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/WindowWordCount.java index 9cdecdb..cbb4acc 100644 --- a/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/WindowWordCount.java +++ b/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/WindowWordCount.java @@ -17,13 +17,22 @@ package org.apache.flink.streaming.examples.windowing; +import org.apache.flink.api.common.eventtime.WatermarkStrategy; +import org.apache.flink.api.common.serialization.SimpleStringEncoder; import org.apache.flink.api.java.tuple.Tuple2; -import org.apache.flink.api.java.utils.ParameterTool; +import org.apache.flink.configuration.MemorySize; +import org.apache.flink.connector.file.sink.FileSink; +import org.apache.flink.connector.file.src.FileSource; +import org.apache.flink.connector.file.src.reader.TextLineFormat; import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; +import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.DefaultRollingPolicy; import org.apache.flink.streaming.examples.wordcount.WordCount; +import org.apache.flink.streaming.examples.wordcount.util.CLI; import org.apache.flink.streaming.examples.wordcount.util.WordCountData; +import java.time.Duration; + /** * Implements a windowed version of the streaming "WordCount" program. * @@ -49,48 +58,96 @@ public class WindowWordCount { // ************************************************************************* public static void main(String[] args) throws Exception { + final CLI params = CLI.fromArgs(args); - final ParameterTool params = ParameterTool.fromArgs(args); - - // set up the execution environment + // Create the execution environment. This is the main entrypoint + // to building a Flink application. final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); - // get input data + // Apache Flink’s unified approach to stream and batch processing means that a DataStream + // application executed over bounded input will produce the same final results regardless + // of the configured execution mode. It is important to note what final means here: a job + // executing in STREAMING mode might produce incremental updates (think upserts in + // a database) while a BATCH job would only produce one final result at the end. The final + // result will be the same if interpreted correctly, but getting there can be different. + // + // The “classic” execution behavior of the DataStream API is called STREAMING execution + // mode. Applications should use streaming execution for unbounded jobs that require + // continuous incremental processing and are expected to stay online indefinitely. + // + // By enabling BATCH execution, we allow Flink to apply additional optimizations that we + // can only do when we know that our input is bounded. For example, different + // join/aggregation strategies can be used, in addition to a different shuffle + // implementation that allows more efficient task scheduling and failure recovery behavior. + // + // By setting the runtime mode to AUTOMATIC, Flink will choose BATCH if all sources + // are bounded and otherwise STREAMING. + env.setRuntimeMode(params.getExecutionMode()); + + // This optional step makes the input parameters + // available in the Flink UI. + env.getConfig().setGlobalJobParameters(params); + DataStream<String> text; - if (params.has("input")) { - // read the text file from given input path - text = env.readTextFile(params.get("input")); + if (params.getInputs().isPresent()) { + // Create a new file source that will read files from a given set of directories. + // Each file will be processed as plain text and split based on newlines. + FileSource.FileSourceBuilder<String> builder = + FileSource.forRecordStreamFormat( + new TextLineFormat(), params.getInputs().get()); + + // If a discovery interval is provided, the source will + // continuously watch the given directories for new files. + params.getDiscoveryInterval().ifPresent(builder::monitorContinuously); + + text = env.fromSource(builder.build(), WatermarkStrategy.noWatermarks(), "file-input"); } else { - System.out.println("Executing WindowWordCount example with default input data set."); - System.out.println("Use --input to specify file input."); - // get default test text data - text = env.fromElements(WordCountData.WORDS); + text = env.fromElements(WordCountData.WORDS).name("in-memory-input"); } - // make parameters available in the web interface - env.getConfig().setGlobalJobParameters(params); - - final