前面简单的介绍了elasticsearch。现在开始使用，当我们面对海量数据的时候，如果想把数据导入到es，肯定不能想以前那样单条导入，我使用的是spark导入到es的批量导入。第三方依赖包：这里写链接内容
可以到这里下载相应的依赖包解压使用。PS：在使用的时候一定要记得spark和scala以及es的对应关系，我使用的是spark1.6.2 ，scala使用的是2.10.4，在使用的时spark-es的jar包elasticsearch-spark_2.10-2.3.3，该版本对应的编译的时候使用的是scala2.10.5.。开始没注意导致出现了一个问题：错误提示为：java.lang.NoSuchMethodError: scala.Predef$.ArrowAssoc(Ljava/lang/Object;)Ljava/lang/Object;
好了，下面开始介绍使用spark-es：
导入依赖包到spark环境变量：
spark使用es的时候，需要配置es的相应参数两种方式：
1）、val conf = new SparkConf( ).setAppName( "loadToES" ) conf.set("es.nodes", "192.168.0.23") conf.set("es.port", "9200") conf.set("es.cluster.name","elasticsearch") val sc = new SparkContext( conf )
2）、val configuration = Map("es.nodes"->"192.168.0.23","es.port"->"9200","es.cluster.name"->"elasticsearch")
spark使用es的时候分为导入和读取，spark分别提供如下两个接口：
EsSpark和EsSparkSQL，分别使用spark-rdd和spark-DataFrame的方式进行读取和写入
EsSpark-rdd读取：
1）def esRDD(sc: SparkContext): RDD[(String, Map[String, AnyRef])]
2）def esRDD(sc: SparkContext, resource: String, query: String, cfg: Map[String, String]): RDD[(String, Map[String, AnyRef])]
3）def esJsonRDD(sc: SparkContext, resource: String, query: String, cfg: Map[String, String]): RDD[(String, String)]
例如：sc.esRDD(“radio/artists”, “q=me*”)
查询出create an RDD streaming all the documents matching me* from index radio/artists

val conf = new JobConf()
conf.set(“es.resource”, “radio/artists”)
conf.set(“es.query”, “q=me*”)
val esRDD = sc.hadoopRDD(conf,
classOf[EsInputFormat[Text, MapWritable]],
classOf[Text], classOf[MapWritable]))
val docCount = esRDD.count();

val conf = new Configuration()
conf.set(“es.resource”, “radio/artists”)
conf.set(“es.query”, “q=me*”)
val esRDD = sc.newAPIHadoopRDD(conf,
classOf[EsInputFormat[Text, MapWritable]],
classOf[Text], classOf[MapWritable]))
val docCount = esRDD.count();

EsSpark-rdd批量写入：
1)def saveToEs(rdd: RDD[_], resource: String, cfg: Map[String, String])
2)def saveToEsWithMeta[K,V](rdd: RDD[(K,V)], resource: String, cfg: Map[String, String])
3)saveJsonToEs(rdd: RDD[_], resource: String, cfg: Map[String, String])

EsSpark-DataFrame批量写入：
1）def esDF(sc: SQLContext, resource: String, query: String)
// Spark 1.3 style
val df = sql.load(“spark/index”, “org.elasticsearch.spark.sql”)

// Spark 1.4 style
val df = sql.read.format(“org.elasticsearch.spark.sql”).load(“spark/index”)
// Spark 1.5 style
val df = sql.read.format(“es”).load(“spark/index”)
val sql = new SQLContext…
// options for Spark 1.3 need to include the target path/resource
val options13 = Map(“path” -> “spark/index”,
“pushdown” -> “true”,
“es.nodes” -> “someNode”, “es.port” -> “9200”)

// Spark 1.3 style
val spark13DF = sql.load(“org.elasticsearch.spark.sql”, options13)

// options for Spark 1.4 - the path/resource is specified separately
val options = Map(“pushdown” -> “true”, “es.nodes” -> “someNode”, “es.port” -> “9200”)

// Spark 1.4 style
val spark14DF = sql.read.format(“org.elasticsearch.spark.sql”)
.options(options).load(“spark/index”)

pushdown option - specific to Spark data sources

sqlContext.sql(
“CREATE TEMPORARY TABLE myIndex ” +
“USING org.elasticsearch.spark.sql ” +
“OPTIONS ( resource ‘spark/index’, nodes ‘spark/index’)” ) “

include

es.read.field.include = name, address.

exclude

es.read.field.exclude = *.created
2）

def saveToEs(srdd: DataFrame, resource: String, cfg: Map[String, String])

import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.SQLContext._

import org.elasticsearch.spark.sql._

val sqlContext = new SQLContext(sc)
case class Person(name: String, surname: String, age: Int)
val people = sc.textFile(“people.txt”)
.map(_.split(“,”))
.map(p => Person(p(0), p(1), p(2).trim.toInt))
.toDF()
people.saveToEs(“spark/people”)

使用案例：

使用自定义的_id:
方法一：
val rdd = sc.textFile("hdfs://master:9000/es.nb").map( x => (Hashing.md5().hashString( x,Charsets.UTF_8 ).asLong(),x))
    val relation = sqlContext.createDataFrame( rdd ).toDF("key","val")
    EsSparkSQL.saveToEs( relation,"es/info",configuration )
1
2
3
4
5

val otp = Map(“iata” -> “OTP”, “name” -> “Otopeni”)
val muc = Map(“iata” -> “MUC”, “name” -> “Munich”)
val sfo = Map(“iata” -> “SFO”, “name” -> “San Fran”)

// instance of SparkContext
val sc = …

val airportsRDD = sc.makeRDD(Seq((1, otp), (2, muc), (3, sfo)))
pairRDD.saveToEsWithMeta(airportsRDD, “airports/2015”)
此方式实现的是指定_id;

方法二：
import org.elasticsearch.spark.rdd.Metadata._ （1）

val otp = Map(“iata” -> “OTP”, “name” -> “Otopeni”)
val muc = Map(“iata” -> “MUC”, “name” -> “Munich”)
val sfo = Map(“iata” -> “SFO”, “name” -> “San Fran”)

// metadata for each document
// note it’s not required for them to have the same structure
val otpMeta = Map(ID -> 1, TTL -> “3h”) （2）
val mucMeta = Map(ID -> 2, VERSION -> “23”) （3）
val sfoMeta = Map(ID -> 3) （4）

// instance of SparkContext
val sc = …

val airportsRDD = sc.makeRDD(Seq((otpMeta, otp), (mucMeta, muc), (sfoMeta, sfo))) （5）
pairRDD.saveToEsWithMeta(airportsRDD, “airports/2015”) （6）
（1）导入这个包，即元数据的枚举类型
（2）ID with a value of 1 and TTL with a value of 3h

其他的方法大家可以尝试一下。小杨也是刚接触elasticsearch，处于摸索阶段，希望大家多多指导。欢迎来访，qq：791279468