kafka RecordAccumulator 源码
kafka RecordAccumulator 代码
文件路径：/clients/src/main/java/org/apache/kafka/clients/producer/internals/RecordAccumulator.java
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* 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.
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package org.apache.kafka.clients.producer.internals;
import java.nio.ByteBuffer;
import java.util.ArrayDeque;
import java.util.ArrayList;
import java.util.Collections;
import java.util.Deque;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import java.util.Set;
import java.util.concurrent.ConcurrentMap;
import java.util.concurrent.atomic.AtomicInteger;
import org.apache.kafka.clients.ApiVersions;
import org.apache.kafka.clients.producer.Callback;
import org.apache.kafka.clients.producer.RecordMetadata;
import org.apache.kafka.common.utils.ProducerIdAndEpoch;
import org.apache.kafka.common.Cluster;
import org.apache.kafka.common.KafkaException;
import org.apache.kafka.common.Node;
import org.apache.kafka.common.PartitionInfo;
import org.apache.kafka.common.TopicPartition;
import org.apache.kafka.common.errors.UnsupportedVersionException;
import org.apache.kafka.common.header.Header;
import org.apache.kafka.common.metrics.Metrics;
import org.apache.kafka.common.record.AbstractRecords;
import org.apache.kafka.common.record.CompressionRatioEstimator;
import org.apache.kafka.common.record.CompressionType;
import org.apache.kafka.common.record.MemoryRecords;
import org.apache.kafka.common.record.MemoryRecordsBuilder;
import org.apache.kafka.common.record.Record;
import org.apache.kafka.common.record.RecordBatch;
import org.apache.kafka.common.record.TimestampType;
import org.apache.kafka.common.utils.CopyOnWriteMap;
import org.apache.kafka.common.utils.LogContext;
import org.apache.kafka.common.utils.Time;
import org.slf4j.Logger;
/**
* This class acts as a queue that accumulates records into {@link MemoryRecords}
* instances to be sent to the server.
* <p>
* The accumulator uses a bounded amount of memory and append calls will block when that memory is exhausted, unless
* this behavior is explicitly disabled.
*/
public class RecordAccumulator {
private final LogContext logContext;
private final Logger log;
private volatile boolean closed;
private final AtomicInteger flushesInProgress;
private final AtomicInteger appendsInProgress;
private final int batchSize;
private final CompressionType compression;
private final int lingerMs;
private final long retryBackoffMs;
private final int deliveryTimeoutMs;
private final long partitionAvailabilityTimeoutMs; // latency threshold for marking partition temporary unavailable
private final boolean enableAdaptivePartitioning;
private final BufferPool free;
private final Time time;
private final ApiVersions apiVersions;
private final ConcurrentMap<String /*topic*/, TopicInfo> topicInfoMap = new CopyOnWriteMap<>();
private final ConcurrentMap<Integer /*nodeId*/, NodeLatencyStats> nodeStats = new CopyOnWriteMap<>();
private final IncompleteBatches incomplete;
// The following variables are only accessed by the sender thread, so we don't need to protect them.
private final Set<TopicPartition> muted;
private final Map<String, Integer> nodesDrainIndex;
private final TransactionManager transactionManager;
private long nextBatchExpiryTimeMs = Long.MAX_VALUE; // the earliest time (absolute) a batch will expire.
/**
* Create a new record accumulator
*
* @param logContext The log context used for logging
* @param batchSize The size to use when allocating {@link MemoryRecords} instances
* @param compression The compression codec for the records
* @param lingerMs An artificial delay time to add before declaring a records instance that isn't full ready for
* sending. This allows time for more records to arrive. Setting a non-zero lingerMs will trade off some
* latency for potentially better throughput due to more batching (and hence fewer, larger requests).
* @param retryBackoffMs An artificial delay time to retry the produce request upon receiving an error. This avoids
* exhausting all retries in a short period of time.
* @param deliveryTimeoutMs An upper bound on the time to report success or failure on record delivery
* @param partitionerConfig Partitioner config
* @param metrics The metrics
* @param metricGrpName The metric group name
* @param time The time instance to use
* @param apiVersions Request API versions for current connected brokers
* @param transactionManager The shared transaction state object which tracks producer IDs, epochs, and sequence
* numbers per partition.
* @param bufferPool The buffer pool
*/
public RecordAccumulator(LogContext logContext,
int batchSize,
CompressionType compression,
int lingerMs,
long retryBackoffMs,
int deliveryTimeoutMs,
PartitionerConfig partitionerConfig,
Metrics metrics,
String metricGrpName,
Time time,
ApiVersions apiVersions,
TransactionManager transactionManager,
BufferPool bufferPool) {
this.logContext = logContext;
this.log = logContext.logger(RecordAccumulator.class);
this.closed = false;
this.flushesInProgress = new AtomicInteger(0);
this.appendsInProgress = new AtomicInteger(0);
this.batchSize = batchSize;
this.compression = compression;
this.lingerMs = lingerMs;
this.retryBackoffMs = retryBackoffMs;
this.deliveryTimeoutMs = deliveryTimeoutMs;
this.enableAdaptivePartitioning = partitionerConfig.enableAdaptivePartitioning;
this.partitionAvailabilityTimeoutMs = partitionerConfig.partitionAvailabilityTimeoutMs;
this.free = bufferPool;
this.incomplete = new IncompleteBatches();
this.muted = new HashSet<>();
this.time = time;
this.apiVersions = apiVersions;
nodesDrainIndex = new HashMap<>();
this.transactionManager = transactionManager;
registerMetrics(metrics, metricGrpName);
}
/**
* Create a new record accumulator with default partitioner config
*
* @param logContext The log context used for logging
* @param batchSize The size to use when allocating {@link MemoryRecords} instances
* @param compression The compression codec for the records
* @param lingerMs An artificial delay time to add before declaring a records instance that isn't full ready for
* sending. This allows time for more records to arrive. Setting a non-zero lingerMs will trade off some
* latency for potentially better throughput due to more batching (and hence fewer, larger requests).
