基于FLink實作的實時安全檢測（一段時間內連續登錄失敗20次后，下一次登錄成功場景）-有解無憂

研發背景

公司安全部目前針對內部系統的網路訪問日志的安全審計，大部分都是T+1時效，每日當天，啟動Python撰寫的定時任務，完成昨日的日志審計和檢測，定時任務運行完成后，統一進行企業微信告警推送，這種方案在目前的網路環境和人員規模下，呈現兩個痛點，一是面對日益頻繁的網路攻擊、釣魚鏈接，T+1的定時任務，難以及時進行告警，因此也難以有效避免如關鍵資訊泄露等問題，二是目前以Python為主的單機定時任務，針對不同場景的處理時效，從一小時到十幾小時不等，效率低下，為解決以上問題，本人協助公司安全部同時對告警采集平臺進行改造，由之前的python單機任務處理，切換到基于Flink集群的并行處理，且告警推送時效，由之前的T+1天，提升到秒級實時告警，本次改造涉及網路日志審計的多個常見場景，如埠掃描、黑名單統計、例外流量、連續惡意登錄等，本次以一段時間內連續登錄失敗20次后，下一次登錄成功場景來進行介紹，

場景描述

針對一個內部系統，如郵件系統，公司員工的訪問行為日志，存放于kafka，我們希望對于一個用戶賬號在同一個IP下，任意的3分鐘時間內，連續登錄郵件系統20次失敗，下一次登錄成功，這種場景能夠及時獲取并推送到企業微信某個指定的安全介面人，kafka中的資料，能夠通過某個關鍵字，區分當前網路訪問是否一次登錄事件，且有訪問時間（也就是事件時間），在決議到符合需求的用戶賬號之后，第一時間進行企業微信告警推送，并將其這段時間內的訪問行為，寫入下游ElasticSearch，

組件版本

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

日志結構

IP和賬號皆為測驗使用，

{
   "user": "wangxm",
   "client_ip": "110.68.6.182",
   "source": "login",
   "loginname": "[email protected]",
   "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"
 }

技術方案

上述場景，可考慮使用FlinkCEP及Flink的滑動視窗進行實作，由于本人在采用FlinkCEP的方案進行代碼撰寫除錯后，發現并不能滿足，因此改用滑動視窗進行實作，

關鍵代碼

主入口類

主入口類，創建了flink環境、設定了基礎引數，創建了kafkaSource,接入訊息后，進行了映射、過濾，并設定了水位線，進行了分組，之后設定了滑動視窗，在視窗內進行了事件統計，將復合條件的事件收集回傳并寫入ElasticSearch，

針對map、filter、keyBy、window等算子，都單獨進行了撰寫，后面會一一列出來，

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處理在3分鐘內連續登錄失敗20次后登錄成功的場景
 * 采用滑動視窗來實作
 * @author wangxiaomin 2022-06-01
 */

@Slf4j
public class MailMsg extends BaseBean {

    /**
     * Flink作業名稱
     */
    public static final  String JobName = "告警采集平臺——連續登錄失敗后登錄成功告警";
    /**
     * Kafka訊息名
     */
    public static final  String KafkaSourceName = "Kafka Source for AlarmPlatform About Mail Topic";

    public MailMsg(){
        log.info("初始化滑動視窗場景告警程式");
    }

    /**
     * 執行邏輯統計場景，實作告警推送
     */
    public static void execute(){


        //① 創建Flink執行環境并設定checkpoint等必要的引數
        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 篩選登錄訊息，創建初始登錄事件流
        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 設定水位線
        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("增加水位線");

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

        //⑥ 轉化為滑動視窗
        WindowedStream<com.data.dev.common.javabean.kafkaMailTopic.MailMsg, String, TimeWindow> loginWindowDs = loginKeyedDs.window(SlidingEventTimeWindows.of(Time.seconds(180L),Time.seconds(90L)));

        //⑦ 在視窗內進行邏輯統計
        SingleOutputStreamOperator<MailMsgAlarm> loginWindowsDealDs  = loginWindowDs.process(new WindowProcessFuncImpl()).name("視窗處理邏輯");

        //⑧ 將結果轉化為通用DataStream<String>格式
        SingleOutputStreamOperator<String> resultDs  = loginWindowsDealDs.map(new AlarmMsgToStringMapper()).name("視窗結果轉化為標準格式");

        //⑨ 將最終結果寫入ES
        resultDs.addSink(SinkToEs.getEsSinkBuilder(ElasticSearchInfo.ES_LOGIN_FAIL_INDEX_NAME,ElasticSearchInfo.ES_INDEX_TYPE_DEFAULT).build());

        //⑩ 提交Flink集群進行執行
        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;

