kafka api中给出2类直接获取流的接口:createStream和createDirectStream。

createStream比较简单,只需topic、groupid、zookeeper就可以直接获取流,brokers和offset都是黑盒无需进行控制,但在项目中往往不受控。以下是部分源码:

/**
* Create an input stream that pulls messages from Kafka Brokers.
* @param ssc StreamingContext object
* @param zkQuorum Zookeeper quorum (hostname:port,hostname:port,..)
* @param groupId The group id for this consumer
* @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed
* in its own thread
* @param storageLevel Storage level to use for storing the received objects
* (default: StorageLevel.MEMORY_AND_DISK_SER_2)
* @return DStream of (Kafka message key, Kafka message value)
*/
def createStream(
ssc: StreamingContext,
zkQuorum: String,
groupId: String,
topics: Map[String, Int],
storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2
): ReceiverInputDStream[(String, String)] = {
val kafkaParams = Map[String, String](
"zookeeper.connect" -> zkQuorum, "group.id" -> groupId,
"zookeeper.connection.timeout.ms" -> "10000")
createStream[String, String, StringDecoder, StringDecoder](
ssc, kafkaParams, topics, storageLevel)
}

KafkaUtils.createStream

createDirectStream直接去操作kafka,需要自己手动保存offset,方法的注释写的还是很明白的,以下是部分源码:

/**
* Create an input stream that directly pulls messages from Kafka Brokers
* without using any receiver. This stream can guarantee that each message
* from Kafka is included in transformations exactly once (see points below).
*
* Points to note:
* - No receivers: This stream does not use any receiver. It directly queries Kafka
* - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked
* by the stream itself. For interoperability with Kafka monitoring tools that depend on
* Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application.
* You can access the offsets used in each batch from the generated RDDs (see
* [[org.apache.spark.streaming.kafka.HasOffsetRanges]]).
* - Failure Recovery: To recover from driver failures, you have to enable checkpointing
* in the [[StreamingContext]]. The information on consumed offset can be
* recovered from the checkpoint. See the programming guide for details (constraints, etc.).
* - End-to-end semantics: This stream ensures that every records is effectively received and
* transformed exactly once, but gives no guarantees on whether the transformed data are
* outputted exactly once. For end-to-end exactly-once semantics, you have to either ensure
* that the output operation is idempotent, or use transactions to output records atomically.
* See the programming guide for more details.
*
* @param ssc StreamingContext object
* @param kafkaParams Kafka <a href="http://kafka.apache.org/documentation.html#configuration">
* configuration parameters</a>. Requires "metadata.broker.list" or "bootstrap.servers"
* to be set with Kafka broker(s) (NOT zookeeper servers) specified in
* host1:port1,host2:port2 form.
* @param fromOffsets Per-topic/partition Kafka offsets defining the (inclusive)
* starting point of the stream
* @param messageHandler Function for translating each message and metadata into the desired type
* @tparam K type of Kafka message key
* @tparam V type of Kafka message value
* @tparam KD type of Kafka message key decoder
* @tparam VD type of Kafka message value decoder
* @tparam R type returned by messageHandler
* @return DStream of R
*/
def createDirectStream[
K: ClassTag,
V: ClassTag,
KD <: Decoder[K]: ClassTag,
VD <: Decoder[V]: ClassTag,
R: ClassTag] (
ssc: StreamingContext,
kafkaParams: Map[String, String],
fromOffsets: Map[TopicAndPartition, Long],
messageHandler: MessageAndMetadata[K, V] => R
): InputDStream[R] = {
val cleanedHandler = ssc.sc.clean(messageHandler)
new DirectKafkaInputDStream[K, V, KD, VD, R](
ssc, kafkaParams, fromOffsets, cleanedHandler)
}

KafkaUtils.createDirectStream

/**
* Create an input stream that directly pulls messages from Kafka Brokers
* without using any receiver. This stream can guarantee that each message
* from Kafka is included in transformations exactly once (see points below).
*
* Points to note:
* - No receivers: This stream does not use any receiver. It directly queries Kafka
* - Offsets: This does not use Zookeeper to store offsets. The consumed offsets are tracked
* by the stream itself. For interoperability with Kafka monitoring tools that depend on
* Zookeeper, you have to update Kafka/Zookeeper yourself from the streaming application.
* You can access the offsets used in each batch from the generated RDDs (see
* [[org.apache.spark.streaming.kafka.HasOffsetRanges]]).
* - Failure Recovery: To recover from driver failures, you have to enable checkpointing
* in the [[StreamingContext]]. The information on consumed offset can be
* recovered from the checkpoint. See the programming guide for details (constraints, etc.).
* - End-to-end semantics: This stream ensures that every records is effectively received and
* transformed exactly once, but gives no guarantees on whether the transformed data are
* outputted exactly once. For end-to-end exactly-once semantics, you have to either ensure
* that the output operation is idempotent, or use transactions to output records atomically.
* See the programming guide for more details.
*
* @param ssc StreamingContext object
* @param kafkaParams Kafka <a href="http://kafka.apache.org/documentation.html#configuration">
* configuration parameters</a>. Requires "metadata.broker.list" or "bootstrap.servers"
* to be set with Kafka broker(s) (NOT zookeeper servers), specified in
* host1:port1,host2:port2 form.
* If not starting from a checkpoint, "auto.offset.reset" may be set to "largest" or "smallest"
* to determine where the stream starts (defaults to "largest")
* @param topics Names of the topics to consume
* @tparam K type of Kafka message key
* @tparam V type of Kafka message value
* @tparam KD type of Kafka message key decoder
* @tparam VD type of Kafka message value decoder
* @return DStream of (Kafka message key, Kafka message value)
*/
def createDirectStream[
K: ClassTag,
V: ClassTag,
KD <: Decoder[K]: ClassTag,
VD <: Decoder[V]: ClassTag] (
ssc: StreamingContext,
kafkaParams: Map[String, String],
topics: Set[String]
): InputDStream[(K, V)] = {
val messageHandler = (mmd: MessageAndMetadata[K, V]) => (mmd.key, mmd.message)
val kc = new KafkaCluster(kafkaParams)
val fromOffsets = getFromOffsets(kc, kafkaParams, topics)
new DirectKafkaInputDStream[K, V, KD, VD, (K, V)](
ssc, kafkaParams, fromOffsets, messageHandler)
}

