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

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
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。

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 {

复制代码

  @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() {

  }

}

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

启动测试

启动 Zookeeper、Kafka 集群
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
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}
检查HFDS是否生成数据
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")
复制代码
```
     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
最终 HDFS 文件