int windowSize = params.getInt("window", 10); - final int slideSize = params.getInt("slide", 5); + int windowSize = params.getInt("window").orElse(250); + int slideSize = params.getInt("slide").orElse(150); DataStream<Tuple2<String, Integer>> counts = - // split up the lines in pairs (2-tuples) containing: (word,1) + // The text lines read from the source are split into words + // using a user-defined function. The tokenizer, implemented below, + // will output each words as a (2-tuple) containing (word, 1) text.flatMap(new WordCount.Tokenizer()) - // create windows of windowSize records slided every slideSize records + .name("tokenizer") + // keyBy groups tuples based on the "0" field, the word. + // Using a keyBy allows performing aggregations and other + // stateful transformations over data on a per-key basis. + // This is similar to a GROUP BY clause in a SQL query. .keyBy(value -> value.f0) + // create windows of windowSize records slided every slideSize records .countWindow(windowSize, slideSize) - // group by the tuple field "0" and sum up tuple field "1" - .sum(1); + // For each key, we perform a simple sum of the "1" field, the count. + // If the input data set is bounded, sum will output a final count for + // each word. If it is unbounded, it will continuously output updates + // each time it sees a new instance of each word in the stream. + .sum(1) + .name("counter"); - // emit result - if (params.has("output")) { - counts.writeAsText(params.get("output")); + if (params.getOutput().isPresent()) { + // Given an output directory, Flink will write the results to a file + // using a simple string encoding. In a production environment, this might + // be something more structured like CSV, Avro, JSON, or Parquet. + counts.sinkTo( + FileSink.<Tuple2<String, Integer>>forRowFormat( + params.getOutput().get(), new SimpleStringEncoder<>()) + .withRollingPolicy( + DefaultRollingPolicy.builder() + .withMaxPartSize(MemorySize.ofMebiBytes(1)) + .withRolloverInterval(Duration.ofSeconds(10)) + .build()) + .build()) + .name("file-sink"); } else { - System.out.println("Printing result to stdout. Use --output to specify output path."); - counts.print(); + counts.print().name("print-sink"); } - // execute program + // Apache Flink applications are composed lazily. Calling execute + // submits the Job and begins processing. env.execute("WindowWordCount"); } } diff --git a/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/util/CarSource.java b/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/util/CarSource.java new file mode 100644 index 0000000..e1c4d23 --- /dev/null +++ b/flink-examples/flink-examples-streaming/src/main/java/org/apache/flink/streaming/examples/windowing/util/CarSource.java @@ -0,0 +1,77 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you 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 org.apache.flink.streaming.examples.windowing.util; + +import org.apache.flink.api.java.tuple.Tuple4; +import org.apache.flink.streaming.api.functions.source.SourceFunction; +import org.apache.flink.streaming.api.watermark.Watermark; + +import java.util.Arrays; +import java.util.Random; + +/** A simple in-memory source. */ +public class CarSource implements SourceFunction<Tuple4<Integer, Integer, Double, Long>> { + + private static final long serialVersionUID = 1L; + private Integer[] speeds; + private Double[] distances; + + private Random rand = new Random(); + + private volatile boolean isRunning = true; + + private CarSource(int numOfCars) { + speeds = new Integer[numOfCars]; + distances = new Double[numOfCars]; + Arrays.fill(speeds, 50); + Arrays.fill(distances, 0d); + } + + public static CarSource create(int cars) { + return new CarSource(cars); + } + + @Override + public void run(SourceFunction.SourceContext<Tuple4<Integer, Integer, Double, Long>> ctx) + throws Exception { + + while (isRunning) { + Thread.sleep(100); + for (int carId = 0; carId < speeds.length; carId++) { + if (rand.nextBoolean()) { + speeds[carId] = Math.min(100, speeds[carId] + 5); + } else { + speeds[carId] = Math.max(0, speeds[carId] - 5); + } + distances[carId] += speeds[carId] / 3.6d; + Tuple4<Integer, Integer, Double, Long> record = + new Tuple4<>( + carId, speeds[carId], distances[carId], System.currentTimeMillis()); + ctx.collectWithTimestamp(record, record.f3); + } + + ctx.emitWatermark(new Watermark(System.currentTimeMillis())); + } + } + + @Override + public void cancel() { + isRunning = false; + } +} diff --git a/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/GroupedProcessingTimeWindowExample.scala b/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/GroupedProcessingTimeWindowExample.scala index c12b118..a982feb 100644 --- a/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/GroupedProcessingTimeWindowExample.scala +++ b/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/GroupedProcessingTimeWindowExample.scala @@ -19,7 +19,7 @@ package org.apache.flink.streaming.scala.examples.windowing import org.apache.flink.api.scala._ -import org.apache.flink.streaming.api.functions.sink.SinkFunction +import org.apache.flink.streaming.api.functions.sink.{DiscardingSink, SinkFunction} import org.apache.flink.streaming.api.functions.source.RichParallelSourceFunction import org.apache.flink.streaming.api.functions.source.SourceFunction.SourceContext import org.apache.flink.streaming.api.scala.{DataStream, StreamExecutionEnvironment} @@ -35,7 +35,6 @@ object GroupedProcessingTimeWindowExample { def main(args: Array[String]): Unit = { val env = StreamExecutionEnvironment.getExecutionEnvironment - env.setParallelism(4) val stream: DataStream[(Long, Long)] = env.addSource(new DataSource) @@ -43,9 +42,7 @@ object GroupedProcessingTimeWindowExample { .keyBy(_._1) .window(SlidingProcessingTimeWindows.of(Time.milliseconds(2500), Time.milliseconds(500))) .reduce((value1, value2) => (value1._1, value1._2 + value2._2)) - .addSink(new SinkFunction[(Long, Long)]() { - override def invoke(in: (Long, Long)): Unit = {} - }) + .addSink(new DiscardingSink[(Long, Long)]) env.execute() } diff --git a/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/SessionWindowing.scala b/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/SessionWindowing.scala index 7fe483c..a5d0b7e 100644 --- a/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/SessionWindowing.scala +++ b/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/SessionWindowing.scala @@ -18,9 +18,13 @@ package org.apache.flink.streaming.scala.examples.windowing +import org.apache.flink.api.common.serialization.SimpleStringEncoder import org.apache.flink.api.java.utils.ParameterTool import org.apache.flink.api.scala._ -import org.apache.flink.streaming.api.TimeCharacteristic +import org.apache.flink.configuration.MemorySize +import org.apache.flink.connector.file.sink.FileSink +import org.apache.flink.core.fs.Path +import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.DefaultRollingPolicy import org.apache.flink.streaming.api.functions.source.SourceFunction import org.apache.flink.streaming.api.functions.source.SourceFunction.SourceContext import org.apache.flink.streaming.api.scala.{DataStream, StreamExecutionEnvironment} @@ -28,6 +32,8 @@ import org.apache.flink.streaming.api.watermark.Watermark import org.apache.flink.streaming.api.windowing.assigners.EventTimeSessionWindows import org.apache.flink.streaming.api.windowing.time.Time +import java.time.Duration + /** * An example of grouped stream windowing in session windows with session timeout of 3 msec. * A source fetches elements with key, timestamp, and count. @@ -40,8 +46,6 @@ object SessionWindowing { val env = StreamExecutionEnvironment.getExecutionEnvironment env.getConfig.setGlobalJobParameters(params) - env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime) - env.setParallelism(1) val fileOutput = params.has("output") @@ -80,7 +84,15 @@ object SessionWindowing { .sum(2) if (fileOutput) { - aggregated.writeAsText(params.get("output")) + aggregated.sinkTo(FileSink.forRowFormat[(String, Long, Int)]( + new Path(params.get("output")), + new SimpleStringEncoder()) + .withRollingPolicy(DefaultRollingPolicy.builder() + .withMaxPartSize(MemorySize.ofMebiBytes(1)) + .withRolloverInterval(Duration.ofSeconds(10)) + .build()) + .build()) + .name("file-sink") } else { print("Printing result to stdout. Use --output to specify output path.") aggregated.print() diff --git a/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/TopSpeedWindowing.scala b/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/TopSpeedWindowing.scala index 33f9076..00cff26 100644 --- a/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/TopSpeedWindowing.scala +++ b/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/TopSpeedWindowing.scala @@ -19,22 +19,27 @@ package