下面来说一下一个日志处理平台Kafka，关于Kafka的基本概念先做一个说明：
1.producer：
消息生产者，发布消息到 kafka 集群的终端或服务。
2.broker：
kafka 集群中包含的服务器。
3.topic：
每条发布到 kafka 集群的消息属于的类别，即 kafka 是面向 topic 的。
4.partition：
partition 是物理上的概念，每个 topic 包含一个或多个 partition。kafka 分配的单位是 partition。
5.consumer：
从 kafka 集群中消费消息的终端或服务。
6.Consumer group：
high-level consumer API 中，每个 consumer 都属于一个 consumer group，每条消息只能被 consumer group 中的一个 Consumer 消费，但可以被多个 consumer group 消费。
7.replica：
partition 的副本，保障 partition 的高可用。
8.leader：
replica 中的一个角色， producer 和 consumer 只跟 leader 交互。
9.follower：
replica 中的一个角色，从 leader 中复制数据。
10.controller：
kafka 集群中的其中一个服务器，用来进行 leader election 以及 各种 failover。
12.zookeeper：
kafka 通过 zookeeper 来存储集群的 meta 信息。
下面我们就来说一下Kafka的producer的设计。producer 采用 push 模式将消息发布到 broker，每条消息都被 append 到 patition 中，属于顺序写磁盘（顺序写磁盘效率比随机写内存要高，保障 kafka 吞吐率）。
producer 发送消息到 broker 时，会根据分区算法选择将其存储到哪一个 partition。
先来看一下KafkaProducer这个类，通过这个类操作Kafka的producer。先来看一下这个类有哪些属性：
private final String clientId;
// Visible for testing
final Metrics metrics;
private final Partitioner partitioner;
private final int maxRequestSize;
private final long totalMemorySize;
private final Metadata metadata;
private final RecordAccumulator accumulator;
private final Sender sender;
private final Thread ioThread;
private final CompressionType compressionType;
private final Sensor errors;
private final Time time;
private final Serializer keySerializer;
private final Serializer valueSerializer;
private final ProducerConfig producerConfig;
private final long maxBlockTimeMs;
private final ProducerInterceptors interceptors;
private final ApiVersions apiVersions;
private final TransactionManager transactionManager;
private TransactionalRequestResult initTransactionsResult;
主要来看一下partitioner这个对象，这是一个接口Partitioner，定义了producer将消息发送到哪一个partition的算法。默认的实现是DefaultPartitioner，看一下这个类的partition方法
public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {
List partitions = cluster.partitionsForTopic(topic);
int numPartitions = partitions.size();
if (keyBytes == null) {
int nextValue = nextValue(topic);
List availablePartitions = cluster.availablePartitionsForTopic(topic);
if (availablePartitions.size() > 0) {
int part = Utils.toPositive(nextValue) % availablePartitions.size();
return availablePartitions.get(part).partition();
} else {
// no partitions are available, give a non-available partition
return Utils.toPositive(nextValue) % numPartitions;
}
} else {
// hash the keyBytes to choose a partition
return Utils.toPositive(Utils.murmur2(keyBytes)) % numPartitions;
}
}
其路由机制为：
指定了 patition，则直接使用；
未指定 patition 但指定 key，通过对 key 的 value 进行hash 选出一个 patition
-
patition 和 key 都未指定，使用轮询选出一个 patition。
再到KafkaProducer的doSend方法里int partition = partition(record, serializedKey, serializedValue, cluster); tp = new TopicPartition(record.topic(), partition);
调用了partition方法，再看一下partition方法
private int partition(ProducerRecord record, byte[] serializedKey, byte[] serializedValue, Cluster cluster) {
Integer partition = record.partition();
return partition != null ?