* @param retryBackoffMs An artificial delay time to retry the produce request upon receiving an error. This avoids
* exhausting all retries in a short period of time.
* @param deliveryTimeoutMs An upper bound on the time to report success or failure on record delivery
* @param metrics The metrics
* @param metricGrpName The metric group name
* @param time The time instance to use
* @param apiVersions Request API versions for current connected brokers
* @param transactionManager The shared transaction state object which tracks producer IDs, epochs, and sequence
* numbers per partition.
* @param bufferPool The buffer pool
*/
public RecordAccumulator(LogContext logContext,
int batchSize,
CompressionType compression,
int lingerMs,
long retryBackoffMs,
int deliveryTimeoutMs,
Metrics metrics,
String metricGrpName,
Time time,
ApiVersions apiVersions,
TransactionManager transactionManager,
BufferPool bufferPool) {
this(logContext,
batchSize,
compression,
lingerMs,
retryBackoffMs,
deliveryTimeoutMs,
new PartitionerConfig(),
metrics,
metricGrpName,
time,
apiVersions,
transactionManager,
bufferPool);
}
private void registerMetrics(Metrics metrics, String metricGrpName) {
metrics.addMetric(
metrics.metricName("waiting-threads", metricGrpName,
"The number of user threads blocked waiting for buffer memory to enqueue their records"),
(config, now) -> free.queued());
metrics.addMetric(
metrics.metricName("buffer-total-bytes", metricGrpName,
"The maximum amount of buffer memory the client can use (whether or not it is currently used)."),
(config, now) -> free.totalMemory());
metrics.addMetric(
metrics.metricName("buffer-available-bytes", metricGrpName,
"The total amount of buffer memory that is not being used (either unallocated or in the free list)."),
(config, now) -> free.availableMemory());
}
private void setPartition(AppendCallbacks callbacks, int partition) {
if (callbacks != null)
callbacks.setPartition(partition);
}
/**
* Check if partition concurrently changed, or we need to complete previously disabled partition change.
*
* @param topic The topic
* @param topicInfo The topic info
* @param partitionInfo The built-in partitioner's partition info
* @param deque The partition queue
* @param nowMs The current time, in milliseconds
* @param cluster THe cluster metadata
* @return 'true' if partition changed and we need to get new partition info and retry,
* 'false' otherwise
*/
private boolean partitionChanged(String topic,
TopicInfo topicInfo,
BuiltInPartitioner.StickyPartitionInfo partitionInfo,
Deque<ProducerBatch> deque, long nowMs,
Cluster cluster) {
if (topicInfo.builtInPartitioner.isPartitionChanged(partitionInfo)) {
log.trace("Partition {} for topic {} switched by a concurrent append, retrying",
partitionInfo.partition(), topic);
return true;
}
// We might have disabled partition switch if the queue had incomplete batches.
// Check if all batches are full now and switch .
if (allBatchesFull(deque)) {
topicInfo.builtInPartitioner.updatePartitionInfo(partitionInfo, 0, cluster, true);
if (topicInfo.builtInPartitioner.isPartitionChanged(partitionInfo)) {
log.trace("Completed previously disabled switch for topic {} partition {}, retrying",
topic, partitionInfo.partition());
return true;
}
}
return false;
}
/**
* Add a record to the accumulator, return the append result
* <p>
* The append result will contain the future metadata, and flag for whether the appended batch is full or a new batch is created
* <p>
*
* @param topic The topic to which this record is being sent
* @param partition The partition to which this record is being sent or RecordMetadata.UNKNOWN_PARTITION
* if any partition could be used
* @param timestamp The timestamp of the record
* @param key The key for the record
* @param value The value for the record
* @param headers the Headers for the record
* @param callbacks The callbacks to execute
* @param maxTimeToBlock The maximum time in milliseconds to block for buffer memory to be available
* @param abortOnNewBatch A boolean that indicates returning before a new batch is created and
* running the partitioner's onNewBatch method before trying to append again
* @param nowMs The current time, in milliseconds
* @param cluster The cluster metadata
*/
public RecordAppendResult append(String topic,
int partition,
long timestamp,
byte[] key,
byte[] value,
Header[] headers,
AppendCallbacks callbacks,
long maxTimeToBlock,
boolean abortOnNewBatch,
long nowMs,
Cluster cluster) throws InterruptedException {
TopicInfo topicInfo = topicInfoMap.computeIfAbsent(topic, k -> new TopicInfo(logContext, k, batchSize));
// We keep track of the number of appending thread to make sure we do not miss batches in
// abortIncompleteBatches().
appendsInProgress.incrementAndGet();
ByteBuffer buffer = null;
if (headers == null) headers = Record.EMPTY_HEADERS;
try {
// Loop to retry in case we encounter partitioner's race conditions.
while (true) {
// If the message doesn't have any partition affinity, so we pick a partition based on the broker
// availability and performance. Note, that here we peek current partition before we hold the
// deque lock, so we'll need to make sure that it's not changed while we were waiting for the
// deque lock.
final BuiltInPartitioner.StickyPartitionInfo partitionInfo;
final int effectivePartition;
if (partition == RecordMetadata.UNKNOWN_PARTITION) {
partitionInfo = topicInfo.builtInPartitioner.peekCurrentPartitionInfo(cluster);
effectivePartition = partitionInfo.partition();
} else {
partitionInfo = null;
effectivePartition = partition;
}
// Now that we know the effective partition, let the caller know.
setPartition(callbacks, effectivePartition);
// check if we have an in-progress batch
Deque<ProducerBatch> dq = topicInfo.batches.computeIfAbsent(effectivePartition, k -> new ArrayDeque<>());
synchronized (dq) {
// After taking the lock, validate that the partition hasn't changed and retry.
