- 环境准备
1.1虚拟机准备
克隆三台虚拟机（hadoop101、hadoop102、hadoop103），配置好对应主机的网络IP、主机名称、关闭防火墙。
设置hadoop102、hadoop103、hadoop104的主机对应内存分别是：4G、4G、4G
1.2配置免密登录
- 配置ssh免密登录
[root@hadoop101 ~]# vim /etc/hosts
127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4
::1 localhost localhost.localdomain localhost6 localhost6.localdomain6
192.168.1.101 hadoop101
192.168.1.102 hadoop102
192.168.1.103 hadoop103
[root@hadoop101 ~]# ssh-keygen -t rsa
[root@hadoop101 ~]# ssh-copy-id hadoop101
[root@hadoop101 ~]# ssh-copy-id hadoop102
[root@hadoop101 ~]# ssh-copy-id hadoop103
其余两台机器同样操作一遍
1.3安装jdk
- 卸载linux上原有open jdk，其余两台机器同样操作进行卸载
[root@hadoop101 ~]# rpm -qa | grep jdk
java-1.8.0-openjdk-1.8.0.161-2.b14.el7.x86_64
copy-jdk-configs-3.3-2.el7.noarch
java-1.8.0-openjdk-headless-1.8.0.161-2.b14.el7.x86_64
[root@hadoop101 ~]# rpm -e --nodeps java-1.8.0-openjdk-1.8.0.161-2.b14.el7.x86_64
[root@hadoop101 ~]# rpm -e --nodeps copy-jdk-configs-3.3-2.el7.noarch
[root@hadoop101 ~]# rpm -e --nodeps java-1.8.0-openjdk-headless-1.8.0.161-2.b14.el7.x86_64
- 创建软件包存放目录
[root@hadoop101 ~]# mkdir /opt/software
[root@hadoop101 ~]# cd /opt/software/
（3）上传jdk安装包并进行解压,添加环境变量
[root@hadoop101 software]# mkdir /opt/module
[root@hadoop101 software]# tar -zxvf jdk-8u211-linux-x64.tar.gz -C /opt/module/
/opt/module/jdk1.8.0_211
[root@hadoop101 jdk1.8.0_211]# vim /etc/profile
在profile结尾处加上jdk路径
#JAVA_HOME
export JAVA_HOME=/opt/module/jdk1.8.0_211
export PATH=$PATH:$JAVA_HOME/bin
（4）source下
[root@hadoop101 jdk1.8.0_211]# source /etc/profile
[root@hadoop101 jdk1.8.0_211]# java -version
java version "1.8.0_211"
Java(TM) SE Runtime Environment (build 1.8.0_211-b12)
Java HotSpot(TM) 64-Bit Server VM (build 25.211-b12, mixed mode)
[root@hadoop101 jdk1.8.0_211]#
- 将module包的jdk路径传输到其余两台机器上，并配置jdk环境变量source下
[root@hadoop101 module]# scp -r /opt/module/jdk1.8.0_211/ hadoop102:/opt/module/
[root@hadoop101 module]# scp -r /opt/module/jdk1.8.0_211/ hadoop103:/opt/module/
[root@hadoop101 module]# scp /etc/profile hadoop102:/etc/
[root@hadoop101 module]# scp /etc/profile hadoop103:/etc/
[root@hadoop102 module]# source /etc/profile
[root@hadoop102 module]# java -version
java version "1.8.0_211"
Java(TM) SE Runtime Environment (build 1.8.0_211-b12)
Java HotSpot(TM) 64-Bit Server VM (build 25.211-b12, mixed mode)
[root@hadoop103 ~]# source /etc/profile
[root@hadoop103 ~]# java -version
java version "1.8.0_211"
Java(TM) SE Runtime Environment (build 1.8.0_211-b12)
Java HotSpot(TM) 64-Bit Server VM (build 25.211-b12, mixed mode)
1.4关闭防火墙
[root@hadoop101 ~]# systemctl stop firewalld.service
[root@hadoop101 ~]# systemctl disable firewalld.service
[root@hadoop102 ~]# systemctl stop firewalld.service
[root@hadoop102 ~]# systemctl disable firewalld.service
[root@hadoop103 ~]# systemctl stop firewalld.service
[root@hadoop103 ~]# systemctl disable firewalld.service
- 安装Zookeeper 3.5.7
（1）上传压缩包到software文件夹，并进行解压
[root@hadoop101 module]# cd /opt/software/
[root@hadoop101 software]# tar -zxvf apache-zookeeper-3.5.7-bin.tar.gz -C /opt/module/
（2）分发到各节点
[root@hadoop101 software]# cd /opt/module/
[root@hadoop101 module]# scp -r apache-zookeeper-3.5.7-bin/ hadoop102:/opt/module/
[root@hadoop101 module]# scp -r apache-zookeeper-3.5.7-bin/ hadoop103:/opt/module/
- 在zookeeper目录创建zkData目录
[root@hadoop101 module]# cd apache-zookeeper-3.5.7-bin/
[root@hadoop101 apache-zookeeper-3.5.7-bin]# mkdir zkData
- 在zkData目录下创建myid文件,写上对应比编号1并保存
[root@hadoop101 apache-zookeeper-3.5.7-bin]# cd zkData/
[root@hadoop101 zkData]# vim myid
1
（5）分发zkData目录
[root@hadoop101 zkData]# cd ..