partition :
partitioner.partition(
record.topic(), record.key(), serializedKey, record.value(), serializedValue, cluster);
}
就是调用partitioner的partition方法，选择一个partition进行发送的。
一般情况下存在三种情况：
- At most once 消息可能会丢，但绝不会重复传输
- At least one 消息绝不会丢，但可能会重复传输
- Exactly once 每条消息肯定会被传输一次且仅传输一次
当 producer 向 broker 发送消息时，一旦这条消息被 commit，由于 replication 的存在，它就不会丢。但是如果 producer 发送数据给 broker 后，遇到网络问题而造成通信中断，那 Producer 就无法判断该条消息是否已经 commit。所以目前默认情况下一条消息从 producer 到 broker 是确保了 At least once，可通过设置 producer 异步发送实现At most once。
下面来看一下producer是怎么发送数据的。Kafka会把要发送的数据封装成ProducerRecord，我们来看一下这个类都有哪些属性
private final String topic;
private final Integer partition;
private final Headers headers;
private final K key;
private final V value;
private final Long timestamp;
在ProducerRecord里保存了topic,partition,和路由的key，value就是要发送的数据。
再来看一下doSend方法
RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey,
serializedValue, headers, interceptCallback, remainingWaitMs);
把封装好的TopicPartition对象和要发送的数据添加到RecordAccumulator里，RecordAccumulator是一个消息缓存区，里面存放了要发送的数据。我们来看一下这个类的append方法
public RecordAppendResult append(TopicPartition tp,
long timestamp,
byte[] key,
byte[] value,
Header[] headers,
Callback callback,
long maxTimeToBlock) throws InterruptedException {
// 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 {
// check if we have an in-progress batch
Deque dq = getOrCreateDeque(tp);
synchronized (dq) {
if (closed)
throw new KafkaException("Producer closed while send in progress");
RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callback, dq);
if (appendResult != null)
return appendResult;
}
// we don't have an in-progress record batch try to allocate a new batch
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 {}", size, tp.topic(), tp.partition());
buffer = free.allocate(size, maxTimeToBlock);
synchronized (dq) {
// Need to check if producer is closed again after grabbing the dequeue lock.
if (closed)
throw new KafkaException("Producer closed while send in progress");
RecordAppendResult appendResult = tryAppend(timestamp, key, value, headers, callback, dq);
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, maxUsableMagic);
ProducerBatch batch = new ProducerBatch(tp, recordsBuilder, time.milliseconds());
FutureRecordMetadata future = Utils.notNull(batch.tryAppend(timestamp, key, value, headers, callback, time.milliseconds()));
dq.addLast(batch);
incomplete.add(batch);
// Don't deallocate this buffer in the finally block as it's being used in the record batch
buffer = null;
return new RecordAppendResult(future, dq.size() > 1 || batch.isFull(), true);
}
} finally {
if (buffer != null)
free.deallocate(buffer);
appendsInProgress.decrementAndGet();
}
}
其实就是写到一个ByteBuffer里面，封装成一个FutureRecordMetadata对象，加入到队列里面。
然后我们来看一下Sender这个类。这个Sender其实就是一个线程，轮询消息缓冲区上的数据，然后发送。先来看一下run方法
void run(long now) {
if (transactionManager != null) {
try {
if (transactionManager.shouldResetProducerStateAfterResolvingSequences())
// Check if the previous run expired batches which requires a reset of the producer state.
transactionManager.resetProducerId();
if (!transactionManager.isTransactional()) {
// this is an idempotent producer, so make sure we have a producer id
maybeWaitForProducerId();
} else if (transactionManager.hasUnresolvedSequences() && !transactionManager.hasFatalError()) {
transactionManager.transitionToFatalError(
new KafkaException("The client hasn't received acknowledgment for " +
"some previously sent messages and can no longer retry them. It isn't safe to continue."));
} else if (transactionManager.hasInFlightTransactionalRequest() || maybeSendTransactionalRequest(now)) {
// as long as there are outstanding transactional requests, we simply wait for them to return
client.poll(retryBackoffMs, now);
return;
}
// do not continue sending if the transaction manager is in a failed state or if there
// is no producer id (for the idempotent case).
if (transactionManager.hasFatalError() || !transactionManager.hasProducerId()) {
RuntimeException lastError = transactionManager.lastError();
if (lastError != null)
maybeAbortBatches(lastError);
client.poll(retryBackoffMs, now);
return;
} else if (transactionManager.hasAbortableError()) {
accumulator.abortUndrainedBatches(transactionManager.lastError());
}
} catch (AuthenticationException e) {
// This is already logged as error, but propagated here to perform any clean ups.
