https://github.com/wurstmeister/storm-kafka-0.8-plus
http://blog.csdn.net/xeseo/article/details/18615761
准备，一些相关类
GlobalPartitionInformation (storm.kafka.trident)
记录partitionid和broker的关系
GlobalPartitionInformation info = new GlobalPartitionInformation();
info.addPartition(0, new Broker("10.1.110.24",9092));
info.addPartition(0, new Broker("10.1.110.21",9092));可以静态的生成GlobalPartitionInformation，向上面代码一样
也可以动态的从zk获取，推荐这种方式
从zk获取就会用到DynamicBrokersReader
DynamicBrokersReader
核心就是从zk上读出partition和broker的对应关系
操作zk都是使用curator框架
核心函数，
/**
* Get all partitions with their current leaders
*/
public GlobalPartitionInformation getBrokerInfo() {
GlobalPartitionInformation globalPartitionInformation = new GlobalPartitionInformation();
try {
int numPartitionsForTopic = getNumPartitions(); //从zk取得partition的数目
String brokerInfoPath = brokerPath();
for (int partition = 0; partition < numPartitionsForTopic; partition++) {
int leader = getLeaderFor(partition); //从zk获取partition的leader broker
String path = brokerInfoPath + "/" + leader;
try {
byte[] brokerData = _curator.getData().forPath(path);
Broker hp = getBrokerHost(brokerData); //从zk获取broker的host:port
globalPartitionInformation.addPartition(partition, hp);//生成GlobalPartitionInformation
} catch (org.apache.zookeeper.KeeperException.NoNodeException e) {
LOG.error("Node {} does not exist ", path);
}
}
} catch (Exception e) {
throw new RuntimeException(e);
}
LOG.info("Read partition info from zookeeper: " + globalPartitionInformation);
return globalPartitionInformation;
}
DynamicPartitionConnections
维护到每个broker的connection，并记录下每个broker上对应的partitions
核心数据结构，为每个broker维持一个ConnectionInfo
Map<Broker, ConnectionInfo> _connections = new HashMap();
ConnectionInfo的定义，包含连接该broker的SimpleConsumer和记录partitions的set
static class ConnectionInfo {
SimpleConsumer consumer;
Set<Integer> partitions = new HashSet();
public ConnectionInfo(SimpleConsumer consumer) {
this.consumer = consumer;
}
}核心函数，就是register
public SimpleConsumer register(Broker host, int partition) {
if (!_connections.containsKey(host)) {
_connections.put(host, new ConnectionInfo(new SimpleConsumer(host.host, host.port, _config.socketTimeoutMs, _config.bufferSizeBytes, _config.clientId)));
}
ConnectionInfo info = _connections.get(host);
info.partitions.add(partition);
return info.consumer;
}
PartitionManager
关键核心逻辑，用于管理一个partiiton的读取状态
先理解下面几个变量，
Long _emittedToOffset;
Long _committedTo;
SortedSet<Long> _pending = new TreeSet<Long>();
LinkedList<MessageAndRealOffset> _waitingToEmit = new LinkedList<MessageAndRealOffset>();
kafka对于一个partition，一定是从offset从小到大按顺序读的，并且这里为了保证不读丢数据，会定期的将当前状态即offset写入zk
几个中间状态，
从kafka读到的offset，_emittedToOffset
从kafka读到的messages会放入_waitingToEmit，放入这个list，我们就认为一定会被emit，所以emittedToOffset可以认为是从kafka读到的offset
已经成功处理的offset，lastCompletedOffset
由于message是要在storm里面处理的，其中是可能fail的，所以正在处理的offset是缓存在_pending中的
如果_pending为空，那么lastCompletedOffset=_emittedToOffset
如果_pending不为空，那么lastCompletedOffset为pending list里面第一个offset，因为后面都还在等待ack
public long lastCompletedOffset() {
if (_pending.isEmpty()) {
return _emittedToOffset;
} else {
return _pending.first();
}
}
已经写入zk的offset，_committedTo
我们需要定期将lastCompletedOffset，写入zk，否则crash后，我们不知道上次读到哪儿了
所以_committedTo <= lastCompletedOffset
完整过程，
1. 初始化，
关键就是注册partition，然后初始化offset，以知道从哪里开始读
public PartitionManager(DynamicPartitionConnections connections, String topologyInstanceId, ZkState state, Map stormConf, SpoutConfig spoutConfig, Partition id) {