if (partitionChanged(topic, topicInfo, partitionInfo, dq, nowMs, cluster))
continue;
RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callbacks, dq, nowMs);
if (appendResult != null) {
// If queue has incomplete batches we disable switch (see comments in updatePartitionInfo).
boolean enableSwitch = allBatchesFull(dq);
topicInfo.builtInPartitioner.updatePartitionInfo(partitionInfo, appendResult.appendedBytes, cluster, enableSwitch);
return appendResult;
}
}
// we don't have an in-progress record batch try to allocate a new batch
if (abortOnNewBatch) {
// Return a result that will cause another call to append.
return new RecordAppendResult(null, false, false, true, 0);
}
if (buffer == null) {
byte maxUsableMagic = apiVersions.maxUsableProduceMagic();
int size = Math.max(this.batchSize, AbstractRecords.estimateSizeInBytesUpperBound(maxUsableMagic, compression, key, value, headers));
log.trace("Allocating a new {} byte message buffer for topic {} partition {} with remaining timeout {}ms", size, topic, partition, maxTimeToBlock);
// This call may block if we exhausted buffer space.
buffer = free.allocate(size, maxTimeToBlock);
// Update the current time in case the buffer allocation blocked above.
// NOTE: getting time may be expensive, so calling it under a lock
// should be avoided.
nowMs = time.milliseconds();
}
synchronized (dq) {
// After taking the lock, validate that the partition hasn't changed and retry.
if (partitionChanged(topic, topicInfo, partitionInfo, dq, nowMs, cluster))
continue;
RecordAppendResult appendResult = appendNewBatch(topic, effectivePartition, dq, timestamp, key, value, headers, callbacks, buffer, nowMs);
// Set buffer to null, so that deallocate doesn't return it back to free pool, since it's used in the batch.
if (appendResult.newBatchCreated)
buffer = null;
// If queue has incomplete batches we disable switch (see comments in updatePartitionInfo).
boolean enableSwitch = allBatchesFull(dq);
topicInfo.builtInPartitioner.updatePartitionInfo(partitionInfo, appendResult.appendedBytes, cluster, enableSwitch);
return appendResult;
}
}
} finally {
free.deallocate(buffer);
appendsInProgress.decrementAndGet();
}
}
/**
* Append a new batch to the queue
*
* @param topic The topic
* @param partition The partition (cannot be RecordMetadata.UNKNOWN_PARTITION)
* @param dq The queue
* @param timestamp The timestamp of the record
* @param key The key for the record
* @param value The value for the record
* @param headers the Headers for the record
* @param callbacks The callbacks to execute
* @param buffer The buffer for the new batch
* @param nowMs The current time, in milliseconds
*/
private RecordAppendResult appendNewBatch(String topic,
int partition,
Deque<ProducerBatch> dq,
long timestamp,
byte[] key,
byte[] value,
Header[] headers,
AppendCallbacks callbacks,
ByteBuffer buffer,
long nowMs) {
assert partition != RecordMetadata.UNKNOWN_PARTITION;
RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callbacks, dq, nowMs);
if (appendResult != null) {
// Somebody else found us a batch, return the one we waited for! Hopefully this doesn't happen often...
return appendResult;
}
MemoryRecordsBuilder recordsBuilder = recordsBuilder(buffer, apiVersions.maxUsableProduceMagic());
ProducerBatch batch = new ProducerBatch(new TopicPartition(topic, partition), recordsBuilder, nowMs);
FutureRecordMetadata future = Objects.requireNonNull(batch.tryAppend(timestamp, key, value, headers,
callbacks, nowMs));
dq.addLast(batch);
incomplete.add(batch);
return new RecordAppendResult(future, dq.size() > 1 || batch.isFull(), true, false, batch.estimatedSizeInBytes());
}
private MemoryRecordsBuilder recordsBuilder(ByteBuffer buffer, byte maxUsableMagic) {
if (transactionManager != null && maxUsableMagic < RecordBatch.MAGIC_VALUE_V2) {
throw new UnsupportedVersionException("Attempting to use idempotence with a broker which does not " +
"support the required message format (v2). The broker must be version 0.11 or later.");
}
return MemoryRecords.builder(buffer, maxUsableMagic, compression, TimestampType.CREATE_TIME, 0L);
}
/**
* Check if all batches in the queue are full.
*/
private boolean allBatchesFull(Deque<ProducerBatch> deque) {
// Only the last batch may be incomplete, so we just check that.
ProducerBatch last = deque.peekLast();
return last == null || last.isFull();
}
/**
* Try to append to a ProducerBatch.
*
* If it is full, we return null and a new batch is created. We also close the batch for record appends to free up
* resources like compression buffers. The batch will be fully closed (ie. the record batch headers will be written
* and memory records built) in one of the following cases (whichever comes first): right before send,
* if it is expired, or when the producer is closed.
*/
private RecordAppendResult tryAppend(long timestamp, byte[] key, byte[] value, Header[] headers,
Callback callback, Deque<ProducerBatch> deque, long nowMs) {
if (closed)
throw new KafkaException("Producer closed while send in progress");
ProducerBatch last = deque.peekLast();
if (last != null) {
int initialBytes = last.estimatedSizeInBytes();
FutureRecordMetadata future = last.tryAppend(timestamp, key, value, headers, callback, nowMs);
if (future == null) {
last.closeForRecordAppends();
} else {
int appendedBytes = last.estimatedSizeInBytes() - initialBytes;
return new RecordAppendResult(future, deque.size() > 1 || last.isFull(), false, false, appendedBytes);
}
}
return null;
}
private boolean isMuted(TopicPartition tp) {
return muted.contains(tp);
}
public void resetNextBatchExpiryTime() {
nextBatchExpiryTimeMs = Long.MAX_VALUE;
}
public void maybeUpdateNextBatchExpiryTime(ProducerBatch batch) {
if (batch.createdMs + deliveryTimeoutMs > 0) {
// the non-negative check is to guard us against potential overflow due to setting
// a large value for deliveryTimeoutMs
nextBatchExpiryTimeMs = Math.min(nextBatchExpiryTimeMs, batch.createdMs + deliveryTimeoutMs);
} else {
log.warn("Skipping next batch expiry time update due to addition overflow: "
+ "batch.createMs={}, deliveryTimeoutMs={}", batch.createdMs, deliveryTimeoutMs);
}
}
/**
* Get a list of batches which have been sitting in the accumulator too long and need to be expired.
