写一个flink的es sink,带密码验证
时间: 2023-03-31 18:02:00 浏览: 311
好的,我可以回答这个问题。您可以按照以下步骤编写一个带密码验证的 Flink 的 Elasticsearch Sink:
1. 首先,您需要在 Maven 中添加 Elasticsearch 的依赖项,以便在代码中使用 Elasticsearch 的 API。
2. 接下来,您需要创建一个 Elasticsearch 的连接,包括 Elasticsearch 的主机名、端口号和用户名/密码等信息。
3. 然后,您可以使用 Flink 的 Elasticsearch Sink API,将数据写入 Elasticsearch 中。在创建 Elasticsearch Sink 时,您需要指定 Elasticsearch 的索引名称、类型和 ID 等信息。
4. 最后,您可以在 Elasticsearch Sink 中添加密码验证,以确保只有授权用户才能访问 Elasticsearch。
需要注意的是,具体实现方式可能因 Elasticsearch 版本和 Flink 版本而异,您需要根据实际情况进行调整。
相关问题
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");
}
}
```
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