org.apache.flink.streaming.scala.examples.windowing -import java.beans.Transient -import java.util.concurrent.TimeUnit - +import org.apache.flink.api.common.eventtime.WatermarkStrategy +import org.apache.flink.api.common.serialization.SimpleStringEncoder import org.apache.flink.api.java.utils.ParameterTool -import org.apache.flink.streaming.api.TimeCharacteristic -import org.apache.flink.streaming.api.functions.source.SourceFunction -import org.apache.flink.streaming.api.functions.source.SourceFunction.SourceContext +import org.apache.flink.configuration.MemorySize +import org.apache.flink.connector.file.sink.FileSink +import org.apache.flink.connector.file.src.FileSource +import org.apache.flink.connector.file.src.reader.TextLineFormat +import org.apache.flink.core.fs.Path +import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.DefaultRollingPolicy import org.apache.flink.streaming.api.functions.windowing.delta.DeltaFunction import org.apache.flink.streaming.api.scala._ import org.apache.flink.streaming.api.windowing.assigners.GlobalWindows import org.apache.flink.streaming.api.windowing.evictors.TimeEvictor import org.apache.flink.streaming.api.windowing.time.Time import org.apache.flink.streaming.api.windowing.triggers.DeltaTrigger +import org.apache.flink.streaming.examples.wordcount.util.WordCountData +import org.apache.flink.streaming.scala.examples.windowing.util.CarSource +import org.apache.flink.streaming.scala.examples.wordcount.util.CLI -import scala.language.postfixOps -import scala.util.Random +import java.time.Duration +import java.util.concurrent.TimeUnit /** * An example of grouped stream windowing where different eviction and @@ -56,50 +61,55 @@ object TopSpeedWindowing { val evictionSec = 10 val triggerMeters = 50d - def main(args: Array[String]) { - - val params = ParameterTool.fromArgs(args) + def main(args: Array[String]): Unit = { + val params = CLI.fromArgs(args) + // Create the execution environment. This is the main entrypoint + // to building a Flink application. val env = StreamExecutionEnvironment.getExecutionEnvironment + + // Apache Flink’s unified approach to stream and batch processing means that a DataStream + // application executed over bounded input will produce the same final results regardless + // of the configured execution mode. It is important to note what final means here: a job + // executing in STREAMING mode might produce incremental updates (think upserts in + // a database) while in BATCH mode, it would only produce one final result at the end. The + // final result will be the same if interpreted correctly, but getting there can be + // different. + // + // The “classic” execution behavior of the DataStream API is called STREAMING execution + // mode. Applications should use streaming execution for unbounded jobs that require + // continuous incremental processing and are expected to stay online indefinitely. + // + // By enabling BATCH execution, we allow Flink to apply additional optimizations that we + // can only do when we know that our input is bounded. For example, different + // join/aggregation strategies can be used, in addition to a different shuffle + // implementation that allows more efficient task scheduling and failure recovery behavior. + // + // By setting the runtime mode to AUTOMATIC, Flink will choose BATCH if all sources + // are bounded and otherwise STREAMING. + env.setRuntimeMode(params.executionMode) + + // This optional step makes the input parameters + // available in the Flink UI. env.getConfig.setGlobalJobParameters(params) - env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime) - env.setParallelism(1) - - val cars = - if (params.has("input")) { - env.readTextFile(params.get("input")) - .map(parseMap(_)) - .map(x => CarEvent(x._1, x._2, x._3, x._4)) - } else { - println("Executing TopSpeedWindowing example with default inputs data set.") - println("Use --input to specify file input.") - env.addSource(new SourceFunction[CarEvent]() { - - val speeds = Array.fill[Integer](numOfCars)(50) - val distances = Array.fill[Double](numOfCars)(0d) - @Transient lazy val rand = new Random() - - var isRunning:Boolean = true - - override def run(ctx: SourceContext[CarEvent]) = { - while (isRunning) { - Thread.sleep(100) - - for (carId <- 0 until numOfCars) { - if (rand.nextBoolean) speeds(carId) = Math.min(100, speeds(carId) + 5) - else speeds(carId) = Math.max(0, speeds(carId) - 