_partition = id;
_connections = connections;
_spoutConfig = spoutConfig;
_topologyInstanceId = topologyInstanceId;
_consumer = connections.register(id.host, id.partition); //注册partition到connections，并生成simpleconsumer
_state = state;
_stormConf = stormConf;
String jsonTopologyId = null;
Long jsonOffset = null;
String path = committedPath();
try {
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"); // 从zk中读出commited offset
}
} catch (Throwable e) {
LOG.warn("Error reading and/or parsing at ZkNode: " + path, e);
}
if (jsonTopologyId == null || jsonOffset == null) { // zk中没有记录，那么根据spoutConfig.startOffsetTime设置offset，Earliest或Latest
_committedTo = KafkaUtils.getOffset(_consumer, spoutConfig.topic, id.partition, spoutConfig);
LOG.info("No partition information found, using configuration to determine offset");
} 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");
} else {
_committedTo = jsonOffset;
}
_emittedToOffset = _committedTo; // 初始化时，中间状态都是一致的
}
2. 从kafka读取messages，放到_waitingToEmit
从kafka中读到数据ByteBufferMessageSet，
把需要emit的msg，MessageAndRealOffset，放到_waitingToEmit
把没完成的offset放到pending
更新emittedToOffset
private void fill() {
ByteBufferMessageSet msgs = KafkaUtils.fetchMessages(_spoutConfig, _consumer, _partition, _emittedToOffset);
for (MessageAndOffset msg : msgs) {
_pending.add(_emittedToOffset);
_waitingToEmit.add(new MessageAndRealOffset(msg.message(), _emittedToOffset));
_emittedToOffset = msg.nextOffset();
}
}其中fetch message的逻辑如下，
public static ByteBufferMessageSet fetchMessages(KafkaConfig config, SimpleConsumer consumer, Partition partition, long offset) {
ByteBufferMessageSet msgs = null;
String topic = config.topic;
int partitionId = partition.partition;
for (int errors = 0; errors < 2 && msgs == null; errors++) { // 容忍两次错误
FetchRequestBuilder builder = new FetchRequestBuilder();
FetchRequest fetchRequest = builder.addFetch(topic, partitionId, offset, config.fetchSizeBytes).
clientId(config.clientId).build();
FetchResponse fetchResponse;
try {
fetchResponse = consumer.fetch(fetchRequest);
} catch (Exception e) {
if (e instanceof ConnectException) {
throw new FailedFetchException(e);
} else {
throw new RuntimeException(e);
}
}
if (fetchResponse.hasError()) { // 主要处理offset outofrange的case，通过getOffset从earliest或latest读
KafkaError error = KafkaError.getError(fetchResponse.errorCode(topic, partitionId));
if (error.equals(KafkaError.OFFSET_OUT_OF_RANGE) && config.useStartOffsetTimeIfOffsetOutOfRange && errors == 0) {
long startOffset = getOffset(consumer, topic, partitionId, config.startOffsetTime);
LOG.warn("Got fetch request with offset out of range: [" + offset + "]; " +
"retrying with default start offset time from configuration. " +
"configured start offset time: [" + config.startOffsetTime + "] offset: [" + startOffset + "]");
offset = startOffset;
} else {
String message = "Error fetching data from [" + partition + "] for topic [" + topic + "]: [" + error + "]";
LOG.error(message);
throw new FailedFetchException(message);
}
} else {
msgs = fetchResponse.messageSet(topic, partitionId);
}
}
return msgs;
}
3. emit msg
从_waitingToEmit中取到msg，转换成tuple，然后通过collector.emit发出去
public EmitState next(SpoutOutputCollector collector) {
if (_waitingToEmit.isEmpty()) {
fill();
}
while (true) {
MessageAndRealOffset toEmit = _waitingToEmit.pollFirst();
if (toEmit == null) {
return EmitState.NO_EMITTED;
}
Iterable<List<Object>> tups = KafkaUtils.generateTuples(_spoutConfig, toEmit.msg);