log.trace("Authentication exception while processing transactional request: {}", e);
transactionManager.authenticationFailed(e);
}
}
long pollTimeout = sendProducerData(now);
client.poll(pollTimeout, now);
}
然后由KafkaClient去发送数据。来看一下KafkaProducer的newSender方法
Sender newSender(LogContext logContext, KafkaClient kafkaClient, Metadata metadata) {
int maxInflightRequests = configureInflightRequests(producerConfig, transactionManager != null);
int requestTimeoutMs = producerConfig.getInt(ProducerConfig.REQUEST_TIMEOUT_MS_CONFIG);
ChannelBuilder channelBuilder = ClientUtils.createChannelBuilder(producerConfig, time);
ProducerMetrics metricsRegistry = new ProducerMetrics(this.metrics);
Sensor throttleTimeSensor = Sender.throttleTimeSensor(metricsRegistry.senderMetrics);
KafkaClient client = kafkaClient != null ? kafkaClient : new NetworkClient(
new Selector(producerConfig.getLong(ProducerConfig.CONNECTIONS_MAX_IDLE_MS_CONFIG),
this.metrics, time, "producer", channelBuilder, logContext),
metadata,
clientId,
maxInflightRequests,
producerConfig.getLong(ProducerConfig.RECONNECT_BACKOFF_MS_CONFIG),
producerConfig.getLong(ProducerConfig.RECONNECT_BACKOFF_MAX_MS_CONFIG),
producerConfig.getInt(ProducerConfig.SEND_BUFFER_CONFIG),
producerConfig.getInt(ProducerConfig.RECEIVE_BUFFER_CONFIG),
requestTimeoutMs,
ClientDnsLookup.forConfig(producerConfig.getString(ProducerConfig.CLIENT_DNS_LOOKUP_CONFIG)),
time,
true,
apiVersions,
throttleTimeSensor,
logContext);
int retries = configureRetries(producerConfig, transactionManager != null, log);
short acks = configureAcks(producerConfig, transactionManager != null, log);
return new Sender(logContext,
client,
metadata,
this.accumulator,
maxInflightRequests == 1,
producerConfig.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG),
acks,
retries,
metricsRegistry.senderMetrics,
time,
requestTimeoutMs,
producerConfig.getLong(ProducerConfig.RETRY_BACKOFF_MS_CONFIG),
this.transactionManager,
apiVersions);
}
来看一下NetworkClient的poll方法
public List poll(long timeout, long now) {
ensureActive();
if (!abortedSends.isEmpty()) {
// If there are aborted sends because of unsupported version exceptions or disconnects,
// handle them immediately without waiting for Selector#poll.
List responses = new ArrayList<>();
handleAbortedSends(responses);
completeResponses(responses);
return responses;
}
long metadataTimeout = metadataUpdater.maybeUpdate(now);
try {
this.selector.poll(Utils.min(timeout, metadataTimeout, defaultRequestTimeoutMs));
} catch (IOException e) {
log.error("Unexpected error during I/O", e);
}
// process completed actions
long updatedNow = this.time.milliseconds();
List responses = new ArrayList<>();
handleCompletedSends(responses, updatedNow);
handleCompletedReceives(responses, updatedNow);
handleDisconnections(responses, updatedNow);
handleConnections();
handleInitiateApiVersionRequests(updatedNow);
handleTimedOutRequests(responses, updatedNow);
completeResponses(responses);
return responses;
}
调用selector的poll方法，轮询io事件，并处理请求的数据。
看一下Sender的sendProducerData方法
private long sendProducerData(long now) {
Cluster cluster = metadata.fetch();
// get the list of partitions with data ready to send
RecordAccumulator.ReadyCheckResult result = this.accumulator.ready(cluster, now);
// if there are any partitions whose leaders are not known yet, force metadata update
if (!result.unknownLeaderTopics.isEmpty()) {
// The set of topics with unknown leader contains topics with leader election pending as well as
// topics which may have expired. Add the topic again to metadata to ensure it is included
// and request metadata update, since there are messages to send to the topic.
for (String topic : result.unknownLeaderTopics)
this.metadata.add(topic);
log.debug("Requesting metadata update due to unknown leader topics from the batched records: {}",
result.unknownLeaderTopics);
this.metadata.requestUpdate();
}
// remove any nodes we aren't ready to send to
Iterator iter = result.readyNodes.iterator();
long notReadyTimeout = Long.MAX_VALUE;
while (iter.hasNext()) {
Node node = iter.next();
if (!this.client.ready(node, now)) {
iter.remove();
notReadyTimeout = Math.min(notReadyTimeout, this.client.pollDelayMs(node, now));
}
}
// create produce requests
Map> batches = this.accumulator.drain(cluster, result.readyNodes, this.maxRequestSize, now);
addToInflightBatches(batches);
if (guaranteeMessageOrder) {
// Mute all the partitions drained
for (List batchList : batches.values()) {
for (ProducerBatch batch : batchList)
this.accumulator.mutePartition(batch.topicPartition);
}
}
accumulator.resetNextBatchExpiryTime();
List expiredInflightBatches = getExpiredInflightBatches(now);
List expiredBatches = this.accumulator.expiredBatches(now);
expiredBatches.addAll(expiredInflightBatches);
// Reset the producer id if an expired batch has previously been sent to the broker. Also update the metrics
// for expired batches. see the documentation of @TransactionState.resetProducerId to understand why
// we need to reset the producer id here.
