项目实践|基于Flink的用户行为日志分析系统

用户行为日志分析是实时数据处理很常见的一个应用场景，比如常见的PV、UV统计。本文将基于Flink从0到1构建一个用户行为日志分析系统，包括架构设计与代码实现。本文分享将完整呈现日志分析系统的数据处理链路，通过本文，你可以了解到：

• 基于discuz搭建一个论坛平台
• Flume日志收集系统使用方式
• Apache日志格式分析
• Flume与Kafka集成
• 日志分析处理流程
• 架构设计与完整的代码实现

项目简介

本文分享会从0到1基于Flink实现一个实时的用户行为日志分析系统，基本架构图如下：

首先会先搭建一个论坛平台，对论坛平台产生的用户点击日志进行分析。然后使用Flume日志收集系统对产生的Apache日志进行收集，并将其推送到Kafka。接着我们使用Flink对日志进行实时分析处理，将处理之后的结果写入MySQL供前端应用可视化展示。本文主要实现以下三个指标计算：

• 统计热门板块，即访问量最高的板块
• 统计热门文章，即访问量最高的帖子文章
• 统计不同客户端对版块和文章的总访问量

基于discuz搭建一个论坛平台

安装XAMPP

• 下载

wget https://www.apachefriends.org/xampp-files/5.6.33/xampp-linux-x64-5.6.33-0-installer.run

• 安装

# 赋予文件执行权限
chmod u+x xampp-linux-x64-5.6.33-0-installer.run
# 运行安装文件
./xampp-linux-x64-5.6.33-0-installer.run

• 配置环境变量

  将以下内容加入到 ~/.bash_profile

export XAMPP=/opt/lampp/
export PATH=$PATH:$XAMPP:$XAMPP/bin

• 刷新环境变量

source ~/.bash_profile

• 启动XAMPP

xampp restart

• MySQL的root用户密码和权限修改

#修改root用户密码为123qwe 
update mysql.user set password=PASSWORD('123qwe') where user='root'; 
flush privileges;  
#赋予root用户远程登录权限 
grant all privileges on *.* to 'root'@'%' identified by '123qwe' with grant option;
flush privileges; 

安装Discuz

• 下载discuz

wget http://download.comsenz.com/DiscuzX/3.2/Discuz_X3.2_SC_UTF8.zip

• 安装

#删除原有的web应用  
rm -rf /opt/lampp/htdocs/*
unzip Discuz_X3.2_SC_UTF8.zip –d /opt/lampp/htdocs/
cd /opt/lampp/htdocs/  
mv upload/*   
#修改目录权限 
chmod 777 -R /opt/lampp/htdocs/config/
chmod 777 -R /opt/lampp/htdocs/data/
chmod 777 -R /opt/lampp/htdocs/uc_client/  
chmod 777 -R /opt/lampp/htdocs/uc_server/ 

Discuz基本操作

• 自定义版块
• 进入discuz后台：http://kms-4/admin.php
• 点击顶部的论坛菜单
• 按照页面提示创建所需版本，可以创建父子版块

Discuz帖子/版块存储数据库表介

-- 登录ultrax数据库
mysql -uroot -p123 ultrax 
-- 查看包含帖子id及标题对应关系的表
-- tid, subject（文章id、标题）
select tid, subject from pre_forum_post limit 10;
-- fid, name（版块id、标题）
select fid, name from pre_forum_forum limit 40;

当我们在各个板块添加帖子之后，如下所示：

修改日志格式

• 查看访问日志

# 日志默认地址  
/opt/lampp/logs/access_log 
# 实时查看日志命令  
tail –f /opt/lampp/logs/access_log

• 修改日志格式

Apache配置文件名称为httpd.conf，完整路径为/opt/lampp/etc/httpd.conf。由于默认的日志类型为common类型，总共有7个字段。为了获取更多的日志信息，我们需要将其格式修改为combined格式，该日志格式共有9个字段。修改方式如下：

