基于FLink实现实时安全检测的示例代码

目录

• 研发背景
• 场景描述
• 组件版本
• 日志结构
• 技术方案
• 关键代码
• 主入口类
• mapper算子
• filter算子
• keyBy算子
• 窗口函数（核心代码）
• 最后一次map算子
• ElasticSearch工具类
• 事件实体类
• 消息实体类

研发背景

公司安全部目前针对内部系统的网络访问日志的安全审计，大部分都是T+1时效，每日当天，启动Python编写的定时任务，完成昨日的日志审计和检测，定时任务运行完成后，统一进行企业微信告警推送。这种方案在目前的网络环境和人员规模下，呈现两个痛点，一是面对日益频繁的网络攻击、钓鱼链接，T+1的定时任务，难以及时进行告警，因此也难以有效避免如关键信息泄露等问题，二是目前以Python为主的单机定时任务，针对不同场景的处理时效，从一小时到十几小时不等，效率低下。为解决以上问题，本人协助公司安全部同时对告警采集平台进行改造，由之前的python单机任务处理，切换到基于Flink集群的并行处理，且告警推送时效，由之前的T+1天，提升到秒级实时告警。本次改造涉及网络日志审计的多个常见场景，如端口扫描、黑名单统计、异常流量、连续恶意登录等。本次以一段时间内连续登录失败20次后，下一次登录成功场景来进行介绍。

场景描述

针对一个内部系统，如邮件系统，公司员工的访问行为日志，存放于kafka，我们希望对于一个用户账号在同一个IP下，任意的3分钟时间内，连续登录邮件系统20次失败，下一次登录成功，这种场景能够及时获取并推送到企业微信某个指定的安全接口人。kafka中的数据，能够通过某个关键字，区分当前网络访问是否一次登录事件，且有访问时间（也就是事件时间）。在解析到符合需求的用户账号之后，第一时间进行企业微信告警推送，并将其这段时间内的访问行为，写入下游ElasticSearch。

组件版本

• Flink-1.14.4
• Java8
• ElasticSearch-7.3.2
• Kafka-2.12_2.8.1

日志结构

IP和账号皆为测试使用。

{
   "user": "wangxm",
   "client_ip": "110.68.6.182",
   "source": "login",
   "loginname": "wangxm@test.com",
   "IP": "110.8.148.58",
   "timestamp": "17:58:12",
   "@timestamp": "2022-04-20T09:58:13.647Z",
   "ip": "110.7.231.25",
   "clienttype": "POP3",
   "result": "success",
   "@version": "1"
 }

技术方案

上述场景，可考虑使用FlinkCEP及Flink的滑动窗口进行实现。由于本人在采用FlinkCEP的方案进行代码编写调试后，发现并不能满足，因此改用滑动窗口进行实现。

关键代码

主入口类

主入口类，创建了flink环境、设置了基础参数，创建了kafkaSource,接入消息后，进行了映射、过滤，并设置了水位线，进行了分组，之后设置了滑动窗口，在窗口内进行了事件统计，将复合条件的事件收集返回并写入ElasticSearch。

针对map、filter、keyBy、window等算子，都单独进行了编写，后面会一一列出来。

package com.data.dev.flink.mailTopic.main;

import com.data.dev.common.javabean.BaseBean;
import com.data.dev.common.javabean.kafkaMailTopic.MailMsgAlarm;
import com.data.dev.elasticsearch.ElasticSearchInfo;
import com.data.dev.elasticsearch.SinkToEs;
import com.data.dev.flink.FlinkEnv;
import com.data.dev.flink.mailTopic.OperationForLoginFailCheck.*;
import com.data.dev.kafka.KafkaSourceBuilder;
import com.data.dev.key.ConfigurationKey;
import com.data.dev.utils.TimeUtils;
import lombok.extern.slf4j.Slf4j;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.connector.kafka.source.KafkaSource;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.windowing.assigners.SlidingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;

import java.time.Duration;

/**
 * Flink处理在3分钟内连续登录失败20次后登录成功的场景
 * 采用滑动窗口来实现
 * @author wangxiaomin 2022-06-01
 */