*/
public List<ProducerBatch> expiredBatches(long now) {
List<ProducerBatch> expiredBatches = new ArrayList<>();
for (TopicInfo topicInfo : topicInfoMap.values()) {
for (Deque<ProducerBatch> deque : topicInfo.batches.values()) {
// expire the batches in the order of sending
synchronized (deque) {
while (!deque.isEmpty()) {
ProducerBatch batch = deque.getFirst();
if (batch.hasReachedDeliveryTimeout(deliveryTimeoutMs, now)) {
deque.poll();
batch.abortRecordAppends();
expiredBatches.add(batch);
} else {
maybeUpdateNextBatchExpiryTime(batch);
break;
}
}
}
}
}
return expiredBatches;
}
public long getDeliveryTimeoutMs() {
return deliveryTimeoutMs;
}
/**
* Re-enqueue the given record batch in the accumulator. In Sender.completeBatch method, we check
* whether the batch has reached deliveryTimeoutMs or not. Hence we do not do the delivery timeout check here.
*/
public void reenqueue(ProducerBatch batch, long now) {
batch.reenqueued(now);
Deque<ProducerBatch> deque = getOrCreateDeque(batch.topicPartition);
synchronized (deque) {
if (transactionManager != null)
insertInSequenceOrder(deque, batch);
else
deque.addFirst(batch);
}
}
/**
* Split the big batch that has been rejected and reenqueue the split batches in to the accumulator.
* @return the number of split batches.
*/
public int splitAndReenqueue(ProducerBatch bigBatch) {
// Reset the estimated compression ratio to the initial value or the big batch compression ratio, whichever
// is bigger. There are several different ways to do the reset. We chose the most conservative one to ensure
// the split doesn't happen too often.
CompressionRatioEstimator.setEstimation(bigBatch.topicPartition.topic(), compression,
Math.max(1.0f, (float) bigBatch.compressionRatio()));
Deque<ProducerBatch> dq = bigBatch.split(this.batchSize);
int numSplitBatches = dq.size();
Deque<ProducerBatch> partitionDequeue = getOrCreateDeque(bigBatch.topicPartition);
while (!dq.isEmpty()) {
ProducerBatch batch = dq.pollLast();
incomplete.add(batch);
// We treat the newly split batches as if they are not even tried.
synchronized (partitionDequeue) {
if (transactionManager != null) {
// We should track the newly created batches since they already have assigned sequences.
transactionManager.addInFlightBatch(batch);
insertInSequenceOrder(partitionDequeue, batch);
} else {
partitionDequeue.addFirst(batch);
}
}
}
return numSplitBatches;
}
// We will have to do extra work to ensure the queue is in order when requests are being retried and there are
// multiple requests in flight to that partition. If the first in flight request fails to append, then all the
// subsequent in flight requests will also fail because the sequence numbers will not be accepted.
//
// Further, once batches are being retried, we are reduced to a single in flight request for that partition. So when
// the subsequent batches come back in sequence order, they will have to be placed further back in the queue.
//
// Note that this assumes that all the batches in the queue which have an assigned sequence also have the current
// producer id. We will not attempt to reorder messages if the producer id has changed, we will throw an
// IllegalStateException instead.
private void insertInSequenceOrder(Deque<ProducerBatch> deque, ProducerBatch batch) {
// When we are re-enqueueing and have enabled idempotence, the re-enqueued batch must always have a sequence.
if (batch.baseSequence() == RecordBatch.NO_SEQUENCE)
throw new IllegalStateException("Trying to re-enqueue a batch which doesn't have a sequence even " +
"though idempotency is enabled.");
if (!transactionManager.hasInflightBatches(batch.topicPartition))
throw new IllegalStateException("We are re-enqueueing a batch which is not tracked as part of the in flight " +
"requests. batch.topicPartition: " + batch.topicPartition + "; batch.baseSequence: " + batch.baseSequence());
ProducerBatch firstBatchInQueue = deque.peekFirst();
if (firstBatchInQueue != null && firstBatchInQueue.hasSequence() && firstBatchInQueue.baseSequence() < batch.baseSequence()) {
// The incoming batch can't be inserted at the front of the queue without violating the sequence ordering.
// This means that the incoming batch should be placed somewhere further back.
// We need to find the right place for the incoming batch and insert it there.
// We will only enter this branch if we have multiple inflights sent to different brokers and we need to retry
// the inflight batches.
//
// Since we reenqueue exactly one batch a time and ensure that the queue is ordered by sequence always, it
// is a simple linear scan of a subset of the in flight batches to find the right place in the queue each time.
List<ProducerBatch> orderedBatches = new ArrayList<>();
while (deque.peekFirst() != null && deque.peekFirst().hasSequence() && deque.peekFirst().baseSequence() < batch.baseSequence())
orderedBatches.add(deque.pollFirst());
log.debug("Reordered incoming batch with sequence {} for partition {}. It was placed in the queue at " +
"position {}", batch.baseSequence(), batch.topicPartition, orderedBatches.size());
// Either we have reached a point where there are batches without a sequence (ie. never been drained
// and are hence in order by default), or the batch at the front of the queue has a sequence greater
// than the incoming batch. This is the right place to add the incoming batch.
deque.addFirst(batch);
// Now we have to re insert the previously queued batches in the right order.
for (int i = orderedBatches.size() - 1; i >= 0; --i) {
deque.addFirst(orderedBatches.get(i));
}
// At this point, the incoming batch has been queued in the correct place according to its sequence.
