日志
spark-sql与elasticsearch整合&测试
1. 前置条件
spark是1.4.1版本
elasticsearch是1.7版本
java是1.7版本
2. 依赖jar包
需要使用elasticsearch-hadoop
下载地址：http://mvnrepository.com/artifact/org.elasticsearch/elasticsearch-hadoop/2.2.0-m1
3. 配置
将下载的elasticsearch-hadoop包放置到$SPARK_HOME/lib/下
ls -lh lib
-rw-r----- 1 wangyue wangyue 692K 11月 9 17:49 elasticsearch-hadoop-2.2.0-m1.jar
-rw-rw-r-- 1 wangyue wangyue 946K 9月 10 14:22 mysql-connector-java-5.1.35.jar
配置$SPARK_HOME/conf/spark-env.sh
SPARK_CLASSPATH增加elasticsearch-hadoop-2.2.0-m1.jar包
4. spark-sql写数据到elasticsearch
vim /home/cluster/data/test/people.txt，增加如下测试内容：
zhang,san,20
li,si,30
wang,wu,40
li,bai,100
du,fu,101
启动spark-shell增加如下代码：
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.SQLContext._
import org.elasticsearch.spark.sql._
import org.apache.spark.rdd.RDD._
import sqlContext.implicits._
//创建sqlContext
val sqlContext = new SQLContext(sc)
//定义Person case class
case class Person(name: String, surname: String, age: Int)
//创建DataFrame
val people = sc.textFile("file:///home/cluster/data/test/people.txt").map(_.split(",")).map(p => Person(p(0), p(1), p(2).trim.toInt)).toDF()
people.saveToEs("spark/people")
查询插入到elasticsearch数据：
GET /spark/people/_search
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 5,
"max_score": 1,
"hits": [
{
"_index": "spark",
"_type": "people",
"_id": "AVDrt5MyJpFYJkWM4nwP",
"_score": 1,
"_source": {
"name": "zhang",
"surname": "san",
"age": 20
}
},
{
"_index": "spark",
"_type": "people",
"_id": "AVDrt5SEJpFYJkWM4nwS",
"_score": 1,
"_source": {
"name": "li",
"surname": "bai",
"age": 100
}
},
{
"_index": "spark",
"_type": "people",
"_id": "AVDrt5MyJpFYJkWM4nwR",
"_score": 1,
"_source": {
"name": "wang",
"surname": "wu",
"age": 40
}
},
{
"_index": "spark",
"_type": "people",
"_id": "AVDrt5MyJpFYJkWM4nwQ",
"_score": 1,
"_source": {
"name": "li",
"surname": "si",
"age": 30
}
},
{
"_index": "spark",
"_type": "people",
"_id": "AVDrt5SEJpFYJkWM4nwT",
"_score": 1,
"_source": {
"name": "du",
"surname": "fu",
"age": 101
}
}
]
}
}
5. spark-sql读取elasticsearch数据
Name Default value Description
path Elasticsearch index/type required
pushdown true Whether to translate (push-down) Spark SQL into Elasticsearch
strict false Whether to use exact (not analyzed) matching or not (analyzed)
load或read方式读取：
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.SQLContext._
val sqlContext = new SQLContext(sc)
// options for Spark 1.3 need to include the target path/resource
val options13 = Map("path" -> "spark/people",
"pushdown" -> "true",
"es.nodes" -> "localhost","es.port" -> "9200")
// Spark 1.3 style
val spark13DF = sqlContext.load("org.elasticsearch.spark.sql", options13)
// options for Spark 1.4 - the path/resource is specified separately
val options = Map("pushdown" -> "true", "es.nodes" -> "localhost", "es.port" -> "9200")
// Spark 1.4 style
val spark14DF = sqlContext.read.format("org.elasticsearch.spark.sql").options(options).load("spark/people")
查询name,age:
spark14DF.select("name","age").collect().foreach(println(_))
[zhang,20]
[li,100]
[wang,40]
[li,30]
[du,101]
注册临时表＆查询name
spark14DF.registerTempTable("people")
val results = sqlContext.sql("SELECT name FROM people")
results.map(t => "Name: " + t(0)).collect().foreach(println)
Name: zhang
Name: li
Name: wang
Name: li
Name: du
读取elasticsearch数据并创建临时表myPeople
sqlContext.sql(
"CREATE TEMPORARY TABLE myPeople " +
"USING org.elasticsearch.spark.sql " +
"OPTIONS ( resource 'spark/people', nodes 'localhost:9200')" )
sqlContext.sql("select * from myPeople").collect.foreach(println)
[20,zhang,san]
[100,li,bai]
[40,wang,wu]
[30,li,si]
[101,du,fu]
sqlContext.sql(
"CREATE TEMPORARY TABLE myPeople " +
"USING org.elasticsearch.spark.sql " +
"OPTIONS ( resource 'spark/people', nodes 'localhost:9200',scroll_size '20')" )
因为使用.会导致语法异常,应该用_风格代替它. 因此,在这个例子中es.scroll.size 变成 scroll_size(由于加载数据时候es可以删除)。注意这只工作在spark1.3／1.4的spark有更严格的解析器
esDF方式读取elasticsearch数据：
import org.apache.spark.sql.SQLContext
import org.elasticsearch.spark.sql._
val sqlContext = new SQLContext(sc)
val people = sqlContext.esDF("spark/people")
// check the associated schema
println(people.schema.treeString)
root
|-- age: long (nullable = true)
|-- name: string (nullable = true)
|-- surname: string (nullable = true)
// get only the wang
val wangs = sqlContext.esDF("spark/people","?q=wang" )
wangs.show()
+---+----+-------+
|age|name|surname|
+---+----+-------+
| 40|wang| wu|
+---+----+-------+
参考文章：
https://www.elastic.co/guide/en/elasticsearch/hadoop/master/spark.html#spark-sql
作者:stark_summer
出处：http://blog.csdn.net/stark_summer/article/details/49743687
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