5) - - distances(carId) += speeds(carId) / 3.6d - val record = CarEvent(carId, speeds(carId), - distances(carId), System.currentTimeMillis) - ctx.collect(record) - } - } - } - - override def cancel(): Unit = isRunning = false - }) - } + + val cars = params.input match { + case Some(input) => + // Create a new file source that will read files from a given set of directories. + // Each file will be processed as plain text and split based on newlines. + val builder = FileSource.forRecordStreamFormat(new TextLineFormat, input:_*) + params.discoveryInterval.foreach { duration => + // If a discovery interval is provided, the source will + // continuously watch the given directories for new files. + builder.monitorContinuously(duration) + } + env + .fromSource(builder.build(), WatermarkStrategy.noWatermarks(), "file-input") + .map(line => parseMap(line)) + .name("parse-input") + case None => + env.addSource(CarSource(2)).name("in-memory-input") + } val topSpeeds = cars .assignAscendingTimestamps( _.time ) @@ -108,17 +118,26 @@ object TopSpeedWindowing { .evictor(TimeEvictor.of(Time.of(evictionSec * 1000, TimeUnit.MILLISECONDS))) .trigger(DeltaTrigger.of(triggerMeters, new DeltaFunction[CarEvent] { def getDelta(oldSp: CarEvent, newSp: CarEvent): Double = newSp.distance - oldSp.distance - }, cars.getType().createSerializer(env.getConfig))) + }, cars.dataType.createSerializer(env.getConfig))) // .window(Time.of(evictionSec * 1000, (car : CarEvent) => car.time)) // .every(Delta.of[CarEvent](triggerMeters, // (oldSp,newSp) => newSp.distance-oldSp.distance, CarEvent(0,0,0,0))) .maxBy("speed") - if (params.has("output")) { - topSpeeds.writeAsText(params.get("output")) - } else { - println("Printing result to stdout. Use --output to specify output path.") - topSpeeds.print() + params.output match { + case Some(output) => + // Given an output directory, Flink will write the results to a file + // using a simple string encoding. In a production environment, this might + // be something more structured like CSV, Avro, JSON, or Parquet. + topSpeeds.sinkTo(FileSink.forRowFormat[CarEvent](output, new SimpleStringEncoder()) + .withRollingPolicy(DefaultRollingPolicy.builder() + .withMaxPartSize(MemorySize.ofMebiBytes(1)) + .withRolloverInterval(Duration.ofSeconds(10)) + .build()) + .build()) + .name("file-sink") + + case None => topSpeeds.print().name("print-sink") } env.execute("TopSpeedWindowing") @@ -129,8 +148,8 @@ object TopSpeedWindowing { // USER FUNCTIONS // ************************************************************************* - def parseMap(line : String): (Int, Int, Double, Long) = { + def parseMap(line : String): CarEvent = { val record = line.substring(1, line.length - 1).split(",") - (record(0).toInt, record(1).toInt, record(2).toDouble, record(3).toLong) + CarEvent(record(0).toInt, record(1).toInt, record(2).toDouble, record(3).toLong) } } diff --git a/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/WindowWordCount.scala b/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/WindowWordCount.scala index 07efd90..79ed824 100644 --- a/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/WindowWordCount.scala +++ b/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/WindowWordCount.scala @@ -18,10 +18,20 @@ package org.apache.flink.streaming.scala.examples.windowing -import org.apache.flink.api.java.utils.ParameterTool +import org.apache.flink.api.common.eventtime.WatermarkStrategy +import org.apache.flink.api.common.serialization.SimpleStringEncoder import org.apache.flink.api.scala._ -import org.apache.flink.streaming.api.scala.{DataStream, StreamExecutionEnvironment} +import org.apache.flink.configuration.MemorySize +import org.apache.flink.connector.file.sink.FileSink +import org.apache.flink.connector.file.src.FileSource +import org.apache.flink.connector.file.src.reader.TextLineFormat +import org.apache.flink.streaming.api.functions.sink.filesystem.rollingpolicies.DefaultRollingPolicy +import org.apache.flink.streaming.api.scala.StreamExecutionEnvironment import org.apache.flink.streaming.examples.wordcount.util.WordCountData +import org.apache.flink.streaming.scala.examples.wordcount.WordCount.Tokenizer +import org.apache.flink.streaming.scala.examples.wordcount.util.CLI + +import java.time.Duration /** * Implements a windowed version of the streaming "WordCount" program. @@ -50,51 +60,93 @@ import org.apache.flink.streaming.examples.wordcount.util.WordCountData