} else {
deque.addFirst(batch);
}
}
/**
* Add the leader to the ready nodes if the batch is ready
*
* @param nowMs The current time
* @param exhausted 'true' is the buffer pool is exhausted
* @param part The partition
* @param leader The leader for the partition
* @param waitedTimeMs How long batch waited
* @param backingOff Is backing off
* @param full Is batch full
* @param nextReadyCheckDelayMs The delay for next check
* @param readyNodes The set of ready nodes (to be filled in)
* @return The delay for next check
*/
private long batchReady(long nowMs, boolean exhausted, TopicPartition part, Node leader,
long waitedTimeMs, boolean backingOff, boolean full,
long nextReadyCheckDelayMs, Set<Node> readyNodes) {
if (!readyNodes.contains(leader) && !isMuted(part)) {
long timeToWaitMs = backingOff ? retryBackoffMs : lingerMs;
boolean expired = waitedTimeMs >= timeToWaitMs;
boolean transactionCompleting = transactionManager != null && transactionManager.isCompleting();
boolean sendable = full
|| expired
|| exhausted
|| closed
|| flushInProgress()
|| transactionCompleting;
if (sendable && !backingOff) {
readyNodes.add(leader);
} else {
long timeLeftMs = Math.max(timeToWaitMs - waitedTimeMs, 0);
// Note that this results in a conservative estimate since an un-sendable partition may have
// a leader that will later be found to have sendable data. However, this is good enough
// since we'll just wake up and then sleep again for the remaining time.
nextReadyCheckDelayMs = Math.min(timeLeftMs, nextReadyCheckDelayMs);
}
}
return nextReadyCheckDelayMs;
}
/**
* Iterate over partitions to see which one have batches ready and collect leaders of those partitions
* into the set of ready nodes. If partition has no leader, add the topic to the set of topics with
* no leader. This function also calculates stats for adaptive partitioning.
*
* @param cluster The cluster metadata
* @param nowMs The current time
* @param topic The topic
* @param topicInfo The topic info
* @param nextReadyCheckDelayMs The delay for next check
* @param readyNodes The set of ready nodes (to be filled in)
* @param unknownLeaderTopics The set of topics with no leader (to be filled in)
* @return The delay for next check
*/
private long partitionReady(Cluster cluster, long nowMs, String topic,
TopicInfo topicInfo,
long nextReadyCheckDelayMs, Set<Node> readyNodes, Set<String> unknownLeaderTopics) {
ConcurrentMap<Integer, Deque<ProducerBatch>> batches = topicInfo.batches;
// Collect the queue sizes for available partitions to be used in adaptive partitioning.
int[] queueSizes = null;
int[] partitionIds = null;
if (enableAdaptivePartitioning && batches.size() >= cluster.partitionsForTopic(topic).size()) {
// We don't do adaptive partitioning until we scheduled at least a batch for all
// partitions (i.e. we have the corresponding entries in the batches map), we just
// do uniform. The reason is that we build queue sizes from the batches map,
// and if an entry is missing in the batches map, then adaptive partitioning logic
// won't know about it and won't switch to it.
queueSizes = new int[batches.size()];
partitionIds = new int[queueSizes.length];
}
int queueSizesIndex = -1;
boolean exhausted = this.free.queued() > 0;
for (Map.Entry<Integer, Deque<ProducerBatch>> entry : batches.entrySet()) {
TopicPartition part = new TopicPartition(topic, entry.getKey());
// Advance queueSizesIndex so that we properly index available
// partitions. Do it here so that it's done for all code paths.
Node leader = cluster.leaderFor(part);
if (leader != null && queueSizes != null) {
++queueSizesIndex;
assert queueSizesIndex < queueSizes.length;
partitionIds[queueSizesIndex] = part.partition();
}
Deque<ProducerBatch> deque = entry.getValue();
final long waitedTimeMs;
final boolean backingOff;
final int dequeSize;
final boolean full;
// This loop is especially hot with large partition counts.
// We are careful to only perform the minimum required inside the
// synchronized block, as this lock is also used to synchronize producer threads
// attempting to append() to a partition/batch.
synchronized (deque) {
// Deques are often empty in this path, esp with large partition counts,
// so we exit early if we can.
ProducerBatch batch = deque.peekFirst();
if (batch == null) {
continue;
}
waitedTimeMs = batch.waitedTimeMs(nowMs);
backingOff = batch.attempts() > 0 && waitedTimeMs < retryBackoffMs;
dequeSize = deque.size();
full = dequeSize > 1 || batch.isFull();
}
if (leader == null) {
// This is a partition for which leader is not known, but messages are available to send.
// Note that entries are currently not removed from batches when deque is empty.
unknownLeaderTopics.add(part.topic());
} else {
if (queueSizes != null)
queueSizes[queueSizesIndex] = dequeSize;
if (partitionAvailabilityTimeoutMs > 0) {
// Check if we want to exclude the partition from the list of available partitions
// if the broker hasn't responded for some time.
NodeLatencyStats nodeLatencyStats = nodeStats.get(leader.id());
if (nodeLatencyStats != null) {
// NOTE: there is no synchronization between reading metrics,
// so we read ready time first to avoid accidentally marking partition
// unavailable if we read while the metrics are being updated.
long readyTimeMs = nodeLatencyStats.readyTimeMs;
if (readyTimeMs - nodeLatencyStats.drainTimeMs > partitionAvailabilityTimeoutMs)
--queueSizesIndex;
}
}
nextReadyCheckDelayMs = batchReady(nowMs, exhausted, part, leader, waitedTimeMs, backingOff,
full, nextReadyCheckDelayMs, readyNodes);
}
}
// We've collected the queue sizes for partitions of this topic, now we can calculate
// load stats. NOTE: the stats are calculated in place, modifying the
// queueSizes array.
topicInfo.builtInPartitioner.updatePartitionLoadStats(queueSizes, partitionIds, queueSizesIndex + 1);
return nextReadyCheckDelayMs;
}
/**
* Get a list of nodes whose partitions are ready to be sent, and the earliest time at which any non-sendable
* partition will be ready; Also return the flag for whether there are any unknown leaders for the accumulated
* partition batches.
