为Spark结构化流媒体编写ElasticsearchSink,可以通过以下步骤实现:
下面是一个示例代码:
import org.apache.spark.sql.streaming.StreamingQuery
import org.apache.spark.sql.{ForeachWriter, Row, SparkSession}
import org.elasticsearch.action.index.IndexRequest
import org.elasticsearch.client.{RequestOptions, RestHighLevelClient}
import org.elasticsearch.common.xcontent.XContentType
import org.elasticsearch.common.xcontent.json.JsonXContent
class ElasticsearchSink(elasticsearchHost: String, elasticsearchPort: Int, index: String, `type`: String) extends ForeachWriter[Row] {
private var client: RestHighLevelClient = _
override def open(partitionId: Long, version: Long): Boolean = {
// 创建Elasticsearch连接
client = new RestHighLevelClient(RestClient.builder(new HttpHost(elasticsearchHost, elasticsearchPort)))
true
}
override def process(row: Row): Unit = {
// 将数据写入Elasticsearch
val json = JsonXContent.contentBuilder().startObject()
for (i <- 0 until row.length) {
json.field(row.schema.fieldNames(i), row.get(i))
}
json.endObject()
val request = new IndexRequest(index, `type`).source(json, XContentType.JSON)
client.index(request, RequestOptions.DEFAULT)
}
override def close(errorOrNull: Throwable): Unit = {
// 关闭Elasticsearch连接
if (client != null) {
client.close()
}
}
}
object Main {
def main(args: Array[String]): Unit = {
val spark = SparkSession.builder()
.appName("Spark Elasticsearch Sink")
.master("local[*]")
.getOrCreate()
val data = spark.readStream
.format("kafka")
.option("kafka.bootstrap.servers", "localhost:9092")
.option("subscribe", "topic")
.load()
val query = data.writeStream
.foreach(new ElasticsearchSink("localhost", 9200, "index", "type"))
.start()
query.awaitTermination()
}
}
在上述示例代码中,我们创建了一个ElasticsearchSink类,实现了ForeachWriter接口,并在process方法中将数据写入Elasticsearch。然后,在main方法中,我们使用SparkSession创建了一个StreamingQuery,并将ElasticsearchSink应用到流式数据上。
注意:在实际使用中,需要根据具体的需求和环境进行配置和调整。此外,还可以根据需要添加异常处理、性能优化等功能。
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