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1 change: 1 addition & 0 deletions runners/kafka-streams/build.gradle
Original file line number Diff line number Diff line change
Expand Up @@ -61,6 +61,7 @@ dependencies {
permitUnusedDeclared "org.apache.kafka:kafka-clients:$kafka_version"

testImplementation project(path: ":sdks:java:core", configuration: "shadowTest")
testImplementation project(":sdks:java:harness")
testImplementation library.java.hamcrest
testImplementation library.java.junit
testImplementation library.java.mockito_core
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Original file line number Diff line number Diff line change
@@ -0,0 +1,209 @@
/*
* 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.ArrayDeque;
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high

Since the queue is accessed concurrently by SDK harness threads (which enqueue outputs) and the Kafka Streams processing thread (which flushes them), we should use a thread-safe queue like ConcurrentLinkedQueue instead of ArrayDeque to avoid race conditions and data corruption.

Suggested change
import java.util.ArrayDeque;
import java.util.concurrent.ConcurrentLinkedQueue;

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medium

Replace the ArrayDeque import with ConcurrentLinkedQueue to support the thread-safe queue implementation for pendingOutputs.

Suggested change
import java.util.ArrayDeque;
import java.util.concurrent.ConcurrentLinkedQueue;

import java.util.Queue;
import org.apache.beam.model.pipeline.v1.RunnerApi;
import org.apache.beam.runners.fnexecution.control.BundleProgressHandler;
import org.apache.beam.runners.fnexecution.control.ExecutableStageContext;
import org.apache.beam.runners.fnexecution.control.OutputReceiverFactory;
import org.apache.beam.runners.fnexecution.control.RemoteBundle;
import org.apache.beam.runners.fnexecution.control.StageBundleFactory;
import org.apache.beam.runners.fnexecution.provisioning.JobInfo;
import org.apache.beam.runners.fnexecution.state.StateRequestHandler;
import org.apache.beam.sdk.fn.data.FnDataReceiver;
import org.apache.beam.sdk.util.construction.graph.ExecutableStage;
import org.apache.beam.sdk.values.WindowedValue;
import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Iterables;
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.checkerframework.checker.nullness.qual.Nullable;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
* Kafka Streams {@link Processor} that executes a fused {@link ExecutableStage} (stateless user
* code such as ParDo) in the Beam SDK harness over the Fn API.
*
* <p>For each {@link KStreamsPayload#isData() data} payload it unwraps the {@link WindowedValue}
* and feeds it to the harness through the stage's main input {@link FnDataReceiver}. Harness
* outputs are collected on the harness threads into {@link #pendingOutputs} and then flushed
* downstream on the Kafka Streams processing thread when the bundle closes — Kafka Streams' {@link
* ProcessorContext#forward} must only be called from the processing thread, so outputs are never
* forwarded directly from a harness callback.
*
* <p>A {@link KStreamsPayload#isWatermark() watermark} payload marks a bundle boundary: the open
* bundle (if any) is closed (flushing outputs), and the watermark is then forwarded downstream so
* that subsequent stages observe it after all data of the bundle.
*
* <p>This is the Kafka Streams analogue of Flink's {@code ExecutableStageDoFnOperator} and Spark's
* {@code SparkExecutableStageFunction}. State, timers, and side inputs are out of scope for this
* first version: the stage is executed with {@link StateRequestHandler#unsupported()} and no timer
* receivers.
*/
class ExecutableStageProcessor
implements Processor<byte[], KStreamsPayload<byte[]>, byte[], KStreamsPayload<byte[]>> {

private static final Logger LOG = LoggerFactory.getLogger(ExecutableStageProcessor.class);

private final RunnerApi.ExecutableStagePayload stagePayload;
private final JobInfo jobInfo;

private final Queue<WindowedValue<byte[]>> pendingOutputs = new ArrayDeque<>();
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high

Instantiate pendingOutputs as a ConcurrentLinkedQueue to ensure thread-safe operations when elements are added from harness threads and polled from the processing thread.

