Flink与ElasticSearch集成_flinkdcd 自定义集成es

Flink与ElasticSearch集成

在生产中，经常会使用Flink和ES集成，将Flink处理后的数据存放在ES之中，我们了解Flink是个实时处理的框架，其输出Sink可以由用户自定义输出，所以就可以将Flink处理后的数据通过自定义的Sink输出到ES中。

1. 首先要添加相关的依赖

   <dependency>
       <groupId>org.apache.flink</groupId>
       <artifactId>flink-scala_2.12</artifactId>
       <version>1.12.0</version>
   </dependency>
   
   <dependency>
       <groupId>org.apache.flink</groupId>
       <artifactId>flink-streaming-scala_2.12</artifactId>
       <version>1.12.0</version>
   </dependency>
   
   <dependency>
       <groupId>org.apache.flink</groupId>
       <artifactId>flink-clients_2.12</artifactId>
       <version>1.12.0</version>
   </dependency>
   
   <dependency>
       <groupId>org.apache.flink</groupId>
       <artifactId>flink-connector-elasticsearch7_2.11</artifactId>
       <version>1.12.0</version>
   </dependency>
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2. 创建flink的执行环境

   StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
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3. 使用socket当作数据源

   DataStreamSource<String> source = env.socketTextStream("localhost", 9999);
   
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4. 创建ES连接

   List<HttpHost> httpHosts = new ArrayList<>();
   httpHosts.add(new HttpHost("127.0.0.1", 9200, "http"));
   ElasticsearchSink.Builder<String> esSinkBuilder = new ElasticsearchSink.Builder<>(
   	httpHosts, new ElasticsearchSinkFunction<String>() {
   		public IndexRequest createIndexRequest(String element) {
   			Map<String, String> json = new HashMap<>();
                json.put("data", element);
   			return Requests.indexRequest()
   			.index("my-index")
   			.source(json);
               }
               @Override
               public void process(String element, RuntimeContext ctx, RequestIndexer indexer) {
               indexer.add(createIndexRequest(element));
   		}
   	}
   );
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5. 将ES作为Sink

   source.addSink(esSinkBuilder.build());
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6. 执行

   env.execute("flink-es");
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