/**
 *  邏輯統計場景告警推送ES訊息體
 *  @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　消費mail主題的訊息，過濾其中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("篩選原始的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 編程，需要進行key選取
 */
@Slf4j
public class LoginKeySelector extends BaseBean implements KeySelector<MailMsg, String> {
    @Override
    public String getKey(MailMsg mailMsg) {
        return mailMsg.getUser() + "@" + mailMsg.getClient_ip();
    }
}

視窗函式（核心代碼）

這里我們主要考慮使用一個事件串列，用來存盤每一個視窗期內得到的連續登錄，當檢測到登陸失敗的事件，即存入事件串列中，之后判斷下一次登錄失敗事件，如果檢測到登錄成功事件，但此時登錄失敗的次數不足20次，則清空loginEventList，等待下一次檢測，一旦符合視窗內連續登錄失敗超過20次且下一次登錄成功這個事件，則清空此時的loginEventList并將當前登錄成功的事件進行告警推送，

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;

/**
 *  滑動視窗內復雜事件決議邏輯實作
 *  @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("收集到的登錄事件【" + mailMsg + "】");

            if (mailMsg.getResult().equals("fail")) { //開始檢測當前視窗內的事件，并將失敗的事件收集到loginEventList
                log.info("開始檢測當前視窗內的事件，并將失敗的事件收集到loginEventList");
                loginEventList.add(mailMsg);
            } else if (mailMsg.getResult().equals("success") && loginEventList.size() < 20) {//如果檢測到登錄成功事件，但此時登錄失敗的次數不足20次，則清空loginEventList，等待下一次檢測
                log.info("檢測到登錄成功事件，但此時登錄失敗的次數為【" + loginEventList.size() + "】不足20次，清空loginEventList，等待下一次檢測");
                loginEventList.clear();
            } else if (mailMsg.getResult().equals("success") && loginEventList.size() >= 20) {
                mailMsgAlarm = getMailMsgAlarm(loginEventList,mailMsg);
                log.info("檢測到登錄成功的事件,此時視窗內連續登錄失敗的次數為【" + mailMsgAlarm.getFailTimes() + "】");

                //一旦符合視窗內連續登錄失敗超過20次且下一次登錄成功這個事件，則清空此時的loginEventList并將當前登錄成功的事件進行告警推送；
                loginEventList.clear();
                doAlarmPush(mailMsgAlarm);

                collector.collect(mailMsgAlarm);//將當前登錄成功的事件進行收集上報
            } else {
                log.info(mailMsg.getUser() + "當前已連續：【" + loginEventList.size() + "】 次登錄失敗");
            }
        }
    }


    /**
     * 2022年6月17日15:03:06
     * @param eventList:當前視窗內的事件串列
     * @param eventCurrent：當前登錄成功的事件
     * @return mailMsgAlarm：告警訊息體
     */
    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 :當前構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){//如果是白名單IP，不告警
            log.info("當前登錄用戶【" + userKey + "】屬于白名單IP");
        }else {
            //IP歸屬查詢結果、企業微信推送告警
            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;

/**
 *  邏輯統計場景告警推送ES訊息體
 *  @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工具類

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流計算結果寫入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
     * 獲取ES連接資訊
     * @return esInfoMap:ES連接資訊持久化
     */
    public static HashMap<String,String > getElasticSearchInfo(){
        log.info("獲取ES連接資訊：【 " + "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:寫入索引名稱
     * @param esType：寫入索引型別
     * @return ElasticsearchSink.Builder<String>：構建器
     */
    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("寫入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());
                    }
                }
        );


        //定義es的連接配置  帶用戶名密碼
        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;
    }

}

事件物體類

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
 * 邏輯統計場景告警事件
 */
@Data
public class MailMsgAlarm extends BaseBean {


    /**
     * 當前登錄成功的事件
     */
   public  MailMsg mailMsg;

    /**
     * 當前捕獲的告警主鍵：username@client_ip
     */
   public  String alarmKey;

    /**
     * 第一次登錄失敗的事件時間
     */
   public  String startTime;

    /**
     * 連續登錄失敗后下一次登錄成功的事件時間
     */
   public  String endTime;

    /**
     * 連續登錄失敗的次數
     */
   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());
    }
}

訊息物體類

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": "[email protected]",
 *   "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登錄用戶
 * client_ip 來源ip
 * source 型別
 * loginname 登錄用戶郵箱地址
 * ip 目標前端ip
 * timestamp 發送時間
 * &#064;timestamp  發送日期時間
 * IP 郵件日志發送來源IP
 * clienttype 客戶端登錄型別
 * result 登錄狀態
 */

@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 + "'" +
                "}";
    }

}

源代碼已去掉敏感資訊，地址：https://gitee.com/wangxm-2270/alarmCollectByFlink.git

轉載請註明出處，本文鏈接：https://www.uj5u.com/shujuku/544905.html

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