07用户行为日志数据采集

用户行为数据由Flume从Kafka直接同步到HDFS，由于离线数仓采用Hive的分区表按天统计，所以目标路径要包含一层日期。具体数据流向如下图所示。

按照规划，该Flume需将Kafka中topic_log的数据发往HDFS。并且对每天产生的用户行为日志进行区分，将不同天的数据发往HDFS不同天的路径。

此处选择KafkaSource、FileChannel、HDFSSink。关键配置如下：

日志消费者 Flume 实操

1. 在hadoop101 节点的Flume 的 job目录下创建 kafka_to_hdfs_log.conf，内容如下

   配置注释：

   • FileChannel优化：配置 dataDirsk可以通过逗号分隔指向多个路径，每个路径对应不同硬盘，可以增加吞吐量。

   • 新增checkpointDir和backupCheckpointDir也尽量配置在不同硬盘对应的目录中，保证checkpoint坏掉后，可以快速使用backupCheckpointDir恢复数据

     #定义组件
     a1.sources=r1
     a1.channels=c1
     a1.sinks=k1

     #配置source1
     a1.sources.r1.type = org.apache.flume.source.kafka.KafkaSource
     a1.sources.r1.batchSize = 5000
     a1.sources.r1.batchDurationMillis = 2000
     a1.sources.r1.kafka.bootstrap.servers = hadoop101:9092,hadoop102:9092
     a1.sources.r1.kafka.topics=topic_log
     a1.sources.r1.interceptors = i1
     a1.sources.r1.interceptors.i1.type = com.logan.gmall.flume.interceptor.TimestampInterceptor$Builder

     #配置channel
     a1.channels.c1.type = file
     a1.channels.c1.checkpointDir = /opt/module/flume/checkpoint/behavior1
     a1.channels.c1.dataDirs = /opt/module/flume/data/behavior1
     a1.channels.c1.maxFileSize = 2146435071
     a1.channels.c1.capacity = 1000000
     a1.channels.c1.keep-alive = 6

     #配置sink
     a1.sinks.k1.type = hdfs
     a1.sinks.k1.hdfs.path = /origin_data/gmall/log/topic_log/%Y-%m-%d
     a1.sinks.k1.hdfs.filePrefix = log
     a1.sinks.k1.hdfs.round = false

     a1.sinks.k1.hdfs.rollInterval = 10
     a1.sinks.k1.hdfs.rollSize = 134217728
     a1.sinks.k1.hdfs.rollCount = 0

     #控制输出文件类型
     a1.sinks.k1.hdfs.fileType = CompressedStream
     a1.sinks.k1.hdfs.codeC = gzip

     #组装
     a1.sources.r1.channels = c1
     a1.sinks.k1.channel = c1

2. HDFS Sink 优化

   • HDFS存入大量小文件，有什么影响？
     元数据层面：每个小文件都有一份元数据，其中包括文件路径，文件名，所有者，所属组，权限，创建时间等，这些信息都保存在Namenode内存中。所以小文件过多，会占用Namenode服务器大量内存，影响Namenode性能和使用寿命。
   • HDFS小文件处理。
     官方默认三个参数配置写入HDFS 后会产生小文件: hdfs.rollInterval, hdfs.rollSize, hdfs.rollCount。
     本次配置的参数为hdfs.rollInterval=3600，hdfs.rollSize=134217728，hdfs.rollCount =0。意味着文件在达到128M时会滚动生成新文件，或者文件超过 3600 秒会生成新文件。

3. 编写 Flume 拦截器

   • 解决问题
     

   • 在com.logan.gmall.flume.interceptor包下创建TimestampInterceptor类

     package com.logan.gmall.flume.interceptor;

     import com.alibaba.fastjson.JSONObject;
     import org.apache.flume.Context;
     import org.apache.flume.Event;
     import org.apache.flume.interceptor.Interceptor;

     import java.nio.charset.StandardCharsets;
     import java.util.List;
     import java.util.Map;

     public class TimestampInterceptor implements Interceptor {

       @Override
       public void initialize() {
     
       }
     
       @Override
       public Event intercept(Event event) {
           // 获取header和body数据
           Map<String, String> headers = event.getHeaders();
           String body = new String(event.getBody(), StandardCharsets.UTF_8);
     
           // 将body转换成JsonObject类型
           JSONObject jsonObject = JSONObject.parseObject(body);
     
           // 将header中的timestamp时间转换成body中的timestamp（解决数据漂移问题）
           String ts = jsonObject.getString("ts");
           headers.put("timestamp", ts);
     
           return event;
       }
     
       @Override
       public List<Event> intercept(List<Event> list) {
           for (Event event : list) {
               intercept(event);
           }
           return list;
       }
     
       public static class Builder implements Interceptor.Builder{
     
           @Override
           public Interceptor build() {
               return new TimestampInterceptor();
           }
     
           @Override
           public void configure(Context context) {
     
           }
       }
       @Override
       public void close() {
     
       }

     }

4. 将打好的包放入到hadoop101的/opt/module/flume/lib文件夹下

启动测试

1. 启动 Zookeeper、Kafka 集群

2. 启动 hadoop101 的消费Flume

   [logan@hadoop101 flume]$ bin/flume-ng agent -n a1 -c conf/ -f job/kafka_to_hdfs_log.conf -Dflume.root.logger=info,console

3. 生成模拟数据[logan@hadoop101 ~]$ vim /opt/module/applog/log/app.2023-12-14.log

   {"common":{"ar":"110000","ba":"vivo","ch":"oppo","is_new":"0","md":"vivo iqoo3","mid":"mid_70997","os":"Android 11.0","uid":"776","vc":"v2.1.134"},"start":{"entry":"icon","loading_time":11968,"open_ad_id":16,"open_ad_ms":7891,"open_ad_skip_ms":0},"ts":1672503309000}
   {"common":{"ar":"110000","ba":"vivo","ch":"oppo","is_new":"0","md":"vivo iqoo3","mid":"mid_70997","os":"Android 11.0","uid":"776","vc":"v2.1.134"},"displays":[{"display_type":"activity","item":"2","item_type":"activity_id","order":1,"pos_id":1},{"display_type":"activity","item":"2","item_type":"activity_id","order":2,"pos_id":1},{"display_type":"query","item":"9","item_type":"sku_id","order":3,"pos_id":1},{"display_type":"query","item":"18","item_type":"sku_id","order":4,"pos_id":4},{"display_type":"promotion","item":"35","item_type":"sku_id","order":5,"pos_id":4},{"display_type":"query","item":"35","item_type":"sku_id","order":6,"pos_id":4},{"display_type":"recommend","item":"13","item_type":"sku_id","order":7,"pos_id":5}],"page":{"during_time":14287,"page_id":"home"},"ts":1672503309000}

4. 检查HFDS是否生成数据

5. 当 HDFS 生成数据后，增加[logan@hadoop101 bin]$ vim f2.sh

   #!/bin/bash

   case $1 in
   "start")
   echo " --------启动 hadoop101 日志数据flume-------"
   ssh hadoop101 "nohup /opt/module/flume/bin/flume-ng agent -n a1 -c /opt/module/flume/conf -f /opt/module/flume/job/kafka_to_hdfs_log.conf >/dev/null 2>&1 &"
   ;;
   "stop")

        echo " --------停止 hadoop101 日志数据flume-------"
        ssh hadoop101 "ps -ef | grep kafka_to_hdfs_log | grep -v grep |awk '{print \$2}' | xargs -n1 kill"

   ;;
   esac

6. 最终 HDFS 文件