if (tups != null) {
for (List<Object> tup : tups) {
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;
}
}可以看看转换tuple的过程，
可以看到是通过kafkaConfig.scheme.deserialize来做转换
public static Iterable<List<Object>> generateTuples(KafkaConfig kafkaConfig, Message msg) {
Iterable<List<Object>> tups;
ByteBuffer payload = msg.payload();
ByteBuffer key = msg.key();
if (key != null && kafkaConfig.scheme instanceof KeyValueSchemeAsMultiScheme) {
tups = ((KeyValueSchemeAsMultiScheme) kafkaConfig.scheme).deserializeKeyAndValue(Utils.toByteArray(key), Utils.toByteArray(payload));
} else {
tups = kafkaConfig.scheme.deserialize(Utils.toByteArray(payload));
}
return tups;
}所以你使用时，需要定义scheme逻辑，
spoutConfig.scheme = new SchemeAsMultiScheme(new TestMessageScheme());
public class TestMessageScheme implements Scheme {
private static final Logger LOGGER = LoggerFactory.getLogger(TestMessageScheme.class);
@Override
public List<Object> deserialize(byte[] bytes) {
try {
String msg = new String(bytes, "UTF-8");
return new Values(msg);
} catch (InvalidProtocolBufferException e) {
LOGGER.error("Cannot parse the provided message!");
}
return null;
}
@Override
public Fields getOutputFields() {
return new Fields("msg");
}
}
4. 定期的commit offset
public void commit() {
long lastCompletedOffset = lastCompletedOffset();
if (lastCompletedOffset != lastCommittedOffset()) {
Map<Object, Object> data = 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;
} else {
LOG.info("No new offset for " + _partition + " for topology: " + _topologyInstanceId);
}
}
5. 最后关注一下，fail时的处理
首先作者没有cache message，而只是cache offset
所以fail的时候，他是无法直接replay的，在他的注释里面写了，不这样做的原因是怕内存爆掉
所以他的做法是，当一个offset fail的时候， 直接将_emittedToOffset回滚到当前fail的这个offset
下次从Kafka fetch的时候会从_emittedToOffset开始读，这样做的好处就是依赖kafka做replay，问题就是会有重复问题
所以使用时，一定要考虑，是否可以接受重复问题
public void fail(Long offset) {
//TODO: should it use in-memory ack set to skip anything that's been acked but not committed???
// things might get crazy with lots of timeouts
if (_emittedToOffset > offset) {
_emittedToOffset = offset;
_pending.tailSet(offset).clear();
}
}
KafkaSpout
最后来看看KafkaSpout
1. 初始化
关键就是初始化DynamicPartitionConnections和_coordinator
public void open(Map conf, final TopologyContext context, final SpoutOutputCollector collector) {
_collector = collector;
Map stateConf = new HashMap(conf);
List<String> zkServers = _spoutConfig.zkServers;
if (zkServers == null) {
zkServers = (List<String>) conf.get(Config.STORM_ZOOKEEPER_SERVERS);
}
Integer zkPort = _spoutConfig.zkPort;
if (zkPort == null) {
zkPort = ((Number) conf.get(Config.STORM_ZOOKEEPER_PORT)).intValue();
}
stateConf.put(Config.TRANSACTIONAL_ZOOKEEPER_SERVERS, zkServers);
stateConf.put(Config.TRANSACTIONAL_ZOOKEEPER_PORT, zkPort);
stateConf.put(Config.TRANSACTIONAL_ZOOKEEPER_ROOT, _spoutConfig.zkRoot);
_state = new ZkState(stateConf);
_connections = new DynamicPartitionConnections(_spoutConfig, KafkaUtils.makeBrokerReader(conf, _spoutConfig));
// using TransactionalState like this is a hack
int totalTasks = context.getComponentTasks(context.getThisComponentId()).size();
if (_spoutConfig.hosts instanceof StaticHosts) {
_coordinator = new StaticCoordinator(_connections, conf, _spoutConfig, _state, context.getThisTaskIndex(), totalTasks, _uuid);
} else {
_coordinator = new ZkCoordinator(_connections, conf, _spoutConfig, _state, context.getThisTaskIndex(), totalTasks, _uuid);
}
}看看_coordinator 是干嘛的？