[root@hadoop101 apache-zookeeper-3.5.7-bin]# scp -r zkData/ hadoop102:/opt/module/apache-zookeeper-3.5.7-bin/
[root@hadoop101 apache-zookeeper-3.5.7-bin]# scp -r zkData/ hadoop103:/opt/module/apache-zookeeper-3.5.7-bin/
（6）配置zoo.cfg
[root@hadoop101 apache-zookeeper-3.5.7]# cd conf/
[root@hadoop101 conf]# mv zoo_sample.cfg zoo.cfg
[root@hadoop101 conf]# vim zoo.cfg
修改数据存储路径
dataDir=/opt/module/apache-zookeeper-3.5.7-bin/zkData
在文件末尾处增加集群配置
server.1=hadoop101:2888:3888
server.2=hadoop102:2888:3888
server.3=hadoop103:2888:3888
分发zoo.cfg
[root@hadoop101 conf]# scp zoo.cfg hadoop102:/opt/module/apache-zookeeper-3.5.7-bin/conf/
[root@hadoop101 conf]# scp zoo.cfg hadoop103:/opt/module/apache-zookeeper-3.5.7-bin/conf/
（7）修改其余两台机器的myid,分别为2,3
[root@hadoop102 apache-zookeeper-3.5.7]# vim zkData/myid
2
[root@hadoop103 apache-zookeeper-3.5.7]# vim zkData/myid
3
- 启动集群
[root@hadoop101 ~]# /opt/module/apache-zookeeper-3.5.7-bin/bin/zkServer.sh start
[root@hadoop102~]# /opt/module/apache-zookeeper-3.5.7-bin/bin/zkServer.sh start
[root@hadoop103 ~]# /opt/module/apache-zookeeper-3.5.7-bin/bin/zkServer.sh start
- 安装Hadoop 3.1.3
3.1HDFS HA搭建
- 上传压缩包到software文件夹，并进行解压
[root@hadoop101 module]# cd /opt/software/
[root@hadoop101 software]# tar -zxvf hadoop-3.1.3.tar.gz -C /opt/module/
- 分发opt目录下hadoop文件夹
[root@hadoop101 software]# cd /opt/module/
[root@hadoop101 module]# scp -r hadoop-3.1.3/ hadoop102:/opt/module/
[root@hadoop101 module]# scp -r hadoop-3.1.3/ hadoop103:/opt/module/
（3）配置hadoop环境变量，结尾处加上hadoop路径，其余两台机器同样操作
[root@hadoop101 hadoop-3.1.3]# vim /etc/profile
#HADOOP_HOME
export HADOOP_HOME=/opt/module/hadoop-3.1.3
export PATH=$PATH:$HADOOP_HOME/bin:$HADOOP_HOME/sbin
[root@hadoop101 hadoop-3.1.3]# source /etc/profile
[root@hadoop101 hadoop-3.1.3]# hadoop version
Hadoop 3.1.3
Source code repository https://gitbox.apache.org/repos/asf/hadoop.git -r ba631c436b806728f8ec2f54ab1e289526c90579
Compiled by ztang on 2019-09-12T02:47Z
Compiled with protoc 2.5.0
From source with checksum ec785077c385118ac91aadde5ec9799
This command was run using /opt/module/hadoop-3.1.3/share/hadoop/common/hadoop-common-3.1.3.jar
（4）配置nameservice，编写hdfs-sitx.xml
[root@hadoop101 hadoop-3.1.3]# cd etc/hadoop/
[root@hadoop101 hadoop]# vim hdfs-site.xml
- 编写core-site.xml
- 在hdfs.xml添加故障自动转移
[root@hadoop101 hadoop]# vim hdfs-site.xml
- 在core-site.xml添加zookeeper地址
3.2ResouceManager HA搭建
- 编写yarn-site.xml
[root@hadoop101 hadoop]# vim yarn-site.xml
3.3启动集群
- 配置workers(老版本为slaves)
[root@hadoop101 hadoop]# vim workers
hadoop101
hadoop102
Hadoop103
（2）分发配置文件
[root@hadoop101 hadoop]# cd ..