KafkaUtils.createDirectStream

项目中需要的是手动去控制这个偏移量,由此可以看到多了2个参数:fromOffsets: Map[TopicAndPartition, Long] 和 messageHandler: MessageAndMetadata[K, V] => R。

获取fromOffsets的思路应该就是:

1. 连接到zk

2. 获取topic和partitions

3. 遍历topic的partitions,读取每个partitions的offset(存在zk中的地址为:/consumers/[group id]/offsets/[topic]/[0 ... N])

4. 有可能读取的路径为空,那么得去取leader中的offset

因此,对应代码:(可以参考这些源码:kafka.utils.ZkUtils,org.apache.spark.streaming.kafka.KafkaUtils,kafka.tools.GetOffsetShell,及其对应的调用类)

private def getOffset = {
val fromOffset: mutable.Map[TopicAndPartition, Long] = mutable.Map() val (zkClient, zkConnection) = ZkUtils.createZkClientAndConnection(kafkaZkQuorum, kafkaZkSessionTimeout, kafkaZkSessionTimeout)
val zkUtil = new ZkUtils(zkClient, zkConnection, false) zkUtil.getPartitionsForTopics(kafkaTopic.split(",").toSeq)
.foreach({ topic2Partition =>
val topic = topic2Partition._1
val partitions = topic2Partition._2
val topicDirs = new ZKGroupTopicDirs(groupId, topic) partitions.foreach(partition => {
val zkPath = s"${topicDirs.consumerOffsetDir}/$partition"
zkUtil.makeSurePersistentPathExists(zkPath) val untilOffset = zkUtil.zkClient.readData[String](zkPath) val tp = TopicAndPartition(topic, partition)
val offset = {
if (null == untilOffset)
getLatestLeaderOffsets(tp, zkUtil)
else untilOffset.toLong
}
fromOffset += (tp -> offset)
}
)
})
zkUtil.close()
fromOffset.toMap
}

getOffset

获取messageHandler,就跟其第二个构造函数一样即可:

messageHandler = (mmd: MessageAndMetadata[K, V]) => (mmd.key, mmd.message)

messageHandler

接着就是getLatestLeaderOffsets:

private def getLatestLeaderOffsets(tp: TopicAndPartition, zkUtil: ZkUtils): Long = {
try {
val brokerId = zkUtil.getLeaderForPartition(tp.topic, tp.partition).get
val brokerInfoString = zkUtil.readDataMaybeNull(s"${ZkUtils.BrokerIdsPath}/$brokerId")._1.get
val brokerInfo = Json.parseFull(brokerInfoString).get.asInstanceOf[Map[String, Any]] val host = brokerInfo("host").asInstanceOf[String]
val port = brokerInfo("port").asInstanceOf[Int] val consumer = new SimpleConsumer(host, port, 10000, 100000, "getLatestLeaderOffsets")
val request = OffsetRequest(Map(tp -> PartitionOffsetRequestInfo(OffsetRequest.LatestTime, 1)))
val offsets = consumer.getOffsetsBefore(request).partitionErrorAndOffsets(tp).offsets offsets.head
} catch {
case _ => throw new Exception("获取最新offset异常:" + TopicAndPartition)
}
}

getLatestLeaderOffsets

最后就是调用的方式了:

KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder, (String, String)](ssc,
kafkaParams, getOffset, (mmd: MessageAndMetadata[String, String]) => (mmd.key, mmd.message))

KafkaUtils.createDirectStream

由于要从灾难中还原,做到7*24,需要设置checkpoint,业务逻辑需要包含在checkpoint的方法里,代码如下:

def main(args: Array[String]): Unit = {

    val run = gatewayIsEnable || urlAnalysIsEnable

    if (run) {

      val ssc = StreamingContext.getOrCreate(checkpointDir, createStreamingContext _)

      ssc.start()
ssc.awaitTermination()
}
} def createStreamingContext() = {
val duration = SysConfig.duration(2) val sparkConf = new SparkConf().setAppName("cmhi")
val ssc = new StreamingContext(sparkConf, Seconds(duration)) ssc.checkpoint(checkpointDir) Osgi.init(ssc, debug) ssc
}

main

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