@Slf4j
public class MailMsg extends BaseBean {

    /**
     * Flink作业名称
     */
    public static final  String JobName = "告警采集平台——连续登录失败后登录成功告警";
    /**
     * Kafka消息名
     */
    public static final  String KafkaSourceName = "Kafka Source for AlarmPlatform About Mail Topic";

    public MailMsg(){
        log.info("初始化滑动窗口场景告警程序");
    }

    /**
     * 执行逻辑统计场景，实现告警推送
     */
    public static void execute(){

        //① 创建Flink执行环境并设置checkpoint等必要的参数
        StreamExecutionEnvironment env = FlinkEnv.getFlinkEnv();
        KafkaSource<String> kafkaSource = KafkaSourceBuilder.getKafkaSource(ConfigurationKey.KAFKA_MAIL_TOPIC_NAME,ConfigurationKey.KAFKA_MAIL_CONSUMER_GROUP_ID) ;
        DataStreamSource<String> kafkaMailMsg = env.fromSource(kafkaSource, WatermarkStrategy.forBoundedOutOfOrderness(Duration.ofMillis(10)), KafkaSourceName);

        //② 筛选登录消息，创建初始登录事件流
        SingleOutputStreamOperator<com.data.dev.common.javabean.kafkaMailTopic.MailMsg> loginMapDs = kafkaMailMsg.map(new MsgToBeanMapper()).name("Map算子加工");
        SingleOutputStreamOperator<com.data.dev.common.javabean.kafkaMailTopic.MailMsg> loginFilterDs = loginMapDs.filter(new MailMsgForLoginFilter()).name("Filter算子加工");

        //③ 设置水位线
        WatermarkStrategy<com.data.dev.common.javabean.kafkaMailTopic.MailMsg> watermarkStrategy = WatermarkStrategy.<com.data.dev.common.javabean.kafkaMailTopic.MailMsg>forBoundedOutOfOrderness(Duration.ofMinutes(1))
                        .withTimestampAssigner((mailMsg, timestamp) -> TimeUtils.switchUTCToBeijingTimestamp(mailMsg.getTimestamp_datetime()));
        SingleOutputStreamOperator<com.data.dev.common.javabean.kafkaMailTopic.MailMsg> loginWmDs = loginFilterDs.assignTimestampsAndWatermarks(watermarkStrategy.withIdleness(Duration.ofMinutes(3))).name("增加水位线");

        //④ 设置主键
        KeyedStream<com.data.dev.common.javabean.kafkaMailTopic.MailMsg, String> loginKeyedDs = loginWmDs.keyBy(new LoginKeySelector());

        //⑥ 转化为滑动窗口
        WindowedStream<com.data.dev.common.javabean.kafkaMailTopic.MailMsg, String, TimeWindow> loginWindowDs = loginKeyedDs.window(SlidingEventTimeWindows.of(Time.seconds(180L),Time.seconds(90L)));

        //⑦ 在窗口内进行逻辑统计
        SingleOutputStreamOperator<MailMsgAlarm> loginWindowsDealDs  = loginWindowDs.process(new WindowProcessFuncImpl()).name("窗口处理逻辑");

        //⑧ 将结果转化为通用DataStream<String>格式
        SingleOutputStreamOperator<String> resultDs  = loginWindowsDealDs.map(new AlarmMsgToStringMapper()).name("窗口结果转化为标准格式");

        //⑨ 将最终结果写入ES
        resultDs.addSink(SinkToEs.getEsSinkBuilder(ElasticSearchInfo.ES_LOGIN_FAIL_INDEX_NAME,ElasticSearchInfo.ES_INDEX_TYPE_DEFAULT).build());

        //⑩ 提交Flink集群进行执行
        FlinkEnv.envExec(env,JobName);

    }
}

mapper算子

package com.data.dev.flink.mailTopic.OperationForLoginFailCheck;

import com.alibaba.fastjson.JSON;
import com.data.dev.common.javabean.BaseBean;
import com.data.dev.common.javabean.kafkaMailTopic.MailMsgAlarm;
import lombok.extern.slf4j.Slf4j;
import org.apache.flink.api.common.functions.MapFunction;

/**
 *  逻辑统计场景告警推送ES消息体
 *  @author wangxiaoming-ghq 2022-06-01
 */
@Slf4j
public   class AlarmMsgToStringMapper extends BaseBean implements MapFunction<MailMsgAlarm, String> {