if (!expiredBatches.isEmpty())
log.trace("Expired {} batches in accumulator", expiredBatches.size());
for (ProducerBatch expiredBatch : expiredBatches) {
String errorMessage = "Expiring " + expiredBatch.recordCount + " record(s) for " + expiredBatch.topicPartition
+ ":" + (now - expiredBatch.createdMs) + " ms has passed since batch creation";
failBatch(expiredBatch, -1, NO_TIMESTAMP, new TimeoutException(errorMessage), false);
if (transactionManager != null && expiredBatch.inRetry()) {
// This ensures that no new batches are drained until the current in flight batches are fully resolved.
transactionManager.markSequenceUnresolved(expiredBatch.topicPartition);
}
}
sensors.updateProduceRequestMetrics(batches);
// If we have any nodes that are ready to send + have sendable data, poll with 0 timeout so this can immediately
// loop and try sending more data. Otherwise, the timeout will be the smaller value between next batch expiry
// time, and the delay time for checking data availability. Note that the nodes may have data that isn't yet
// sendable due to lingering, backing off, etc. This specifically does not include nodes with sendable data
// that aren't ready to send since they would cause busy looping.
long pollTimeout = Math.min(result.nextReadyCheckDelayMs, notReadyTimeout);
pollTimeout = Math.min(pollTimeout, this.accumulator.nextExpiryTimeMs() - now);
pollTimeout = Math.max(pollTimeout, 0);
if (!result.readyNodes.isEmpty()) {
log.trace("Nodes with data ready to send: {}", result.readyNodes);
// if some partitions are already ready to be sent, the select time would be 0;
// otherwise if some partition already has some data accumulated but not ready yet,
// the select time will be the time difference between now and its linger expiry time;
// otherwise the select time will be the time difference between now and the metadata expiry time;
pollTimeout = 0;
}
sendProduceRequests(batches, now);
return pollTimeout;
}
其实就是从RecordAccumulator里取出要发送的消息，然后放到一个map里，这个map的key是节点的id，value是ProducerBatch的list，这个类是producer发送消息的处理类，然后我们来看一下sendProduceRequests方法
private void sendProduceRequests(Map> collated, long now) {
for (Map.Entry> entry : collated.entrySet())
sendProduceRequest(now, entry.getKey(), acks, requestTimeoutMs, entry.getValue());
}
再到sendProduceRequest方法
private void sendProduceRequest(long now, int destination, short acks, int timeout, List batches) {
if (batches.isEmpty())
return;
Map produceRecordsByPartition = new HashMap<>(batches.size());
final Map recordsByPartition = new HashMap<>(batches.size());
// find the minimum magic version used when creating the record sets
byte minUsedMagic = apiVersions.maxUsableProduceMagic();
for (ProducerBatch batch : batches) {
if (batch.magic() < minUsedMagic)
minUsedMagic = batch.magic();
}
for (ProducerBatch batch : batches) {
TopicPartition tp = batch.topicPartition;
MemoryRecords records = batch.records();
// down convert if necessary to the minimum magic used. In general, there can be a delay between the time
// that the producer starts building the batch and the time that we send the request, and we may have
// chosen the message format based on out-dated metadata. In the worst case, we optimistically chose to use
// the new message format, but found that the broker didn't support it, so we need to down-convert on the
// client before sending. This is intended to handle edge cases around cluster upgrades where brokers may
// not all support the same message format version. For example, if a partition migrates from a broker
// which is supporting the new magic version to one which doesn't, then we will need to convert.
