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.flink;
  
  import static org.hamcrest.MatcherAssert.assertThat;
  import static org.hamcrest.Matchers.equalTo;
  import static org.hamcrest.Matchers.nullValue;
  import static org.junit.Assert.assertNull;
  
  import java.util.Collections;
  import java.util.HashMap;
  import java.util.List;
  import java.util.Map;
  import java.util.UUID;
  import org.apache.beam.runners.flink.FlinkStreamingPipelineTranslator.FlinkAutoBalancedShardKeyShardingFunction;
  import org.apache.beam.sdk.Pipeline;
  import org.apache.beam.sdk.coders.KvCoder;
  import org.apache.beam.sdk.coders.StringUtf8Coder;
  import org.apache.beam.sdk.coders.VarLongCoder;
  import org.apache.beam.sdk.options.PipelineOptions;
  import org.apache.beam.sdk.options.PipelineOptionsFactory;
  import org.apache.beam.sdk.state.TimeDomain;
  import org.apache.beam.sdk.state.TimerSpec;
  import org.apache.beam.sdk.state.TimerSpecs;
  import org.apache.beam.sdk.transforms.Count;
  import org.apache.beam.sdk.transforms.Create;
  import org.apache.beam.sdk.transforms.DoFn;
  import org.apache.beam.sdk.transforms.GroupByKey;
  import org.apache.beam.sdk.transforms.ParDo;
  import org.apache.beam.sdk.util.SerializableUtils;
  import org.apache.beam.sdk.values.KV;
  import org.apache.beam.sdk.values.PCollection;
  import org.apache.beam.sdk.values.ShardedKey;
  import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Iterables;
  import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Lists;
  import org.apache.flink.runtime.jobgraph.JobGraph;
  import org.apache.flink.runtime.state.KeyGroupRangeAssignment;
  import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
  import org.junit.Assert;
  import org.junit.Test;
  
  /** Tests if overrides are properly applied. */
  // TODO(https://github.com/apache/beam/issues/21230): Remove when new version of errorprone is
  // released (2.11.0)
  @SuppressWarnings("unused")
  public class FlinkStreamingPipelineTranslatorTest {
  
    @Test
    public void testAutoBalanceShardKeyResolvesMaxParallelism() {
  
      int parallelism = 3;
      assertThat(
          new FlinkAutoBalancedShardKeyShardingFunction<>(parallelism, -1, StringUtf8Coder.of())
              .getMaxParallelism(),
          equalTo(KeyGroupRangeAssignment.computeDefaultMaxParallelism(parallelism)));
      assertThat(
          new FlinkAutoBalancedShardKeyShardingFunction<>(parallelism, 0, StringUtf8Coder.of())
              .getMaxParallelism(),
          equalTo(KeyGroupRangeAssignment.computeDefaultMaxParallelism(parallelism)));
    }
  
    @Test
    public void testAutoBalanceShardKeyCacheIsNotSerialized() throws Exception {
  
      FlinkAutoBalancedShardKeyShardingFunction<String, String> fn =
          new FlinkAutoBalancedShardKeyShardingFunction<>(2, 2, StringUtf8Coder.of());
  
      assertNull(fn.getCache());
  
      fn.assignShardKey("target/destination1", "one", 10);
      fn.assignShardKey("target/destination2", "two", 10);
  
      assertThat(fn.getCache().size(), equalTo(2));
      assertThat(SerializableUtils.clone(fn).getCache(), nullValue());
    }
  
    @Test
    public void testAutoBalanceShardKeyCacheIsStable() throws Exception {
  
      int numShards = 50;
  
      FlinkAutoBalancedShardKeyShardingFunction<String, String> fn =
          new FlinkAutoBalancedShardKeyShardingFunction<>(
              numShards / 2, numShards * 2, StringUtf8Coder.of());
  
      List<KV<String, String>> inputs = Lists.newArrayList();
      for (int i = 0; i < numShards * 100; i++) {
        inputs.add(KV.of("target/destination/1", UUID.randomUUID().toString()));
        inputs.add(KV.of("target/destination/2", UUID.randomUUID().toString()));
        inputs.add(KV.of("target/destination/3", UUID.randomUUID().toString()));
      }
  
      Map<KV<String, Integer>, ShardedKey<Integer>> generatedKeys = new HashMap<>();
      for (KV<String, String> input : inputs) {
        ShardedKey<Integer> shardKey = fn.assignShardKey(input.getKey(), input.getValue(), numShards);
        generatedKeys.put(KV.of(input.getKey(), shardKey.getShardNumber()), shardKey);
      }
  
      // let's create new sharding function instance, shuffle inputs and check if we generate same
      // shard keys
      fn =
          new FlinkAutoBalancedShardKeyShardingFunction<>(
              numShards / 2, numShards * 2, StringUtf8Coder.of());
  
      Collections.shuffle(inputs);
      for (KV<String, String> input : inputs) {
        ShardedKey<Integer> shardKey = fn.assignShardKey(input.getKey(), input.getValue(), numShards);
        ShardedKey<Integer> expectedShardKey =
            generatedKeys.get(KV.of(input.getKey(), shardKey.getShardNumber()));
        if (expectedShardKey != null) {
          assertThat(shardKey, equalTo(expectedShardKey));
        }
      }
    }
  