    @Override
    public String map(MailMsgAlarm mailMsgAlarm) throws Exception {
        return JSON.toJSONString(mailMsgAlarm);
    }
}

filter算子

package com.data.dev.flink.mailTopic.OperationForLoginFailCheck;

import com.data.dev.common.javabean.BaseBean;
import com.data.dev.common.javabean.kafkaMailTopic.MailMsg;
import lombok.extern.slf4j.Slf4j;
import org.apache.flink.api.common.functions.FilterFunction;

/**
 * ②　消费mail主题的消息，过滤其中login的事件
 * @author wangxiaoming-ghq 2022-06-01
 */
@Slf4j
public class MailMsgForLoginFilter extends BaseBean implements FilterFunction<MailMsg> {
    @Override
    public boolean filter(MailMsg mailMsg) {
        if("login".equals(mailMsg.getSource())) {
            log.info("筛选原始的login事件：【" + mailMsg + "】");
        }
        return "login".equals(mailMsg.getSource());
    }
}

keyBy算子

package com.data.dev.flink.mailTopic.OperationForLoginFailCheck;

import com.data.dev.common.javabean.BaseBean;
import com.data.dev.common.javabean.kafkaMailTopic.MailMsg;
import lombok.extern.slf4j.Slf4j;
import org.apache.flink.api.java.functions.KeySelector;

/**
 * CEP 编程，需要进行key选取
 */
@Slf4j
public class LoginKeySelector extends BaseBean implements KeySelector<MailMsg, String> {
    @Override
    public String getKey(MailMsg mailMsg) {
        return mailMsg.getUser() + "@" + mailMsg.getClient_ip();
    }
}

窗口函数（核心代码）

这里我们主要考虑使用一个事件列表，用来存储每一个窗口期内得到的连续登录，当检测到登陆失败的事件，即存入事件列表中，之后判断下一次登录失败事件，如果检测到登录成功事件，但此时登录失败的次数不足20次，则清空loginEventList，等待下一次检测。一旦符合窗口内连续登录失败超过20次且下一次登录成功这个事件，则清空此时的loginEventList并将当前登录成功的事件进行告警推送。

package com.data.dev.flink.mailTopic.OperationForLoginFailCheck;

import com.data.dev.common.javabean.kafkaMailTopic.MailMsg;
import com.data.dev.common.javabean.kafkaMailTopic.MailMsgAlarm;
import com.data.dev.utils.HttpUtils;
import com.data.dev.utils.IPUtils;
import lombok.extern.slf4j.Slf4j;
import org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;

import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;

/**
 *  滑动窗口内复杂事件解析逻辑实现
 *  @author wangxiaoming-ghq 2022-06-01
 */
@Slf4j
public   class WindowProcessFuncImpl extends  ProcessWindowFunction<MailMsg, MailMsgAlarm, String, TimeWindow> implements Serializable {
    @Override
    public void process(String key, ProcessWindowFunction<MailMsg, MailMsgAlarm, String, TimeWindow>.Context context, Iterable<MailMsg> iterable, Collector<MailMsgAlarm> collector) {

        List<MailMsg> loginEventList = new ArrayList<>();
        MailMsgAlarm mailMsgAlarm;
        for (MailMsg mailMsg : iterable) {
            log.info("收集到的登录事件【" + mailMsg + "】");

            if (mailMsg.getResult().equals("fail")) { //开始检测当前窗口内的事件，并将失败的事件收集到loginEventList
                log.info("开始检测当前窗口内的事件，并将失败的事件收集到loginEventList");
                loginEventList.add(mailMsg);
            } else if (mailMsg.getResult().equals("success") && loginEventList.size() < 20) {//如果检测到登录成功事件，但此时登录失败的次数不足20次，则清空loginEventList，等待下一次检测
                log.info("检测到登录成功事件，但此时登录失败的次数为【" + loginEventList.size() + "】不足20次，清空loginEventList，等待下一次检测");
                loginEventList.clear();
            } else if (mailMsg.getResult().equals("success") && loginEventList.size() >= 20) {
                mailMsgAlarm = getMailMsgAlarm(loginEventList,mailMsg);
                log.info("检测到登录成功的事件,此时窗口内连续登录失败的次数为【" + mailMsgAlarm.getFailTimes() + "】");

                //一旦符合窗口内连续登录失败超过20次且下一次登录成功这个事件，则清空此时的loginEventList并将当前登录成功的事件进行告警推送；
                loginEventList.clear();
                doAlarmPush(mailMsgAlarm);

                collector.collect(mailMsgAlarm);//将当前登录成功的事件进行收集上报
            } else {
                log.info(mailMsg.getUser() + "当前已连续：【" + loginEventList.size() + "】 次登录失败");
            }
        }
    }