这很关键，因为我们一般都会开多个并发的kafkaspout，类似于high-level中的consumer group，如何保证这些并发的线程不冲突？
使用和highlevel一样的思路，一个partition只会有一个spout消费，这样就避免处理麻烦的访问互斥问题(kafka做访问互斥很麻烦，试着想想)
是根据当前spout的task数和partition数来分配，task和partitioin的对应关系的，并且为每个partition建立PartitionManager
这里首先看到totalTasks就是当前这个spout component的task size
StaticCoordinator和ZkCoordinator的差别就是， 从StaticHost还是从Zk读到partition的信息，简单起见，看看StaticCoordinator实现
public class StaticCoordinator implements PartitionCoordinator {
Map<Partition, PartitionManager> _managers = new HashMap<Partition, PartitionManager>();
List<PartitionManager> _allManagers = new ArrayList();
public StaticCoordinator(DynamicPartitionConnections connections, Map stormConf, SpoutConfig config, ZkState state, int taskIndex, int totalTasks, String topologyInstanceId) {
StaticHosts hosts = (StaticHosts) config.hosts;
List<Partition> myPartitions = KafkaUtils.calculatePartitionsForTask(hosts.getPartitionInformation(), totalTasks, taskIndex);
for (Partition myPartition : myPartitions) {// 建立PartitionManager
_managers.put(myPartition, new PartitionManager(connections, topologyInstanceId, state, stormConf, config, myPartition));
}
_allManagers = new ArrayList(_managers.values());
}
@Override
public List<PartitionManager> getMyManagedPartitions() {
return _allManagers;
}
public PartitionManager getManager(Partition partition) {
return _managers.get(partition);
}
}其中分配的逻辑在calculatePartitionsForTask
public static List<Partition> calculatePartitionsForTask(GlobalPartitionInformation partitionInformation, int totalTasks, int taskIndex) {
Preconditions.checkArgument(taskIndex < totalTasks, "task index must be less that total tasks");
List<Partition> partitions = partitionInformation.getOrderedPartitions();
int numPartitions = partitions.size();
List<Partition> taskPartitions = new ArrayList<Partition>();
for (int i = taskIndex; i < numPartitions; i += totalTasks) {// 平均分配，
Partition taskPartition = partitions.get(i);
taskPartitions.add(taskPartition);
}
logPartitionMapping(totalTasks, taskIndex, taskPartitions);
return taskPartitions;
}
2. nextTuple
逻辑写的比较tricky，其实只要从一个partition读成功一次
只所以要for，是当EmitState.NO_EMITTED时，需要遍历后面的partition以保证读成功一次
@Override
public void nextTuple() {
List<PartitionManager> managers = _coordinator.getMyManagedPartitions();
for (int i = 0; i < managers.size(); i++) { // in case the number of managers decreased
_currPartitionIndex = _currPartitionIndex % managers.size(); //_currPartitionIndex初始为0，每次依次读一个partition
EmitState state = managers.get(_currPartitionIndex).next(_collector); //调用PartitonManager.next去emit数据
if (state != EmitState.EMITTED_MORE_LEFT) { //当EMITTED_MORE_LEFT时，还有数据，可以继续读，不需要+1
_currPartitionIndex = (_currPartitionIndex + 1) % managers.size();
}
if (state != EmitState.NO_EMITTED) { //当EmitState.NO_EMITTED时，表明partition的数据已经读完，也就是没有读到数据，所以不能break
break;
}
} long now = System.currentTimeMillis();
if ((now - _lastUpdateMs) > _spoutConfig.stateUpdateIntervalMs) {
commit(); //定期commit
}
}
定期commit的逻辑，遍历去commit每个PartitionManager
private void commit() {
_lastUpdateMs = System.currentTimeMillis();
for (PartitionManager manager : _coordinator.getMyManagedPartitions()) {
manager.commit();
}
}
3. Ack和Fail
直接调用PartitionManager
@Override
public void ack(Object msgId) {
KafkaMessageId id = (KafkaMessageId) msgId;
PartitionManager m = _coordinator.getManager(id.partition);
if (m != null) {
m.ack(id.offset);
}
} @Override
public void fail(Object msgId) {
