junaiddshaukat commented on code in PR #39141: URL: https://github.com/apache/beam/pull/39141#discussion_r3490470460
########## runners/kafka-streams/src/main/java/org/apache/beam/runners/kafka/streams/translation/GroupByKeyProcessor.java: ########## @@ -0,0 +1,182 @@ +/* + * 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.kafka.streams.translation; + +import java.util.ArrayList; +import java.util.List; +import org.apache.beam.sdk.coders.Coder; +import org.apache.beam.sdk.coders.CoderException; +import org.apache.beam.sdk.coders.IterableCoder; +import org.apache.beam.sdk.transforms.windowing.BoundedWindow; +import org.apache.beam.sdk.util.CoderUtils; +import org.apache.beam.sdk.values.KV; +import org.apache.beam.sdk.values.WindowedValue; +import org.apache.beam.sdk.values.WindowedValues; +import org.apache.kafka.streams.processor.api.Processor; +import org.apache.kafka.streams.processor.api.ProcessorContext; +import org.apache.kafka.streams.processor.api.Record; +import org.apache.kafka.streams.state.KeyValueIterator; +import org.apache.kafka.streams.state.KeyValueStore; +import org.checkerframework.checker.nullness.qual.Nullable; +import org.joda.time.Instant; + +/** + * Executes a {@code GroupByKey} (GlobalWindow, default trigger, no allowed lateness). + * + * <p>Records arrive on the repartition topic keyed by the encoded Beam key, so every value of a key + * is co-located here. Each value is appended to a per-key buffer in a Kafka Streams state store. + * Watermark reports are fed to a {@link WatermarkManager}; when the input watermark reaches {@link + * BoundedWindow#TIMESTAMP_MAX_VALUE} (the end of the global window) every buffered key is emitted + * once as {@code KV<K, Iterable<V>>} and the buffer cleared, then the watermark is forwarded + * downstream. + * + * <p>Buffering whole value lists and re-encoding on each append is O(n^2) per key; fine for this + * first GroupByKey, and replaced when this moves to runner-core {@code GroupAlsoByWindow}. + */ +class GroupByKeyProcessor + implements Processor<byte[], KStreamsPayload<?>, byte[], KStreamsPayload<?>> { + + private final String stateStoreName; + private final Coder<Object> keyCoder; + private final IterableCoder<@Nullable Object> bufferCoder; + + private final WatermarkManager watermarkManager = new WatermarkManager(); + private Instant lastForwardedWatermark = BoundedWindow.TIMESTAMP_MIN_VALUE; + // The global window fires exactly once, when the watermark first reaches its end. Later watermark + // reports (e.g. the same terminal watermark broadcast across repartition partitions) must not + // re-fire. + private boolean fired = false; + + private @Nullable ProcessorContext<byte[], KStreamsPayload<?>> context; + private @Nullable KeyValueStore<byte[], byte[]> store; + + GroupByKeyProcessor( + String stateStoreName, Coder<Object> keyCoder, Coder<@Nullable Object> valueCoder) { + this.stateStoreName = stateStoreName; + this.keyCoder = keyCoder; + this.bufferCoder = IterableCoder.of(valueCoder); + } + + @Override + public void init(ProcessorContext<byte[], KStreamsPayload<?>> context) { + this.context = context; + this.store = context.getStateStore(stateStoreName); + } + + @Override + public void process(Record<byte[], KStreamsPayload<?>> record) { + KStreamsPayload<?> payload = record.value(); + if (payload.isData()) { + byte[] encodedKey = record.key(); + Object element = payload.getData().getValue(); + if (encodedKey == null || element == null) { + throw new IllegalStateException("GroupByKey data record is missing its key or value"); + } + appendValue(encodedKey, element); + return; + } + WatermarkPayload report = payload.asWatermark(); + watermarkManager.observe( + report.getSourcePartition(), + new Instant(report.getWatermarkMillis()), + report.getTotalSourcePartitions()); + Instant advanced = watermarkManager.advance(); + if (!fired && !advanced.isBefore(BoundedWindow.TIMESTAMP_MAX_VALUE)) { + fireAll(record); + fired = true; + } + if (advanced.isAfter(lastForwardedWatermark)) { + lastForwardedWatermark = advanced; + forwardWatermark(record, advanced.getMillis()); + } + } + + private void appendValue(byte[] encodedKey, Object kvObject) { + KV<?, ?