kafka + spark Streaming + Tranquility Server发送数据到druid

　　花了很长时间尝试druid官网上说的Tranquility嵌入代码进行实时发送数据到druid，结果失败了，各种各样的原因造成了失败，现在还没有找到原因，在IDEA中可以跑起，放到线上就死活不行，有成功了的同仁希望贴个链接供我来学习学习；后来又尝试了从kafka实时发送到druid，还是有些错误(现在已经解决, 后面再记录一下)；最后没办法呀，使用Tranquility Server呗 _ _!

Tranquility Server的配置和启动请移步：https://github.com/druid-io/tranquility/blob/master/docs/server.md

(一)、在启动了自己定制的server之后可以利用druid bin目录下的generate-example-metrics生成测试数据 (定制的server.json如下)

server.json的配置

{
  "dataSources" : {
    "reynold_metrics" : {
      "spec" : {
        "dataSchema" : {
          "dataSource" : "reynold_metrics",
          "parser" : {
            "type" : "string",
            "parseSpec" : {
              "timestampSpec" : {
                "column" : "timestamp",
                "format" : "auto"
              },
              "dimensionsSpec" : {
                "dimensions" : [],
                "dimensionExclusions" : [
                  "timestamp",
                  "value"
                ]
              },
              "format" : "json"
            }
          },
          "granularitySpec" : {
            "type" : "uniform",
            "segmentGranularity" : "hour",
            "queryGranularity" : "none"
          },
          "metricsSpec" : [
            {
              "type" : "count",
              "name" : "count"
            },
            {
              "name" : "value_sum",
              "type" : "doubleSum",
              "fieldName" : "value"
            },
            {
              "fieldName" : "value",
              "name" : "value_min",
              "type" : "doubleMin"
            },
            {
              "type" : "doubleMax",
              "name" : "value_max",
              "fieldName" : "value"
            }
          ]
        },
　　　　　"tuningConfig" : {
          "type" : "realtime",
          "maxRowsInMemory" : "",
          "intermediatePersistPeriod" : "PT10M",
          "windowPeriod" : "PT10M"
        }
      },
      "properties" : {
        "task.partitions" : "",
        "task.replicants" : ""
      }
    }
  },
  "properties" : {
    "zookeeper.connect" : "reynold-master:2181,reynold-slave02:2181,reynold-slave03:2181",
    "druid.discovery.curator.path" : "/druid/discovery",
    "druid.selectors.indexing.serviceName" : "druid/overlord",
    "http.port" : "",
    "http.threads" : ""
  }
}

(二)、创建kafka的topic并往里面发送数据

删除topic：kafka-topics  --delete --topic reynold --zookeeper localhost:
创建topic：kafka-topics  --create --topic reynold --zookeeper localhost: --partitions  --replication-factor
消费数据：kafka-console-consumer --topic reynold --zookeeper localhost: --from-beginning
生产数据：kafka-console-producer --broker-list reynold-master: --topic reynold

{"count": 1, "value_min": 74.0, "timestamp": "2017-03-09T02:34:24.000Z", "value_max": 74.0, "metricType": "request/latency", "server": "www5.example.com", "http_method": "GET", "value_sum": 74.0, "http_code": "200", "unit": "milliseconds", "page": "/"}
{"count": 1, "value_min": 75.0, "timestamp": "2017-03-09T02:34:24.000Z", "value_max": 75.0, "metricType": "request/latency", "server": "www5.example.com", "http_method": "GET", "value_sum": 75.0, "http_code": "200", "unit": "milliseconds", "page": "/list"}
{"count": 1, "value_min": 143.0, "timestamp": "2017-03-09T02:38:06.000Z", "value_max": 143.0, "metricType": "request/latency", "server": "www2.example.com", "http_method": "GET", "value_sum": 143.0, "http_code": "200", "unit": "milliseconds", "page": "/"}

