聊聊storm-kafka-client的ProcessingGuarantee

    /**
     * This enum controls when the tuple with the {@link ConsumerRecord} for an offset is marked as processed,
     * i.e. when the offset can be committed to Kafka. The default value is AT_LEAST_ONCE.
     * The commit interval is controlled by {@link KafkaSpoutConfig#getOffsetsCommitPeriodMs() }, if the mode commits on an interval.
     * NO_GUARANTEE may be removed in a later release without warning, we're still evaluating whether it makes sense to keep.
     */
    @InterfaceStability.Unstable
    public enum ProcessingGuarantee {
        /**
         * An offset is ready to commit only after the corresponding tuple has been processed and acked (at least once). If a tuple fails or
         * times out it will be re-emitted, as controlled by the {@link KafkaSpoutRetryService}. Commits synchronously on the defined
         * interval.
         */
        AT_LEAST_ONCE,
        /**
         * Every offset will be synchronously committed to Kafka right after being polled but before being emitted to the downstream
         * components of the topology. The commit interval is ignored. This mode guarantees that the offset is processed at most once by
         * ensuring the spout won't retry tuples that fail or time out after the commit to Kafka has been done
         */
        AT_MOST_ONCE,
        /**
         * The polled offsets are ready to commit immediately after being polled. The offsets are committed periodically, i.e. a message may
         * be processed 0, 1 or more times. This behavior is similar to setting enable.auto.commit=true in the consumer, but allows the
         * spout to control when commits occur. Commits asynchronously on the defined interval.
         */
        NO_GUARANTEE,
    }

public class KafkaSpout<K, V> extends BaseRichSpout {
​
    //Initial delay for the commit and subscription refresh timers
    public static final long TIMER_DELAY_MS = 500;
​
    // timer == null only if the processing guarantee is at-most-once
    private transient Timer commitTimer;
​
    // Tuples that were successfully acked/emitted. These tuples will be committed periodically when the commit timer expires,
    // or after a consumer rebalance, or during close/deactivate. Always empty if processing guarantee is none or at-most-once.
    private transient Map<TopicPartition, OffsetManager> offsetManagers;
​
    // Records that have been polled and are queued to be emitted in the nextTuple() call. One record is emitted per nextTuple()
    private transient Map<TopicPartition, List<ConsumerRecord<K, V>>> waitingToEmit;
​
    //......
​
    @Override
    public void open(Map conf, TopologyContext context, SpoutOutputCollector collector) {
        this.context = context;
​
        // Spout internals
        this.collector = collector;
​
        // Offset management
        firstPollOffsetStrategy = kafkaSpoutConfig.getFirstPollOffsetStrategy();
​
        // Retries management
        retryService = kafkaSpoutConfig.getRetryService();
​
        tupleListener = kafkaSpoutConfig.getTupleListener();
​
        if (kafkaSpoutConfig.getProcessingGuarantee() != KafkaSpoutConfig.ProcessingGuarantee.AT_MOST_ONCE) {
            // In at-most-once mode the offsets are committed after every poll, and not periodically as controlled by the timer
            commitTimer = new Timer(TIMER_DELAY_MS, kafkaSpoutConfig.getOffsetsCommitPeriodMs(), TimeUnit.MILLISECONDS);
        }
        refreshSubscriptionTimer = new Timer(TIMER_DELAY_MS, kafkaSpoutConfig.getPartitionRefreshPeriodMs(), TimeUnit.MILLISECONDS);
​
        offsetManagers = new HashMap<>();
        emitted = new HashSet<>();
        waitingToEmit = new HashMap<>();
        commitMetadataManager = new CommitMetadataManager(context, kafkaSpoutConfig.getProcessingGuarantee());
​
        tupleListener.open(conf, context);
        if (canRegisterMetrics()) {
            registerMetric();
        }
​
        LOG.info("Kafka Spout opened with the following configuration: {}", kafkaSpoutConfig);
    }
​
    //......
​
}

public class Timer {
    private final long delay;
    private final long period;
    private final TimeUnit timeUnit;
    private final long periodNanos;
    private long start;
​
    //......
​
    /**
     * Checks if a call to this method occurs later than {@code period} since the timer was initiated or reset. If that is the
     * case the method returns true, otherwise it returns false. Each time this method returns true, the counter is reset
     * (re-initiated) and a new cycle will start.
     *
     * @return true if the time elapsed since the last call returning true is greater than {@code period}. Returns false
     * otherwise.
     */
    public boolean isExpiredResetOnTrue() {
        final boolean expired = Time.nanoTime() - start >= periodNanos;
        if (expired) {
            start = Time.nanoTime();
        }
        return expired;
    }
}