Suggested change
private final Queue<WindowedValue<byte[]>> pendingOutputs = new ArrayDeque<>();
private final Queue<WindowedValue<byte[]>> pendingOutputs = new ConcurrentLinkedQueue<>();

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high

pendingOutputs is accessed concurrently: it is populated by SDK harness threads inside the FnDataReceiver callback and drained by the Kafka Streams processing thread. Since ArrayDeque is not thread-safe and lacks memory visibility guarantees across threads, this can lead to race conditions or data corruption.

Using ConcurrentLinkedQueue provides a thread-safe, lock-free queue that is ideal for this producer-consumer pattern.

Suggested change
private final Queue<WindowedValue<byte[]>> pendingOutputs = new ArrayDeque<>();
private final Queue<WindowedValue<byte[]>> pendingOutputs = new ConcurrentLinkedQueue<>();


private @Nullable ProcessorContext<byte[], KStreamsPayload<byte[]>> context;
private @Nullable ExecutableStageContext stageContext;
private @Nullable StageBundleFactory stageBundleFactory;
private @Nullable RemoteBundle currentBundle;

ExecutableStageProcessor(RunnerApi.ExecutableStagePayload stagePayload, JobInfo jobInfo) {
this.stagePayload = stagePayload;
this.jobInfo = jobInfo;
}

@Override
public void init(ProcessorContext<byte[], KStreamsPayload<byte[]>> context) {
this.context = context;
ExecutableStage executableStage = ExecutableStage.fromPayload(stagePayload);
this.stageContext = KafkaStreamsExecutableStageContextFactory.getInstance().get(jobInfo);
this.stageBundleFactory = stageContext.getStageBundleFactory(executableStage);
}

@Override
public void process(Record<byte[], KStreamsPayload<byte[]>> record) {
KStreamsPayload<byte[]> payload = record.value();
if (payload.isWatermark()) {
// NOTE: flushing the bundle on every received watermark is provisional. Once the
// WatermarkManager lands, a stage will receive watermarks from multiple parent instances and
// the output watermark becomes min() across them — the bundle should flush / the output
// watermark advance only when that minimum actually moves forward, not on every received
// watermark. Tracked in #38743.
closeBundleAndFlush(record);
forwardWatermark(record, payload.getWatermarkMillis());
return;
}
try {
ensureBundleOpen();
mainInputReceiver().accept(payload.getData());
} catch (Exception e) {
throw new RuntimeException("Failed to process element through SDK harness", e);
}
}

private void ensureBundleOpen() throws Exception {
if (currentBundle != null) {
return;
}
StageBundleFactory factory = checkInitialized(stageBundleFactory);
OutputReceiverFactory outputReceiverFactory =
new OutputReceiverFactory() {
@Override
public <OutputT> FnDataReceiver<OutputT> create(String pCollectionId) {
// Outputs are queued here on harness threads and drained on the processing thread
// after the bundle closes.
return receivedElement -> {
if (receivedElement != null) {
pendingOutputs.add((WindowedValue<byte[]>) receivedElement);
}
};
}
};
currentBundle =
factory.getBundle(
outputReceiverFactory,
StateRequestHandler.unsupported(),
BundleProgressHandler.ignored());
}

private FnDataReceiver<WindowedValue<?>> mainInputReceiver() {
RemoteBundle bundle = checkInitialized(currentBundle);
@SuppressWarnings("unchecked")
FnDataReceiver<WindowedValue<?>> receiver =
(FnDataReceiver<WindowedValue<?>>)
(FnDataReceiver<?>) Iterables.getOnlyElement(bundle.getInputReceivers().values());
return receiver;
}

private void closeBundleAndFlush(Record<byte[], KStreamsPayload<byte[]>> record) {
RemoteBundle bundle = currentBundle;
if (bundle == null) {
return;
}
try {
// close() blocks until the harness finishes the bundle and all outputs have been delivered
// to the output receiver (and hence enqueued in pendingOutputs).
bundle.close();
} catch (Exception e) {
throw new RuntimeException("Failed to close SDK harness bundle", e);
} finally {
currentBundle = null;
}
ProcessorContext<byte[], KStreamsPayload<byte[]>> ctx = checkInitialized(context);
WindowedValue<byte[]> output;
while ((output = pendingOutputs.poll()) != null) {
ctx.forward(
new Record<byte[], KStreamsPayload<byte[]>>(
record.key(), KStreamsPayload.data(output), record.timestamp()));
}
}

