spark-streaming-kafka交互问题

name := "test"
version := "0.0.2"
scalaVersion := "2.11.8"
val sparkVersion = "2.2.0"
libraryDependencies += "org.apache.spark" % "spark-core_2.11" % "2.2.0"// % Provided
libraryDependencies += "org.apache.spark" % "spark-streaming-kafka-0-10-assembly_2.11" % "2.2.0" //% Provided
libraryDependencies += "org.apache.spark" % "spark-streaming_2.11" % "2.2.0"// % Provided

import org.apache.kafka.clients.consumer.ConsumerRecord
import org.apache.spark.streaming.Seconds
import org.apache.kafka.common.serialization.StringDeserializer
import org.apache.spark.streaming.kafka010.ConsumerStrategies.Subscribe
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.kafka010.KafkaUtils
import org.apache.spark.streaming.kafka010.LocationStrategies.PreferConsistent
import org.apache.spark.SparkConf

object stu_live_test {
    def main(args:Array[String]){
        val conf = new SparkConf().setAppName("test")
        val ssc = new StreamingContext(conf, Seconds(2))
        println("hello")
        val kafkaParams = Map[String, Object](
            "bootstrap.servers" -> ip,
            "group.id" -> "test_kafka1102",
            "key.deserializer" -> classOf[StringDeserializer],
            "value.deserializer" -> classOf[StringDeserializer],
            "auto.offset.reset" -> "latest",
            "enable.auto.commit" -> (false: java.lang.Boolean)
   //         "partition.assignment.strategy" -> "range"

        );
        val tops = "artsuseronline"
        val topics = tops.split(",").toSet
        println(topics);

        val stream = KafkaUtils.createDirectStream[String, String](
            ssc,
            PreferConsistent,
            Subscribe[String, String](topics,kafkaParams));
        println("****************************9999");
        val lines = stream.map(record => record.toString());
  //      println(lines);
        lines.print();
        println("dfsdfsdf");
        ssc.start();
        ssc.awaitTermination();

    }
}

以上是sbt代码封装以及scala代码,sbt打包后即可运行。

环境spark2.2.0 scala 2.11.8 kafka 0.10

中间遇到问题如下:

Exception in thread “streaming-start” java.lang.NoSuchMethodError: org.apache.kafka.clients.consumer.KafkaConsumer.subscribe(Ljava/util/Collection;)V

 搜索之后反馈是版本冲突问题

搜到的解决办法有:export SPARK_KAFKA_VERSION=0.10  增加kafka-client-10的依赖等等。

spark/jars中之前链接kafka-08下载了spark-streaming-kafka-0-8-assembly_2.11-2.2.0.jar

将这个删掉，增加了spark-streaming-kafka-0-10_2.11-2.2.0.jar，运行失败，增加kafka_client_10.jar包，运行失败

删掉以上Jar包，增加spark-streaming-kafka-0-10-assembly_2.11-2.2.0.jar。运行出数据，但是显示序列化失败，于是在Map转换中将输出强制类型转换成string，输出成功。

当kafka 8 与 10同时存在时，调用8运行没有问题，10有问题。

streaming-kafka jar包应单独调用，不能存在于spark/jar环境中

以上是未加sasl认证的kafka0.10获取代码

以下是增加sasl认证代码

需要额外增加kafka_client_jass.conf文件，在文件中加入用户密码信息

如下：

KafkaClient {
        org.apache.kafka.common.security.plain.PlainLoginModule required
        username="name"
        password="psw";
};

kafkaparam增加如下：

        val kafkaParams = Map[String, Object](
            "bootstrap.servers" -> ip,
            "group.id" -> "test_kafka11",
            "key.deserializer" -> classOf[StringDeserializer],
            "value.deserializer" -> classOf[StringDeserializer],
            "auto.offset.reset" -> "latest",
            "sasl.plain.username" -> name,
            "sasl.plain.password" -> psw,
            "security.protocol" -> "SASL_PLAINTEXT",
            "sasl.mechanism" -> "PLAIN",
            "enable.auto.commit" -> (false: java.lang.Boolean)

        );

提交中增加conf

spark-submit --class scala_class_name --driver-java-options="-Djava.security.auth.login.config=kafka_client_jass.conf" xxxxx.jar