> kv = (KV<?, ?>) kvObject; + KeyValueStore<byte[], byte[]> kvStore = checkInitialized(store); + byte[] existing = kvStore.get(encodedKey); + List<@Nullable Object> values = existing == null ? new ArrayList<>() : decodeBuffer(existing); + values.add(kv.getValue()); + kvStore.put(encodedKey, encodeBuffer(values)); + } + + private void fireAll(Record<byte[], KStreamsPayload<?>> trigger) { + ProcessorContext<byte[], KStreamsPayload<?>> ctx = checkInitialized(context); + KeyValueStore<byte[], byte[]> kvStore = checkInitialized(store); + List<byte[]> firedKeys = new ArrayList<>(); + try (KeyValueIterator<byte[], byte[]> it = kvStore.all()) { + while (it.hasNext()) { + org.apache.kafka.streams.KeyValue<byte[], byte[]> entry = it.next(); + Object key = decodeKey(entry.key); + List<@Nullable Object> values = decodeBuffer(entry.value); + WindowedValue<KV<Object, Iterable<@Nullable Object>>> output = + WindowedValues.valueInGlobalWindow(KV.of(key, (Iterable<@Nullable Object>) values)); Review Comment: Good catch — fixed. Used WindowedValues.timestampedValueInGlobalWindow with GlobalWindow.INSTANCE.maxTimestamp() (END_OF_GLOBAL_WINDOW) rather than the literal TIMESTAMP_MAX_VALUE: that's the window-end timestamp the framework expects (it leaves the standard margin before end-of-time) and what the other runners emit for a global-window pane. Same effect as the suggestion — the output is no longer at MIN so it won't be dropped as late. ########## runners/kafka-streams/src/test/java/org/apache/beam/runners/kafka/streams/translation/GroupByKeyTest.java: ########## @@ -0,0 +1,184 @@ +/* + * 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.kafka.streams.translation; + +import static org.hamcrest.CoreMatchers.hasItems; +import static org.hamcrest.CoreMatchers.is; +import static org.hamcrest.MatcherAssert.assertThat; + +import java.time.Duration; +import java.util.ArrayList; +import java.util.Collections; +import java.util.List; +import java.util.Properties; +import org.apache.beam.model.pipeline.v1.RunnerApi; +import org.apache.beam.runners.fnexecution.provisioning.JobInfo; +import org.apache.beam.runners.kafka.streams.KafkaStreamsPipelineOptions; +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.VarIntCoder; +import org.apache.beam.sdk.options.PipelineOptions; +import org.apache.beam.sdk.options.PipelineOptionsFactory; +import org.apache.beam.sdk.options.PortablePipelineOptions; +import org.apache.beam.sdk.testing.CrashingRunner; +import org.apache.beam.sdk.transforms.DoFn; +import org.apache.beam.sdk.transforms.GroupByKey; +import org.apache.beam.sdk.transforms.Impulse; +import org.apache.beam.sdk.transforms.ParDo; +import org.apache.beam.sdk.util.construction.Environments; +import org.apache.beam.sdk.util.construction.PTransformTranslation; +import org.apache.beam.sdk.util.construction.PipelineOptionsTranslation; +import org.apache.beam.sdk.util.construction.PipelineTranslation; +import org.apache.beam.sdk.values.KV; +import org.apache.kafka.common.serialization.ByteArrayDeserializer; +import org.apache.kafka.common.serialization.ByteArraySerializer; +import org.apache.kafka.common.serialization.Serdes; +import org.apache.kafka.streams.StreamsConfig; +import org.apache.kafka.streams.TestInputTopic; +import org.apache.kafka.streams.TestOutputTopic; +import org.apache.kafka.streams.Topology; +import org.apache.kafka.streams.TopologyTestDriver; +import org.apache.kafka.streams.test.TestRecord; +import org.junit.Test; + +/** + * End-to-end test of GroupByKey: {@code Impulse -> emit KVs -> GroupByKey -> record