# 启用组合日志文件
CustomLog "logs/access_log" combined

• 重新加载配置文件

xampp reload

Apache日志格式介绍

192.168.10.1 - - [30/Aug/2020:15:53:15 +0800] "GET /forum.php?mod=forumdisplay&fid=43 HTTP/1.1" 200 30647 "http://kms-4/forum.php" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.135 Safari/537.36"

上面的日志格式共有9个字段，分别用空格隔开。每个字段的具体含义如下：

192.168.10.1 ##(1)客户端的IP地址
- ## (2)客户端identity标识,该字段为"-"
- ## (3)客户端userid标识,该字段为"-"
[30/Aug/2020:15:53:15 +0800] ## (4)服务器完成请求处理时的时间
"GET /forum.php?mod=forumdisplay&fid=43 HTTP/1.1" ## (5)请求类型 请求的资源 使用的协议
200 ## (6)服务器返回给客户端的状态码，200表示成功
30647 ## (7)返回给客户端不包括响应头的字节数，如果没有信息返回，则此项应该是"-"
"http://kms-4/forum.php" ## (8)Referer请求头
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.135 Safari/537.36" ## (9)客户端的浏览器信息

关于上面的日志格式，可以使用正则表达式进行匹配：

(\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}) (\S+) (\S+) (\[.+?\]) (\"(.*?)\") (\d{3}) (\S+) (\"(.*?)\") (\"(.*?)\")

Flume与Kafka集成

本文使用Flume对产生的Apache日志进行收集，然后推送至Kafka。需要启动Flume agent对日志进行收集，对应的配置文件如下：

# agent的名称为a1
a1.sources = source1
a1.channels = channel1
a1.sinks = sink1

# set source
a1.sources.source1.type = TAILDIR
a1.sources.source1.filegroups = f1
a1.sources.source1.filegroups.f1 = /opt/lampp/logs/access_log
a1sources.source1.fileHeader = flase

# 配置sink
a1.sinks.sink1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.sink1.brokerList=kms-2:9092,kms-3:9092,kms-4:9092
a1.sinks.sink1.topic= user_access_logs
a1.sinks.sink1.kafka.flumeBatchSize = 20
a1.sinks.sink1.kafka.producer.acks = 1
a1.sinks.sink1.kafka.producer.linger.ms = 1
a1.sinks.sink1.kafka.producer.compression.type = snappy

# 配置channel
a1.channels.channel1.type = file
a1.channels.channel1.checkpointDir = /home/kms/data/flume_data/checkpoint
a1.channels.channel1.dataDirs= /home/kms/data/flume_data/data

# 配置bind
a1.sources.source1.channels = channel1
a1.sinks.sink1.channel = channel1

知识点：

Taildir Source相比Exec Source、Spooling Directory Source的优势是什么？

TailDir Source：断点续传、多目录。Flume1.6以前需要自己自定义Source记录每次读取文件位置,实现断点续传

Exec Source：可以实时收集数据,但是在Flume不运行或者Shell命令出错的情况下,数据将会丢失

Spooling Directory Source：监控目录,不支持断点续传

值得注意的是，上面的配置是直接将原始日志push到Kafka。除此之外，我们还可以自定义Flume的拦截器对原始日志先进行过滤处理，同时也可以实现将不同的日志push到Kafka的不同Topic中。

启动Flume Agent

将启动Agent的命令封装成shell脚本:**start-log-collection.sh **,脚本内容如下：

#!/bin/bash
echo "start log agent !!!"
/opt/modules/apache-flume-1.9.0-bin/bin/flume-ng agent --conf-file /opt/modules/apache-flume-1.9.0-bin/conf/log_collection.conf --name a1 -Dflume.root.logger=INFO,console 

查看push到Kafka的日志数据

将控制台消费者命令封装成shell脚本：kafka-consumer.sh，脚本内容如下：

#!/bin/bash
echo "kafka consumer "
bin/kafka-console-consumer.sh  --bootstrap-server kms-2.apache.com:9092,kms-3.apache.com:9092,kms-4.apache.com:9092  --topic $1 --from-beginning