KafkaMessageId id = (KafkaMessageId) msgId;
PartitionManager m = _coordinator.getManager(id.partition);
if (m != null) {
m.fail(id.offset);
}
}
4. declareOutputFields
所以在scheme里面需要定义，deserialize和getOutputFields
@Override
public void declareOutputFields(OutputFieldsDeclarer declarer) {
declarer.declare(_spoutConfig.scheme.getOutputFields());
}
Metrics
再来看下Metrics，关键学习一下如何在storm里面加metrics
在spout.open里面初始化了下面两个metrics
kafkaOffset
反映出每个partition的earliestTimeOffset，latestTimeOffset，和latestEmittedOffset，其中latestTimeOffset - latestEmittedOffset就是spout lag
除了反映出每个partition的，还会算出所有的partitions的总数据
context.registerMetric("kafkaOffset", new IMetric() {
KafkaUtils.KafkaOffsetMetric _kafkaOffsetMetric = new KafkaUtils.KafkaOffsetMetric(_spoutConfig.topic, _connections);
@Override
public Object getValueAndReset() {
List<PartitionManager> pms = _coordinator.getMyManagedPartitions(); //从coordinator获取pms的信息
Set<Partition> latestPartitions = new HashSet();
for (PartitionManager pm : pms) {
latestPartitions.add(pm.getPartition());
}
_kafkaOffsetMetric.refreshPartitions(latestPartitions); //根据最新的partition信息删除metric中已经不存在的partition的统计信息
for (PartitionManager pm : pms) {
_kafkaOffsetMetric.setLatestEmittedOffset(pm.getPartition(), pm.lastCompletedOffset()); //更新metric中每个partition的已经完成的offset
}
return _kafkaOffsetMetric.getValueAndReset();
}
}, _spoutConfig.metricsTimeBucketSizeInSecs);_kafkaOffsetMetric.getValueAndReset，其实只是get，不需要reset
@Override
public Object getValueAndReset() {
try {
long totalSpoutLag = 0;
long totalEarliestTimeOffset = 0;
long totalLatestTimeOffset = 0;
long totalLatestEmittedOffset = 0;
HashMap ret = new HashMap();
if (_partitions != null && _partitions.size() == _partitionToOffset.size()) {
for (Map.Entry<Partition, Long> e : _partitionToOffset.entrySet()) {
Partition partition = e.getKey();
SimpleConsumer consumer = _connections.getConnection(partition);
long earliestTimeOffset = getOffset(consumer, _topic, partition.partition, kafka.api.OffsetRequest.EarliestTime());
long latestTimeOffset = getOffset(consumer, _topic, partition.partition, kafka.api.OffsetRequest.LatestTime());
long latestEmittedOffset = e.getValue();
long spoutLag = latestTimeOffset - latestEmittedOffset;
ret.put(partition.getId() + "/" + "spoutLag", spoutLag);
ret.put(partition.getId() + "/" + "earliestTimeOffset", earliestTimeOffset);
ret.put(partition.getId() + "/" + "latestTimeOffset", latestTimeOffset);
ret.put(partition.getId() + "/" + "latestEmittedOffset", latestEmittedOffset);
totalSpoutLag += spoutLag;
totalEarliestTimeOffset += earliestTimeOffset;
totalLatestTimeOffset += latestTimeOffset;
totalLatestEmittedOffset += latestEmittedOffset;
}
ret.put("totalSpoutLag", totalSpoutLag);
ret.put("totalEarliestTimeOffset", totalEarliestTimeOffset);
ret.put("totalLatestTimeOffset", totalLatestTimeOffset);
ret.put("totalLatestEmittedOffset", totalLatestEmittedOffset);
return ret;
} else {
LOG.info("Metrics Tick: Not enough data to calculate spout lag.");
}
} catch (Throwable t) {
LOG.warn("Metrics Tick: Exception when computing kafkaOffset metric.", t);
}
return null;
}
kafkaPartition
反映出从Kafka fetch数据的情况，fetchAPILatencyMax，fetchAPILatencyMean，fetchAPICallCount 和 fetchAPIMessageCount
context.registerMetric("kafkaPartition", new IMetric() {
@Override
public Object getValueAndReset() {
List<PartitionManager> pms = _coordinator.getMyManagedPartitions();
Map concatMetricsDataMaps = new HashMap();
for (PartitionManager pm : pms) {
