Spark Streaming篇3：Spark Streaming 把数据发往kafka，并实现去重
废话不多说，直接上干货
package com.iflytek.kafka
import java.util.Properties
import com.iflytek.kafkaManager.KafkaSink
import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.log4j.{Level, Logger}
import org.apache.spark.broadcast.Broadcast
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.functions.lit
import org.apache.spark.sql.types._
import org.apache.spark.sql.{Dataset, Row, SparkSession, _}
import org.apache.spark.streaming.dstream.{DStream, InputDStream}
import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe
import org.apache.spark.streaming.kafka010.KafkaUtils
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
import org.apache.spark.streaming.{Seconds, StreamingContext}
/*
需求：
利用sparkstreaming实时过滤30s内重复出现的人
*/
object WC2Kafka2 {
def main(args: Array[String]): Unit = {
Logger.getRootLogger.setLevel(Level.ERROR)
System.setProperty("HADOOP_USER_NAME","root")
System.setProperty("user.name","root")
val warehouse = "hdfs://cdh01:8020/user/hive/warehouse/carbon.store"
val sparkSession: SparkSession = SparkSession.builder()
.appName("xx2")
.master("local[2]")
.config("spark.testing.memory", "471859200")
.config("spark.steaming.kafka.maxRatePerPartition","10")
.config("spark.sql.streaming.schemaInference", "true")
.getOrCreate()
val ssc = new StreamingContext(sparkSession.sparkContext, Seconds(3))
val kafkaParams = Map[String, Object](
"bootstrap.servers" -> "cdh01:9092",
"key.deserializer" -> classOf[StringDeserializer],
"value.deserializer" -> classOf[StringDeserializer],
"group.id" -> "xytest1101",
"auto.offset.reset" -> "latest",//earliest latest
"enable.auto.commit" -> (false: java.lang.Boolean)
)
val topics = Array("pd_ry_txjl")
val stream: InputDStream[ConsumerRecord[String, String]] ={
KafkaUtils.createDirectStream[String, String](
ssc,
PreferConsistent,
Subscribe[String, String](topics, kafkaParams))}
val value: DStream[String] = stream.map(_.value())
val kafkaProducerConfig = {
val p = new Properties()
p.setProperty("bootstrap.servers","cdh01:9092")
p.setProperty("key.serializer","org.apache.kafka.common.serialization.StringSerializer")
p.setProperty("value.serializer","org.apache.kafka.common.serialization.StringSerializer")
p
}
val kafkaProducer: Broadcast[KafkaSink[String, String]] =
{sparkSession.sparkContext.broadcast(KafkaSink[String, String](kafkaProducerConfig))}
val windowWords: DStream[String] = value.window(Seconds(6), Seconds(6))
val schema = {StructType(Seq(
StructField("id",StringType,true),
StructField("name",StringType,true),
StructField("age",StringType,true),
StructField("time",TimestampType,true)))}
windowWords.foreachRDD(rdd=>{
import org.apache.spark.sql.functions._
import sparkSession.implicits._
val rdd1: RDD[String] = rdd.cache()
val ds: Dataset[String] = sparkSession.createDataset(rdd1)
val frameAll: Dataset[Row] = {//下面是解析2层嵌套的json
ds.select(get_json_object(col("value").cast("string"), "$.body") as ("body"))
.select(get_json_object(col("body"), "$.body") as ("body"))
.select(from_json(col("body"), schema) as ("parsed_value"))
.select(
col("parsed_value").getItem("id") as ("id"),
col("parsed_value").getItem("name") as ("name"),
col("parsed_value").getItem("age") as ("age"),
from_unixtime((col("parsed_value").getItem("time").cast("double")/1000), "yyyy-MM-dd HH:mm:ss").cast("TIMESTAMP") as ("time"))
.filter("id is not null")
}
frameAll.createOrReplaceTempView("frameAll")
sparkSession.sqlContext.cacheTable("frameAll")
val distancted: Dataset[Row] = frameAll.dropDuplicates(Array("name","id"))
distancted.createOrReplaceTempView("distancted")
sparkSession.sqlContext.cacheTable("distancted")
sendKafka(distancted,kafkaProducer)
sparkSession.sqlContext.uncacheTable("frameAll")
sparkSession.sqlContext.uncacheTable("distancted")
rdd1.unpersist(true)
})
ssc.start()
ssc.awaitTermination()
}
//把数据再包装成之前的2层嵌套json，发往kafka
def sendKafka(distancted:DataFrame,kafkaProducer:Broadcast[KafkaSink[String, String]]):Unit={
if(!distancted.rdd.isEmpty()){
distancted.toJSON.withColumnRenamed("value", "body")
.withColumn("skynet.priority", lit("4"))
.toJSON.withColumnRenamed("value", "properties")
.toJSON.withColumnRenamed("value", "body")
.withColumn("skynet.msg.location", lit("LOCAL"))