    @Test
    public void testAutoBalanceShardKeyCacheMaxSize() throws Exception {
  
      FlinkAutoBalancedShardKeyShardingFunction<String, String> fn =
          new FlinkAutoBalancedShardKeyShardingFunction<>(2, 2, StringUtf8Coder.of());
  
      for (int i = 0; i < FlinkAutoBalancedShardKeyShardingFunction.CACHE_MAX_SIZE * 2; i++) {
        fn.assignShardKey(UUID.randomUUID().toString(), "one", 2);
      }
  
      assertThat(
          fn.getCache().size(), equalTo(FlinkAutoBalancedShardKeyShardingFunction.CACHE_MAX_SIZE));
    }
  
    @Test
    public void testStatefulParDoAfterCombineChaining() {
      final JobGraph stablePartitioning = getStatefulParDoAfterCombineChainingJobGraph(true);
      final JobGraph unstablePartitioning = getStatefulParDoAfterCombineChainingJobGraph(false);
      // We expect an extra shuffle stage for unstable partitioning.
      Assert.assertEquals(
          1,
          Iterables.size(unstablePartitioning.getVertices())
              - Iterables.size(stablePartitioning.getVertices()));
    }
  
    private JobGraph getStatefulParDoAfterCombineChainingJobGraph(boolean stablePartitioning) {
      final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
      final FlinkStreamingPipelineTranslator translator =
          new FlinkStreamingPipelineTranslator(env, PipelineOptionsFactory.create(), true);
      final PipelineOptions pipelineOptions = PipelineOptionsFactory.create();
      pipelineOptions.setRunner(FlinkRunner.class);
      final Pipeline pipeline = Pipeline.create(pipelineOptions);
      PCollection<KV<String, Long>> aggregate =
          pipeline
              .apply(Create.of("foo", "bar").withCoder(StringUtf8Coder.of()))
              .apply(Count.perElement());
      if (!stablePartitioning) {
        // When we insert any element-wise "map" operation between aggregation and stateful ParDo, we
        // can no longer assume that partitioning did not change, therefore we need an extra shuffle
        aggregate = aggregate.apply(ParDo.of(new StatelessIdentityDoFn<>()));
      }
      aggregate.apply(ParDo.of(new StatefulNoopDoFn<>()));
      translator.translate(pipeline);
      return env.getStreamGraph().getJobGraph();
    }
  
    @Test
    public void testStatefulParDoAfterGroupByKeyChaining() {
      final JobGraph stablePartitioning = getStatefulParDoAfterGroupByKeyChainingJobGraph(true);
      final JobGraph unstablePartitioning = getStatefulParDoAfterGroupByKeyChainingJobGraph(false);
      // We expect an extra shuffle stage for unstable partitioning.
      Assert.assertEquals(
          1,
          Iterables.size(unstablePartitioning.getVertices())
              - Iterables.size(stablePartitioning.getVertices()));
    }
  
    private JobGraph getStatefulParDoAfterGroupByKeyChainingJobGraph(boolean stablePartitioning) {
      final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
      final FlinkStreamingPipelineTranslator translator =
          new FlinkStreamingPipelineTranslator(env, PipelineOptionsFactory.create(), true);
      final PipelineOptions pipelineOptions = PipelineOptionsFactory.create();
      pipelineOptions.setRunner(FlinkRunner.class);
      final Pipeline pipeline = Pipeline.create(pipelineOptions);
      PCollection<KV<String, Iterable<Long>>> aggregate =
          pipeline
              .apply(
                  Create.of(KV.of("foo", 1L), KV.of("bar", 1L))
                      .withCoder(KvCoder.of(StringUtf8Coder.of(), VarLongCoder.of())))
              .apply(GroupByKey.create());
      if (!stablePartitioning) {
        // When we insert any element-wise "map" operation between aggregation and stateful ParDo, we
        // can no longer assume that partitioning did not change, therefore we need an extra shuffle
        aggregate = aggregate.apply(ParDo.of(new StatelessIdentityDoFn<>()));
      }
      aggregate.apply(ParDo.of(new StatefulNoopDoFn<>()));
      translator.translate(pipeline);
      return env.getStreamGraph().getJobGraph();
    }
  
    private static class StatelessIdentityDoFn<KeyT, ValueT>
        extends DoFn<KV<KeyT, ValueT>, KV<KeyT, ValueT>> {
  
      @ProcessElement
      public void processElement(ProcessContext ctx) {
        ctx.output(ctx.element());
      }
    }
  
    private static class StatefulNoopDoFn<KeyT, ValueT> extends DoFn<KV<KeyT, ValueT>, Void> {
  
      @TimerId("my-timer")
      private final TimerSpec myTimer = TimerSpecs.timer(TimeDomain.EVENT_TIME);
  
      @ProcessElement
      public void processElement() {
        // noop
      }
  
      @OnTimer("my-timer")
      public void onMyTimer() {
        // noop
      }
    }
  }
  