* <p>
* A destination node is ready to send data if:
* <ol>
* <li>There is at least one partition that is not backing off its send
* <li><b>and</b> those partitions are not muted (to prevent reordering if
* {@value org.apache.kafka.clients.producer.ProducerConfig#MAX_IN_FLIGHT_REQUESTS_PER_CONNECTION}
* is set to one)</li>
* <li><b>and <i>any</i></b> of the following are true</li>
* <ul>
* <li>The record set is full</li>
* <li>The record set has sat in the accumulator for at least lingerMs milliseconds</li>
* <li>The accumulator is out of memory and threads are blocking waiting for data (in this case all partitions
* are immediately considered ready).</li>
* <li>The accumulator has been closed</li>
* </ul>
* </ol>
*/
public ReadyCheckResult ready(Cluster cluster, long nowMs) {
Set<Node> readyNodes = new HashSet<>();
long nextReadyCheckDelayMs = Long.MAX_VALUE;
Set<String> unknownLeaderTopics = new HashSet<>();
// Go topic by topic so that we can get queue sizes for partitions in a topic and calculate
// cumulative frequency table (used in partitioner).
for (Map.Entry<String, TopicInfo> topicInfoEntry : this.topicInfoMap.entrySet()) {
final String topic = topicInfoEntry.getKey();
nextReadyCheckDelayMs = partitionReady(cluster, nowMs, topic, topicInfoEntry.getValue(), nextReadyCheckDelayMs, readyNodes, unknownLeaderTopics);
}
return new ReadyCheckResult(readyNodes, nextReadyCheckDelayMs, unknownLeaderTopics);
}
/**
* Check whether there are any batches which haven't been drained
*/
public boolean hasUndrained() {
for (TopicInfo topicInfo : topicInfoMap.values()) {
for (Deque<ProducerBatch> deque : topicInfo.batches.values()) {
synchronized (deque) {
if (!deque.isEmpty())
return true;
}
}
}
return false;
}
private boolean shouldStopDrainBatchesForPartition(ProducerBatch first, TopicPartition tp) {
ProducerIdAndEpoch producerIdAndEpoch = null;
if (transactionManager != null) {
if (!transactionManager.isSendToPartitionAllowed(tp))
return true;
producerIdAndEpoch = transactionManager.producerIdAndEpoch();
if (!producerIdAndEpoch.isValid())
// we cannot send the batch until we have refreshed the producer id
return true;
if (!first.hasSequence()) {
if (transactionManager.hasInflightBatches(tp) && transactionManager.hasStaleProducerIdAndEpoch(tp)) {
// Don't drain any new batches while the partition has in-flight batches with a different epoch
// and/or producer ID. Otherwise, a batch with a new epoch and sequence number
// 0 could be written before earlier batches complete, which would cause out of sequence errors
return true;
}
if (transactionManager.hasUnresolvedSequence(first.topicPartition))
// Don't drain any new batches while the state of previous sequence numbers
// is unknown. The previous batches would be unknown if they were aborted
// on the client after being sent to the broker at least once.
return true;
}
int firstInFlightSequence = transactionManager.firstInFlightSequence(first.topicPartition);
if (firstInFlightSequence != RecordBatch.NO_SEQUENCE && first.hasSequence()
&& first.baseSequence() != firstInFlightSequence)
// If the queued batch already has an assigned sequence, then it is being retried.
// In this case, we wait until the next immediate batch is ready and drain that.
// We only move on when the next in line batch is complete (either successfully or due to
// a fatal broker error). This effectively reduces our in flight request count to 1.
return true;
}
return false;
}
private List<ProducerBatch> drainBatchesForOneNode(Cluster cluster, Node node, int maxSize, long now) {
int size = 0;
List<PartitionInfo> parts = cluster.partitionsForNode(node.id());
List<ProducerBatch> ready = new ArrayList<>();
/* to make starvation less likely each node has it's own drainIndex */
int drainIndex = getDrainIndex(node.idString());
int start = drainIndex = drainIndex % parts.size();
do {
PartitionInfo part = parts.get(drainIndex);
TopicPartition tp = new TopicPartition(part.topic(), part.partition());
updateDrainIndex(node.idString(), drainIndex);
drainIndex = (drainIndex + 1) % parts.size();
// Only proceed if the partition has no in-flight batches.
if (isMuted(tp))
continue;
Deque<ProducerBatch> deque = getDeque(tp);
if (deque == null)
continue;
final ProducerBatch batch;
synchronized (deque) {
// invariant: !isMuted(tp,now) && deque != null
ProducerBatch first = deque.peekFirst();
if (first == null)
continue;
// first != null
boolean backoff = first.attempts() > 0 && first.waitedTimeMs(now) < retryBackoffMs;
// Only drain the batch if it is not during backoff period.
if (backoff)
continue;
if (size + first.estimatedSizeInBytes() > maxSize && !ready.isEmpty()) {
// there is a rare case that a single batch size is larger than the request size due to
// compression; in this case we will still eventually send this batch in a single request
break;
} else {
if (shouldStopDrainBatchesForPartition(first, tp))
break;
}
batch = deque.pollFirst();
boolean isTransactional = transactionManager != null && transactionManager.isTransactional();
ProducerIdAndEpoch producerIdAndEpoch =
transactionManager != null ? transactionManager.producerIdAndEpoch() : null;
if (producerIdAndEpoch != null && !batch.hasSequence()) {
// If the producer id/epoch of the partition do not match the latest one
// of the producer, we update it and reset the sequence. This should be
// only done when all its in-flight batches have completed. This is guarantee
// in `shouldStopDrainBatchesForPartition`.
transactionManager.maybeUpdateProducerIdAndEpoch(batch.topicPartition);
// If the batch already has an assigned sequence, then we should not change the producer id and
// sequence number, since this may introduce duplicates. In particular, the previous attempt
// may actually have been accepted, and if we change the producer id and sequence here, this
// attempt will also be accepted, causing a duplicate.
