最近在使用storm做一个实时计算的项目,Spout需要从 KAFKA 集群中读取数据，为了提高开发效率，直接使用了Storm提供的KAFKA插件。今天抽空看了一下KafkaSpout的源码，记录下心得体会。
KafkaSpout基于kafka.javaapi.consumer.SimpleConsumer实现了consumer客户端的功能，包括 partition的分配，消费状态的维护（offset）。同时KafkaSpout使用了storm的可靠API，并实现了spout的ack 和 fail机制。KafkaSpout的基本处理流程如下：
1． 建立zookeeper客户端，在zookeeper zk_root + “/topics/” + _topic + “/partitions” 路径下获取到partition列表
2．针对每个partition 到路径Zk_root + “/topics/” + _topic + “/partitions”+”/” + partition_id + “/state”下面获取到leader partition 所在的broker id
3．到/broker/ids/broker id 路径下获取broker的host 和 port 信息，并保存到Map中Partition_id –-> learder broker
4. 获取spout的任务个数和当前任务的index，然后再根据partition的个数来分配当前spout 所消费的partition列表
5. 针对所消费的每个broker建立一个SimpleConsumer对象用来从kafka上获取数据
6. 提交当前partition的消费信息到zookeeper上面保存
下面对几个关键点进行下分析：
一、partition 的分配策略
1. 在KafkaSpout中获取spout的task的个数，也就是consumer的个数，代码如下：
int totalTasks = context.getComponentTasks(context.getThisComponentId()).size();
2. 在KafkaSpout中获取当前spout的 task index，注意，task index和task id是不同的，task id是当前spout在整个topology中的id，而task index是当前spout在组件中的id，取值范围为[0, spout_task_number-1],代码如下：
_coordinator = new ZkCoordinator(_connections, conf, _spoutConfig, _state, context.getThisTaskIndex(), totalTasks, _uuid);
3. 获取partiton与leader partition所在broker的映射关系，代码的调用顺序如下：
ZkCoordinator：
GlobalPartitionInformation brokerInfo = _reader.getBrokerInfo();
DynamicBrokersReader：
/**
* Get all partitions with their current leaders
*/
public GlobalPartitionInformation getBrokerInfo() throws SocketTimeoutException {
GlobalPartitionInformation globalPartitionInformation = new GlobalPartitionInformation();
try {
int numPartitionsForTopic = getNumPartitions();
String brokerInfoPath = brokerPath();
for (int partition = 0; partition < numPartitionsForTopic; partition++) {
int leader = getLeaderFor(partition);
String path = brokerInfoPath + "/" + leader;
try {
byte[] brokerData = _curator.getData().forPath(path);
Broker hp = getBrokerHost(brokerData);
globalPartitionInformation.addPartition(partition, hp);
} catch (org.apache.zookeeper.KeeperException.NoNodeException e) {
LOG.error("Node {} does not exist ", path);
}
}
} catch (SocketTimeoutException e) {
throw e;
} catch (Exception e) {
throw new RuntimeException(e);
}
LOG.info("Read partition info from zookeeper: " + globalPartitionInformation);
return globalPartitionInformation;
}
4. 获取当前spout消费的partition
KafkaUtils：
public static List<Partition> calculatePartitionsForTask(GlobalPartitionInformation partitionInformation, int totalTasks, int taskIndex) {
Preconditions.checkArgument(taskIndex < totalTasks, "task index must be less that total tasks");
//获取所有的排序后的partition列表
List<Partition> partitions = partitionInformation.getOrderedPartitions();
int numPartitions = partitions.size();
if (numPartitions < totalTasks) {
LOG.warn("there are more tasks than partitions (tasks: " + totalTasks + "; partitions: " + numPartitions + "), some tasks will be idle");
}
List<Partition> taskPartitions = new ArrayList<Partition>();
//此处是核心分配算法，举个例子来说明分配策略
