content: /*
   * 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.beam.runners.spark.aggregators.metrics.sink;
  
  import static org.hamcrest.MatcherAssert.assertThat;
  import static org.hamcrest.Matchers.is;
  import static org.hamcrest.Matchers.nullValue;
  
  import org.apache.beam.runners.spark.SparkContextRule;
  import org.apache.beam.runners.spark.SparkPipelineOptions;
  import org.apache.beam.runners.spark.StreamingTest;
  import org.apache.beam.runners.spark.TestSparkPipelineOptions;
  import org.apache.beam.runners.spark.examples.WordCount;
  import org.apache.beam.runners.spark.io.CreateStream;
  import org.apache.beam.sdk.coders.StringUtf8Coder;
  import org.apache.beam.sdk.testing.PAssert;
  import org.apache.beam.sdk.testing.TestPipeline;
  import org.apache.beam.sdk.transforms.Create;
  import org.apache.beam.sdk.transforms.MapElements;
  import org.apache.beam.sdk.transforms.windowing.FixedWindows;
  import org.apache.beam.sdk.transforms.windowing.Window;
  import org.apache.beam.sdk.values.KV;
  import org.apache.beam.sdk.values.PCollection;
  import org.apache.beam.sdk.values.TimestampedValue;
  import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableList;
  import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.ImmutableSet;
  import org.joda.time.Duration;
  import org.joda.time.Instant;
  import org.junit.ClassRule;
  import org.junit.Rule;
  import org.junit.Test;
  import org.junit.experimental.categories.Category;
  import org.junit.rules.ExternalResource;
  
  /**
   * A test that verifies Beam metrics are reported to Spark's metrics sink in both batch and
   * streaming modes.
   */
  public class SparkMetricsSinkTest {
    @ClassRule
    public static SparkContextRule contextRule =
        new SparkContextRule(
            KV.of("spark.metrics.conf.*.sink.memory.class", InMemoryMetrics.class.getName()));
  
    @Rule public ExternalResource inMemoryMetricsSink = new InMemoryMetricsSinkRule();
  
    @Rule
    public final TestPipeline pipeline =
        TestPipeline.fromOptions(contextRule.createPipelineOptions());
  
    private static final ImmutableList<String> WORDS =
        ImmutableList.of("hi there", "hi", "hi sue bob", "hi sue", "", "bob hi");
    private static final ImmutableSet<String> EXPECTED_COUNTS =
        ImmutableSet.of("hi: 5", "there: 1", "sue: 2", "bob: 2");
  
    @Test
    public void testInBatchMode() throws Exception {
      assertThat(InMemoryMetrics.valueOf("emptyLines"), is(nullValue()));
  
      final PCollection<String> output =
          pipeline
              .apply(Create.of(WORDS).withCoder(StringUtf8Coder.of()))
              .apply(new WordCount.CountWords())
              .apply(MapElements.via(new WordCount.FormatAsTextFn()));
      PAssert.that(output).containsInAnyOrder(EXPECTED_COUNTS);
      pipeline.run().waitUntilFinish();
  
      assertThat(InMemoryMetrics.<Double>valueOf("emptyLines"), is(1d));
    }
  
    @Category(StreamingTest.class)
    @Test
    public void testInStreamingMode() throws Exception {
      pipeline.getOptions().as(TestSparkPipelineOptions.class).setStreaming(true);
      assertThat(InMemoryMetrics.valueOf("emptyLines"), is(nullValue()));
  
      Instant instant = new Instant(0);
      CreateStream<String> source =
          CreateStream.of(
                  StringUtf8Coder.of(),
                  Duration.millis(
                      pipeline.getOptions().as(SparkPipelineOptions.class).getBatchIntervalMillis()))
              .emptyBatch()
              .advanceWatermarkForNextBatch(instant)
              .nextBatch(
                  TimestampedValue.of(WORDS.get(0), instant),
                  TimestampedValue.of(WORDS.get(1), instant),
                  TimestampedValue.of(WORDS.get(2), instant))
              .advanceWatermarkForNextBatch(instant.plus(Duration.standardSeconds(2L)))
              .nextBatch(
                  TimestampedValue.of(WORDS.get(3), instant.plus(Duration.standardSeconds(1L))),
                  TimestampedValue.of(WORDS.get(4), instant.plus(Duration.standardSeconds(1L))),
                  TimestampedValue.of(WORDS.get(5), instant.plus(Duration.standardSeconds(1L))))
              .advanceNextBatchWatermarkToInfinity();
      PCollection<String> output =
          pipeline
              .apply(source)
              .apply(
                  Window.<String>into(FixedWindows.of(Duration.standardSeconds(3L)))
                      .withAllowedLateness(Duration.ZERO))
              .apply(new WordCount.CountWords())
              .apply(MapElements.via(new WordCount.FormatAsTextFn()));
      PAssert.that(output).containsInAnyOrder(EXPECTED_COUNTS);
      pipeline.run();
  
      assertThat(InMemoryMetrics.<Double>valueOf("emptyLines"), is(1d));
    }
  }
  