使用下面命令消费Kafka中的数据：

[kms@kms-2 kafka_2.11-2.1.0]$ ./kafka-consumer.sh  user_access_logs

日志分析处理流程

为了方便解释，下面会对重要代码进行讲解，完整代码移步github：https://github.com/jiamx/flink-log-analysis

创建MySQL数据库和目标表

-- 客户端访问量统计
CREATE TABLE `client_ip_access` (
  `client_ip` char(50) NOT NULL COMMENT '客户端ip',
  `client_access_cnt` bigint(20) NOT NULL COMMENT '访问次数',
  `statistic_time` text NOT NULL COMMENT '统计时间',
  PRIMARY KEY (`client_ip`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
-- 热门文章统计
CREATE TABLE `hot_article` (
  `article_id` int(10) NOT NULL COMMENT '文章id',
  `subject` varchar(80) NOT NULL COMMENT '文章标题',
  `article_pv` bigint(20) NOT NULL COMMENT '访问次数',
  `statistic_time` text NOT NULL COMMENT '统计时间',
  PRIMARY KEY (`article_id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;
-- 热门板块统计
CREATE TABLE `hot_section` (
  `section_id` int(10) NOT NULL COMMENT '版块id',
  `name` char(50) NOT NULL COMMENT '版块标题',
  `section_pv` bigint(20) NOT NULL COMMENT '访问次数',
  `statistic_time` text NOT NULL COMMENT '统计时间',
  PRIMARY KEY (`section_id`)
) ENGINE=InnoDB DEFAULT CHARSET=utf8;

AccessLogRecord类

该类封装了日志所包含的字段数据，共有9个字段。

/**
 * 使用lombok
 * 原始日志封装类
 */
@Data
public class AccessLogRecord {
    public String clientIpAddress; // 客户端ip地址
    public String clientIdentity; // 客户端身份标识,该字段为 `-`
    public String remoteUser; // 用户标识,该字段为 `-`
    public String dateTime; //日期,格式为[day/month/yearhourminutesecond zone]
    public String request; // url请求,如：`GET /foo ...`
    public String httpStatusCode; // 状态码，如：200; 404.
    public String bytesSent; // 传输的字节数，有可能是 `-`
    public String referer; // 参考链接,即来源页
    public String userAgent;  // 浏览器和操作系统类型
}

LogParse类

该类是日志解析类，通过正则表达式对日志进行匹配，对匹配上的日志进行按照字段解析。

public class LogParse implements Serializable {

    //构建正则表达式
    private String regex = "(\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}) (\\S+) (\\S+) (\\[.+?\\]) (\\\"(.*?)\\\") (\\d{3}) (\\S+) (\\\"(.*?)\\\") (\\\"(.*?)\\\")";
    private Pattern p = Pattern.compile(regex);

    /*
     *构造访问日志的封装类对象
     * */
    public AccessLogRecord buildAccessLogRecord(Matcher matcher) {
        AccessLogRecord record = new AccessLogRecord();
        record.setClientIpAddress(matcher.group(1));
        record.setClientIdentity(matcher.group(2));
        record.setRemoteUser(matcher.group(3));
        record.setDateTime(matcher.group(4));
        record.setRequest(matcher.group(5));
        record.setHttpStatusCode(matcher.group(6));
        record.setBytesSent(matcher.group(7));
        record.setReferer(matcher.group(8));
        record.setUserAgent(matcher.group(9));
        return record;

    }

    /**
     * @param record:record表示一条apache combined 日志
     * @return 解析日志记录，将解析的日志封装成一个AccessLogRecord类
     */
    public AccessLogRecord parseRecord(String record) {
        Matcher matcher = p.matcher(record);
        if (matcher.find()) {
            return buildAccessLogRecord(matcher);
        }
        return null;
    }

    /**
     * @param request url请求，类型为字符串，类似于 "GET /the-uri-here HTTP/1.1"
     * @return 一个三元组(requestType, uri, httpVersion). requestType表示请求类型，如GET, POST等
     */
    public Tuple3<String, String, String> parseRequestField(String request) {
        //请求的字符串格式为：“GET /test.php HTTP/1.1”，用空格切割
        String[] arr = request.split(" ");
        if (arr.length == 3) {
            return Tuple3.of(arr[0], arr[1], arr[2]);
        } else {
            return null;
        }
    }