//假设spout的并发度是3，当前spout的task index 是 1，总的partition的个数为5，那么当前spout消费的partition id为1,4
for (int i = taskIndex; i < numPartitions; i += totalTasks) {
Partition taskPartition = partitions.get(i);
taskPartitions.add(taskPartition);
}
logPartitionMapping(totalTasks, taskIndex, taskPartitions);
return taskPartitions;
}
二、partition的更新策略
如果出现broker宕机，spout挂掉的情况，那么spout是要重新分配parition的，KafkaSpout并没有监听zookeeper上broker、partition和其他spout的状态，所以当有异常发生的时候KafkaSpout并不知道的，它采用了两种方法来更新partition的分配。
1. 定时更新
根据ZkHosts中的refreshFreqSecs字段来定时更新partition列表，我们可以通过修改配置来更改定时刷新的间隔。每一次调用kafkaspout的nextTuple方法时，都会首先调用ZkCoordinator的getMyManagedPartitions方法来获取当前spout消费的partition列表
public void nextTuple() {
List<PartitionManager> managers = _coordinator.getMyManagedPartitions(); //getMyManagedPartitions方法中会判断是否已经到了该刷新的时间，如果到了就重新分配partition
public List<PartitionManager> getMyManagedPartitions() {
if (_lastRefreshTime == null || (System.currentTimeMillis() - _lastRefreshTime) > _refreshFreqMs) {
refresh();
_lastRefreshTime = System.currentTimeMillis();
}
return _cachedList;
}
2.异常更新
当调用kafkaspout的nextTuple方法出现异常时，强制更新当前spout的partition消费列表
public void nextTuple() {
List<PartitionManager> managers = _coordinator.getMyManagedPartitions();
for (int i = 0; i < managers.size(); i++) { try { EmitState state = managers.get(_currPartitionIndex).next(_collector); } catch (FailedFetchException e) {
_coordinator.refresh();
}
}
三、消费状态的维护
1.首先要分析一下当spout启动的时候是怎么获取初始offset的。在每个spout获取到消费的partition列表时，会针对每个partition来创建PartitionManager对象，下面看一下PartitionManager的初始化过程：
public PartitionManager(DynamicPartitionConnections connections, String topologyInstanceId, ZkState state, Map stormConf, SpoutConfig spoutConfig, Partition id) {
_partition = id;
_connections = connections;
_spoutConfig = spoutConfig;
_topologyInstanceId = topologyInstanceId;
//到连接池里注册partition和partition leader所在的broker host，如果连接池里有该broker的连接，则直接返回该连接、
//如果连接池里没有，则建立broker的连接，并返回连接
_consumer = connections.register(id.host, id.partition);
_state = state;
_stormConf = stormConf;
numberAcked = numberFailed = 0; String jsonTopologyId = null;
Long jsonOffset = null;
//获取zookeeper上offset的提交路径
String path = committedPath();
try {
//从提交路径上读取信息，提取记录的该partition的消费offset
//如果zookeeper上没有该路径则表示当前topic没有被spout消费过
Map<Object, Object> json = _state.readJSON(path);
LOG.info("Read partition information from: " + path + " --> " + json );
if (json != null) {
jsonTopologyId = (String) ((Map<Object, Object>) json.get("topology")).get("id");
jsonOffset = (Long) json.get("offset");
}
} catch (Throwable e) {
LOG.warn("Error reading and/or parsing at ZkNode: " + path, e);
} //从broker上获取当前partition的offset，默认为获取最新的offset，如果用户配置forceFromStart（KafkaConfig），则获取该partition最早的offset，
//也就是consume from beginning
Long currentOffset = KafkaUtils.getOffset(_consumer, spoutConfig.topic, id.partition, spoutConfig); //情况1： 如果从zookeeper上没有获取topology和消费信息，则直接用从broker上获取到的offset
if (jsonTopologyId == null || jsonOffset == null) { // failed to parse JSON?
