依赖
UTF-8
1.8
1.8
1.8
2.11
2.11.8
2.4.0-cdh6.3.2
3.0.0-cdh6.3.2
2.2.1-cdh6.3.2
com.emg
etp-common
0.0.1-SNAPSHOT
org.scala-lang
scala-library
${scala.version}
${jar.scope}
org.scala-lang
scala-compiler
${scala.version}
${jar.scope}
org.scala-lang
scala-reflect
${scala.version}
${jar.scope}
org.apache.spark
spark-core_${scala.binary.version}
${spark.version}
${jar.scope}
org.apache.spark
spark-sql_${scala.binary.version}
${spark.version}
${jar.scope}
org.apache.spark
spark-sql-kafka-0-10_${scala.binary.version}
${spark.version}
${jar.scope}
org.apache.spark
spark-streaming_${scala.binary.version}
${spark.version}
org.apache.kafka
kafka-streams-test-utils
${kafka.version}
org.apache.kafka
kafka-clients
${kafka.version}
org.apache.kafka
kafka_2.12
2.2.1
org.apache.spark
spark-streaming-kafka-0-10_${scala.binary.version}
${spark.version}
org.apache.spark
spark-hive_2.11
${spark.version}
com.alibaba
fastjson
1.2.70
joda-time
joda-time
2.9.2
com.jolbox
bonecp
0.8.0.RELEASE
org.postgresql
postgresql
42.2.12
org.apache.commons
commons-collections4
4.4
org.locationtech.jts
jts-core
1.16.1
mysql
mysql-connector-java
5.1.38
com.uber
h3
3.6.3
org.apache.parquet
parquet-common
1.11.0
org.apache.parquet
parquet-encoding
1.11.0
org.apache.parquet
parquet-column
1.11.0
org.apache.parquet
parquet-hadoop
1.11.0
log4j
log4j
1.2.17
代码
import scala.collection.JavaConversions._
import org.apache.curator.framework.CuratorFrameworkFactory
import org.apache.curator.retry.ExponentialBackoffRetry
import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.kafka.common.TopicPartition
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.InputDStream
import org.apache.spark.streaming.kafka010._
import org.apache.spark.streaming.{Seconds, StreamingContext}
import scala.collection.mutable
object KafkaZookeeperCheckPoint {
// ZK client
val client = {
val client = CuratorFrameworkFactory
.builder
.connectString("bigdata:2181,bigdata:2182,bigdata:2183")
.retryPolicy(new ExponentialBackoffRetry(1000, 3))
.namespace("mykafka")
.build()
client.start()
client
}
// offset 路径起始位置
val Globe_kafkaOffsetPath = "/kafka/offsets"
// 路径确认函数 确认ZK中路径存在，不存在则创建该路径
def ensureZKPathExists(path: String)={
if (client.checkExists().forPath(path) == null) {
client.create().creatingParentsIfNeeded().forPath(path)
}
}
// 保存 新的 offset
def storeOffsets(offsetRange: Array[OffsetRange], groupName:String) = {
for (o <- offsetRange){
val zkPath = s"${Globe_kafkaOffsetPath}/${groupName}/${o.topic}/${o.partition}"
//检查路径
ensureZKPathExists(zkPath)
// 向对应分区第一次写入或者更新Offset 信息
println("---Offset写入ZK------\nTopic：" + o.topic +", Partition:" + o.partition + ", Offset:" + o.untilOffset)
client.setData().forPath(zkPath, o.untilOffset.toString.getBytes())
}
}
def getFromOffset(topic: Array[String], groupName:String):(Map[TopicPartition, Long], Int) = {
// Kafka 0.8和0.10的版本差别，0.10 为 TopicPartition 0.8 TopicAndPartition
var fromOffset: Map[TopicPartition, Long] = Map()
val topic1 = topic(0).toString
// 读取ZK中保存的Offset，作为Dstrem的起始位置。如果没有则创建该路径，并从 0 开始Dstream
val zkTopicPath = s"${Globe_kafkaOffsetPath}/${groupName}/${topic1}"
// 检查路径是否存在
ensureZKPathExists(zkTopicPath)
// 获取topic的子节点，即 分区
val childrens = client.getChildren().forPath(zkTopicPath)
// 遍历分区
val offSets: mutable.Buffer[(TopicPartition, Long)] = for {
p <- childrens
}
yield {
// 遍历读取子节点中的数据：即 offset
val offsetData = client.getData().forPath(s"$zkTopicPath/$p")
// 将offset转为Long
val offSet = java.lang.Long.valueOf(new String(offsetData)).toLong
// 返回 (TopicPartition, Long)
(new TopicPartition(topic1, Integer.parseInt(p)), offSet)
}
println(offSets.toMap)
if(offSets.isEmpty){
(offSets.toMap, 0)
} else {
(offSets.toMap, 1)
}
}
// if (client.checkExists().forPath(zkTopicPath) == null){
//
// (null, 0)
// }
// else {
// val data = client.getData.forPath(zkTopicPath)
// println("----------offset info")
// println(data)
// println(data(0))
// println(data(1))
// val offSets = Map(new TopicPartition(topic1, 0) -> 7332.toLong)
// println(offSets)
// (offSets, 1)
// }
//
// }
def createMyZookeeperDirectKafkaStream(ssc:StreamingContext, kafkaParams:Map[String, Object], topic:Array[String],
groupName:String ):InputDStream[ConsumerRecord[String, String]] = {
// get offset flag = 1 表示基于已有的offset计算 flag = 表示从头开始(最早或者最新，根据Kafka配置)
val (fromOffsets, flag) = getFromOffset(topic, groupName)
var kafkaStream:InputDStream[ConsumerRecord[String, String]] = null
if (flag == 1){
// 加上消息头
//val messageHandler = (mmd:MessageAndMetadata[String, String]) => (mmd.topic, mmd.message())
println(fromOffsets)
kafkaStream = KafkaUtils.createDirectStream(ssc, LocationStrategies.PreferConsistent,
ConsumerStrategies.Subscribe(topic, kafkaParams, fromOffsets))
println(fromOffsets)
println("中断后 Streaming 成功！")
} else {
kafkaStream = KafkaUtils.createDirectStream(ssc, LocationStrategies.PreferConsistent,
ConsumerStrategies.Subscribe(topic, kafkaParams))
println("首次 Streaming 成功！")
}
kafkaStream
}
def main(args: Array[String]): Unit = {
val processInterval = 5
val brokers = "bigdata:9092,bigdata:9093,bigdata:9094"
val topics = Array("zkKafka")
val conf = new SparkConf().setMaster("local[2]").setAppName("kafka checkpoint zookeeper")
// kafka params
val kafkaParams = Map[String, Object](
"bootstrap.servers" -> brokers,
"key.deserializer" -> classOf[StringDeserializer],
"value.deserializer" -> classOf[StringDeserializer],
"group.id" -> "zk_group",
"auto.offset.reset" -> "latest",
"enable.auto.commit" -> (false: java.lang.Boolean)
)
val ssc = new StreamingContext(conf, Seconds(processInterval))
val messages = createMyZookeeperDirectKafkaStream(ssc, kafkaParams, topics, "zk_group")
messages.foreachRDD((rdd) => {
if (!rdd.isEmpty()){
println("###################:"+rdd.count())
}
// 存储新的offset
storeOffsets(rdd.asInstanceOf[HasOffsetRanges].offsetRanges, "zk_group")
})
ssc.start()
ssc.awaitTermination()
}
}