if (!records.hasMatchingMagic(minUsedMagic))
records = batch.records().downConvert(minUsedMagic, 0, time).records();
produceRecordsByPartition.put(tp, records);
recordsByPartition.put(tp, batch);
}
String transactionalId = null;
if (transactionManager != null && transactionManager.isTransactional()) {
transactionalId = transactionManager.transactionalId();
}
ProduceRequest.Builder requestBuilder = ProduceRequest.Builder.forMagic(minUsedMagic, acks, timeout,
produceRecordsByPartition, transactionalId);
RequestCompletionHandler callback = new RequestCompletionHandler() {
public void onComplete(ClientResponse response) {
handleProduceResponse(response, recordsByPartition, time.milliseconds());
}
};
String nodeId = Integer.toString(destination);
ClientRequest clientRequest = client.newClientRequest(nodeId, requestBuilder, now, acks != 0,
requestTimeoutMs, callback);
client.send(clientRequest, now);
log.trace("Sent produce request to {}: {}", nodeId, requestBuilder);
}
调用KafkaClient的send方法进行消息的发送
看一下NetworkClient的send方法
public void send(ClientRequest request, long now) {
doSend(request, false, now);
}
再到doSend方法
private void doSend(ClientRequest clientRequest, boolean isInternalRequest, long now) {
ensureActive();
String nodeId = clientRequest.destination();
if (!isInternalRequest) {
// If this request came from outside the NetworkClient, validate
// that we can send data. If the request is internal, we trust
// that internal code has done this validation. Validation
// will be slightly different for some internal requests (for
// example, ApiVersionsRequests can be sent prior to being in
// READY state.)
if (!canSendRequest(nodeId, now))
throw new IllegalStateException("Attempt to send a request to node " + nodeId + " which is not ready.");
}
AbstractRequest.Builder builder = clientRequest.requestBuilder();
try {
NodeApiVersions versionInfo = apiVersions.get(nodeId);
short version;
// Note: if versionInfo is null, we have no server version information. This would be
// the case when sending the initial ApiVersionRequest which fetches the version
// information itself. It is also the case when discoverBrokerVersions is set to false.
if (versionInfo == null) {
version = builder.latestAllowedVersion();
if (discoverBrokerVersions && log.isTraceEnabled())
log.trace("No version information found when sending {} with correlation id {} to node {}. " +
"Assuming version {}.", clientRequest.apiKey(), clientRequest.correlationId(), nodeId, version);
} else {
version = versionInfo.latestUsableVersion(clientRequest.apiKey(), builder.oldestAllowedVersion(),
builder.latestAllowedVersion());
}
// The call to build may also throw UnsupportedVersionException, if there are essential
// fields that cannot be represented in the chosen version.
doSend(clientRequest, isInternalRequest, now, builder.build(version));
} catch (UnsupportedVersionException unsupportedVersionException) {
// If the version is not supported, skip sending the request over the wire.
// Instead, simply add it to the local queue of aborted requests.
log.debug("Version mismatch when attempting to send {} with correlation id {} to {}", builder,
clientRequest.correlationId(), clientRequest.destination(), unsupportedVersionException);
ClientResponse clientResponse = new ClientResponse(clientRequest.makeHeader(builder.latestAllowedVersion()),
clientRequest.callback(), clientRequest.destination(), now, now,
false, unsupportedVersionException, null, null);
abortedSends.add(clientResponse);
}
}
再到doSend方法
private void doSend(ClientRequest clientRequest, boolean isInternalRequest, long now, AbstractRequest request) {
String destination = clientRequest.destination();
RequestHeader header = clientRequest.makeHeader(request.version());
if (log.isDebugEnabled()) {
int latestClientVersion = clientRequest.apiKey().latestVersion();
if (header.apiVersion() == latestClientVersion) {
log.trace("Sending {} {} with correlation id {} to node {}", clientRequest.apiKey(), request,
clientRequest.correlationId(), destination);
} else {
log.debug("Using older server API v{} to send {} {} with correlation id {} to node {}",
header.apiVersion(), clientRequest.apiKey(), request, clientRequest.correlationId(), destination);
}
}
Send send = request.toSend(destination, header);
InFlightRequest inFlightRequest = new InFlightRequest(
clientRequest,
header,
isInternalRequest,
request,
send,
now);
this.inFlightRequests.add(inFlightRequest);
selector.send(send);
}
封装了一个ClientRequest对象，转成一个Send对象，然后调用selector发送数据。
其实Kafka是通过将数据写入到消息缓冲区中，然后通过Sender线程轮询消息缓冲区中的数据，异步发送出去的。
Kafka的produer设计就介绍到这里了。