//
// Additionally, we update the next sequence number bound for the partition, and also have
// the transaction manager track the batch so as to ensure that sequence ordering is maintained
// even if we receive out of order responses.
batch.setProducerState(producerIdAndEpoch, transactionManager.sequenceNumber(batch.topicPartition), isTransactional);
transactionManager.incrementSequenceNumber(batch.topicPartition, batch.recordCount);
log.debug("Assigned producerId {} and producerEpoch {} to batch with base sequence " +
"{} being sent to partition {}", producerIdAndEpoch.producerId,
producerIdAndEpoch.epoch, batch.baseSequence(), tp);
transactionManager.addInFlightBatch(batch);
}
}
// the rest of the work by processing outside the lock
// close() is particularly expensive
batch.close();
size += batch.records().sizeInBytes();
ready.add(batch);
batch.drained(now);
} while (start != drainIndex);
return ready;
}
private int getDrainIndex(String idString) {
return nodesDrainIndex.computeIfAbsent(idString, s -> 0);
}
private void updateDrainIndex(String idString, int drainIndex) {
nodesDrainIndex.put(idString, drainIndex);
}
/**
* Drain all the data for the given nodes and collate them into a list of batches that will fit within the specified
* size on a per-node basis. This method attempts to avoid choosing the same topic-node over and over.
*
* @param cluster The current cluster metadata
* @param nodes The list of node to drain
* @param maxSize The maximum number of bytes to drain
* @param now The current unix time in milliseconds
* @return A list of {@link ProducerBatch} for each node specified with total size less than the requested maxSize.
*/
public Map<Integer, List<ProducerBatch>> drain(Cluster cluster, Set<Node> nodes, int maxSize, long now) {
if (nodes.isEmpty())
return Collections.emptyMap();
Map<Integer, List<ProducerBatch>> batches = new HashMap<>();
for (Node node : nodes) {
List<ProducerBatch> ready = drainBatchesForOneNode(cluster, node, maxSize, now);
batches.put(node.id(), ready);
}
return batches;
}
public void updateNodeLatencyStats(Integer nodeId, long nowMs, boolean canDrain) {
// Don't bother with updating stats if the feature is turned off.
if (partitionAvailabilityTimeoutMs <= 0)
return;
// When the sender gets a node (returned by the ready() function) that has data to send
// but the node is not ready (and so we cannot drain the data), we only update the
// ready time, then the difference would reflect for how long a node wasn't ready
// to send the data. Then we can temporarily remove partitions that are handled by the
// node from the list of available partitions so that the partitioner wouldn't pick
// this partition.
// NOTE: there is no synchronization for metric updates, so drainTimeMs is updated
// first to avoid accidentally marking a partition unavailable if the reader gets
// values between updates.
NodeLatencyStats nodeLatencyStats = nodeStats.computeIfAbsent(nodeId, id -> new NodeLatencyStats(nowMs));
if (canDrain)
nodeLatencyStats.drainTimeMs = nowMs;
nodeLatencyStats.readyTimeMs = nowMs;
}
/* Visible for testing */
public NodeLatencyStats getNodeLatencyStats(Integer nodeId) {
return nodeStats.get(nodeId);
}
/* Visible for testing */
public BuiltInPartitioner getBuiltInPartitioner(String topic) {
return topicInfoMap.get(topic).builtInPartitioner;
}
/**
* The earliest absolute time a batch will expire (in milliseconds)
*/
public long nextExpiryTimeMs() {
return this.nextBatchExpiryTimeMs;
}
/* Visible for testing */
public Deque<ProducerBatch> getDeque(TopicPartition tp) {
TopicInfo topicInfo = topicInfoMap.get(tp.topic());
if (topicInfo == null)
return null;
return topicInfo.batches.get(tp.partition());
}
/**
* Get the deque for the given topic-partition, creating it if necessary.
*/
private Deque<ProducerBatch> getOrCreateDeque(TopicPartition tp) {
TopicInfo topicInfo = topicInfoMap.computeIfAbsent(tp.topic(), k -> new TopicInfo(logContext, k, batchSize));
return topicInfo.batches.computeIfAbsent(tp.partition(), k -> new ArrayDeque<>());
}
/**
* Deallocate the record batch
*/
public void deallocate(ProducerBatch batch) {
incomplete.remove(batch);
// Only deallocate the batch if it is not a split batch because split batch are allocated outside the
// buffer pool.
if (!batch.isSplitBatch())
free.deallocate(batch.buffer(), batch.initialCapacity());
}
/**
* Package private for unit test. Get the buffer pool remaining size in bytes.
*/
long bufferPoolAvailableMemory() {
return free.availableMemory();
}
/**
* Are there any threads currently waiting on a flush?
*
* package private for test
*/
boolean flushInProgress() {
return flushesInProgress.get() > 0;
}
/**
* Initiate the flushing of data from the accumulator...this makes all requests immediately ready
*/
public void beginFlush() {
this.flushesInProgress.getAndIncrement();
}
/**
* Are there any threads currently appending messages?