(三)、使用spark streaming消费kafka中的数据并通过http发送到druid

object SparkDruid {

  val kafkaParam = Map[String, String](
    "metadata.broker.list" -> "reynold-master:9092,reynold-slave01:9092,reynold-slave02:9092,reynold-slave03:9092",
    "auto.offset.reset" -> "smallest"
  )

  def main(args: Array[String]): Unit = {
    val sparkContext = new SparkContext(new SparkConf().setMaster("local[4]").setAppName("SparkDruid"))
    val ssc = new StreamingContext(sparkContext, Seconds(3))
    val topic: String = "reynold" //消费的 topic 名字
    val topics: Set[String] = Set(topic) //创建 stream 时使用的 topic 名字集合

    var kafkaStream: InputDStream[(String, String)] = null

    kafkaStream = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParam, topics)

    kafkaStream.map(msg => msg._2).foreachRDD { rdd =>
      rdd.foreach(strJson => Https.post("http://reynold-master:8200/v1/post/zcx_metrics", strJson))
    }

    ssc.start()
    ssc.awaitTermination()
  }
}

Https类如下：

import java.io.InputStreamReader

import com.google.common.io.CharStreams
import org.apache.http.client.methods.{HttpGet, HttpPost}
import org.apache.http.entity.StringEntity
import org.apache.http.impl.client.HttpClients

/**  * 通过http请求的方式,可以
  * 1. 向druid里面发送数据
  * 2. 提供一些查询的druid的方法
  * 3. 顺带查询hbase数据的方法
  */
object Https {
  private val httpClient = HttpClients.createDefault()

  def get(url: String): String = {
    val _get = new HttpGet(url)
    val resp = httpClient.execute(_get)
    try {
      if (resp.getStatusLine.getStatusCode != 200) {
        throw new RuntimeException("error: " + resp.getStatusLine)
      }
      CharStreams.toString(new InputStreamReader(resp.getEntity.getContent))
    } finally {
      resp.close()
    }
  }

　//既可以发送数据，也可以请求数据(以结果的形式返回)
  def post(url: String, content: String): String = {
    val _post = new HttpPost(url)
    _post.setHeader("Content-Type", "application/json")
    _post.setEntity(new StringEntity(content,"utf-8"))
    val resp = httpClient.execute(_post)
    try {
      CharStreams.toString(new InputStreamReader(resp.getEntity.getContent))
    } finally {
      resp.close()
    }

  }

  object MapTypeRef extends com.fasterxml.jackson.core.`type`.TypeReference[Map[String, Any]]

  object ListMapTypeRef extends com.fasterxml.jackson.core.`type`.TypeReference[List[Map[String, Any]]]

  def queryHBase(sql: String): List[Map[String, Any]] = {
　　//将request为json格式
    val request  = new String(Mapper.mapper.writeValueAsBytes(Map(
      "action" -> "query",
      "sql" -> sql
    )))
　　//发送json格式的请求
    val resp = post("http://reynold-master:8209/api", request)
    val rs = Mapper.mapper.readValue[Map[String, Any]](resp, MapTypeRef)
    //val rs = Mapper.mapper.readValue[Map[String, Any]](resp, classOf[Map[String, Any]]) 这种方式也可以
    rs("result").asInstanceOf[List[Map[String, Any]]]
  }

  def queryDruid(json: String): String = {
    post("http://reynold-master:18082/druid/v2", json)
  }

  private def getDruid(path: String): String = {
    get("http://reynold-master:18082/druid/v2" + path)
  }

  def druidDataSources(): List[String] = {
    Mapper.mapper.readValue[List[String]](getDruid("/datasources"), ListMapTypeRef)
  }

  def druidDimension(datasource: String): String = {
    getDruid(s"/datasources/$datasource/dimensions")
  }

  def druidMetrics(datasource: String): String = {
    getDruid(s"/datasources/$datasource/metrics")
  }

  def main(args: Array[String]): Unit = {
    println(queryHBase("select * from user_tags limit 2"))
  }

}

Mapper.scala

package com.donews.util

import com.fasterxml.jackson.databind.ObjectMapper
import com.fasterxml.jackson.module.scala.DefaultScalaModule

/**
  * Created by reynold
  */

object Mapper {
  val mapper = new ObjectMapper()
  mapper.registerModule(DefaultScalaModule)
}

注意在pom文件中添加下面的依赖：

<dependency>
    <groupId>org.apache.httpcomponents</groupId>
    <artifactId>httpclient</artifactId>
    <version>4.3.3</version>
</dependency>
<dependency>
    <groupId>com.fasterxml.jackson.module</groupId>
    <artifactId>jackson-module-scala_2.11</artifactId>
    <version>2.4.5</version>
</dependency>