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Add sdf kafka poll latencies #34275

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Add sdf kafka poll latencies #34275

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31585e7
add kafka sdf metrics
Mar 13, 2025
0c5d001
address comments
Mar 13, 2025
5ad81c7
address more comments
Mar 14, 2025
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4 changes: 1 addition & 3 deletions ...rs/core-java/src/main/java/org/apache/beam/runners/core/metrics/MetricsContainerImpl.java
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Original file line number Diff line number Diff line change
Expand Up @@ -321,9 +321,7 @@ public MetricUpdates getUpdates() {

// Add any metricKey labels to the monitoringInfoLabels.
if (!metricName.getLabels().isEmpty()) {
for (Map.Entry<String, String> entry : metricName.getLabels().entrySet()) {
builder.setLabel(entry.getKey(), entry.getValue());
}
builder.setLabels(metricName.getLabels());
}
return builder;
}
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4 changes: 2 additions & 2 deletions ...core-java/src/main/java/org/apache/beam/runners/core/metrics/MonitoringInfoEncodings.java
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Original file line number Diff line number Diff line change
Expand Up @@ -182,12 +182,12 @@ public static double decodeDoubleCounter(ByteString payload) {
}
}

/** Encodes to {@link MonitoringInfoConstants.TypeUrns#PER_WORKER_HISTOGRAM}. */
/** Encodes to {@link MonitoringInfoConstants.TypeUrns#HISTOGRAM}. */
public static ByteString encodeInt64Histogram(HistogramData inputHistogram) {
return inputHistogram.toProto().toByteString();
}

/** Decodes to {@link MonitoringInfoConstants.TypeUrns#PER_WORKER_HISTOGRAM}. */
/** Decodes to {@link MonitoringInfoConstants.TypeUrns#HISTOGRAM}. */
public static HistogramData decodeInt64Histogram(ByteString payload) {
try {
return new HistogramData(HistogramValue.parseFrom(payload));
Expand Down
230 changes: 117 additions & 113 deletions sdks/java/io/kafka/src/main/java/org/apache/beam/sdk/io/kafka/ReadFromKafkaDoFn.java
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Original file line number Diff line number Diff line change
Expand Up @@ -454,122 +454,125 @@ public ProcessContinuation processElement(
final Stopwatch sw = Stopwatch.createStarted();

