写一个flink的es sink,带密码验证的java代码

好的，我可以回答这个问题。以下是一个示例代码，用于将 Flink 流数据写入 Elasticsearch，并带有密码验证：

import org.apache.flink.streaming.api.functions.sink.SinkFunction;
import org.apache.flink.streaming.connectors.elasticsearch.ElasticsearchSink;
import org.apache.flink.streaming.connectors.elasticsearch6.ElasticsearchSinkBuilder;
import org.apache.flink.streaming.connectors.elasticsearch6.ElasticsearchSinkFunction;
import org.apache.http.HttpHost;
import org.elasticsearch.client.Requests;
import org.elasticsearch.common.xcontent.XContentType;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

public class FlinkESSink {

    public static void main(String[] args) throws Exception {
        // Elasticsearch 集群地址
        List<HttpHost> httpHosts = new ArrayList<>();
        httpHosts.add(new HttpHost("localhost", 920, "http"));

        // Elasticsearch 用户名和密码
        final String username = "your_username";
        final String password = "your_password";

        // Flink 流数据
        List<Map<String, Object>> data = new ArrayList<>();
        Map<String, Object> map1 = new HashMap<>();
        map1.put("name", "Alice");
        map1.put("age", 25);
        data.add(map1);
        Map<String, Object> map2 = new HashMap<>();
        map2.put("name", "Bob");
        map2.put("age", 30);
        data.add(map2);

        // ElasticsearchSinkFunction
        ElasticsearchSinkFunction<Map<String, Object>> elasticsearchSinkFunction =
                new ElasticsearchSinkFunction<Map<String, Object>>() {
                    public IndexRequest createIndexRequest(Map<String, Object> element) {
                        return Requests.indexRequest()
                                .index("my-index")
                                .type("_doc")
                                .source(element, XContentType.JSON);
                    }

                    @Override
                    public void process(Map<String, Object> element, RuntimeContext ctx, RequestIndexer indexer) {
                        indexer.add(createIndexRequest(element));
                    }
                };

        // ElasticsearchSink
        ElasticsearchSink.Builder<Map<String, Object>> esSinkBuilder =
                new ElasticsearchSink.Builder<>(httpHosts, elasticsearchSinkFunction);
        esSinkBuilder.setBulkFlushMaxActions(1); // 每条数据都刷新
        esSinkBuilder.setRestClientFactory(restClientBuilder -> {
            restClientBuilder.setMaxRetryTimeoutMillis(60000); // 超时时间
            restClientBuilder.setHttpClientConfigCallback(httpClientBuilder -> {
                httpClientBuilder.disableAuthCaching(); // 禁用缓存
                httpClientBuilder.setDefaultCredentialsProvider(new BasicCredentialsProvider() {{
                    setCredentials(AuthScope.ANY, new UsernamePasswordCredentials(username, password));
                }});
                return httpClientBuilder;
            });
        });

        // Flink 程序
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.fromCollection(data)
                .addSink(esSinkBuilder.build());

        env.execute("FlinkESSink");
    }
}