[root@hadoop101 etc]# scp -r hadoop/ hadoop102:/opt/module/hadoop-3.1.3/etc/
[root@hadoop101 etc]# scp -r hadoop/ hadoop103:/opt/module/hadoop-3.1.3/etc/
（3）在各台机器上启动journalnode服务
[root@hadoop101 hadoop-3.1.3]# sbin/hadoop-daemon.sh start journalnode
[root@hadoop102 hadoop-3.1.3]# sbin/hadoop-daemon.sh start journalnode
[root@hadoop103 hadoop-3.1.3]# sbin/hadoop-daemon.sh start journalnode
（4）在nn1上对namenode进行格式化
[root@hadoop101 hadoop-3.1.3]# bin/hdfs namenode -format
（5）在start-dfs.sh,stop-dfs.sh中配置root用户,顶部配置以下内容
[root@hadoop101 hadoop-3.1.3]# vim sbin/start-dfs.sh
HDFS_DATANODE_USER=root
HADOOP_SECURE_DN_USER=hdfs
HDFS_NAMENODE_USER=root
HDFS_SECONDARYNAMENODE_USER=root
HDFS_JOURNALNODE_USER=root
HDFS_ZKFC_USER=root
[root@hadoop101 hadoop-3.1.3]# vim sbin/stop-dfs.sh
HDFS_DATANODE_USER=root
HADOOP_SECURE_DN_USER=hdfs
HDFS_NAMENODE_USER=root
HDFS_SECONDARYNAMENODE_USER=root
HDFS_JOURNALNODE_USER=root
HDFS_ZKFC_USER=root
（6）在start-yarn.sh,stop-yarn.sh中配置root用户,顶部配置以下内容
[root@hadoop101 hadoop-3.1.3]# vim sbin/start-yarn.sh
YARN_RESOURCEMANAGER_USER=root
HADOOP_SECURE_DN_USER=yarn
YARN_NODEMANAGER_USER=root
[root@hadoop101 hadoop-3.1.3]# vim sbin/stop-yarn.sh
YARN_RESOURCEMANAGER_USER=root
HADOOP_SECURE_DN_USER=yarn
YARN_NODEMANAGER_USER=root
（7）编辑hadoop-env.sh,解开注释，添加JAVA_HOME
[root@hadoop101 hadoop-3.1.3]# vim etc/hadoop/hadoop-env.sh
export JAVA_HOME=/opt/module/jdk1.8.0_211
[root@hadoop102 hadoop-3.1.3]# vim etc/hadoop/hadoop-env.sh
export JAVA_HOME=/opt/module/jdk1.8.0_211
[root@hadoop103 hadoop-3.1.3]# vim etc/hadoop/hadoop-env.sh
export JAVA_HOME=/opt/module/jdk1.8.0_211
（8）分发以上.sh文件
[root@hadoop101 hadoop-3.1.3]# scp -r sbin/ hadoop102:/opt/module/hadoop-3.1.3/
[root@hadoop101 hadoop-3.1.3]# scp -r sbin/ hadoop103:/opt/module/hadoop-3.1.3/
（9）同步,启动nn1的namenode，在 nn2和nn3上进行同步
[root@hadoop101 hadoop-3.1.3]# sbin/hadoop-daemon.sh start namenode
[root@hadoop102 hadoop-3.1.3]# bin/hdfs namenode -bootstrapStandby
[root@hadoop103 hadoop-3.1.3]# bin/hdfs namenode -bootstrapStandby
[root@hadoop102 hadoop-3.1.3]# sbin/hadoop-daemon.sh start namenode
[root@hadoop103 hadoop-3.1.3]# sbin/hadoop-daemon.sh start namenode
（10）关闭所有hdfs服务
[root@hadoop101 hadoop-3.1.3]# sbin/stop-all.sh
（11）初始化HA在Zookeeper中状态：
[root@hadoop101 hadoop-3.1.3]# bin/hdfs zkfc -formatZK
（12）启动集群服务
[root@hadoop101 hadoop-3.1.3]# sbin/start-all.sh
- 安装MySql
4.1安装MySql服务端
（1）卸载MySql依赖，虽然机器上没有装MySql，但是这一步不可少
[root@hadoop101 software]# yum remove mysql-libs
- 下载依赖并安装
[root@hadoop101 software]# yum install libaio
[root@hadoop101 software]# yum -y install autoconf
[root@hadoop101 software]# wget https://downloads.mysql.com/archives/get/p/23/file/MySQL-shared-compat-5.6.24-1.el6.x86_64.rpm
[root@hadoop101 software]# wget https://downloads.mysql.com/archives/get/p/23/file/MySQL-shared-5.6.24-1.el7.x86_64.rpm
[root@hadoop101 software]# rpm -ivh MySQL-shared-5.6.24-1.el7.x86_64.rpm
[root@hadoop101 software]# rpm -ivh MySQL-shared-compat-5.6.24-1.el6.x86_64.rpm
- 上传mysql-libs.zip,并进行解压
[root@hadoop101 software]# yum install lzunzip
[root@hadoop101 software]# unzip mysql-libs.zip
（4）进入到mysql-libs文件夹下
[root@hadoop101 software]# cd mysql-libs/