while (true) {
rawRecords = poll(consumer, kafkaSourceDescriptor.getTopicPartition());
// When there are no records available for the current TopicPartition, self-checkpoint
// and move to process the next element.
if (rawRecords.isEmpty()) {
if (!topicPartitionExists(
kafkaSourceDescriptor.getTopicPartition(),
consumer.partitionsFor(kafkaSourceDescriptor.getTopic()))) {
return ProcessContinuation.stop();
}
if (timestampPolicy != null) {
updateWatermarkManually(timestampPolicy, watermarkEstimator, tracker);
}
return ProcessContinuation.resume();
}
for (ConsumerRecord<byte[], byte[]> rawRecord : rawRecords) {
// If the Kafka consumer returns a record with an offset that is already processed
// the record can be safely skipped. This is needed because there is a possibility
// that the seek() above fails to move the offset to the desired position. In which
// case poll() would return records that are already cnsumed.
if (rawRecord.offset() < startOffset) {
// If the start offset is not reached even after skipping the records for 10 seconds
// then the processing is stopped with a backoff to give the Kakfa server some time
// catch up.
if (sw.elapsed().getSeconds() > 10L) {
LOG.error(
"The expected offset ({}) was not reached even after"
+ " skipping consumed records for 10 seconds. The offset we could"
+ " reach was {}. The processing of this bundle will be attempted"
+ " at a later time.",
expectedOffset,
rawRecord.offset());
return ProcessContinuation.resume()
.withResumeDelay(org.joda.time.Duration.standardSeconds(10L));
}
skippedRecords++;
continue;
}
if (skippedRecords > 0L) {
LOG.warn(
"{} records were skipped due to seek returning an"
+ " earlier position than requested position of {}",
skippedRecords,
expectedOffset);
skippedRecords = 0L;
}
if (!tracker.tryClaim(rawRecord.offset())) {
return ProcessContinuation.stop();
}
try {
KafkaRecord<K, V> kafkaRecord =
new KafkaRecord<>(
rawRecord.topic(),
rawRecord.partition(),
rawRecord.offset(),
ConsumerSpEL.getRecordTimestamp(rawRecord),
ConsumerSpEL.getRecordTimestampType(rawRecord),
ConsumerSpEL.hasHeaders() ? rawRecord.headers() : null,
ConsumerSpEL.deserializeKey(keyDeserializerInstance, rawRecord),
ConsumerSpEL.deserializeValue(valueDeserializerInstance, rawRecord));
int recordSize =
(rawRecord.key() == null ? 0 : rawRecord.key().length)
+ (rawRecord.value() == null ? 0 : rawRecord.value().length);
avgRecordSizeCache
.getUnchecked(kafkaSourceDescriptor)
.update(recordSize, rawRecord.offset() - expectedOffset);
rawSizes.update(recordSize);
expectedOffset = rawRecord.offset() + 1;
Instant outputTimestamp;
// The outputTimestamp and watermark will be computed by timestampPolicy, where the
// WatermarkEstimator should be a manual one.
if (timestampPolicy != null) {
TimestampPolicyContext context =
updateWatermarkManually(timestampPolicy, watermarkEstimator, tracker);
outputTimestamp = timestampPolicy.getTimestampForRecord(context, kafkaRecord);
} else {
Preconditions.checkStateNotNull(this.extractOutputTimestampFn);
outputTimestamp = extractOutputTimestampFn.apply(kafkaRecord);
KafkaMetrics kafkaMetrics = KafkaSinkMetrics.kafkaMetrics();
try {
rawRecords = poll(consumer, kafkaSourceDescriptor.getTopicPartition(), kafkaMetrics);
// When there are no records available for the current TopicPartition, self-checkpoint
// and move to process the next element.
if (rawRecords.isEmpty()) {
if (!topicPartitionExists(
kafkaSourceDescriptor.getTopicPartition(),
consumer.partitionsFor(kafkaSourceDescriptor.getTopic()))) {
return ProcessContinuation.stop();
}
receiver
.get(recordTag)
.outputWithTimestamp(KV.of(kafkaSourceDescriptor, kafkaRecord), outputTimestamp);
} catch (SerializationException e) {
// This exception should only occur during the key and value deserialization when
// creating the Kafka Record
badRecordRouter.route(
receiver,
rawRecord,
null,
e,
"Failure deserializing Key or Value of Kakfa record reading from Kafka");
if (timestampPolicy != null) {
updateWatermarkManually(timestampPolicy, watermarkEstimator, tracker);
}
return ProcessContinuation.resume();
}
for (ConsumerRecord<byte[], byte[]> rawRecord : rawRecords) {
// If the Kafka consumer returns a record with an offset that is already processed
// the record can be safely skipped. This is needed because there is a possibility
// that the seek() above fails to move the offset to the desired position. In which
// case poll() would return records that are already cnsumed.
if (rawRecord.offset() < startOffset) {
// If the start offset is not reached even after skipping the records for 10 seconds
// then the processing is stopped with a backoff to give the Kakfa server some time
// catch up.
if (sw.elapsed().getSeconds() > 10L) {
LOG.error(
"The expected offset ({}) was not reached even after"
+ " skipping consumed records for 10 seconds. The offset we could"
+ " reach was {}. The processing of this bundle will be attempted"
+ " at a later time.",
expectedOffset,
rawRecord.offset());
return ProcessContinuation.resume()
.withResumeDelay(org.joda.time.Duration.standardSeconds(10L));
}
skippedRecords++;
continue;
}
if (skippedRecords > 0L) {
LOG.warn(
"{} records were skipped due to seek returning an"
+ " earlier position than requested position of {}",
skippedRecords,
expectedOffset);
skippedRecords = 0L;
}
if (!tracker.tryClaim(rawRecord.offset())) {
return ProcessContinuation.stop();
}
try {
KafkaRecord<K, V> kafkaRecord =
new KafkaRecord<>(
rawRecord.topic(),
rawRecord.partition(),
rawRecord.offset(),
ConsumerSpEL.getRecordTimestamp(rawRecord),
ConsumerSpEL.getRecordTimestampType(rawRecord),
ConsumerSpEL.hasHeaders() ? rawRecord.headers() : null,
ConsumerSpEL.deserializeKey(keyDeserializerInstance, rawRecord),
ConsumerSpEL.deserializeValue(valueDeserializerInstance, rawRecord));
int recordSize =
(rawRecord.key() == null ? 0 : rawRecord.key().length)
+ (rawRecord.value() == null ? 0 : rawRecord.value().length);
avgRecordSizeCache
.getUnchecked(kafkaSourceDescriptor)
.update(recordSize, rawRecord.offset() - expectedOffset);
rawSizes.update(recordSize);
expectedOffset = rawRecord.offset() + 1;
Instant outputTimestamp;
// The outputTimestamp and watermark will be computed by timestampPolicy, where the
// WatermarkEstimator should be a manual one.
if (timestampPolicy != null) {
TimestampPolicyContext context =
updateWatermarkManually(timestampPolicy, watermarkEstimator, tracker);
outputTimestamp = timestampPolicy.getTimestampForRecord(context, kafkaRecord);
} else {
Preconditions.checkStateNotNull(this.extractOutputTimestampFn);
outputTimestamp = extractOutputTimestampFn.apply(kafkaRecord);
}
receiver
.get(recordTag)
.outputWithTimestamp(KV.of(kafkaSourceDescriptor, kafkaRecord), outputTimestamp);
} catch (SerializationException e) {
// This exception should only occur during the key and value deserialization when
// creating the Kafka Record
badRecordRouter.route(
receiver,
rawRecord,
null,
e,
"Failure deserializing Key or Value of Kakfa record reading from Kafka");
if (timestampPolicy != null) {
updateWatermarkManually(timestampPolicy, watermarkEstimator, tracker);
}
}
}
}