    /**
     * 2022年6月17日15:03:06
     * @param eventList:当前窗口内的事件列表
     * @param eventCurrent：当前登录成功的事件
     * @return mailMsgAlarm：告警消息体
     */
    public static MailMsgAlarm getMailMsgAlarm(List<MailMsg> eventList,MailMsg eventCurrent){

        String alarmKey = eventCurrent.getUser() + "@" + eventCurrent.getClient_ip();
        String loginFailStartTime = eventList.get(0).getTimestamp_datetime();
        String loginSuccessTime = eventCurrent.getTimestamp_datetime();
        int loginFailTimes = eventList.size();

        MailMsgAlarm mailMsgAlarm = new MailMsgAlarm();
        mailMsgAlarm.setMailMsg(eventCurrent);
        mailMsgAlarm.setAlarmKey(alarmKey);
        mailMsgAlarm.setStartTime(loginFailStartTime);
        mailMsgAlarm.setEndTime(loginSuccessTime);
        mailMsgAlarm.setFailTimes(loginFailTimes);

        return mailMsgAlarm;
    }

    /**
     * 2022年6月17日14:47:53
     * @param mailMsgAlarm :当前构建的需要告警的事件
     */
    public void doAlarmPush(MailMsgAlarm mailMsgAlarm){
        String userKey = mailMsgAlarm.getAlarmKey();
        String clientIp = mailMsgAlarm.mailMsg.getClient_ip();
        boolean isWhiteListIp = IPUtils.isWhiteListIp(clientIp);
        if(isWhiteListIp){//如果是白名单IP，不告警
            log.info("当前登录用户【" + userKey + "】属于白名单IP");
        }else {
            //IP归属查询结果、企业微信推送告警
            String user = HttpUtils.getUserByClientIp(clientIp);
            HttpUtils.pushAlarmMsgToWechatWork(user,mailMsgAlarm.toString());
        }
    }
}

最后一次map算子

package com.data.dev.flink.mailTopic.OperationForLoginFailCheck;

import com.alibaba.fastjson.JSON;
import com.data.dev.common.javabean.BaseBean;
import com.data.dev.common.javabean.kafkaMailTopic.MailMsgAlarm;
import lombok.extern.slf4j.Slf4j;
import org.apache.flink.api.common.functions.MapFunction;

/**
 *  逻辑统计场景告警推送ES消息体
 *  @author wangxiaoming-ghq 2022-06-01
 */
@Slf4j
public   class AlarmMsgToStringMapper extends BaseBean implements MapFunction<MailMsgAlarm, String> {

    @Override
    public String map(MailMsgAlarm mailMsgAlarm) throws Exception {
        return JSON.toJSONString(mailMsgAlarm);
    }
}

ElasticSearch工具类

package com.data.dev.elasticsearch;

import com.data.dev.common.javabean.BaseBean;
import com.data.dev.key.ConfigurationKey;
import com.data.dev.key.ElasticSearchKey;
import lombok.extern.slf4j.Slf4j;
import org.apache.flink.api.common.functions.RuntimeContext;
import org.apache.flink.streaming.connectors.elasticsearch.ElasticsearchSinkFunction;
import org.apache.flink.streaming.connectors.elasticsearch.RequestIndexer;
import org.apache.flink.streaming.connectors.elasticsearch7.ElasticsearchSink;
import org.apache.flink.streaming.connectors.elasticsearch7.RestClientFactory;
import org.apache.http.HttpHost;
import org.apache.http.auth.AuthScope;
import org.apache.http.auth.UsernamePasswordCredentials;
import org.apache.http.client.CredentialsProvider;
import org.apache.http.impl.client.BasicCredentialsProvider;
import org.elasticsearch.action.index.IndexRequest;
import org.elasticsearch.client.Requests;

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

/**
 * 2022年6月17日15:15:06
 * @author wangxiaoming-ghq
 * Flink流计算结果写入ES公共方法
 */
@Slf4j
public class SinkToEs extends BaseBean {
    public static final long serialVersionUID = 2L;
    private static final HashMap<String,String> ES_PROPS_MAP = ConfigurationKey.getApplicationProps();
    private static final String HOST = ES_PROPS_MAP.get(ConfigurationKey.ES_HOST);
    private static final String PASSWORD = ES_PROPS_MAP.get(ConfigurationKey.ES_PASSWORD);
    private static final String USERNAME = ES_PROPS_MAP.get(ConfigurationKey.ES_USERNAME);
    private static final String PORT = ES_PROPS_MAP.get(ConfigurationKey.ES_PORT);

