我们实时的流处理入 ElasticSearch 中还是比较麻烦的，虽然说 flink 提供了相关的 sink 接口，但是一般来说仅仅是简单的将数据插入而已，对于优化以及使用用户名和密码登录操作的话，不管官网还是网上，写得零零碎碎的，对于大佬来说可以拼接起来再用，但是对于像我这种菜鸟来说，那简直是看天书一样，一愣一愣的。今天写这个案例主要是项目中涉及了这个需求，废了半条命终于整理出来了，现在做个总结，以便避免初学者再掉坑。
废话不多说，接下来我们开搞。。。
一、启动服务器
[syy@nfdw elasticsearch-7.6.1]$ pwd
/opt/modules/elasticsearch-7.6.1
[syy@nfdw elasticsearch-7.6.1]$ bin/elasticsearch
[syy@nfdw kibana-7.6.1-linux-x86_64]$ pwd
/opt/modules/kibana-7.6.1-linux-x86_64
[syy@nfdw kibana-7.6.1-linux-x86_64]$ bin/kibana
登录 kibana 控制台：http://IP:5601/app/kibana#/dev_tools/console ,登录成功如下：
image.png
二、代码实现
2.1 添加依赖
org.apache.flink
flink-connector-elasticsearch7_2.11
${flink.version}
com.google.code.gson
gson
2.8.6
2.2 主体代码
public class App {
public static void main(String[] args) throws Exception {
// 获取环境对象
StreamExecutionEnvironment env = GetStreamExecutionEnvironment.getEnv();
//请求kafka数据
Properties prop = new Properties();
prop.setProperty("bootstrap.servers","cdh101:9092");
prop.setProperty("group.id","cloudera_mirrormaker");
prop.put("value.serializer","org.apache.kafka.common.serialization.StringSerializer");
FlinkKafkaConsumer011 myConsumer = new FlinkKafkaConsumer011("luchangyin", new SimpleStringSchema() ,prop);
myConsumer.setStartFromLatest(); //最近的
//请求kafka数据
DataStreamSource dataStream = env.addSource(myConsumer);
//dataStream.print(); // {"id":"226","name":"tang tang - 226","sal":280751,"dept":"美女部","ts":1615191802523}
SingleOutputStreamOperator result = dataStream.map(new MapFunction() {
@Override
public Employees map(String s) throws Exception {
Employees emp = MyJsonUtils.str2JsonObj(s);
emp.setEmpStartTime(new Date(emp.getTs()));
emp.setDt(MyDateUtils.getDate2Second(emp.getEmpStartTime()));
return emp;
}
});
//result.print();
// Employees(eId=257, eName=fei fei - 257, eSal=97674.0, eDept=美女部, ts=1615251002894, empStartTime=Tue Mar 09 08:50:02 GMT+08:00 2021, dt=2021-03-09)
// 设置ES的服务器地址
List esAddresses = null;
try {
esAddresses = ESSinkUtil.getEsAddresses("10.122.1.115:9200");
} catch (MalformedURLException e) {
e.printStackTrace();
}
// 我们可以通过调试此方法的三个数值参数进行优化
ESSinkUtil.addSink(esAddresses, "elastic", "123456", 100,100, 1,
5, result, new ElasticsearchSinkFunction() {
@Override
public void process(Employees emp, RuntimeContext runtimeContext, RequestIndexer requestIndexer) {
String indexStr = "employee_"+ MyDateUtils.getTime2Day(emp.getEmpStartTime()).replaceAll("-","");
System.out.println("索引为-> "+ indexStr);
requestIndexer.add(Requests.indexRequest()
.index(indexStr)
.source(GsonUtil.toJSONBytes(emp), XContentType.JSON));
}
});
env.execute("wo xi huan ni");
}
}
2.3 实现SinkEs的工具类：
package com.nfdw.utils;
import org.apache.commons.lang.StringUtils;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.connectors.elasticsearch.ElasticsearchSinkFunction;
import org.apache.flink.streaming.connectors.elasticsearch7.ElasticsearchSink;
import org.apache.http.HttpHost;
import java.net.MalformedURLException;
import java.net.URL;
import java.util.ArrayList;
import java.util.List;
public class ESSinkUtil {
/**
* es sink
*
* @param hosts es hosts
* @param bulkFlushMaxActions bulk flush size
* @param parallelism 并行数
* @param data 数据
* @param func
* @param
*/
public static void addSink(List esAddresses, String userName, String passwd, int bulkFlushMaxActions,
int bulkFlushMaxSizeMb, long bulkFlushInterval, int parallelism,
SingleOutputStreamOperator data, ElasticsearchSinkFunction func) {
//todo:xpack security
ElasticsearchSink.Builder esSinkBuilder = new ElasticsearchSink.Builder<>(esAddresses, func);
// 鉴权，正对写 es 需要密码的场景
if(StringUtils.isNotEmpty(userName) && StringUtils.isNotEmpty(passwd)){
esSinkBuilder.setRestClientFactory(new HDRestClientFactory(userName,passwd));
}
//失败处理策略
esSinkBuilder.setFailureHandler(new RetryRequestFailureHandler());
//bulk
esSinkBuilder.setBulkFlushMaxActions(bulkFlushMaxActions);
esSinkBuilder.setBulkFlushMaxSizeMb(bulkFlushMaxSizeMb);
esSinkBuilder.setBulkFlushInterval(bulkFlushInterval);
//-----------------------------------
data.addSink(esSinkBuilder.build()).setParallelism(parallelism);
}
/**
* 解析配置文件的 es hosts
*
* @param hosts
* @return
* @throws MalformedURLException
*/
public static List getEsAddresses(String hosts) throws MalformedURLException {
String[] hostList = hosts.split(",");
List addresses = new ArrayList<>();
for (String host : hostList) {