    // ======== Next Tuple =======
    @Override
    public void nextTuple() {
        try {
            if (refreshSubscriptionTimer.isExpiredResetOnTrue()) {
                kafkaSpoutConfig.getSubscription().refreshAssignment();
            }
​
            if (commitTimer != null && commitTimer.isExpiredResetOnTrue()) {
                if (isAtLeastOnceProcessing()) {
                    commitOffsetsForAckedTuples(kafkaConsumer.assignment());
                } else if (kafkaSpoutConfig.getProcessingGuarantee() == ProcessingGuarantee.NO_GUARANTEE) {
                    Map<TopicPartition, OffsetAndMetadata> offsetsToCommit =
                        createFetchedOffsetsMetadata(kafkaConsumer.assignment());
                    kafkaConsumer.commitAsync(offsetsToCommit, null);
                    LOG.debug("Committed offsets {} to Kafka", offsetsToCommit);
                }
            }
​
            PollablePartitionsInfo pollablePartitionsInfo = getPollablePartitionsInfo();
            if (pollablePartitionsInfo.shouldPoll()) {
                try {
                    setWaitingToEmit(pollKafkaBroker(pollablePartitionsInfo));
                } catch (RetriableException e) {
                    LOG.error("Failed to poll from kafka.", e);
                }
            }
​
            emitIfWaitingNotEmitted();
        } catch (InterruptException e) {
            throwKafkaConsumerInterruptedException();
        }
    }

    private Map<TopicPartition, OffsetAndMetadata> createFetchedOffsetsMetadata(Set<TopicPartition> assignedPartitions) {
        Map<TopicPartition, OffsetAndMetadata> offsetsToCommit = new HashMap<>();
        for (TopicPartition tp : assignedPartitions) {
            offsetsToCommit.put(tp, new OffsetAndMetadata(kafkaConsumer.position(tp), commitMetadataManager.getCommitMetadata()));
        }
        return offsetsToCommit;
    }

    private void commitOffsetsForAckedTuples(Set<TopicPartition> assignedPartitions) {
        // Find offsets that are ready to be committed for every assigned topic partition
        final Map<TopicPartition, OffsetManager> assignedOffsetManagers = new HashMap<>();
        for (Entry<TopicPartition, OffsetManager> entry : offsetManagers.entrySet()) {
            if (assignedPartitions.contains(entry.getKey())) {
                assignedOffsetManagers.put(entry.getKey(), entry.getValue());
            }
        }
​
        final Map<TopicPartition, OffsetAndMetadata> nextCommitOffsets = new HashMap<>();
        for (Map.Entry<TopicPartition, OffsetManager> tpOffset : assignedOffsetManagers.entrySet()) {
            final OffsetAndMetadata nextCommitOffset = tpOffset.getValue().findNextCommitOffset(commitMetadataManager.getCommitMetadata());
            if (nextCommitOffset != null) {
                nextCommitOffsets.put(tpOffset.getKey(), nextCommitOffset);
            }
        }
​
        // Commit offsets that are ready to be committed for every topic partition
        if (!nextCommitOffsets.isEmpty()) {
            kafkaConsumer.commitSync(nextCommitOffsets);
            LOG.debug("Offsets successfully committed to Kafka [{}]", nextCommitOffsets);
            // Instead of iterating again, it would be possible to commit and update the state for each TopicPartition
            // in the prior loop, but the multiple network calls should be more expensive than iterating twice over a small loop
            for (Map.Entry<TopicPartition, OffsetAndMetadata> tpOffset : nextCommitOffsets.entrySet()) {
                //Update the OffsetManager for each committed partition, and update numUncommittedOffsets
                final TopicPartition tp = tpOffset.getKey();
                long position = kafkaConsumer.position(tp);
                long committedOffset = tpOffset.getValue().offset();
                if (position < committedOffset) {
                    /*
                     * The position is behind the committed offset. This can happen in some cases, e.g. if a message failed, lots of (more
                     * than max.poll.records) later messages were acked, and the failed message then gets acked. The consumer may only be
                     * part way through "catching up" to where it was when it went back to retry the failed tuple. Skip the consumer forward
                     * to the committed offset and drop the current waiting to emit list, since it'll likely contain committed offsets.
                     */
                    LOG.debug("Consumer fell behind committed offset. Catching up. Position was [{}], skipping to [{}]",
                        position, committedOffset);
                    kafkaConsumer.seek(tp, committedOffset);
                    List<ConsumerRecord<K, V>> waitingToEmitForTp = waitingToEmit.get(tp);
                    if (waitingToEmitForTp != null) {
                        //Discard the pending records that are already committed
                        List<ConsumerRecord<K, V>> filteredRecords = new ArrayList<>();
                        for (ConsumerRecord<K, V> record : waitingToEmitForTp) {
                            if (record.offset() >= committedOffset) {
                                filteredRecords.add(record);
                            }
                        }
                        waitingToEmit.put(tp, filteredRecords);
                    }
                }
​
                final OffsetManager offsetManager = assignedOffsetManagers.get(tp);
                offsetManager.commit(tpOffset.getValue());
                LOG.debug("[{}] uncommitted offsets for partition [{}] after commit", offsetManager.getNumUncommittedOffsets(), tp);
            }
        } else {
            LOG.trace("No offsets to commit. {}", this);
        }
    }