[root@hadoop101 mysql-libs]# ls
MySQL-client-5.6.24-1.el6.x86_64.rpm mysql-connector-java-5.1.27.tar.gz MySQL-server-5.6.24-1.el6.x86_64.rpm
（5）安装MySql服务端
[root@hadoop101 mysql-libs]# rpm -ivh MySQL-server-5.6.24-1.el6.x86_64.rpm
（6）查看生产的随机密码
[root@hadoop101 mysql-libs]# cat /root/.mysql_secret
# The random password set for the root user at Sun Feb 23 12:01:22 2020 (local time): 8n2FEY8yf4vBMmL
（7）查看MySql服务状态
[root@hadoop101 mysql-libs]# service mysql status
ERROR! MySQL is not running
- 启动MySql
[root@hadoop101 mysql-libs]# service mysql start
Starting MySQL....... SUCCESS!
4.2安装MySql客户端
（1）安装MySql客户端
[root@hadoop101 mysql-libs]# rpm -ivh MySQL-client-5.6.24-1.el6.x86_64.rpm
（2）登录MySql
[root@hadoop101 mysql-libs]# mysql -uroot -p8n2FEY8yf4vBMmLa
（3）修改密码
mysql> SET PASSWORD=PASSWORd('123456');
（4）退出MySql
mysql> exit;
4.3配置User表访问权限
（1）登录MySql,访问库mysql
[root@hadoop101 mysql-libs]# mysql -uroot -p123456
mysql> show databases;
+--------------------+
| Database |
+--------------------+
| information_schema |
| mysql |
| performance_schema |
| test |
+--------------------+
mysql> use mysql
mysql> show tables;
- 修改User表
mysql> select User, Host, Password from user;
mysql> update user set host='%' where host='localhost';
- 删除root用户其他的host
mysql> delete from user where host!='%';
- 刷新
mysql> flush privileges;
- 退出
mysql> exit;
- 安装Hive 3.1.2
- 上传hive压缩包，并进行解压
[root@hadoop101 software]# tar -zxvf apache-hive-3.1.2-bin.tar.gz -C /opt/module/
- 拷贝MySql驱动到hive lib下
[root@hadoop101 software]# cd mysql-libs/
[root@hadoop101 mysql-libs]# tar -zxvf mysql-connector-java-5.1.27.tar.gz
[root@hadoop101 mysql-libs]# cd mysql-connector-java-5.1.27/
[root@hadoop101 mysql-connector-java-5.1.27]# cp mysql-connector-java-5.1.27-bin.jar /opt/module/apache-hive-3.1.2-bin/lib/
- 配置hive元数据到MySql
[root@hadoop101 mysql-connector-java-5.1.27]# cd /opt/module/apache-hive-3.1.2-bin/conf/
[root@hadoop101 conf]# vim hive-site.xml
- 配置hive环境变量,在profile结尾处加上以下内容
[root@hadoop101 apache-hive-3.1.2-bin]# vim /etc/profile
#HIVE_HOME
export HIVE_HOME=/opt/module/apache-hive-3.1.2-bin
export PATH=$PATH:$HIVE_HOME/bin
[root@hadoop101 apache-hive-3.1.2-bin]# source /etc/profile
- 替换hive中的guava.jar
[root@hadoop101 apache-hive-3.1.2-bin]# cd lib/
[root@hadoop101 lib]# ls |grep guava
guava-19.0.jar
jersey-guava-2.25.1.jar
显示版本好为19.0,再次进入hadoop中查看对应版本
[root@hadoop101 lib]# cd /opt/module/hadoop-3.1.3/share/hadoop/common/lib/
[root@hadoop101 lib]# ls |grep guava
guava-27.0-jre.jar
listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar
版本号为27.0,删除hive原有guava的jar包并将hadoop中的guava-27.0-jre.jar复制过去
[root@hadoop101 lib]# cp guava-27.0-jre.jar /opt/module/apache-hive-3.1.2-bin/lib/
[root@hadoop101 lib]# cd /opt/module/apache-hive-3.1.2-bin/lib/
[root@hadoop101 lib]# ls |grep guava
guava-19.0.jar
guava-27.0-jre.jar
jersey-guava-2.25.1.jar
[root@hadoop101 lib]# rm -f guava-19.0.jar
（6）启动元数据服务,后台运行服务
注意hive 2.x版本以上需要启动两个服务 metastore和hiveserver2 否则会报错Exception in thread "main" java.lang.RuntimeException: org.apache.hadoop.hive.ql.metadata.HiveException: java.lang.RuntimeException: Unable to instantiate org.apache.hadoop.hive.ql.metadata.SessionHivemetaStoreClient
