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在举荐系统中另一个必不可少的数据便是用户行径数据，能够说用户行径数据是举荐系统的基石，巧妇难为无米之炊，因此接下来，咱们就要将用户的行径数据同步到举荐系统数据库中。
在文案举荐系统中，用户行径包含揭发、点击、停留、保藏、分享等，因此这儿咱们定义的用户行径数据的字段包含：出现时间（actionTime）、停留时间（readTime）、频道 ID（channelId）、事件名叫作（action）、用户 ID（userId）、文案 ID（articleId）以及算法 ID（algorithmCombine），这儿采用 json 格式，如下所示 # 揭发的参数
{"actionTime":"2019-04-10 18:15:35","readTime":"","channelId":0,"param":{"action": "exposure", "userId": "2", "articleId": "[18577, 14299]", "algorithmCombine": "C2"}}
# 对文案触发行径的参数
{"actionTime":"2019-04-10 18:15:36","readTime":"","channelId":18,"param":{"action": "click", "userId": "2", "articleId": "18577", "algorithmCombine": "C2"}}
{"actionTime":"2019-04-10 18:15:38","readTime":"1621","channelId":18,"param":{"action": "read", "userId": "2", "articleId": "18577", "algorithmCombine": "C2"}}
{"actionTime":"2019-04-10 18:15:39","readTime":"","channelId":18,"param":{"action": "click", "userId": "1", "articleId": "14299", "algorithmCombine": "C2"}}
{"actionTime":"2019-04-10 18:15:39","readTime":"","channelId":18,"param":{"action": "click", "userId": "2", "articleId": "14299", "algorithmCombine": "C2"}}
{"actionTime":"2019-04-10 18:15:41","readTime":"914","channelId":18,"param":{"action": "read", "userId": "2", "articleId": "14299", "algorithmCombine": "C2"}}
{"actionTime":"2019-04-10 18:15:47","readTime":"7256","channelId":18,"param":{"action": "read", "userId": "1", "articleId": "14299","algorithmCombine": "C2"}}
用户离线行径数据
因为用户行径数据规模庞大，一般是每日更新一次，以供离线计算运用。首要，在 Hive 中创建用户行径数据库 profile 及用户行径表 user_action，设置根据日期进行分区，并匹配 json 格式 -- 创建用户行径数据库
create database if not exists profile comment "use action" location /user/hive/warehouse/profile.db/;
-- 创建用户行径信息表
create table user_action
(
actionTime STRING comment "user actions time",
readTime STRING comment "user reading time",
channelId INT comment "article channel id",
param map comment "action parameter"
)
COMMENT "user primitive action"
PARTITIONED BY (dt STRING) # 根据日期分区
ROW FORMAT SERDE org.apache.hive.hcatalog.data.JsonSerDe # 匹配json格式
LOCATION /user/hive/warehouse/profile.db/user_action;
一般用户行径数据被保留在应用服务器的日志文件中，咱们能够利用 Flume 监听应用服务器上的日志文件，将用户行径数据收集到 Hive 的 user_action 表对应的 HDFS 目录中，Flume 配置如下 a1.sources= s1a1.sinks = k1
a1.channels = c1
a1.sources.s1.channels= c1
a1.sources.s1.type = exec
a1.sources.s1.command = tail -F /root/logs/userClick.log
a1.sources.s1.interceptors=i1 i2
a1.sources.s1.interceptors.i1.type=regex_filter
a1.sources.s1.interceptors.i1.regex=\\{.*\\}
a1.sources.s1.interceptors.i2.type=timestamp# c1
a1.channels.c1.type=memory
a1.channels.c1.capacity=30000
a1.channels.c1.transactionCapacity=1000
# k1
a1.sinks.k1.type=hdfs
a1.sinks.k1.channel=c1
a1.sinks.k1.hdfs.path=hdfs://192.168.19.137:9000/user/hive/warehouse/profile.db/user_action/%Y-%m-%d
a1.sinks.k1.hdfs.useLocalTimeStamp = true
a1.sinks.k1.hdfs.fileType=DataStream
a1.sinks.k1.hdfs.writeFormat=Text
a1.sinks.k1.hdfs.rollInterval=0
a1.sinks.k1.hdfs.rollSize=10240
a1.sinks.k1.hdfs.rollCount=0
a1.sinks.k1.hdfs.idleTimeout=60
编写 Flume 起步脚本 collect_click.sh #!/usr/bin/env bash
export JAVA_HOME=/root/bigdata/jdk
export HADOOP_HOME=/root/bigdata/hadoop
export PATH=$PATH:$JAVA_HOME/bin:$HADOOP_HOME/bin
/root/bigdata/flume/bin/flume-ng agent -c /root/bigdata/flume/conf -f /root/bigdata/flume/conf/collect_click.conf -Dflume.root.logger=INFO,console -name a1Flume 自动生成目录后，需要手动相关 Hive 分区后才可加载到数据 alter table user_action add partition (dt=2019-11-11) location "/user/hive/warehouse/profile.db/user_action/2011-11-11/"
用户实时行径数据