    /**
     * 将apache日志中的英文日期转化为指定格式的中文日期
     *
     * @param dateTime 传入的apache日志中的日期字符串，"[21/Jul/2009:02:48:13 -0700]"
     * @return
     */
    public String parseDateField(String dateTime) throws ParseException {
        // 输入的英文日期格式
        String inputFormat = "dd/MMM/yyyy:HH:mm:ss";
        // 输出的日期格式
        String outPutFormat = "yyyy-MM-dd HH:mm:ss";

        String dateRegex = "\\[(.*?) .+]";
        Pattern datePattern = Pattern.compile(dateRegex);

        Matcher dateMatcher = datePattern.matcher(dateTime);
        if (dateMatcher.find()) {
            String dateString = dateMatcher.group(1);
            SimpleDateFormat dateInputFormat = new SimpleDateFormat(inputFormat, Locale.ENGLISH);
            Date date = dateInputFormat.parse(dateString);

            SimpleDateFormat dateOutFormat = new SimpleDateFormat(outPutFormat);

            String formatDate = dateOutFormat.format(date);
            return formatDate;
        } else {
            return "";
        }
    }

    /**
     * 解析request,即访问页面的url信息解析
     * "GET /about/forum.php?mod=viewthread&tid=5&extra=page%3D1 HTTP/1.1"
     * 匹配出访问的fid:版本id
     * 以及tid：文章id
     * @param request
     * @return
     */
    public Tuple2<String, String> parseSectionIdAndArticleId(String request) {
        // 匹配出前面是"forumdisplay&fid="的数字记为版块id
        String sectionIdRegex = "(\\?mod=forumdisplay&fid=)(\\d+)";
        Pattern sectionPattern = Pattern.compile(sectionIdRegex);
        // 匹配出前面是"tid="的数字记为文章id
        String articleIdRegex = "(\\?mod=viewthread&tid=)(\\d+)";
        Pattern articlePattern = Pattern.compile(articleIdRegex);

        String[] arr = request.split(" ");
        String sectionId = "";
        String articleId = "";
        if (arr.length == 3) {
            Matcher sectionMatcher = sectionPattern.matcher(arr[1]);
            Matcher articleMatcher = articlePattern.matcher(arr[1]);
                sectionId = (sectionMatcher.find()) ? sectionMatcher.group(2) : "";
               articleId = (articleMatcher.find()) ? articleMatcher.group(2) : "";
        }
        return  Tuple2.of(sectionId, articleId);
    }
}

LogAnalysis类

该类是日志处理的基本逻辑

public class LogAnalysis {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment senv = StreamExecutionEnvironment.getExecutionEnvironment();
        // 开启checkpoint，时间间隔为毫秒
        senv.enableCheckpointing(5000L);
        // 选择状态后端
        // 本地测试
        // senv.setStateBackend(new FsStateBackend("file:///E://checkpoint"));
        // 集群运行
        senv.setStateBackend(new FsStateBackend("hdfs://kms-1:8020/flink-checkpoints"));
        // 重启策略
        senv.setRestartStrategy(
                RestartStrategies.fixedDelayRestart(3, Time.of(2, TimeUnit.SECONDS) ));

        EnvironmentSettings settings = EnvironmentSettings.newInstance()
                .useBlinkPlanner()
                .inStreamingMode()
                .build();
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(senv, settings);
        // kafka参数配置
        Properties props = new Properties();
        // kafka broker地址
        props.put("bootstrap.servers", "kms-2:9092,kms-3:9092,kms-4:9092");
        // 消费者组
        props.put("group.id", "log_consumer");
        // kafka 消息的key序列化器
        props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        // kafka 消息的value序列化器
        props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        props.put("auto.offset.reset", "earliest");

        FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<String>(
                "user_access_logs",
                new SimpleStringSchema(),
                props);