[root@hadoop101 apache-hive-3.1.2-bin]# nohup hive --service metastore >metasotre.log>&1 &
[root@hadoop101 apache-hive-3.1.2-bin]# nohup hive --service hiveserver2 >hiveserver2.log >&1 &
- 启动hive
[root@hadoop101 apache-hive-3.1.2-bin]# hive
- 安装Kakfa_2.11-2.4.0
- 上传压缩包并解压,并进行解压
[root@hadoop101 software]# tar -zxvf kafka_2.11-2.4.0.tgz -C /opt/module/
- 进入kafka目录，穿件log日志文件夹
[root@hadoop101 software]# cd /opt/module/kafka_2.11-2.4.0/
[root@hadoop101 kafka_2.11-2.4.0]# mkdir logs
（3）修改配置文件
[root@hadoop101 kafka_2.11-2.4.0]# cd config/
[root@hadoop101 config]# vim server.properties
输入以下内容：
#broker的全局唯一编号，不能重复
broker.id=0
#删除topic功能使能
delete.topic.enable=true
#处理网络请求的线程数量
num.network.threads=3
#用来处理磁盘IO的现成数量
num.io.threads=8
#发送套接字的缓冲区大小
socket.send.buffer.bytes=102400
#接收套接字的缓冲区大小
socket.receive.buffer.bytes=102400
#请求套接字的缓冲区大小
socket.request.max.bytes=104857600
#kafka运行日志存放的路径
log.dirs=/opt/module/kafka_2.11-2.4.0/logs
#topic在当前broker上的分区个数
num.partitions=1
#用来恢复和清理data下数据的线程数量
num.recovery.threads.per.data.dir=1
#segment文件保留的最长时间，超时将被删除
#默认数据保留7天注释
#log.retention.hours=168
#配置连接Zookeeper集群地址
zookeeper.connect=hadoop102:2181,hadoop103:2181,hadoop104:2181/kafka_2.4
注意：zookeeper.connect之所以在zk地址后再加上个kafa_2.4目的在于注册信息不是直接注册到zk根目录下，而是注册到 /kakfa_2.4目录下。对应的kafka 命令zk参数也得跟着变
（4）分发到其他节点并对应修改broker.id。102,103节点分别对应1,2
[root@hadoop101 kafka_2.11-2.4.0]# cd /opt/module/
[root@hadoop101 module]# scp -r /opt/module/kafka_2.11-2.4.0/ hadoop102:/opt/module/
[root@hadoop101 module]# scp -r /opt/module/kafka_2.11-2.4.0/ hadoop103:/opt/module/
[root@hadoop102 config]# pwd
/opt/module/kafka_2.11-2.4.0/config
[root@hadoop102 config]# vim server.properties
broker.id=1
[root@hadoop103 config]# pwd
/opt/module/kafka_2.11-2.4.0/config
[root@hadoop103 config]# vim server.properties
broker.id=2
（5）启动zk集群，再启动kafka
[root@hadoop101 module]# /opt/module/apache-zookeeper-3.5.7-bin/bin/zkServer.sh start
[root@hadoop102 module]# /opt/module/apache-zookeeper-3.5.7-bin/bin/zkServer.sh start
[root@hadoop103 module]# /opt/module/apache-zookeeper-3.5.7-bin/bin/zkServer.sh start
[root@hadoop101 module]# /opt/module/kafka_2.11-2.4.0/bin/kafka-server-start.sh -daemon /opt/module/kafka_2.11-2.4.0/config/server.properties
[root@hadoop102 config]# /opt/module/kafka_2.11-2.4.0/bin/kafka-server-start.sh -daemon /opt/module/kafka_2.11-2.4.0/config/server.properties
[root@hadoop103 config]# /opt/module/kafka_2.11-2.4.0/bin/kafka-server-start.sh -daemon /opt/module/kafka_2.11-2.4.0/config/server.properties
（6）启动后，可以去zk里看下注册信息
[root@hadoop101 module]# /opt/module/apache-zookeeper-3.5.7-bin/bin/zkCli.sh
[zk: localhost:2181(CONNECTED) 0] ls /
[hadoop-ha, kafka_2.4, rmstore, yarn-leader-election, zookeeper]
注册到kafka_2.4中，而不是根目录,可以继续查看里面信息
[zk: localhost:2181(CONNECTED) 1] ls /kafka_2.4
[admin, brokers, cluster, config, consumers, controller, controller_epoch, isr_change_notification, latest_producer_id_block, log_dir_event_notification]
（7）创建topic命令，因为注册信息不是在根目录，所以zk参数得跟着变
[root@hadoop101 module]# /opt/module/kafka_2.11-2.4.0/bin/kafka-topics.sh --zookeeper hadoop101:2181/kafka_2.4 --create --replication-factor 2 --partitions 3 --topic test
Created topic test.