groups}. + * + * <p>GroupByKey shuffles through an internal repartition topic. {@link TopologyTestDriver} does not + * loop a low-level sink topic back into its source, so the test drives the upstream, drains the + * repartition topic, and pipes those records back into it — standing in for the broker round-trip. + * The downstream {@code RecordGroupFn} records each emitted group into a {@link + * SharedTestCollector}. + */ +public class GroupByKeyTest { + + private static final String JOB_ID = "ks-gbk-test"; + private static final String APPLICATION_ID = "ks-gbk-test"; + + /** Emits a few KVs from the single impulse element so there is something to group. */ + private static class EmitKvsFn extends DoFn<byte[], KV<String, Integer>> { + @ProcessElement + public void processElement(OutputReceiver<KV<String, Integer>> out) { + out.output(KV.of("a", 1)); + out.output(KV.of("a", 2)); + out.output(KV.of("b", 3)); + } + } + + /** Records each grouped result as {@code "key=[sorted values]"}. */ + private static class RecordGroupFn extends DoFn<KV<String, Iterable<Integer>>, Void> { + private final SharedTestCollector<String> collector; + + RecordGroupFn(SharedTestCollector<String> collector) { + this.collector = collector; + } + + @ProcessElement + public void processElement(@Element KV<String, Iterable<Integer>> group) { + List<Integer> values = new ArrayList<>(); + group.getValue().forEach(values::add); + Collections.sort(values); + collector.record(group.getKey() + "=" + values); + } + } + + @Test + public void groupsValuesByKeyAndFiresAtWatermark() throws Exception { + try (SharedTestCollector<String> collector = SharedTestCollector.create()) { + Pipeline pipeline = Pipeline.create(pipelineOptions()); + pipeline + .apply("impulse", Impulse.create()) + .apply("emit", ParDo.of(new EmitKvsFn())) + .setCoder(KvCoder.of(StringUtf8Coder.of(), VarIntCoder.of())) + .apply("gbk", GroupByKey.create()) + .apply("record", ParDo.of(new RecordGroupFn(collector))); + + RunnerApi.Pipeline pipelineProto = PipelineTranslation.toProto(pipeline); + KafkaStreamsPipelineOptions options = + pipeline.getOptions().as(KafkaStreamsPipelineOptions.class); + KafkaStreamsPipelineTranslator translator = new KafkaStreamsPipelineTranslator(); + JobInfo jobInfo = + JobInfo.create( + JOB_ID, options.getJobName(), "", PipelineOptionsTranslation.toProto(options)); + KafkaStreamsTranslationContext context = + translator.createTranslationContext(jobInfo, options); + + RunnerApi.Pipeline prepared = translator.prepareForTranslation(pipelineProto); + translator.translate(context, prepared); + String repartitionTopic = GroupByKeyTranslator.repartitionTopic(findGroupByKeyId(prepared)); + + Topology topology = context.getTopology(); + try (TopologyTestDriver driver = new TopologyTestDriver(topology, streamsConfig())) { + // Fire the impulse; the upstream stage emits the KVs and the terminal watermark, which the + // re-key processor sends to the repartition sink. + driver.advanceWallClockTime(Duration.ofSeconds(1)); + driver.advanceWallClockTime(Duration.ofSeconds(1)); + + // Round-trip the repartition topic: drain what the sink wrote and feed it back to the + // source so the GroupByKey processor buffers the values and fires at the watermark. + TestOutputTopic<byte[], byte[]> repartitionOut = + driver.createOutputTopic( + repartitionTopic, new ByteArrayDeserializer(), new ByteArrayDeserializer()); + TestInputTopic<byte[], byte[]> repartitionIn = + driver.createInputTopic( + repartitionTopic, new ByteArraySerializer(), new ByteArraySerializer()); + for (TestRecord<byte[], byte[]> record : repartitionOut.readRecordsToList()) { + repartitionIn.pipeInput(record.key(), record.value()); + } Review Comment: Done — passing the TestRecord straight to pipeInput now. -- This is an automated message from the Apache Git Service. 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