   private PollablePartitionsInfo getPollablePartitionsInfo() {
        if (isWaitingToEmit()) {
            LOG.debug("Not polling. Tuples waiting to be emitted.");
            return new PollablePartitionsInfo(Collections.<TopicPartition>emptySet(), Collections.<TopicPartition, Long>emptyMap());
        }
​
        Set<TopicPartition> assignment = kafkaConsumer.assignment();
        if (!isAtLeastOnceProcessing()) {
            return new PollablePartitionsInfo(assignment, Collections.<TopicPartition, Long>emptyMap());
        }
​
        Map<TopicPartition, Long> earliestRetriableOffsets = retryService.earliestRetriableOffsets();
        Set<TopicPartition> pollablePartitions = new HashSet<>();
        final int maxUncommittedOffsets = kafkaSpoutConfig.getMaxUncommittedOffsets();
        for (TopicPartition tp : assignment) {
            OffsetManager offsetManager = offsetManagers.get(tp);
            int numUncommittedOffsets = offsetManager.getNumUncommittedOffsets();
            if (numUncommittedOffsets < maxUncommittedOffsets) {
                //Allow poll if the partition is not at the maxUncommittedOffsets limit
                pollablePartitions.add(tp);
            } else {
                long offsetAtLimit = offsetManager.getNthUncommittedOffsetAfterCommittedOffset(maxUncommittedOffsets);
                Long earliestRetriableOffset = earliestRetriableOffsets.get(tp);
                if (earliestRetriableOffset != null && earliestRetriableOffset <= offsetAtLimit) {
                    //Allow poll if there are retriable tuples within the maxUncommittedOffsets limit
                    pollablePartitions.add(tp);
                } else {
                    LOG.debug("Not polling on partition [{}]. It has [{}] uncommitted offsets, which exceeds the limit of [{}]. ", tp,
                        numUncommittedOffsets, maxUncommittedOffsets);
                }
            }
        }
        return new PollablePartitionsInfo(pollablePartitions, earliestRetriableOffsets);
    }