        DataStreamSource<String> logSource = senv.addSource(kafkaConsumer);
        // 获取有效的日志数据
        DataStream<AccessLogRecord> availableAccessLog = LogAnalysis.getAvailableAccessLog(logSource);
        // 获取[clienIP,accessDate,sectionId,articleId]
        DataStream<Tuple4<String, String, Integer, Integer>> fieldFromLog = LogAnalysis.getFieldFromLog(availableAccessLog);
        //从DataStream中创建临时视图,名称为logs
        // 添加一个计算字段:proctime,用于维表JOIN
        tEnv.createTemporaryView("logs",
                fieldFromLog,
                $("clientIP"),
                $("accessDate"),
                $("sectionId"),
                $("articleId"),
                $("proctime").proctime());

        // 需求1：统计热门板块
        LogAnalysis.getHotSection(tEnv);
        // 需求2：统计热门文章
       LogAnalysis.getHotArticle(tEnv);
        // 需求3：统计不同客户端ip对版块和文章的总访问量
       LogAnalysis.getClientAccess(tEnv);
        senv.execute("log-analysisi");
    }

    /**
     * 统计不同客户端ip对版块和文章的总访问量
     * @param tEnv
     */
    private static void getClientAccess(StreamTableEnvironment tEnv) {
        // sink表
        // [client_ip,client_access_cnt,statistic_time]
        // [客户端ip,访问次数,统计时间]
        String client_ip_access_ddl = "" +
                "CREATE TABLE client_ip_access (\n" +
                "    client_ip STRING ,\n" +
                "    client_access_cnt BIGINT,\n" +
                "    statistic_time STRING,\n" +
                "    PRIMARY KEY (client_ip) NOT ENFORCED\n" +
                ")WITH (\n" +
                "    'connector' = 'jdbc',\n" +
                "    'url' = 'jdbc:mysql://kms-4:3306/statistics?useUnicode=true&characterEncoding=utf-8',\n" +
                "    'table-name' = 'client_ip_access', \n" +
                "    'driver' = 'com.mysql.jdbc.Driver',\n" +
                "    'username' = 'root',\n" +
                "    'password' = '123qwe'\n" +
                ") ";

        tEnv.executeSql(client_ip_access_ddl);

        String client_ip_access_sql = "" +
                "INSERT INTO client_ip_access\n" +
                "SELECT\n" +
                "    clientIP,\n" +
                "    count(1) AS access_cnt,\n" +
                "    FROM_UNIXTIME(UNIX_TIMESTAMP()) AS statistic_time\n" +
                "FROM\n" +
                "    logs \n" +
                "WHERE\n" +
                "    articleId <> 0 \n" +
                "    OR sectionId <> 0 \n" +
                "GROUP BY\n" +
                "    clientIP "
               ;
        tEnv.executeSql(client_ip_access_sql);

    }

    /**
     * 统计热门文章
     * @param tEnv
     */

    private static void getHotArticle(StreamTableEnvironment tEnv) {
        // JDBC数据源
        // 文章id及标题对应关系的表,[tid, subject]分别为：文章id和标题
        String pre_forum_post_ddl = "" +
                "CREATE TABLE pre_forum_post (\n" +
                "    tid INT,\n" +
                "    subject STRING,\n" +
                "    PRIMARY KEY (tid) NOT ENFORCED\n" +
                ") WITH (\n" +
                "    'connector' = 'jdbc',\n" +
                "    'url' = 'jdbc:mysql://kms-4:3306/ultrax',\n" +
                "    'table-name' = 'pre_forum_post', \n" +
                "    'driver' = 'com.mysql.jdbc.Driver',\n" +
                "    'username' = 'root',\n" +
                "    'password' = '123qwe'\n" +
                ")";
        // 创建pre_forum_post数据源
        tEnv.executeSql(pre_forum_post_ddl);
        // 创建MySQL的sink表
        // [article_id,subject,article_pv,statistic_time]
        // [文章id,标题名称,访问次数,统计时间]
        String hot_article_ddl = "" +
                "CREATE TABLE hot_article (\n" +
                "    article_id INT,\n" +
                "    subject STRING,\n" +
                "    article_pv BIGINT ,\n" +
                "    statistic_time STRING,\n" +
                "    PRIMARY KEY (article_id) NOT ENFORCED\n" +
                ")WITH (\n" +
                "    'connector' = 'jdbc',\n" +
                "    'url' = 'jdbc:mysql://kms-4:3306/statistics?useUnicode=true&characterEncoding=utf-8',\n" +
                "    'table-name' = 'hot_article', \n" +
                "    'driver' = 'com.mysql.jdbc.Driver',\n" +
                "    'username' = 'root',\n" +
                "    'password' = '123qwe'\n" +
                ")";
        tEnv.executeSql(hot_article_ddl);
        // 向MySQL目标表insert数据
        String hot_article_sql = "" +
                "INSERT INTO hot_article\n" +
                "SELECT \n" +
                "    a.articleId,\n" +
                "    b.subject,\n" +
                "    count(1) as article_pv,\n" +
                "    FROM_UNIXTIME(UNIX_TIMESTAMP()) AS statistic_time\n" +
                "FROM logs a \n" +
                "  JOIN pre_forum_post FOR SYSTEM_TIME AS OF a.proctime as b ON a.articleId = b.tid\n" +
                "WHERE a.articleId <> 0\n" +
                "GROUP BY a.articleId,b.subject\n" +
                "ORDER BY count(1) desc\n" +
                "LIMIT 10";

