spark与Elasticsearch整合

maven添加依赖

   
   
       org.apache.spark
       spark-core_2.11
       2.4.3
   
   
   
       org.apache.spark
       spark-sql_2.11
       2.4.3
   
   
   
       org.apache.spark
       spark-streaming_2.11
       2.4.3
   
   
   
       org.apache.spark
       spark-mllib_2.11
       2.4.3
   
   
   
       com.alibaba
       fastjson
       1.2.58
   

   
       org.elasticsearch
       elasticsearch-spark-20_2.11
       7.2.0
   

spark 从 Elasticsearch读取数据

es官方文档

import org.apache.spark.{SparkConf, SparkContext}
import org.elasticsearch.spark._

object ESToSpark {
  def main(args: Array[String]): Unit = {

    val conf = new SparkConf().setAppName("hello world").setMaster("local[*]")
    conf.set("es.index.auto.create", "true")
    conf.set("es.nodes","127.0.0.1")
    conf.set("es.port","9200")

    val sc = new SparkContext(conf)
    val query: String =
      s"""{
           "query": {
             "term": {
               "name": {
                 "value": "鲁仲连"
               }
             }
           }
      }"""
    val rdd = sc.esRDD("phonebills",query)
    rdd.collect().foreach(println)
    println(rdd.count()+" -----------")
    sc.stop()
  }
}

spark 向 Elasticsearch中写入数据

1.先在es中新建索引指定字段

spark字段	es 字段
String	text/keyword
Long	long
String	text/keyword
Integer	number
String/Long	date
Double	number
Int	number

其中向es中写入date类型时，需要先创建索引并指定mapping的format，不然会被当做text类型处理

PUT xxx
{
  "mappings": {
    "properties": {
      "a": {
        "type": "keyword"
      },
      "b": {
        "type": "keyword"
      },
      "c": {
        "type": "long"
      },
      "time": {
        "type": "date",
        "format":["yyyy-MM-dd HH:mm:ss"]
      },
      "d": {
        "type": "text"
      },
      "e": {
        "type": "keyword"
      },
      "f": {
        "type": "text"
      },
      "g": {
        "type": "keyword"
      }
    }
  }
}

    val spark = SparkSession.builder()
      .master("local[8]")
      .config("es.index.auto.create", "true")
      .config("es.nodes", "127.0.0.1")
      .config("es.port", "9200")
      .appName("log")
      .getOrCreate()
    val sc = spark.sparkContext
    // val rdd = sc.textFile(path)
    // case class xx()
    // val log = rdd.map(x=>xx())
    val rlog = spark.createDataFrame(log)
    rlog.saveToEs("dblog")