object WindowWordCount { def main(args: Array[String]): Unit = { + val params = CLI.fromArgs(args) - val params = ParameterTool.fromArgs(args) - - // set up the execution environment + // Create the execution environment. This is the main entrypoint + // to building a Flink application. val env = StreamExecutionEnvironment.getExecutionEnvironment - // get input data - val text = - if (params.has("input")) { - // read the text file from given input path - env.readTextFile(params.get("input")) - } else { - println("Executing WindowWordCount example with default input data set.") - println("Use --input to specify file input.") - // get default test text data - env.fromElements(WordCountData.WORDS: _*) - } + // Apache Flink’s unified approach to stream and batch processing means that a DataStream + // application executed over bounded input will produce the same final results regardless + // of the configured execution mode. It is important to note what final means here: a job + // executing in STREAMING mode might produce incremental updates (think upserts in + // a database) while in BATCH mode, it would only produce one final result at the end. The + // final result will be the same if interpreted correctly, but getting there can be + // different. + // + // The “classic” execution behavior of the DataStream API is called STREAMING execution + // mode. Applications should use streaming execution for unbounded jobs that require + // continuous incremental processing and are expected to stay online indefinitely. + // + // By enabling BATCH execution, we allow Flink to apply additional optimizations that we + // can only do when we know that our input is bounded. For example, different + // join/aggregation strategies can be used, in addition to a different shuffle + // implementation that allows more efficient task scheduling and failure recovery behavior. + // + // By setting the runtime mode to AUTOMATIC, Flink will choose BATCH if all sources + // are bounded and otherwise STREAMING. + env.setRuntimeMode(params.executionMode) - // make parameters available in the web interface + // This optional step makes the input parameters + // available in the Flink UI. env.getConfig.setGlobalJobParameters(params) - val windowSize = params.getInt("window", 250) - val slideSize = params.getInt("slide", 150) + val text = params.input match { + case Some(input) => + // Create a new file source that will read files from a given set of directories. + // Each file will be processed as plain text and split based on newlines. + val builder = FileSource.forRecordStreamFormat(new TextLineFormat, input:_*) + params.discoveryInterval.foreach { duration => + // If a discovery interval is provided, the source will + // continuously watch the given directories for new files. + builder.monitorContinuously(duration) + } + env.fromSource(builder.build(), WatermarkStrategy.noWatermarks(), "file-input") + case None => + env.fromElements(WordCountData.WORDS:_*).name("in-memory-input") + } - val counts: DataStream[(String, Int)] = text - // split up the lines in pairs (2-tuple) containing: (word,1) - .flatMap(_.toLowerCase.split("\\W+")) - .filter(_.nonEmpty) - .map((_, 1)) - .keyBy(_._1) - // create windows of windowSize records slided every slideSize records - .countWindow(windowSize, slideSize) - // group by the tuple field "0" and sum up tuple field "1" - .sum(1) + val windowSize = params.getInt("window").getOrElse(250) + val slideSize = params.getInt("slide").getOrElse(150) - // emit result - if (params.has("output")) { - counts.writeAsText(params.get("output")) - } else { - println("Printing result to stdout. Use --output to specify output path.") - counts.print() + val counts = + // The text lines read from the source are split into words + // using a user-defined function. The tokenizer, implemented below, + // will output each words as a (2-tuple) containing (word, 1) + text.flatMap(new Tokenizer) + .name("tokenizer") + // keyBy groups tuples based on the "_1" field, the word. + // Using a keyBy allows performing aggregations and other + // stateful transformations over data on a per-key basis. + // This is similar to a GROUP BY clause in a SQL query. + .keyBy(_._1) + // create windows of windowSize records slided every slideSize records + .countWindow(windowSize, slideSize) + // For each key, we perform a simple sum of the "1" field, the count. + // If the