    // ======== poll =========
    private ConsumerRecords<K, V> pollKafkaBroker(PollablePartitionsInfo pollablePartitionsInfo) {
        doSeekRetriableTopicPartitions(pollablePartitionsInfo.pollableEarliestRetriableOffsets);
        Set<TopicPartition> pausedPartitions = new HashSet<>(kafkaConsumer.assignment());
        Iterator<TopicPartition> pausedIter = pausedPartitions.iterator();
        while (pausedIter.hasNext()) {
            if (pollablePartitionsInfo.pollablePartitions.contains(pausedIter.next())) {
                pausedIter.remove();
            }
        }
        try {
            kafkaConsumer.pause(pausedPartitions);
            final ConsumerRecords<K, V> consumerRecords = kafkaConsumer.poll(kafkaSpoutConfig.getPollTimeoutMs());
            ackRetriableOffsetsIfCompactedAway(pollablePartitionsInfo.pollableEarliestRetriableOffsets, consumerRecords);
            final int numPolledRecords = consumerRecords.count();
            LOG.debug("Polled [{}] records from Kafka",
                numPolledRecords);
            if (kafkaSpoutConfig.getProcessingGuarantee() == KafkaSpoutConfig.ProcessingGuarantee.AT_MOST_ONCE) {
                //Commit polled records immediately to ensure delivery is at-most-once.
                Map<TopicPartition, OffsetAndMetadata> offsetsToCommit =
                    createFetchedOffsetsMetadata(kafkaConsumer.assignment());
                kafkaConsumer.commitSync(offsetsToCommit);
                LOG.debug("Committed offsets {} to Kafka", offsetsToCommit);
            }
            return consumerRecords;
        } finally {
            kafkaConsumer.resume(pausedPartitions);
        }
    }
​
    private void doSeekRetriableTopicPartitions(Map<TopicPartition, Long> pollableEarliestRetriableOffsets) {
        for (Entry<TopicPartition, Long> retriableTopicPartitionAndOffset : pollableEarliestRetriableOffsets.entrySet()) {
            //Seek directly to the earliest retriable message for each retriable topic partition
            kafkaConsumer.seek(retriableTopicPartitionAndOffset.getKey(), retriableTopicPartitionAndOffset.getValue());
        }
    }
​
    private void ackRetriableOffsetsIfCompactedAway(Map<TopicPartition, Long> earliestRetriableOffsets,
        ConsumerRecords<K, V> consumerRecords) {
        for (Entry<TopicPartition, Long> entry : earliestRetriableOffsets.entrySet()) {
            TopicPartition tp = entry.getKey();
            List<ConsumerRecord<K, V>> records = consumerRecords.records(tp);
            if (!records.isEmpty()) {
                ConsumerRecord<K, V> record = records.get(0);
                long seekOffset = entry.getValue();
                long earliestReceivedOffset = record.offset();
                if (seekOffset < earliestReceivedOffset) {
                    //Since we asked for tuples starting at seekOffset, some retriable records must have been compacted away.
                    //Ack up to the first offset received if the record is not already acked or currently in the topology
                    for (long i = seekOffset; i < earliestReceivedOffset; i++) {
                        KafkaSpoutMessageId msgId = retryService.getMessageId(new ConsumerRecord<>(tp.topic(), tp.partition(), i, null, null));
                        if (!offsetManagers.get(tp).contains(msgId) && !emitted.contains(msgId)) {
                            LOG.debug("Record at offset [{}] appears to have been compacted away from topic [{}], marking as acked", i, tp);
                            retryService.remove(msgId);
                            emitted.add(msgId);
                            ack(msgId);
                        }
                    }
                }
            }
        }
    }

    private void emitIfWaitingNotEmitted() {
        Iterator<List<ConsumerRecord<K, V>>> waitingToEmitIter = waitingToEmit.values().iterator();
        outerLoop:
        while (waitingToEmitIter.hasNext()) {
            List<ConsumerRecord<K, V>> waitingToEmitForTp = waitingToEmitIter.next();
            while (!waitingToEmitForTp.isEmpty()) {
                final boolean emittedTuple = emitOrRetryTuple(waitingToEmitForTp.remove(0));
                if (emittedTuple) {
                    break outerLoop;
                }
            }
            waitingToEmitIter.remove();
        }
    }