    /**
     * 2022年6月17日15:17:55
     * 获取ES连接信息
     * @return esInfoMap:ES连接信息持久化
     */
    public static HashMap<String,String > getElasticSearchInfo(){
        log.info("获取ES连接信息：【 " + "HOST="+HOST + "PORT="+PORT+"USERNAME="+USERNAME+"PASSWORD=********" + " 】");
        HashMap<String,String> esInfoMap = new HashMap<>();
        esInfoMap.put(ElasticSearchKey.HOST,HOST);
        esInfoMap.put(ElasticSearchKey.PASSWORD,PASSWORD);
        esInfoMap.put(ElasticSearchKey.USERNAME,USERNAME);
        esInfoMap.put(ElasticSearchKey.PORT,PORT);

        return esInfoMap;
    }

    /**
     * @param esIndexName:写入索引名称
     * @param esType：写入索引类型
     * @return ElasticsearchSink.Builder<String>：构建器
     */
    public static ElasticsearchSink.Builder<String> getEsSinkBuilder(String esIndexName,String esType){
        HashMap<String, String> esInfoMap = getElasticSearchInfo();
        List<HttpHost> httpHosts = new ArrayList<>();
        httpHosts.add(new HttpHost(String.valueOf(esInfoMap.get(ElasticSearchKey.HOST)), Integer.parseInt(esInfoMap.get(ElasticSearchKey.PORT)), "http"));

        ElasticsearchSink.Builder<String> esSinkBuilder = new ElasticsearchSink.Builder<>(
                httpHosts,
                new ElasticsearchSinkFunction<String>() {

                    public IndexRequest createIndexRequest() {
                        Map<String, String> json = new HashMap<>();
                        //log.info("写入ES的data:【"+json+"】");
                        IndexRequest index  = Requests.indexRequest()
                                .index(esIndexName)
                                .type(esType)
                                .source(json);
                        return index;
                    }

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

        //定义es的连接配置  带用户名密码
        RestClientFactory restClientFactory = restClientBuilder -> {
            CredentialsProvider credentialsProvider = new BasicCredentialsProvider();
            credentialsProvider.setCredentials(
                    AuthScope.ANY,
                    new UsernamePasswordCredentials(
                            String.valueOf(esInfoMap.get(ElasticSearchKey.USERNAME)),
                            String.valueOf(esInfoMap.get(ElasticSearchKey.PASSWORD))
                    )
            );
            restClientBuilder.setHttpClientConfigCallback(httpAsyncClientBuilder -> {
                httpAsyncClientBuilder.disableAuthCaching();
                return httpAsyncClientBuilder.setDefaultCredentialsProvider(credentialsProvider);
            });
        };

        esSinkBuilder.setRestClientFactory(restClientFactory);
        return esSinkBuilder;
    }

}

事件实体类

package com.data.dev.common.javabean.kafkaMailTopic;

import com.data.dev.common.javabean.BaseBean;
import lombok.Data;

import java.util.Objects;

/**
 * @author wangxiaoming-ghq 2022-05-15
 * 逻辑统计场景告警事件
 */
@Data
public class MailMsgAlarm extends BaseBean {

    /**
     * 当前登录成功的事件
     */
   public  MailMsg mailMsg;

    /**
     * 当前捕获的告警主键：username@client_ip
     */
   public  String alarmKey;

    /**
     * 第一次登录失败的事件时间
     */
   public  String startTime;

    /**
     * 连续登录失败后下一次登录成功的事件时间
     */
   public  String endTime;

    /**
     * 连续登录失败的次数
     */
   public  int failTimes;

    @Override
    public String toString() {
        return "{" +
                "  'mailMsg_login_success':'" + mailMsg + "'" +
                ", 'alarmKey':'" + alarmKey + "'" +
                ", 'start_login_time_in3min':'"  +startTime + "'" +
                ", 'end_login_time_in3min':'"  +endTime + "'" +
                ", 'login_fail_times':'"  +failTimes +  "'" +
                "}";
    }

    public MailMsgAlarm() {
    }

    @Override
    public boolean equals(Object o) {
        if (this == o) return true;
        if (!(o instanceof MailMsgAlarm)) return false;
        MailMsgAlarm that = (MailMsgAlarm) o;
        return getFailTimes() == that.getFailTimes() && getMailMsg().equals(that.getMailMsg()) && getAlarmKey().equals(that.getAlarmKey()) && getStartTime().equals(that.getStartTime()) && getEndTime().equals(that.getEndTime());
    }