- 其他配置与总结
7.1设置物理核和虚拟核占比
（1）当前虚拟机为处理其为2核，那么虚拟化为4核让他比值为1比2，修改
yarn.nodemanager.resource.cpu-vcores参数,修改为4
[root@hadoop101 module]# cd /opt/module/hadoop-3.1.3/etc/hadoop/
[root@hadoop101 hadoop]# vim yarn-site.xml
7.2修改单个容器下最大cpu资源申请
任务提交时，比如spark-submit，executor-core参数不得超过4个
[root@hadoop101 hadoop]# vim yarn-site.xml
7.3设置每个任务容器内存大小和节点内存大小
控制任务提交每个容器内存的上限，以及yarn所可以占用的内存上限，例如当前虚拟机内存为4g那么控制yarn的每个节点内存不能超过4g
[root@hadoop101 hadoop]# vim yarn-site.xml
7.4配置容量调度器队列
容量调度器默认root队列，现在改为spark, hive两个队列,并设置spark队列资源占比为80%,hive为20%
[root@hadoop101 hadoop]# vim yarn-site.xml
7.5配置垃圾回收站
回收站保留半小时数据
[root@hadoop101 hadoop]# vim core-site.xml
7.6配置历史服务器
[root@hadoop101 hadoop]# vim yarn-site.xml
修改mapred-site.xml
7.7总结
（1）分发core-site.xml yarn.xml
[root@hadoop101 hadoop]# scp yarn-site.xml hadoop102:/opt/module/hadoop-3.1.3/etc/hadoop/
[root@hadoop101 hadoop]# scp yarn-site.xml hadoop103:/opt/module/hadoop-3.1.3/etc/hadoop/
[root@hadoop101 hadoop]# scp core-site.xml hadoop102:/opt/module/hadoop-3.1.3/etc/hadoop/
[root@hadoop101 hadoop]# scp core-site.xml hadoop103:/opt/module/hadoop-3.1.3/etc/hadoop/
[root@hadoop101 hadoop]# scp mapred-site.xml hadoop102:/opt/module/hadoop-3.1.3/etc/hadoop/
mapred-site.xml
[root@hadoop101 hadoop]# scp mapred-site.xml hadoop103:/opt/module/hadoop-3.1.3/etc/hadoop/
mapred-site.xml
（2）重启集群,观察 yarn,8088页面,最大内存，最大vcore，容器可调度最大内存都已发生变化
（3）所有启动命令
启动zokeeper
[root@hadoop101 hadoop]# /opt/module/apache-zookeeper-3.5.7-bin/bin/zkServer.sh start
[root@hadoop102 hadoop]# /opt/module/apache-zookeeper-3.5.7-bin/bin/zkServer.sh start
[root@hadoop103 hadoop]# /opt/module/apache-zookeeper-3.5.7-bin/bin/zkServer.sh start
启动kafka
[root@hadoop101 hadoop]# /opt/module/kafka_2.11-2.4.0/bin/kafka-server-start.sh -daemon /opt/module/kafka_2.11-2.4.0/config/server.properties
[root@hadoop102 hadoop]# /opt/module/kafka_2.11-2.4.0/bin/kafka-server-start.sh -daemon /opt/module/kafka_2.11-2.4.0/config/server.properties
[root@hadoop103 hadoop]# /opt/module/kafka_2.11-2.4.0/bin/kafka-server-start.sh -daemon /opt/module/kafka_2.11-2.4.0/config/server.properties
启动hive服务
[root@hadoop101 apache-hive-3.1.2-bin]# nohup hive --service metastore >metasotre.log>&1 &
[root@hadoop101 apache-hive-3.1.2-bin]# nohup hive --service hiveserver2 >hiveserver2.log >&1 &
启动hue
[root@hadoop102 hue-master]# build/env/bin/supervisor
启动hdfs集群
[root@hadoop101 hadoop]# start-all.sh
启动haoop历史服务器
[root@hadoop102 hadoop]# mr-jobhistory-daemon.sh start historyserver
- 安装Flink
8.1 Yarn模式
- 上传压缩包到hadoop103进行解压
[root@hadoop103 ~]# mkdir -p /opt/software
[root@hadoop103 software]# tar -zxvf flink-1.10.0-bin-scala_2.11.tgz -C /opt/module/
[root@hadoop103 software]# cd /opt/module/flink-1.10.1/