.withColumn("skynet.priority", lit("4"))
.toJSON.withColumnRenamed("value", "properties")
.toJSON.foreach(rdd=>{
kafkaProducer.value.send("xytest",rdd.toString)
})
}
}
}
KafkaSink通用类
package com.iflytek.kafkaManager
import java.util.concurrent.Future
import org.apache.kafka.clients.producer.{KafkaProducer, ProducerRecord, RecordMetadata}
class KafkaSink[K, V](createProducer: () => KafkaProducer[K, V])
extends Serializable {
//这样写可以避免序列化错误
@transient
lazy val producer = createProducer()
//发送消息
def send(topic: String, key: K, value: V): Future[RecordMetadata] =
producer.send(new ProducerRecord[K, V](topic, key, value))
def send(topic: String, value: V): Future[RecordMetadata] =
producer.send(new ProducerRecord[K, V](topic, value))
}
object KafkaSink {
import scala.collection.JavaConversions._
def apply[K, V](config: Map[String, Object]): KafkaSink[K, V] = {
val createProducerFunc = () => {
// 新建kafka Producer
val producer = new KafkaProducer[K, V](config)
sys.addShutdownHook {
// 确保在excutor的jvm关闭前
// kafka producer 将缓存中的所有信息写入kafka
producer.close()
}
producer
}
new KafkaSink(createProducerFunc)
}
def apply[K, V](config: java.util.Properties): KafkaSink[K, V] = apply(config.toMap)
}
pom如下：
<properties>
<spark.version>2.3.2</spark.version>
<scala.version>2.11.8</scala.version>
<hbase.version>1.2.1</hbase.version>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>${spark.version}</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>${spark.version}</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-hive_2.11</artifactId>
<version>${spark.version}</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-yarn_2.11</artifactId>
<version>${spark.version}</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>${spark.version}</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>${spark.version}</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming-kafka-0-10_2.11</artifactId>
<version>${spark.version}</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql-kafka-0-10_2.11</artifactId>
<version>${spark.version}</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>com.alibaba</groupId>
<artifactId>fastjson</artifactId>
<version>1.2.47</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-clients</artifactId>
<version>0.11.0.0</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>5.1.31</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>c3p0</groupId>
<artifactId>c3p0</artifactId>
<version>0.9.1.2</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>com.jolbox</groupId>
<artifactId>bonecp</artifactId>
<version>0.8.0.RELEASE</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.apache.zookeeper</groupId>
<artifactId>zookeeper</artifactId>
<version>3.4.13</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-client</artifactId>
<version>${hbase.version}</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-common</artifactId>
<version>${hbase.version}</version>
<scope>compile</scope>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-server</artifactId>
<version>${hbase.version}</version>
<scope>compile</scope>
</dependency>
<!--<dependency>-->
<!--<groupId>org.apache.hadoop</groupId>-->
<!--<artifactId>hadoop-client</artifactId>-->
<!--<version>2.7.2</version>-->
<!--</dependency>-->
<!--<!–guava和hadoop版本得对应–>-->
<!--<dependency>-->
<!--<groupId>com.google.guava</groupId>-->
<!--<artifactId>guava</artifactId>-->
<!--<version>18.0</version>-->
<!--</dependency>-->
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
<scope>compile</scope>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.0</version>
<executions>
<execution>
<id>compile-scala</id>
<phase>compile</phase>
<goals>
<goal>add-source</goal>
<goal>compile</goal>
</goals>
</execution>
<execution>
<id>test-compile-scala</id>
<phase>test-compile</phase>
<goals>
<goal>add-source</goal>
<goal>testCompile</goal>
</goals>
</execution>
</executions>
<configuration>
<scalaVersion>2.11.8</scalaVersion>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.6.1</version>
<configuration>
<source>1.8</source>
<target>1.8</target>
<compilerArgs>
<arg>-extdirs</arg>
<arg>${project.basedir}/lib</arg>
</compilerArgs>
</configuration>
</plugin>
</plugins>
</build>