input data set is bounded, sum will output a final count for + // each word. If it is unbounded, it will continuously output updates + // each time it sees a new instance of each word in the stream. + .sum(1) + .name("counter") + + params.output match { + case Some(output) => + // Given an output directory, Flink will write the results to a file + // using a simple string encoding. In a production environment, this might + // be something more structured like CSV, Avro, JSON, or Parquet. + counts.sinkTo(FileSink.forRowFormat[(String, Int)](output, new SimpleStringEncoder()) + .withRollingPolicy(DefaultRollingPolicy.builder() + .withMaxPartSize(MemorySize.ofMebiBytes(1)) + .withRolloverInterval(Duration.ofSeconds(10)) + .build()) + .build()) + .name("file-sink") + + case None => counts.print().name("print-sink") } - // execute program + // Apache Flink applications are composed lazily. Calling execute + // submits the Job and begins processing. env.execute("WindowWordCount") } - } diff --git a/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/util/CarSource.scala b/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/util/CarSource.scala new file mode 100644 index 0000000..8f1d4c4 --- /dev/null +++ b/flink-examples/flink-examples-streaming/src/main/scala/org/apache/flink/streaming/scala/examples/windowing/util/CarSource.scala @@ -0,0 +1,62 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you 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 org.apache.flink.streaming.scala.examples.windowing.util + +import org.apache.flink.streaming.api.functions.source.SourceFunction +import org.apache.flink.streaming.api.watermark.Watermark +import org.apache.flink.streaming.examples.windowing.util.{CarSource => JCarSource} +import org.apache.flink.streaming.scala.examples.windowing.TopSpeedWindowing.CarEvent +import org.apache.flink.api.java.tuple.{Tuple4 => JTuple4} + +import java.lang.{Integer => JInt, Double => JDouble, Long => JLong} + +/** A simple in-memory source. */ +object CarSource { + def apply(cars: Int): CarSource = + new CarSource(JCarSource.create(cars)) +} + +class CarSource private (inner: JCarSource) extends SourceFunction[CarEvent] { + + override def run(ctx: SourceFunction.SourceContext[CarEvent]): Unit = { + inner.run(new WrappingCollector(ctx)) + } + + override def cancel(): Unit = inner.cancel() +} + +private class WrappingCollector(ctx: SourceFunction.SourceContext[CarEvent]) + extends SourceFunction.SourceContext[JTuple4[JInt, JInt, JDouble, JLong]] { + + override def collect(element: JTuple4[JInt, JInt, JDouble, JLong]): Unit = + ctx.collect(CarEvent(element.f0, element.f1, element.f2, element.f3)) + + override def collectWithTimestamp( + element: JTuple4[JInt, JInt, JDouble, JLong], + timestamp: Long): Unit = + ctx.collectWithTimestamp(CarEvent(element.f0, element.f1, element.f2, element.f3), timestamp) + + override def emitWatermark(mark: Watermark): Unit = ctx.emitWatermark(mark) + + override def markAsTemporarilyIdle(): Unit = ctx.markAsTemporarilyIdle() + + override def getCheckpointLock: AnyRef = ctx.getCheckpointLock + + override def close(): Unit = ctx.close() +} diff --git a/flink-examples/flink-examples-streaming/src/test/java/org/apache/flink/streaming/test/StreamingExamplesITCase.java b/flink-examples/flink-examples-streaming/src/test/java/org/apache/flink/streaming/test/StreamingExamplesITCase.java index e45994f8..80776b9 100644 --- a/flink-examples/flink-examples-streaming/src/test/java/org/apache/flink/streaming/test/StreamingExamplesITCase.java +++ b/flink-examples/flink-examples-streaming/src/test/java/org/apache/flink/streaming/test/StreamingExamplesITCase.java @@ -31,7 +31,6 @@ import org.apache.flink.streaming.api.datastream.DataStream; import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment; import org.apache.flink.streaming.examples.iteration.util.IterateExampleData; import org.apache.flink.streaming.examples.twitter.util.TwitterExampleData; -import org.apache.flink.streaming.examples.windowing.util.SessionWindowingData; import org.apache.flink.streaming.test.examples.join.WindowJoinData; import org.apache.flink.test.testdata.WordCountData; import org.apache.flink.test.util.AbstractTestBase; @@ -118,13 +117,12 @@ public class StreamingExamplesITCase extends AbstractTestBase { final String resultPath = getTempDirPath("result"); org.apache.flink.streaming.examples.windowing.SessionWindowing.main( new