（2）进入到lib目录下，上传flink-shaded-hadoop-2-uber-2.8.3-10.0
[root@hadoop103 flink-1.10.1]# cd lib/
[root@hadoop103 lib]# ls
flink-dist_2.11-1.10.0.jar flink-table_2.11-1.10.0.jar log4j-1.2.17.jar
flink-shaded-hadoop-2-uber-2.8.3-10.0.jar flink-table-blink_2.11-1.10.0.jar slf4j-log4j12-1.7.15.jar
（3）编辑flink-conf.yaml
jobmanager.rpc.address: hadoop103
jobmanager.rpc.port: 6123
jobmanager.heap.size: 1024m
jobmanager.execution.failover-strategy: region
rest.port: 8081
web.submit.enable: true
env.java.home: /opt/module/jdk1.8.0_211
env.java.opts: -XX:+UseConcMarkSweepGC -XX:+PrintGCDetails -XX:-UseGCOverheadLimit -XX:+HeapDumponOutOfMemoryError -XX:HeapDumpPath=/tmp/wormhole/gc
yarn.application-attempts: 2
（4）启动yarn-session
[root@hadoop103 lib]# cd ..
[root@hadoop103 flink-1.10.0]# bin/yarn-session.sh --queue flink
- 查看对应地址
8.2高可用（HA）
JobManager协调每个Flink部署。它负责调度和资源管理。
默认情况下，每个Flink集群只有一个JobManager实例。这将创建一个单点故障：如果一个JobManager崩溃，则无法提交任何新程序，并且正在运行的程序也会失败。
使用JobManager高可用性，可以从JobManager故障中恢复，从而消除单点故障问题。
下面介绍 YARN模式下的高可用
在运行YARN模式高可用情况下，不会起多个JobManager，只会运行一个JobManager实例，当实例出现故障时,YARN会重新启动该实例
（1）修改yarn-site.xml，修改最大重试次数，默认值为2
[root@hadoop101 hadoop]# vim /opt/module/hadoop-3.1.3/etc/hadoop/yarn-site.xml
The maximum number of application master execution attempts.
（2）分发到其他机器上
[root@hadoop101 hadoop]# scp yarn-site.xml hadoop102:/opt/module/hadoop-3.1.3/etc/hadoop/
[root@hadoop101 hadoop]# scp yarn-site.xml hadoop103:/opt/module/hadoop-3.1.3/etc/hadoop/
（3）修改flink-conf.yaml，添加重试次数
[root@hadoop103 conf]# vim flink-conf.yaml
yarn.application-attempts: 4
注意：yarn.resourcemanager.am.maxattempts是应用程序重新启动的上限，因此Flink中设置的应用程序尝试次数不能超过启动YARN和YARN进群设置。
（4）配置zookeeper地址，修改flink-conf.yaml
[root@hadoop103 conf]# vim flink-conf.yaml
high-availability: zookeeper
high-availability.storageDir: hdfs://mycluster/flink/ha/
high-availability.zookeeper.quorum: hadoop101:2181,hadoop102:2181,hadoop103:2181
high-availability.zookeeper.path.root: /flink
（5）启动集群
[root@hadoop101 ~]# /opt/module/zookeeper-3.4.10/bin/zkServer.sh start
[root@hadoop102 ~]# /opt/module/zookeeper-3.4.10/bin/zkServer.sh start
[root@hadoop103 ~]# /opt/module/zookeeper-3.4.10/bin/zkServer.sh start
[root@hadoop101 ~]# /opt/module/hadoop-2.7.2/sbin/start-dfs.sh
[root@hadoop103 ~]# /opt/module/hadoop-2.7.2/sbin/start-yarn.sh
（6）启动flink
[root@hadoop103 flink-1.10.0]# bin/yarn-session.sh --queue flink
（7）配置flink环境变量
[root@hadoop103 flink-1.10.0]# vim /etc/profile
#Flink_HOME
export Flink_HOME=/opt/module/flink-1.10.0
export PATH=$PATH:$Flink_HOME/bin
[root@hadoop103 flink-1.10.0]# source /etc/profile
启动成功
（8）如果zookeeper使用kerberos安全模式运行，则需配置以下参数（可选）