        tEnv.executeSql(hot_article_sql);

    }

    /**
     * 统计热门板块
     *
     * @param tEnv
     */
    public static void getHotSection(StreamTableEnvironment tEnv) {

        // 板块id及其名称对应关系表,[fid, name]分别为：版块id和板块名称
        String pre_forum_forum_ddl = "" +
                "CREATE TABLE pre_forum_forum (\n" +
                "    fid INT,\n" +
                "    name STRING,\n" +
                "    PRIMARY KEY (fid) NOT ENFORCED\n" +
                ") WITH (\n" +
                "    'connector' = 'jdbc',\n" +
                "    'url' = 'jdbc:mysql://kms-4:3306/ultrax',\n" +
                "    'table-name' = 'pre_forum_forum', \n" +
                "    'driver' = 'com.mysql.jdbc.Driver',\n" +
                "    'username' = 'root',\n" +
                "    'password' = '123qwe',\n" +
                "    'lookup.cache.ttl' = '10',\n" +
                "    'lookup.cache.max-rows' = '1000'" +
                ")";
        // 创建pre_forum_forum数据源
        tEnv.executeSql(pre_forum_forum_ddl);

        // 创建MySQL的sink表
        // [section_id,name,section_pv,statistic_time]
        // [板块id,板块名称,访问次数,统计时间]
        String hot_section_ddl = "" +
                "CREATE TABLE hot_section (\n" +
                "    section_id INT,\n" +
                "    name STRING ,\n" +
                "    section_pv BIGINT,\n" +
                "    statistic_time STRING,\n" +
                "    PRIMARY KEY (section_id) NOT ENFORCED  \n" +
                ") WITH (\n" +
                "    'connector' = 'jdbc',\n" +
                "    'url' = 'jdbc:mysql://kms-4:3306/statistics?useUnicode=true&characterEncoding=utf-8',\n" +
                "    'table-name' = 'hot_section', \n" +
                "    'driver' = 'com.mysql.jdbc.Driver',\n" +
                "    'username' = 'root',\n" +
                "    'password' = '123qwe'\n" +
                ")";

        // 创建sink表:hot_section
        tEnv.executeSql(hot_section_ddl);

        //统计热门板块
        // 使用日志流与MySQL的维表数据进行JOIN
        // 从而获取板块名称
        String hot_section_sql = "" +
                "INSERT INTO hot_section\n" +
                "SELECT\n" +
                "    a.sectionId,\n" +
                "    b.name,\n" +
                "    count(1) as section_pv,\n" +
                "    FROM_UNIXTIME(UNIX_TIMESTAMP()) AS statistic_time \n" +
                "FROM\n" +
                "    logs a\n" +
                "    JOIN pre_forum_forum FOR SYSTEM_TIME AS OF a.proctime as b ON a.sectionId = b.fid \n" +
                "WHERE\n" +
                "    a.sectionId <> 0 \n" +
                "GROUP BY a.sectionId, b.name\n" +
                "ORDER BY count(1) desc\n" +
                "LIMIT 10";
        // 执行数据insert
        tEnv.executeSql(hot_section_sql);