if (host.startsWith("http")) {
URL url = new URL(host);
addresses.add(new HttpHost(url.getHost(), url.getPort()));
} else {
String[] parts = host.split(":", 2);
if (parts.length > 1) {
addresses.add(new HttpHost(parts[0], Integer.parseInt(parts[1])));
} else {
throw new MalformedURLException("invalid elasticsearch hosts format");
}
}
}
return addresses;
}
}
2.4 设置密码操作类 HDRestClientFactory:
package com.nfdw.utils;
import org.apache.flink.streaming.connectors.elasticsearch7.RestClientFactory;
import org.apache.http.auth.AuthScope;
import org.apache.http.auth.UsernamePasswordCredentials;
import org.apache.http.client.CredentialsProvider;
import org.apache.http.client.config.RequestConfig;
import org.apache.http.impl.client.BasicCredentialsProvider;
import org.apache.http.impl.nio.client.HttpAsyncClientBuilder;
import org.elasticsearch.client.RestClientBuilder;
public class HDRestClientFactory implements RestClientFactory {
private String userName;
private String password;
transient CredentialsProvider credentialsProvider;
public HDRestClientFactory(String userName, String password) {
this.userName = userName;
this.password = password;
}
@Override
public void configureRestClientBuilder(RestClientBuilder restClientBuilder) {
if (credentialsProvider == null) {
credentialsProvider = new BasicCredentialsProvider();
credentialsProvider.setCredentials(AuthScope.ANY, new UsernamePasswordCredentials(userName, password));
}
restClientBuilder.setHttpClientConfigCallback(new RestClientBuilder.HttpClientConfigCallback() {
@Override
public HttpAsyncClientBuilder customizeHttpClient(HttpAsyncClientBuilder httpAsyncClientBuilder) {
return httpAsyncClientBuilder.setDefaultCredentialsProvider(credentialsProvider);
}
}).setRequestConfigCallback(new RestClientBuilder.RequestConfigCallback() {
@Override
public RequestConfig.Builder customizeRequestConfig(RequestConfig.Builder builder) {
builder.setConnectTimeout(5000);
builder.setSocketTimeout(60000);
builder.setConnectionRequestTimeout(2000);
return builder;
}
});
}
}
2.5 创建失败策列处理类 RetryRequestFailureHandler :
package com.nfdw.utils;
import lombok.extern.slf4j.Slf4j;
import org.apache.flink.streaming.connectors.elasticsearch.ActionRequestFailureHandler;
import org.apache.flink.streaming.connectors.elasticsearch.RequestIndexer;
import org.apache.flink.util.ExceptionUtils;
import org.elasticsearch.action.ActionRequest;
import org.elasticsearch.common.util.concurrent.EsRejectedExecutionException;
import java.io.IOException;
import java.net.SocketTimeoutException;
import java.util.Optional;
@Slf4j
public class RetryRequestFailureHandler implements ActionRequestFailureHandler {
public RetryRequestFailureHandler() {
}
@Override
public void onFailure(ActionRequest actionRequest, Throwable throwable, int i, RequestIndexer requestIndexer) throws Throwable {
if (ExceptionUtils.findThrowable(throwable, EsRejectedExecutionException.class).isPresent()) {
requestIndexer.add(new ActionRequest[]{actionRequest});
} else {
if (ExceptionUtils.findThrowable(throwable, SocketTimeoutException.class).isPresent()) {
return;
} else {
Optional exp = ExceptionUtils.findThrowable(throwable, IOException.class);
if (exp.isPresent()) {
IOException ioExp = exp.get();
if (ioExp != null && ioExp.getMessage() != null && ioExp.getMessage().contains("max retry timeout")) {
log.error(ioExp.getMessage());
return;
}
}
}
throw throwable;
}
}
}
2.6 创建一个 gson 解析类：
package com.nfdw.utils;
import com.google.gson.Gson;
import com.google.gson.GsonBuilder;
import java.lang.reflect.Type;
import java.nio.charset.Charset;
public class GsonUtil {
private final static Gson gson = new Gson();
private final static Gson disableHtmlEscapingGson = new GsonBuilder().disableHtmlEscaping().create();
public static T fromJson(String value, Class type) {
return gson.fromJson(value, type);
}
public static T fromJson(String value, Type type) {
return gson.fromJson(value, type);
}
public static String toJson(Object value) {
return gson.toJson(value);
}
public static String toJsonDisableHtmlEscaping(Object value) {
return disableHtmlEscapingGson.toJson(value);
}
public static byte[] toJSONBytes(Object value) {
return gson.toJson(value).getBytes(Charset.forName("UTF-8"));
}
}
三、运行程序结果查询如下
image.png
这里需要注意的一点是：sinkEs 的流必须是 SingleOutputStreamOperator 的对象，至于优化就是调节工具类中的那几个数值参数即可，好了，Flink 对 ES 的操作到此为止，希望能够帮助到你哦。。。