    /**
     * Creates a tuple from the kafka record and emits it if it was never emitted or it is ready to be retried.
     *
     * @param record to be emitted
     * @return true if tuple was emitted. False if tuple has been acked or has been emitted and is pending ack or fail
     */
    private boolean emitOrRetryTuple(ConsumerRecord<K, V> record) {
        final TopicPartition tp = new TopicPartition(record.topic(), record.partition());
        final KafkaSpoutMessageId msgId = retryService.getMessageId(record);
​
        if (offsetManagers.containsKey(tp) && offsetManagers.get(tp).contains(msgId)) {   // has been acked
            LOG.trace("Tuple for record [{}] has already been acked. Skipping", record);
        } else if (emitted.contains(msgId)) {   // has been emitted and it is pending ack or fail
            LOG.trace("Tuple for record [{}] has already been emitted. Skipping", record);
        } else {
            final OffsetAndMetadata committedOffset = kafkaConsumer.committed(tp);
            if (isAtLeastOnceProcessing()
                && committedOffset != null 
                && committedOffset.offset() > record.offset()
                && commitMetadataManager.isOffsetCommittedByThisTopology(tp, committedOffset, Collections.unmodifiableMap(offsetManagers))) {
                // Ensures that after a topology with this id is started, the consumer fetch
                // position never falls behind the committed offset (STORM-2844)
                throw new IllegalStateException("Attempting to emit a message that has already been committed."
                    + " This should never occur when using the at-least-once processing guarantee.");
            }
​
            final List<Object> tuple = kafkaSpoutConfig.getTranslator().apply(record);
            if (isEmitTuple(tuple)) {
                final boolean isScheduled = retryService.isScheduled(msgId);
                // not scheduled <=> never failed (i.e. never emitted), or scheduled and ready to be retried
                if (!isScheduled || retryService.isReady(msgId)) {
                    final String stream = tuple instanceof KafkaTuple ? ((KafkaTuple) tuple).getStream() : Utils.DEFAULT_STREAM_ID;
​
                    if (!isAtLeastOnceProcessing()) {
                        if (kafkaSpoutConfig.isTupleTrackingEnforced()) {
                            collector.emit(stream, tuple, msgId);
                            LOG.trace("Emitted tuple [{}] for record [{}] with msgId [{}]", tuple, record, msgId);
                        } else {
                            collector.emit(stream, tuple);
                            LOG.trace("Emitted tuple [{}] for record [{}]", tuple, record);
                        }
                    } else {
                        emitted.add(msgId);
                        offsetManagers.get(tp).addToEmitMsgs(msgId.offset());
                        if (isScheduled) {  // Was scheduled for retry and re-emitted, so remove from schedule.
                            retryService.remove(msgId);
                        }
                        collector.emit(stream, tuple, msgId);
                        tupleListener.onEmit(tuple, msgId);
                        LOG.trace("Emitted tuple [{}] for record [{}] with msgId [{}]", tuple, record, msgId);
                    }
                    return true;
                }
            } else {
                /*if a null tuple is not configured to be emitted, it should be marked as emitted and acked immediately
                * to allow its offset to be commited to Kafka*/
                LOG.debug("Not emitting null tuple for record [{}] as defined in configuration.", record);
                if (isAtLeastOnceProcessing()) {
                    msgId.setNullTuple(true);
                    offsetManagers.get(tp).addToEmitMsgs(msgId.offset());
                    ack(msgId);
                }
            }
        }
        return false;
    }

   // ======== Ack =======
    @Override
    public void ack(Object messageId) {
        if (!isAtLeastOnceProcessing()) {
            return;
        }
​
        // Only need to keep track of acked tuples if commits to Kafka are controlled by
        // tuple acks, which happens only for at-least-once processing semantics
        final KafkaSpoutMessageId msgId = (KafkaSpoutMessageId) messageId;
​
        if (msgId.isNullTuple()) {
            //a null tuple should be added to the ack list since by definition is a direct ack
            offsetManagers.get(msgId.getTopicPartition()).addToAckMsgs(msgId);
            LOG.debug("Received direct ack for message [{}], associated with null tuple", msgId);
            tupleListener.onAck(msgId);
            return;
        }
​
        if (!emitted.contains(msgId)) {
            LOG.debug("Received ack for message [{}], associated with tuple emitted for a ConsumerRecord that "
                        + "came from a topic-partition that this consumer group instance is no longer tracking "
                        + "due to rebalance/partition reassignment. No action taken.", msgId);
        } else {
            Validate.isTrue(!retryService.isScheduled(msgId), "The message id " + msgId + " is queued for retry while being acked."
                        + " This should never occur barring errors in the RetryService implementation or the spout code.");
            offsetManagers.get(msgId.getTopicPartition()).addToAckMsgs(msgId);
            emitted.remove(msgId);
        }
        tupleListener.onAck(msgId);
    }

