Spark 读写 es 数据（scala 版）-Toy模板网

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1. spark 读取 ES

import org.apache.spark.sql.SparkSession
import org.elasticsearch.spark.rdd.EsSpark

object esReadToHdfs {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().appName("es_read").getOrCreate()
    val sc = spark.sparkContext

    val options = Map(
      "es.index.auto.create" -> "true",
      "es.nodes.wan.only" -> "true",
      "es.nodes" -> "29.29.29.29:9200,29.29.29.29:9200",
      "es.port" -> "9200",
      "es.mapping.id" -> "id"
    )

    // 返回 RDD[(String, String]]
    // 元组：第一个：esmapping.id、第二个 json 字符串
    val resultRDD = EsSpark.esJsonRDD(sc, options).map(x => x._2)

    //    // 返回 RDD[(String, Map[String, AnyDef]]
    //    val resultRDD = EsSpark.esRDD(sc, options)

  }
}

读取 hdfs 文件

[hadoop@hadoop1 apps]$ hadoop fs -cat hdfs://hadoop1:9000/people.json
{"name":"Michael"}
{"name":"Andy", "age":30}
{"name":"Justin", "age":19}

解析采用 fast-json：

1、pom.xml

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-sql_2.11</artifactId>
    <version>2.1.1</version>
</dependency>

<dependency>
    <groupId>com.alibaba</groupId>
    <artifactId>fastjson</artifactId>
    <version>1.2.47</version>
</dependency>

2、main 文件

package top.midworld.spark1031.create_df

import org.apache.spark.sql.SparkSession
import java.security.MessageDigest
import com.alibaba.fastjson.{JSON, JSONException, JSONObject}

object SaveToEs {
  def main(args: Array[String]): Unit = {
      // 提交到集群，去掉 master，不能采用 local[2]
    val spark = SparkSession.builder.appName("create_rdd").master("local[2]").getOrCreate()
    val sc = spark.sparkContext


    val rdd = sc.textFile("hdfs://hadoop1:9000/people.json").map {
      //      x => JSON.parseObject(x).get("name")
      x =>
        val data = JSON.parseObject(x)
        val name = data.get("name").toString
        val md5 = hashMD5(name)	// name md5 
        data.put("@id", md5)	// 添加新的 key、value
        data
    }

    rdd.collect().foreach(println)
    sc.stop()
    spark.stop()
  }

  def hashMD5(url: String): String = {
    val md5 = MessageDigest.getInstance("MD5")
    val encoded = md5.digest((url).getBytes())
    encoded.map("%02x".format(_)).mkString
  }
}

运行结果：

{"name":"aaa","@id":"3e06fa3927cbdf4e9d93ba4541acce86"}
{"name":"aaa","@id":"0d2366f384b6c702db8e9dd8b74534db","age":30}
{"name":"aaa","@id":"06475174d922e7dcbb3ed34c0236dbdf","age":19}

2. spark 写入 ES

1、pom.xml

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-core_2.11</artifactId>
    <version>2.1.1</version>
</dependency>

<dependency>
    <groupId>org.apache.spark</groupId>
    <artifactId>spark-sql_2.11</artifactId>
    <version>2.1.1</version>
</dependency>

<dependency>
    <groupId>org.elasticsearch</groupId>
    <artifactId>elasticsearch-spark-20_2.11</artifactId>
    <version>6.0.0</version>
</dependency>

2、main 文件

package top.midworld.spark1031.create_df

import org.apache.spark.sql.SparkSession

import java.security.MessageDigest
import com.alibaba.fastjson.{JSON, JSONException, JSONObject}
import org.apache.spark.SparkConf
import org.elasticsearch.spark._

case class People(name: String, age: Int)

object SaveToEs {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder.appName("create_rdd").master("local[2]").getOrCreate()
    val sc = spark.sparkContext

    val conf = new SparkConf().setAppName("save_to_es")
    conf.set("es.nodes", "hadoop1:9200,hadoop2:9200,hadoop3:9200")
    conf.set("es.port", "9200")
    conf.set("es.index.auto.create", "true")

    // 将 Map 对象写入 es
    val aa = Map("one" -> 1, "two" -> 2, "three" -> 3, "id" -> 11111)
    val bb = Map("OTP" -> "Otopeni", "SFO" -> "San Fran", "id" -> 2222)
    sc.makeRDD(Seq(aa, bb)).saveToEs("index_name/docs", Map("es.mapping.id" -> "id")) // docs 是 doc_type

    // 将 case class对象写入ElasticSearch
    val p1 = People("rose", 18)
    val p2 = People("lila", 19)
    sc.makeRDD(Seq(p1, p2)).saveToEs("index_name/docs")

    // 以上都是通过隐士转换才有 saveToEs 方法插入 es，也可以采用显示方法
    import org.elasticsearch.spark.rdd.EsSpark

    val rdd_case_class = sc.makeRDD(Seq(p1, p2))
    EsSpark.saveJsonToEs(rdd_case_class, "index_name/docs")

    // 将Json字符串写入ElasticSearch
    val json1 = """{"id" : 1, "name" : "rose", "age" : "18"}"""
    val json2 = """{"id" : 2, "name" : "lila", "age" : "19"}"""
    sc.makeRDD(Seq(json1, json2)).saveJsonToEs("index_name/docs")

    // 自定义 es.mapping.id，不指定 es 也会生成唯一的 20 字符长度的 id
    // 第三个参数指定 es.mapping.id 为数据中的 id 字段
    sc.makeRDD(Seq(json1, json2)).saveJsonToEs("index_name/docs", Map("es.mapping.id" -> "id"))

    sc.stop()
    spark.stop()
  }
}