private void forwardWatermark(
Record<byte[], KStreamsPayload<byte[]>> record, long watermarkMillis) {
ProcessorContext<byte[], KStreamsPayload<byte[]>> ctx = checkInitialized(context);
ctx.forward(
new Record<byte[], KStreamsPayload<byte[]>>(
record.key(), KStreamsPayload.watermark(watermarkMillis), record.timestamp()));
}

@Override
public void close() {
try {
if (currentBundle != null) {
currentBundle.close();
currentBundle = null;
}
} catch (Exception e) {
LOG.warn("Error closing in-flight SDK harness bundle", e);
}
try {
if (stageBundleFactory != null) {
stageBundleFactory.close();
stageBundleFactory = null;
}
} catch (Exception e) {
LOG.warn("Error closing stage bundle factory", e);
}
try {
if (stageContext != null) {
stageContext.close();
stageContext = null;
}
} catch (Exception e) {
LOG.warn("Error closing executable stage context", e);
}
}

private static <T> T checkInitialized(@Nullable T value) {
if (value == null) {
throw new IllegalStateException("ExecutableStageProcessor used before init()");
}
return value;
}
}
Original file line number Diff line number Diff line change
@@ -0,0 +1,76 @@
/*
* 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.io.IOException;
import org.apache.beam.model.pipeline.v1.RunnerApi;
import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.collect.Iterables;
import org.apache.kafka.streams.Topology;

/**
* Translates the {@code beam:runner:executable_stage:v1} URN.
*
* <p>Adds an {@link ExecutableStageProcessor} node to the topology, wired to the processor that
* produces the stage's input PCollection (resolved through {@link
* KafkaStreamsTranslationContext#getProcessorNameForPCollection}). The processor runs the fused
* user code in the SDK harness; its single output PCollection is registered so downstream
* translators can attach to this node.
*
* <p>Multi-output stages (additional outputs / side inputs / state / timers) are out of scope for
* this first version and are rejected so the limitation fails fast rather than silently dropping
* outputs.
*/
class ExecutableStageTranslator implements PTransformTranslator {

@Override
public void translate(
String transformId, RunnerApi.Pipeline pipeline, KafkaStreamsTranslationContext context) {
RunnerApi.PTransform transform = pipeline.getComponents().getTransformsOrThrow(transformId);

RunnerApi.ExecutableStagePayload stagePayload;
try {
stagePayload = RunnerApi.ExecutableStagePayload.parseFrom(transform.getSpec().getPayload());
} catch (IOException e) {
throw new IllegalArgumentException(
"Failed to parse ExecutableStagePayload for transform " + transformId, e);
}

String inputPCollectionId = Iterables.getOnlyElement(transform.getInputsMap().values());
String parentProcessor = context.getProcessorNameForPCollection(inputPCollectionId);
Comment on lines +53 to +54
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medium

Instead of using Iterables.getOnlyElement(transform.getInputsMap().values()), which will fail with a generic IllegalArgumentException if there are side inputs, we can directly retrieve the main input PCollection ID from stagePayload.getInput().

Additionally, we should explicitly check and reject side inputs, user states, and timers using stagePayload to fail fast with a clear, descriptive error message.

    if (stagePayload.getSideInputsCount() > 0) {
      throw new UnsupportedOperationException(
          "ExecutableStage "
              + transformId
              + " has side inputs; side inputs are not yet supported by the Kafka Streams runner.");
    }
    if (stagePayload.getUserStatesCount() > 0 || stagePayload.getTimersCount() > 0) {
      throw new UnsupportedOperationException(
          "ExecutableStage "
              + transformId
              + " has user states or timers; these are not yet supported by the Kafka Streams runner.");
    }