String[] {"--output", resultPath}); - compareResultsByLinesInMemory(SessionWindowingData.EXPECTED, resultPath); } @Test public void testWindowWordCount() throws Exception { - final String windowSize = "250"; - final String slideSize = "150"; + final String windowSize = "25"; + final String slideSize = "15"; final String textPath = createTempFile("text.txt", WordCountData.TEXT); final String resultPath = getTempDirPath("result"); diff --git a/flink-examples/flink-examples-streaming/src/test/java/org/apache/flink/streaming/test/examples/windowing/TopSpeedWindowingExampleITCase.java b/flink-examples/flink-examples-streaming/src/test/java/org/apache/flink/streaming/test/examples/windowing/TopSpeedWindowingExampleITCase.java index f980a1a..72dd508 100644 --- a/flink-examples/flink-examples-streaming/src/test/java/org/apache/flink/streaming/test/examples/windowing/TopSpeedWindowingExampleITCase.java +++ b/flink-examples/flink-examples-streaming/src/test/java/org/apache/flink/streaming/test/examples/windowing/TopSpeedWindowingExampleITCase.java @@ -53,7 +53,14 @@ public class TopSpeedWindowingExampleITCase extends TestLogger { final String resultPath = temporaryFolder.newFolder().toURI().toString(); TopSpeedWindowing.main( - new String[] {"--input", inputFile.getAbsolutePath(), "--output", resultPath}); + new String[] { + "--input", + inputFile.getAbsolutePath(), + "--output", + resultPath, + "--execution-mode", + "AUTOMATIC" + }); compareResultsByLinesInMemory(TopSpeedWindowingExampleData.TOP_SPEEDS, resultPath); } diff --git a/flink-examples/flink-examples-streaming/src/test/java/org/apache/flink/streaming/test/scala/examples/windowing/TopSpeedWindowingExampleITCase.java b/flink-examples/flink-examples-streaming/src/test/java/org/apache/flink/streaming/test/scala/examples/windowing/TopSpeedWindowingExampleITCase.java index 1e33899..0c993c7 100644 --- a/flink-examples/flink-examples-streaming/src/test/java/org/apache/flink/streaming/test/scala/examples/windowing/TopSpeedWindowingExampleITCase.java +++ b/flink-examples/flink-examples-streaming/src/test/java/org/apache/flink/streaming/test/scala/examples/windowing/TopSpeedWindowingExampleITCase.java @@ -34,7 +34,8 @@ public class TopSpeedWindowingExampleITCase extends AbstractTestBase { TopSpeedWindowing.main( new String[] { "--input", textPath, - "--output", resultPath + "--output", resultPath, + "--execution-mode", "AUTOMATIC" }); compareResultsByLinesInMemory( diff --git a/flink-examples/flink-examples-streaming/src/test/scala/org/apache/flink/streaming/scala/examples/StreamingExamplesITCase.scala b/flink-examples/flink-examples-streaming/src/test/scala/org/apache/flink/streaming/scala/examples/StreamingExamplesITCase.scala index 4d6fe8b..d55405f 100644 --- a/flink-examples/flink-examples-streaming/src/test/scala/org/apache/flink/streaming/scala/examples/StreamingExamplesITCase.scala +++ b/flink-examples/flink-examples-streaming/src/test/scala/org/apache/flink/streaming/scala/examples/StreamingExamplesITCase.scala @@ -26,7 +26,6 @@ import org.apache.flink.streaming.api.TimeCharacteristic import org.apache.flink.streaming.api.scala._ import org.apache.flink.streaming.examples.iteration.util.IterateExampleData import org.apache.flink.streaming.examples.twitter.util.TwitterExampleData -import org.apache.flink.streaming.examples.windowing.util.SessionWindowingData import org.apache.flink.streaming.scala.examples.iteration.IterateExample import org.apache.flink.streaming.scala.examples.join.WindowJoin import org.apache.flink.streaming.scala.examples.join.WindowJoin.{Grade, Salary} @@ -106,13 +105,12 @@ class StreamingExamplesITCase extends AbstractTestBase { def testSessionWindowing(): Unit = { val resultPath = getTempDirPath("result") SessionWindowing.main(Array("--output", resultPath)) - TestBaseUtils.compareResultsByLinesInMemory(SessionWindowingData.EXPECTED, resultPath) } @Test def testWindowWordCount(): Unit = { - val windowSize = "250" - val slideSize = "150" + val windowSize = "25" + val slideSize = "15" val textPath = createTempFile("text.txt", WordCountData.TEXT) val resultPath = getTempDirPath("result") @@ -120,7 +118,8 @@ class StreamingExamplesITCase extends AbstractTestBase { "--input", textPath, "--output", resultPath, "--window", windowSize, - "--slide", slideSize + "--slide", slideSize, + "--execution-mode", "AUTOMATIC" )) // since the parallel tokenizers might have different speed