*/
private boolean appendsInProgress() {
return appendsInProgress.get() > 0;
}
/**
* Mark all partitions as ready to send and block until the send is complete
*/
public void awaitFlushCompletion() throws InterruptedException {
try {
// Obtain a copy of all of the incomplete ProduceRequestResult(s) at the time of the flush.
// We must be careful not to hold a reference to the ProduceBatch(s) so that garbage
// collection can occur on the contents.
// The sender will remove ProducerBatch(s) from the original incomplete collection.
for (ProduceRequestResult result : this.incomplete.requestResults())
result.await();
} finally {
this.flushesInProgress.decrementAndGet();
}
}
/**
* Check whether there are any pending batches (whether sent or unsent).
*/
public boolean hasIncomplete() {
return !this.incomplete.isEmpty();
}
/**
* This function is only called when sender is closed forcefully. It will fail all the
* incomplete batches and return.
*/
public void abortIncompleteBatches() {
// We need to keep aborting the incomplete batch until no thread is trying to append to
// 1. Avoid losing batches.
// 2. Free up memory in case appending threads are blocked on buffer full.
// This is a tight loop but should be able to get through very quickly.
do {
abortBatches();
} while (appendsInProgress());
// After this point, no thread will append any messages because they will see the close
// flag set. We need to do the last abort after no thread was appending in case there was a new
// batch appended by the last appending thread.
abortBatches();
this.topicInfoMap.clear();
}
/**
* Go through incomplete batches and abort them.
*/
private void abortBatches() {
abortBatches(new KafkaException("Producer is closed forcefully."));
}
/**
* Abort all incomplete batches (whether they have been sent or not)
*/
void abortBatches(final RuntimeException reason) {
for (ProducerBatch batch : incomplete.copyAll()) {
Deque<ProducerBatch> dq = getDeque(batch.topicPartition);
synchronized (dq) {
batch.abortRecordAppends();
dq.remove(batch);
}
batch.abort(reason);
deallocate(batch);
}
}
/**
* Abort any batches which have not been drained
*/
void abortUndrainedBatches(RuntimeException reason) {
for (ProducerBatch batch : incomplete.copyAll()) {
Deque<ProducerBatch> dq = getDeque(batch.topicPartition);
boolean aborted = false;
synchronized (dq) {
if ((transactionManager != null && !batch.hasSequence()) || (transactionManager == null && !batch.isClosed())) {
aborted = true;
batch.abortRecordAppends();
dq.remove(batch);
}
}
if (aborted) {
batch.abort(reason);
deallocate(batch);
}
}
}
public void mutePartition(TopicPartition tp) {
muted.add(tp);
}
public void unmutePartition(TopicPartition tp) {
muted.remove(tp);
}
/**
* Close this accumulator and force all the record buffers to be drained
*/
public void close() {
this.closed = true;
this.free.close();
}
/**
* Partitioner config for built-in partitioner
*/
public static final class PartitionerConfig {
private final boolean enableAdaptivePartitioning;
private final long partitionAvailabilityTimeoutMs;
/**
* Partitioner config
*
* @param enableAdaptivePartitioning If it's true, partition switching adapts to broker load, otherwise partition
* switching is random.
* @param partitionAvailabilityTimeoutMs If a broker cannot process produce requests from a partition
* for the specified time, the partition is treated by the partitioner as not available.
* If the timeout is 0, this logic is disabled.
*/
public PartitionerConfig(boolean enableAdaptivePartitioning, long partitionAvailabilityTimeoutMs) {
this.enableAdaptivePartitioning = enableAdaptivePartitioning;
this.partitionAvailabilityTimeoutMs = partitionAvailabilityTimeoutMs;
}
public PartitionerConfig() {
this(false, 0);
}
}
/*
* Metadata about a record just appended to the record accumulator
*/
public final static class RecordAppendResult {
public final FutureRecordMetadata future;
public final boolean batchIsFull;
public final boolean newBatchCreated;
public final boolean abortForNewBatch;
public final int appendedBytes;
public RecordAppendResult(FutureRecordMetadata future,
boolean batchIsFull,
boolean newBatchCreated,
boolean abortForNewBatch,
int appendedBytes) {
this.future = future;
this.batchIsFull = batchIsFull;
this.newBatchCreated = newBatchCreated;
this.abortForNewBatch = abortForNewBatch;
this.appendedBytes = appendedBytes;
}
}
/*
* The callbacks passed into append
*/
public interface AppendCallbacks extends Callback {
/**
* Called to set partition (when append is called, partition may not be calculated yet).
* @param partition The partition
*/
void setPartition(int partition);
}
/*
* The set of nodes that have at least one complete record batch in the accumulator
*/
public final static class ReadyCheckResult {
public final Set<Node> readyNodes;
public final long nextReadyCheckDelayMs;
public final Set<String> unknownLeaderTopics;
public ReadyCheckResult(Set<Node> readyNodes, long nextReadyCheckDelayMs, Set<String> unknownLeaderTopics) {
this.readyNodes = readyNodes;
this.nextReadyCheckDelayMs = nextReadyCheckDelayMs;
this.unknownLeaderTopics = unknownLeaderTopics;
}
}
/**
* Per topic info.
*/
private static class TopicInfo {
public final ConcurrentMap<Integer /*partition*/, Deque<ProducerBatch>> batches = new CopyOnWriteMap<>();
public final BuiltInPartitioner builtInPartitioner;
public TopicInfo(LogContext logContext, String topic, int stickyBatchSize) {
builtInPartitioner = new BuiltInPartitioner(logContext, topic, stickyBatchSize);
}
}
/**
* Node latency stats for each node that are used for adaptive partition distribution
* Visible for testing
*/
public final static class NodeLatencyStats {
volatile public long readyTimeMs; // last time the node had batches ready to send
volatile public long drainTimeMs; // last time the node was able to drain batches
NodeLatencyStats(long nowMs) {
readyTimeMs = nowMs;
drainTimeMs = nowMs;
}
}
}
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