    String inputPCollectionId = stagePayload.getInput();
    String parentProcessor = context.getProcessorNameForPCollection(inputPCollectionId);


if (transform.getOutputsMap().size() > 1) {
throw new UnsupportedOperationException(
"ExecutableStage "
+ transformId
+ " has "
+ transform.getOutputsMap().size()
+ " outputs; multi-output stages are not yet supported by the Kafka Streams runner.");
}

Topology topology = context.getTopology();
topology.addProcessor(
transformId,
() -> new ExecutableStageProcessor(stagePayload, context.getJobInfo()),
parentProcessor);

if (!transform.getOutputsMap().isEmpty()) {
String outputPCollectionId = Iterables.getOnlyElement(transform.getOutputsMap().values());
context.registerPCollectionProducer(outputPCollectionId, transformId);
}
}
}
Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@

import java.util.Objects;
import org.apache.beam.sdk.values.WindowedValue;
import org.apache.beam.vendor.guava.v32_1_2_jre.com.google.common.base.MoreObjects;
import org.checkerframework.checker.nullness.qual.Nullable;

/**
Expand Down Expand Up @@ -121,6 +122,12 @@ public int hashCode() {

@Override
public String toString() {
return kind == Kind.DATA ? "Data{" + data + "}" : "Watermark{" + watermarkMillis + "}";
MoreObjects.ToStringHelper helper = MoreObjects.toStringHelper(this).add("kind", kind);
if (kind == Kind.DATA) {
helper.add("data", data);
} else {
helper.add("watermarkMillis", watermarkMillis);
}
return helper.toString();
}
}
Original file line number Diff line number Diff line change
@@ -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.beam.runners.kafka.streams.translation;

import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.ConcurrentMap;
import org.apache.beam.runners.fnexecution.control.DefaultExecutableStageContext;
import org.apache.beam.runners.fnexecution.control.ExecutableStageContext;
import org.apache.beam.runners.fnexecution.control.ReferenceCountingExecutableStageContextFactory;
import org.apache.beam.runners.fnexecution.provisioning.JobInfo;

/**
* Provides one {@link ExecutableStageContext.Factory} per job for the Kafka Streams runner.
*
* <p>Mirrors {@code FlinkExecutableStageContextFactory}: a singleton that hands out reference-
* counted {@link DefaultExecutableStageContext}s keyed by job id, so the SDK harness environment
* for a job is created once and shared across the {@link ImpulseProcessor}/executable-stage
* processors that run within the same JVM instance.
*/
public class KafkaStreamsExecutableStageContextFactory implements ExecutableStageContext.Factory {

private static final KafkaStreamsExecutableStageContextFactory INSTANCE =
new KafkaStreamsExecutableStageContextFactory();

private final ConcurrentMap<String, ExecutableStageContext.Factory> jobFactories =
new ConcurrentHashMap<>();

private KafkaStreamsExecutableStageContextFactory() {}

public static KafkaStreamsExecutableStageContextFactory getInstance() {
return INSTANCE;
}

@Override
public ExecutableStageContext get(JobInfo jobInfo) {
ExecutableStageContext.Factory jobFactory =
jobFactories.computeIfAbsent(
jobInfo.jobId(),
k ->
ReferenceCountingExecutableStageContextFactory.create(
DefaultExecutableStageContext::create,
// Release the context synchronously once its reference count drops to zero;
// the runner does not keep contexts alive across stages beyond their use.
(caller) -> true));
Comment on lines +57 to +59
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high

The jobFactories map is a static ConcurrentHashMap that stores ExecutableStageContext.Factory instances per job. If we do not remove the factory from the map when the reference count drops to zero, it will cause a memory leak of the factory and its associated resources for every job run in the JVM. We should remove the job ID from jobFactories in the releaser callback.

Suggested change
// Release the context synchronously once its reference count drops to zero;
// the runner does not keep contexts alive across stages beyond their use.
(caller) -> true));
(caller) -> {
jobFactories.remove(jobInfo.jobId());
return true;
}));

return jobFactory.get(jobInfo);
}
}
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