_committedTo = currentOffset;
LOG.info("No partition information found, using configuration to determine offset");
//情况2： 获取到的topology id 不一致 或者用户要求从新获取数据的时候，则从kafka上获取offset
//可以和情况1 合并，在KafkaUtils.getOffset已经判断过forceFromStart，此处无需再次判断
} else if (!topologyInstanceId.equals(jsonTopologyId) && spoutConfig.forceFromStart) {
_committedTo = KafkaUtils.getOffset(_consumer, spoutConfig.topic, id.partition, spoutConfig.startOffsetTime);
LOG.info("Topology change detected and reset from start forced, using configuration to determine offset");
}
//情况3： 使用zookeeper上保留的offset进行消费
else {
_committedTo = jsonOffset;
LOG.info("Read last commit offset from zookeeper: " + _committedTo + "; old topology_id: " + jsonTopologyId + " - new topology_id: " + topologyInstanceId );
}
//如果上次消费的offset已经过了保质期，则直接消费新数据
if (currentOffset - _committedTo > spoutConfig.maxOffsetBehind || _committedTo <= 0) {
LOG.info("Last commit offset from zookeeper: " + _committedTo);
_committedTo = currentOffset;
LOG.info("Commit offset " + _committedTo + " is more than " +
spoutConfig.maxOffsetBehind + " behind, resetting to startOffsetTime=" + spoutConfig.startOffsetTime);
} LOG.info("Starting Kafka " + _consumer.host() + ":" + id.partition + " from offset " + _committedTo);
_emittedToOffset = _committedTo;}
2. 然后看一下partition消费offset是怎么保存和维护的
PartitionManager 中的 _emittedToOffset用来保存当前消费的offset，在每一次获取到消息的时候都会更新这个值
private void fill() { if (!had_failed || failed.contains(cur_offset)) {
numMessages += 1;
_pending.add(cur_offset);
_waitingToEmit.add(new MessageAndRealOffset(msg.message(), cur_offset));
//更新_emittedToOffset
_emittedToOffset = Math.max(msg.nextOffset(), _emittedToOffset);
if (had_failed) {
failed.remove(cur_offset);
}
}
}
_fetchAPIMessageCount.incrBy(numMessages);
}
}
3.提交offset到zookeeper
offset的提交是周期性的，提交的周期可在SpoutConfig中的stateUpdateIntervalMs中来配置。每次调用kafkaspout的nextTuple方法后都会判断是否需要提交offset
public void nextTuple() {
if ((now - _lastUpdateMs) > _spoutConfig.stateUpdateIntervalMs) {
commit();
}
}
如果需要提交则调用kafkaspout的commit方法，使用轮巡的方式提交每个partition的消费状况
private void commit() {
_lastUpdateMs = System.currentTimeMillis();
for (PartitionManager manager : _coordinator.getMyManagedPartitions()) {
manager.commit();
}
}
具体的提交是委托PartitionManager来完成的
public void commit() {
//获取当前要提交的offset，如果有pending的offset的话，就说明还有一些消息没有完成处理，则提交pending消息的最小的offset
//如果没有pending的消息，则提交当前消费的offset
long lastCompletedOffset = lastCompletedOffset();
//用来判断是否有新的offset需要提交
if (_committedTo != lastCompletedOffset) {
LOG.debug("Writing last completed offset (" + lastCompletedOffset + ") to ZK for " + _partition + " for topology: " + _topologyInstanceId);
Map<Object, Object> data = (Map<Object, Object>) ImmutableMap.builder()
.put("topology", ImmutableMap.of("id", _topologyInstanceId,
"name", _stormConf.get(Config.TOPOLOGY_NAME)))
.put("offset", lastCompletedOffset)
.put("partition", _partition.partition)
.put("broker", ImmutableMap.of("host", _partition.host.host,
"port", _partition.host.port))
.put("topic", _spoutConfig.topic).build();
_state.writeJSON(committedPath(), data); _committedTo = lastCompletedOffset;
LOG.debug("Wrote last completed offset (" + lastCompletedOffset + ") to ZK for " + _partition + " for topology: " + _topologyInstanceId);
} else {
LOG.debug("No new offset for " + _partition + " for topology: " + _topologyInstanceId);
}
}
四、kafkaspout ack 和 fail的处理
1. 首先还是说说kafkaspout消息的发送
当调用kafkaspout的nextTuple方法时，kafkaspout委托PartitionManager next方法来发送数据
public void nextTuple() {
List<PartitionManager> managers = _coordinator.getMyManagedPartitions();
for (int i = 0; i < managers.size(); i++) { try {
// in case the number of managers decreased
_currPartitionIndex = _currPartitionIndex % managers.size();