concatMetricsDataMaps.putAll(pm.getMetricsDataMap());
}
return concatMetricsDataMaps;
}
}, _spoutConfig.metricsTimeBucketSizeInSecs);pm.getMetricsDataMap()，
public Map getMetricsDataMap() {
Map ret = new HashMap();
ret.put(_partition + "/fetchAPILatencyMax", _fetchAPILatencyMax.getValueAndReset());
ret.put(_partition + "/fetchAPILatencyMean", _fetchAPILatencyMean.getValueAndReset());
ret.put(_partition + "/fetchAPICallCount", _fetchAPICallCount.getValueAndReset());
ret.put(_partition + "/fetchAPIMessageCount", _fetchAPIMessageCount.getValueAndReset());
return ret;
}更新的逻辑如下，
private void fill() {
long start = System.nanoTime();
ByteBufferMessageSet msgs = KafkaUtils.fetchMessages(_spoutConfig, _consumer, _partition, _emittedToOffset);
long end = System.nanoTime();
long millis = (end - start) / 1000000;
_fetchAPILatencyMax.update(millis);
_fetchAPILatencyMean.update(millis);
_fetchAPICallCount.incr();
int numMessages = countMessages(msgs);
_fetchAPIMessageCount.incrBy(numMessages);
}
我们在读取kafka时，
首先是关心，每个partition的读取状况，这个通过取得KafkaOffset Metrics就可以知道
再者，我们需要replay数据，使用high-level接口的时候可以通过系统提供的工具，这里如何搞？
看下下面的代码，
第一个if，是从配置文件里面没有读到配置的情况
第二个else if，当topologyInstanceId发生变化时，并且forceFromStart为true时，就会取startOffsetTime指定的offset(Latest或Earliest)
这个topologyInstanceId, 每次KafkaSpout对象生成的时候随机产生，
String _uuid = UUID.randomUUID().toString();
Spout对象是在topology提交时，在client端生成一次的，所以如果topology停止，再重新启动，这个id一定会发生变化
所以应该是只需要把forceFromStart设为true，再重启topology，就可以实现replay
if (jsonTopologyId == null || jsonOffset == null) { // failed to parse JSON?
_committedTo = KafkaUtils.getOffset(_consumer, spoutConfig.topic, id.partition, spoutConfig);
LOG.info("No partition information found, using configuration to determine offset");
} 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");
} else {
_committedTo = jsonOffset;
LOG.info("Read last commit offset from zookeeper: " + _committedTo + "; old topology_id: " + jsonTopologyId + " - new topology_id: " + topologyInstanceId );
}
代码例子
storm-kafka的文档很差，最后附上使用的例子
import storm.kafka.KafkaSpout;
import storm.kafka.SpoutConfig;
import storm.kafka.BrokerHosts;
import storm.kafka.ZkHosts;
import storm.kafka.KeyValueSchemeAsMultiScheme;
import storm.kafka.KeyValueScheme; public static class SimplekVScheme implements KeyValueScheme { //定义scheme
@Override
public List<Object> deserializeKeyAndValue(byte[] key, byte[] value){
ArrayList tuple = new ArrayList();
tuple.add(key);
tuple.add(value);
return tuple;
} @Override
public List<Object> deserialize(byte[] bytes) {
ArrayList tuple = new ArrayList();
tuple.add(bytes);
return tuple;
} @Override
public Fields getOutputFields() {
return new Fields("key","value");
} } String topic = “test”; //
String zkRoot = “/kafkastorm”; //
String spoutId = “id”; //读取的status会被存在，/kafkastorm/id下面，所以id类似consumer group BrokerHosts brokerHosts = new ZkHosts("10.1.110.24:2181,10.1.110.22:2181"); SpoutConfig spoutConfig = new SpoutConfig(brokerHosts, topic, zkRoot, spoutId);
spoutConfig.scheme = new KeyValueSchemeAsMultiScheme(new SimplekVScheme()); /*spoutConfig.zkServers = new ArrayList<String>(){{ //只有在local模式下需要记录读取状态时，才需要设置
add("10.118.136.107");
}};
spoutConfig.zkPort = 2181;*/ spoutConfig.forceFromStart = false;
spoutConfig.startOffsetTime = kafka.api.OffsetRequest.EarliestTime();
spoutConfig.metricsTimeBucketSizeInSecs = 6; builder.setSpout(SqlCollectorTopologyDef.KAFKA_SPOUT_NAME, new KafkaSpout(spoutConfig), 1);