backlogBytes.set(
(long)
(BigDecimal.valueOf(
Preconditions.checkStateNotNull(
offsetEstimatorCache.get(kafkaSourceDescriptor).estimate()))
.subtract(BigDecimal.valueOf(expectedOffset), MathContext.DECIMAL128)
.doubleValue()
* avgRecordSize.estimateRecordByteSizeToOffsetCountRatio()));
KafkaMetrics kafkaResults = KafkaSinkMetrics.kafkaMetrics();
kafkaResults.updateBacklogBytes(
kafkaSourceDescriptor.getTopic(),
kafkaSourceDescriptor.getPartition(),
(long)
(BigDecimal.valueOf(
Preconditions.checkStateNotNull(
offsetEstimatorCache.get(kafkaSourceDescriptor).estimate()))
.subtract(BigDecimal.valueOf(expectedOffset), MathContext.DECIMAL128)
.doubleValue()
* avgRecordSize.estimateRecordByteSizeToOffsetCountRatio()));
kafkaResults.flushBufferedMetrics();
backlogBytes.set(
(long)
(BigDecimal.valueOf(
Preconditions.checkStateNotNull(
offsetEstimatorCache.get(kafkaSourceDescriptor).estimate()))
.subtract(BigDecimal.valueOf(expectedOffset), MathContext.DECIMAL128)
.doubleValue()
* avgRecordSize.estimateRecordByteSizeToOffsetCountRatio()));
kafkaMetrics.updateBacklogBytes(
kafkaSourceDescriptor.getTopic(),
kafkaSourceDescriptor.getPartition(),
(long)
(BigDecimal.valueOf(
Preconditions.checkStateNotNull(
offsetEstimatorCache.get(kafkaSourceDescriptor).estimate()))
.subtract(BigDecimal.valueOf(expectedOffset), MathContext.DECIMAL128)
.doubleValue()
* avgRecordSize.estimateRecordByteSizeToOffsetCountRatio()));
} finally {
kafkaMetrics.flushBufferedMetrics();
}
}
}
}
Expand All @@ -583,13 +586,15 @@ private boolean topicPartitionExists(

// see https://github.com/apache/beam/issues/25962
private ConsumerRecords<byte[], byte[]> poll(
Consumer<byte[], byte[]> consumer, TopicPartition topicPartition) {
Consumer<byte[], byte[]> consumer, TopicPartition topicPartition, KafkaMetrics kafkaMetrics) {
final Stopwatch sw = Stopwatch.createStarted();
long previousPosition = -1;
java.time.Duration elapsed = java.time.Duration.ZERO;
java.time.Duration timeout = java.time.Duration.ofSeconds(this.consumerPollingTimeout);
while (true) {
java.time.Duration elapsed = sw.elapsed();
final ConsumerRecords<byte[], byte[]> rawRecords = consumer.poll(timeout.minus(elapsed));
kafkaMetrics.updateSuccessfulRpcMetrics(
topicPartition.topic(), java.time.Duration.ofMillis(elapsed.toMillis()));
if (!rawRecords.isEmpty()) {
// return as we have found some entries
return rawRecords;
Expand All @@ -598,7 +603,6 @@ private ConsumerRecords<byte[], byte[]> poll(
// there was no progress on the offset/position, which indicates end of stream
return rawRecords;
}
elapsed = sw.elapsed();
if (elapsed.toMillis() >= timeout.toMillis()) {
// timeout is over
LOG.warn(
Expand Down
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