EmitState state = managers.get(_currPartitionIndex).next(_collector);
if (state != EmitState.EMITTED_MORE_LEFT) {
_currPartitionIndex = (_currPartitionIndex + 1) % managers.size();
}
}public EmitState next(SpoutOutputCollector collector) {
//判断等待队列是否为空，如果为空则调用fill方法从broker上取数据进行填充
if (_waitingToEmit.isEmpty()) {
fill();
}
while (true) {
MessageAndRealOffset toEmit = _waitingToEmit.pollFirst();
if (toEmit == null) {
return EmitState.NO_EMITTED;
}
//对kafka的消息进行解码
Iterable<List<Object>> tups = KafkaUtils.generateTuples(_spoutConfig, toEmit.msg);
if (tups != null) {
for (List<Object> tup : tups) {
//如果tuple不为null，则发送该tuple，messageID为new KafkaMessageId(_partition, toEmit.offset)
//这样在ack 或者 fail的时候才能根据_partition找到相应的PartitionManager
collector.emit(tup, new KafkaMessageId(_partition, toEmit.offset));
}
break;
} else {
ack(toEmit.offset);
}
}
if (!_waitingToEmit.isEmpty()) {
return EmitState.EMITTED_MORE_LEFT;
} else {
return EmitState.EMITTED_END;
}
}
2. 在PartitionManager会维护一个pending 列表，用来保存已经发送但是没有被成功处理的消息，一个failed列表，用来保存已经失败的消息
3. 当一个消息成功处理时会调用spout的ack方法，kafkaspout会根据message id中包含的partition id 来委托相应的PartitionManager来处理
public void ack(Object msgId) {
KafkaMessageId id = (KafkaMessageId) msgId;
PartitionManager m = _coordinator.getManager(id.partition);
if (m != null) {
m.ack(id.offset);
}
}
//PartitionManager 接收到ack消息后，会判断pending的最早的一条消息是否已经过质保，如果过质保，则清除队列中所有过保的消息
//如果没有过保的消息，则在pending队列中移除当前消息
public void ack(Long offset) {
if (!_pending.isEmpty() && _pending.first() < offset - _spoutConfig.maxOffsetBehind) {
// Too many things pending!
_pending.headSet(offset - _spoutConfig.maxOffsetBehind).clear();
}
_pending.remove(offset);
numberAcked++;
}
4. 当一条消息处理失败时，会调用spout的fail方法，同样，kafkaspout会根据message id中包含的partition id 来委托相应的PartitionManager来处理
public void fail(Object msgId) {
KafkaMessageId id = (KafkaMessageId) msgId;
PartitionManager m = _coordinator.getManager(id.partition);
if (m != null) {
m.fail(id.offset);
}
}
//PartitionManager接收到fail消息，会判断失败的消息是否已经过保，如果过保则忽略掉
public void fail(Long offset) {
if (offset < _emittedToOffset - _spoutConfig.maxOffsetBehind) {
LOG.info(
"Skipping failed tuple at offset=" + offset +
" because it's more than maxOffsetBehind=" + _spoutConfig.maxOffsetBehind +
" behind _emittedToOffset=" + _emittedToOffset
);
}
//如果在保质期内，则加入failed列表，如果没有成功响应的消息，并且失败的消息个数已经超过保质期个数，则认为没有消息成功，系统有问题，丢异常
else {
LOG.debug("failing at offset=" + offset + " with _pending.size()=" + _pending.size() + " pending and _emittedToOffset=" + _emittedToOffset);
failed.add(offset);
numberFailed++;
if (numberAcked == 0 && numberFailed > _spoutConfig.maxOffsetBehind) {
throw new RuntimeException("Too many tuple failures");
}
}
} //对于failed的消息会进行重发
private void fill() {
//如果有失败的消息，则获取第一个的offset
final boolean had_failed = !failed.isEmpty();
if (had_failed) {
offset = failed.first();
} else {
offset = _emittedToOffset;
} ByteBufferMessageSet msgs = null;
try {
msgs = KafkaUtils.fetchMessages(_spoutConfig, _consumer, _partition, offset);
} catch (UpdateOffsetException e) {
_emittedToOffset = KafkaUtils.getOffset(_consumer, _spoutConfig.topic, _partition.partition, _spoutConfig);
LOG.warn("Using new offset: {}", _emittedToOffset);
// fetch failed, so don't update the metrics
return;
}
if (msgs != null) {
int numMessages = 0; for (MessageAndOffset msg : msgs) {
final Long cur_offset = msg.offset();
if (cur_offset < offset) {
// Skip any old offsets.
continue;
}
//如果该消息在failed列表中，则重新发送，并将其从failed列表中删除
if (!had_failed || failed.contains(cur_offset)) {
numMessages += 1;
_pending.add(cur_offset);
_waitingToEmit.add(new MessageAndRealOffset(msg.message(), cur_offset));
_emittedToOffset = Math.max(msg.nextOffset(), _emittedToOffset);
if (had_failed) {
failed.remove(cur_offset);
}
}
}
_fetchAPIMessageCount.incrBy(numMessages);
}
}