   // ======== Fail =======
    @Override
    public void fail(Object messageId) {
        if (!isAtLeastOnceProcessing()) {
            return;
        }
        // Only need to keep track of failed tuples if commits to Kafka are controlled by
        // tuple acks, which happens only for at-least-once processing semantics
        final KafkaSpoutMessageId msgId = (KafkaSpoutMessageId) messageId;
        if (!emitted.contains(msgId)) {
            LOG.debug("Received fail for tuple this spout is no longer tracking."
                + " Partitions may have been reassigned. Ignoring message [{}]", msgId);
            return;
        }
        Validate.isTrue(!retryService.isScheduled(msgId), "The message id " + msgId + " is queued for retry while being failed."
            + " This should never occur barring errors in the RetryService implementation or the spout code.");
​
        msgId.incrementNumFails();
​
        if (!retryService.schedule(msgId)) {
            LOG.debug("Reached maximum number of retries. Message [{}] being marked as acked.", msgId);
            // this tuple should be removed from emitted only inside the ack() method. This is to ensure
            // that the OffsetManager for that TopicPartition is updated and allows commit progression
            tupleListener.onMaxRetryReached(msgId);
            ack(msgId);
        } else {
            tupleListener.onRetry(msgId);
            emitted.remove(msgId);
        }
    }

    private static final RetryEntryTimeStampComparator RETRY_ENTRY_TIME_STAMP_COMPARATOR = new RetryEntryTimeStampComparator();
​
    //This class assumes that there is at most one retry schedule per message id in this set at a time.
    private final Set<RetrySchedule> retrySchedules = new TreeSet<>(RETRY_ENTRY_TIME_STAMP_COMPARATOR);
​
    /**
     * Comparator ordering by timestamp 
     */
    private static class RetryEntryTimeStampComparator implements Serializable, Comparator<RetrySchedule> {
        @Override
        public int compare(RetrySchedule entry1, RetrySchedule entry2) {
            int result = Long.valueOf(entry1.nextRetryTimeNanos()).compareTo(entry2.nextRetryTimeNanos());
            
            if(result == 0) {
                //TreeSet uses compareTo instead of equals() for the Set contract
                //Ensure that we can save two retry schedules with the same timestamp
                result = entry1.hashCode() - entry2.hashCode();
            }
            return result;
        }
    }
​
    @Override
    public boolean schedule(KafkaSpoutMessageId msgId) {
        if (msgId.numFails() > maxRetries) {
            LOG.debug("Not scheduling [{}] because reached maximum number of retries [{}].", msgId, maxRetries);
            return false;
        } else {
            //Remove existing schedule for the message id
            remove(msgId);
            final RetrySchedule retrySchedule = new RetrySchedule(msgId, nextTime(msgId));
            retrySchedules.add(retrySchedule);
            toRetryMsgs.add(msgId);
            LOG.debug("Scheduled. {}", retrySchedule);
            LOG.trace("Current state {}", retrySchedules);
            return true;
        }
    }
​
    @Override
    public Map<TopicPartition, Long> earliestRetriableOffsets() {
        final Map<TopicPartition, Long> tpToEarliestRetriableOffset = new HashMap<>();
        final long currentTimeNanos = Time.nanoTime();
        for (RetrySchedule retrySchedule : retrySchedules) {
            if (retrySchedule.retry(currentTimeNanos)) {
                final KafkaSpoutMessageId msgId = retrySchedule.msgId;
                final TopicPartition tpForMessage = new TopicPartition(msgId.topic(), msgId.partition());
                final Long currentLowestOffset = tpToEarliestRetriableOffset.get(tpForMessage);
                if(currentLowestOffset != null) {
                    tpToEarliestRetriableOffset.put(tpForMessage, Math.min(currentLowestOffset, msgId.offset()));
                } else {
                    tpToEarliestRetriableOffset.put(tpForMessage, msgId.offset());
                }
            } else {
                break;  // Stop searching as soon as passed current time
            }
        }
        LOG.debug("Topic partitions with entries ready to be retried [{}] ", tpToEarliestRetriableOffset);
        return tpToEarliestRetriableOffset;
    }
​
    @Override
    public boolean isReady(KafkaSpoutMessageId msgId) {
        boolean retry = false;
        if (isScheduled(msgId)) {
            final long currentTimeNanos = Time.nanoTime();
            for (RetrySchedule retrySchedule : retrySchedules) {
                if (retrySchedule.retry(currentTimeNanos)) {
                    if (retrySchedule.msgId.equals(msgId)) {
                        retry = true;
                        LOG.debug("Found entry to retry {}", retrySchedule);
                        break; //Stop searching if the message is known to be ready for retry
                    }
                } else {
                    LOG.debug("Entry to retry not found {}", retrySchedule);
                    break;  // Stop searching as soon as passed current time
                }
            }
        }
        return retry;
    }