为了加强举荐的实时性，咱们亦需要收集用户的实时行径数据，以供在线计算运用。这儿利用 Flume 将日志收集到 Kafka，在线计算任务能够从 Kafka 读取用户实时行径数据。首要，开启 zookeeper，以保护进程运行 /root/bigdata/kafka/bin/zookeeper-server-start.sh -daemon /root/bigdata/kafka/config/zookeeper.properties
开启 Kafka /root/bigdata/kafka/bin/kafka-server-start.sh /root/bigdata/kafka/config/server.properties
# 开启信息生产者
/root/bigdata/kafka/bin/kafka-console-producer.sh --broker-list 192.168.19.19092 --sync --topic click-trace
# 开启消费者
/root/bigdata/kafka/bin/kafka-console-consumer.sh --bootstrap-server 192.168.19.137:9092 --topic click-trace
修改 Flume 的日志收集配置文件，添加 c2 和 k2 ，将日志数据收集到 Kafka a1.sources = s1
a1.sinks = k1 k2
a1.channels = c1 c2
a1.sources.s1.channels= c1 c2
a1.sources.s1.type = exec
a1.sources.s1.command = tail -F /root/logs/userClick.log
a1.sources.s1.interceptors=i1 i2
a1.sources.s1.interceptors.i1.type=regex_filter
a1.sources.s1.interceptors.i1.regex=\\{.*\\}
a1.sources.s1.interceptors.i2.type=timestamp
# c1
a1.channels.c1.type=memory
a1.channels.c1.capacity=30000
a1.channels.c1.transactionCapacity=1000
# c2
a1.channels.c2.type=memory
a1.channels.c2.capacity=30000a1.channels.c2.transactionCapacity=1000
# k1
a1.sinks.k1.type=hdfs
a1.sinks.k1.channel=c1
a1.sinks.k1.hdfs.path=hdfs://192.168.19.137:9000/user/hive/warehouse/profile.db/user_action/%Y-%m-%d
a1.sinks.k1.hdfs.useLocalTimeStamp =truea1.sinks.k1.hdfs.fileType=DataStream
a1.sinks.k1.hdfs.writeFormat=Text
a1.sinks.k1.hdfs.rollInterval=0
a1.sinks.k1.hdfs.rollSize=10240
a1.sinks.k1.hdfs.rollCount=0
a1.sinks.k1.hdfs.idleTimeout=60# k2
a1.sinks.k2.channel=c2
a1.sinks.k2.type=org.apache.flume.supervisorctl
咱们能够利用supervisorctl来管理supervisor。sink.kafka.KafkaSink
a1.sinks.k2.kafka.bootstrap.servers=192.168.19.137:9092
a1.sinks.k2.kafka.topic=click-trace
a1.sinks.k2.kafka.batchSize=20
a1.sinks.k2.kafka.producer.requiredAcks=1
编写 Kafka 起步脚本 start_kafka.sh #!/usr/bin/env bash
# 起步zookeeper/root/bigdata/kafka/bin/zookeeper-server-start.sh -daemon /root/bigdata/kafka/config/zookeeper.properties# 起步kafka
/root/bigdata/kafka/bin/kafka-server-start.sh /root/bigdata/kafka/config/server.properties
# 增多topic/root/bigdata/kafka/bin/kafka-topics.sh --zookeeper 192.168.19.137:2181 --create --replication-factor 1 --topic click-trace --partitions 1进程管理
咱们这儿运用 Supervisor 进行进程管理，当进程反常时能够自动重启，Flume 进程配置如下 [program:collect-click]
command=/bin/bash /root/toutiao_project/scripts/collect_click.sh
user=root
autorestart=true
redirect_stderr=true
stdout_logfile=/root/logs/collect.log
loglevel=info
stopsignal=KILL
stopasgroup=true
killasgroup=true
Kafka 进程配置如下 [program:kafka]
command=/bin/bash /root/toutiao_project/scripts/start_kafka.shuser=root
autorestart=true
redirect_stderr=true
stdout_logfile=/root/logs/kafka.log
loglevel=info
stopsignal=KILL
stopasgroup=true
killasgroup=true
起步 Supervisor supervisord -c /etc/supervisord.conf
起步 Kafka 消费者，并在应用服务器日志文件中写入日志数据，Kafka 消费者就可收集到实时行径数据 # 起步Kafka消费者/root/bigdata/kafka/bin/kafka-console-consumer.sh --bootstrap-server 192.168.19.137:9092 --topic click-trace# 写入日志数据
echo{\"actionTime\":\"2019-04-10 21:04:39\",\"readTime\":\"\",\"channelId\":18,\"param\":{\"action\": \"click\", \"userId\": \"2\", \"articleId\": \"14299\", \"algorithmCombine\": \"C2\"}} >> userClick.log# 消费者接收到日志数据
{"actionTime":"2019-04-10 21:04:39","readTime":"","channelId":18,"param":{"action": "click", "userId": "2", "articleId": "14299", "algorithmCombine": "C2"}}
Supervisor 常用命令如下 supervisorctl
> status # 查看程序状态
> start apscheduler # 起步apscheduler单一程序
> stop toutiao:* # 关闭toutiao组程序
> start toutiao:* # 起步toutiao组程序
> restart toutiao:* # 重启toutiao组程序
> update # 重启配置文件修改过的程序
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发表于 2024-9-2 12:11:15