    @Override
    public int hashCode() {
        return Objects.hash(getMailMsg(), getAlarmKey(), getStartTime(), getEndTime(), getFailTimes());
    }
}

消息实体类

package com.data.dev.common.javabean.kafkaMailTopic;

import com.data.dev.common.javabean.BaseBean;
import lombok.Data;

import java.util.Objects;

/**
 * {
 *   "user": "wangxm",
 *   "client_ip": "110.68.6.182",
 *   "source": "login",
 *   "loginname": "wangxm@test.com",
 *   "IP": "110.8.148.58",
 *   "timestamp": "17:58:12",
 *   "@timestamp": "2022-04-20T09:58:13.647Z",
 *   "ip": "110.7.231.25",
 *   "clienttype": "POP3",
 *   "result": "success",
 *   "@version": "1"
 * }
 *
 * user登录用户
 * client_ip 来源ip
 * source 类型
 * loginname 登录用户邮箱地址
 * ip 目标前端ip
 * timestamp 发送时间
 * @timestamp  发送日期时间
 * IP 邮件日志发送来源IP
 * clienttype 客户端登录类型
 * result 登录状态
 */

@Data
public class MailMsg extends BaseBean {
    public String user;
    public String client_ip;
    public String source;
    public String loginName;
    public String mailSenderSourceIp;
    public String timestamp_time;
    public String timestamp_datetime;
    public String ip;
    public String clientType;
    public String result;
    public String version;

    public MailMsg() {
    }

    public MailMsg(String user, String client_ip, String source, String loginName, String mailSenderSourceIp, String timestamp_time, String timestamp_datetime, String ip, String clientType, String result, String version) {
        this.user = user;
        this.client_ip = client_ip;
        this.source = source;
        this.loginName = loginName;
        this.mailSenderSourceIp = mailSenderSourceIp;
        this.timestamp_time = timestamp_time;
        this.timestamp_datetime = timestamp_datetime;
        this.ip = ip;
        this.clientType = clientType;
        this.result = result;
        this.version = version;
    }

    @Override
    public boolean equals(Object o) {
        if (this == o) return true;
        if (!(o instanceof MailMsg)) return false;
        MailMsg mailMsg = (MailMsg) o;
        return getUser().equals(mailMsg.getUser()) && getClient_ip().equals(mailMsg.getClient_ip()) && getSource().equals(mailMsg.getSource()) && getLoginName().equals(mailMsg.getLoginName()) && getMailSenderSourceIp().equals(mailMsg.getMailSenderSourceIp()) && getTimestamp_time().equals(mailMsg.getTimestamp_time()) && getTimestamp_datetime().equals(mailMsg.getTimestamp_datetime()) && getIp().equals(mailMsg.getIp()) && getClientType().equals(mailMsg.getClientType()) && getResult().equals(mailMsg.getResult()) && getVersion().equals(mailMsg.getVersion());
    }

    @Override
    public int hashCode() {
        return Objects.hash(getUser(), getClient_ip(), getSource(), getLoginName(), getMailSenderSourceIp(), getTimestamp_time(), getTimestamp_datetime(), getIp(), getClientType(), getResult(), getVersion());
    }

    @Override
    public String toString() {
        return "{" +
                "  'user':'" + user + "'" +
                ", 'client_ip':'" + client_ip  + "'" +
                ", 'source':'" + source  + "'" +
                ", 'loginName':'" + loginName  + "'" +
                ", 'IP':'" + mailSenderSourceIp + "'" +
                ", 'timestamp':'" + timestamp_time + "'" +
                ", '@timestamp':'" + timestamp_datetime + "'" +
                ", 'ip':'"  + "'" +
                ", 'clientType':'" + clientType  + "'" +
                ", 'result':'" + result  + "'" +
                ", 'version':'" + version + "'" +
                "}";
    }

}

源代码已去掉敏感信息，地址：https://gitee.com/wangxm-2270/alarmCollectByFlink.git

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