    }

    /**
     * 获取[clienIP,accessDate,sectionId,articleId]
     * 分别为客户端ip,访问日期,板块id,文章id
     *
     * @param logRecord
     * @return
     */
    public static DataStream<Tuple4<String, String, Integer, Integer>> getFieldFromLog(DataStream<AccessLogRecord> logRecord) {
        DataStream<Tuple4<String, String, Integer, Integer>> fieldFromLog = logRecord.map(new MapFunction<AccessLogRecord, Tuple4<String, String, Integer, Integer>>() {
            @Override
            public Tuple4<String, String, Integer, Integer> map(AccessLogRecord accessLogRecord) throws Exception {
                LogParse parse = new LogParse();

                String clientIpAddress = accessLogRecord.getClientIpAddress();
                String dateTime = accessLogRecord.getDateTime();
                String request = accessLogRecord.getRequest();
                String formatDate = parse.parseDateField(dateTime);
                Tuple2<String, String> sectionIdAndArticleId = parse.parseSectionIdAndArticleId(request);
                if (formatDate == "" || sectionIdAndArticleId == Tuple2.of("", "")) {

                    return new Tuple4<String, String, Integer, Integer>("0.0.0.0", "0000-00-00 00:00:00", 0, 0);
                }
                Integer sectionId = (sectionIdAndArticleId.f0 == "") ? 0 : Integer.parseInt(sectionIdAndArticleId.f0);
                Integer articleId = (sectionIdAndArticleId.f1 == "") ? 0 : Integer.parseInt(sectionIdAndArticleId.f1);
                return new Tuple4<>(clientIpAddress, formatDate, sectionId, articleId);
            }
        });
        return fieldFromLog;
    }

    /**
     * 筛选可用的日志记录
     *
     * @param accessLog
     * @return
     */
    public static DataStream<AccessLogRecord> getAvailableAccessLog(DataStream<String> accessLog) {
        final LogParse logParse = new LogParse();
        //解析原始日志，将其解析为AccessLogRecord格式
        DataStream<AccessLogRecord> filterDS = accessLog.map(new MapFunction<String, AccessLogRecord>() {
            @Override
            public AccessLogRecord map(String log) throws Exception {
                return logParse.parseRecord(log);
            }
        }).filter(new FilterFunction<AccessLogRecord>() {
            //过滤掉无效日志
            @Override
            public boolean filter(AccessLogRecord accessLogRecord) throws Exception {
                return !(accessLogRecord == null);
            }
        }).filter(new FilterFunction<AccessLogRecord>() {
            //过滤掉状态码非200的记录，即保留请求成功的日志记录
            @Override
            public boolean filter(AccessLogRecord accessLogRecord) throws Exception {
                return !accessLogRecord.getHttpStatusCode().equals("200");
            }
        });
        return filterDS;
    }
}

将上述代码打包上传到集群运行，在执行提交命令之前，需要先将Hadoop的依赖jar包放置在Flink安装目录下的lib文件下：flink-shaded-hadoop-2-uber-2.7.5-10.0.jar，因为我们配置了HDFS上的状态后端，而Flink的release包不含有Hadoop的依赖Jar包。

否则会报如下错误：

Caused by: org.apache.flink.core.fs.UnsupportedFileSystemSchemeException: Hadoop is not in the classpath/dependencies.

提交到集群

编写提交命令脚本

#!/bin/bash
/opt/modules/flink-1.11.1/bin/flink run -m kms-1:8081 \
-c com.jmx.analysis.LogAnalysis \
/opt/softwares/com.jmx-1.0-SNAPSHOT.jar

提交之后，访问Flink的Web界面，查看任务：

此时访问论坛，点击板块和帖子文章，观察数据库变化：

总结

本文主要分享了从0到1构建一个用户行为日志分析系统。首先，基于discuz搭建了论坛平台，针对论坛产生的日志，使用Flume进行收集并push到Kafka中；接着使用Flink对其进行分析处理；最后将处理结果写入MySQL供可视化展示使用。