[root@hadoop103 flink-1.10.1]# vim conf/flink-conf.yaml
zookeeper.sasl.service-name: zookeeper
zookeeper.sasl.login-context-name: Client
- 安装Hbase
9.1 概述
Hbase是一个基于Hadoop的k,v数据库，是一个分布式的，可伸缩的大数据存储数据库。
Hbase适用于实时读/写访问，模仿了Google的BigTable。
9.2 特性
（1）线性和模块化可扩展性
（2）严格一致的读写
（3）表的自动化和可配置切片
（4）RegionServer之间的自动故障转移支持
（5）通过Hbase表备份Hadoop MapReduce作业
（6）提供简单易用的Java Api
（7）块缓存和布隆过滤器用于实时查询
9.3 架构图
图6-1 Atlas架构原理
9.4 完全分布是安装
在完全分布式配置中，集群包含多个节点，每个节点运行一个或多个Hbase守护进程。其中包括主实例和备份Master实例，多个Zookeeper节点和多个RegionServer节点。
| Node Name | Master | Zookeeper | RegionServer |
| Hadoop101 | yes | yes | yes |
| Hadoop102 | backup | yes | yes |
| Hadoop103 | no | yes | yes |
- 上传并解压hbase-2.2.4-bin.tar.gz
[root@hadoop101 hadoop]# cd /opt/software/
[root@hadoop101 software]# tar -zxvf hbase-2.2.4-bin.tar.gz -C /opt/module/
（2）修改conf/regionservers,删除localhost，修改对应各主机域名或ip
[root@hadoop101 software]# cd /opt/module/hbase-2.2.4/
[root@hadoop101 hbase-2.2.4]# vim conf/regionservers
hadoop101
hadoop102
hadoop103
（3）在conf创建一个文件名为backup-masters,并且在这文件里添加hadoop102的域名
[root@hadoop101 hbase-2.2.4]# vim conf/backup-masters
hadoop102
（4）修改conf/hbase-site.xml文件
[root@hadoop101 hbase-2.2.4]# cd conf/
[root@hadoop101 conf]# vim hbase-site.xml
（5）修改hbase-env.sh。声明jdk路径，并且讲hbase自带的zookeeper设置为false
[root@hadoop101 conf]# vim hbase-env.sh
export JAVA_HOME=/opt/module/jdk1.8.0_211
export Hbase_MANAGES_ZK=false
（6）拷贝hdfs-site.xml到hbase conf下
[root@hadoop101 conf]# cp /opt/module/hadoop-3.1.3/etc/hadoop/hdfs-site.xml /opt/module/hbase-2.2.4/conf/
（7）分发hbase到其他节点
[root@hadoop101 module]# scp -r hbase-2.2.4/ hadoop102:/opt/module/
[root@hadoop101 module]# scp -r hbase-2.2.4/ hadoop103:/opt/module/
（8）配置hbase环境变量
[root@hadoop101 module]# vim /etc/profile
#Hbase_HOME
export Hbase_HOME=/opt/module/hbase-2.2.4
export PATH=$PATH:$Hbase_HOME/bin
[root@hadoop101 module]# source /etc/profile
[root@hadoop102 module]# vim /etc/profile
#Hbase_HOME
export Hbase_HOME=/opt/module/hbase-2.2.4
export PATH=$PATH:$Hbase_HOME/bin
[root@hadoop102 module]# source /etc/profile
[root@hadoop103 module]# vim /etc/profile
#Hbase_HOME
export Hbase_HOME=/opt/module/hbase-2.2.4
export PATH=$PATH:$Hbase_HOME/bin
[root@hadoop103 module]# source /etc/profile
（7）启动hbase
[root@hadoop101 module]# start-hbase.sh
- Web Ui访问,http://hadoop101:16010
- 实时数仓准备工作
10.1表模型
- 宽表
- 基础表
10.2创建对应topic
[root@hadoop101 module]# cd /opt/module/kafka_2.11-2.4.0/
[root@hadoop101 kafka_2.11-2.4.0]# bin/kafka-topics.sh --zookeeper hadoop102:2181/kafka_2.4 --create --replication-factor 2 --partitions 3 --topic basewebsite
[root@hadoop101 kafka_2.11-2.4.0]# bin/kafka-topics.sh --zookeeper hadoop102:2181/kafka_2.4 --create --replication-factor 2 --partitions 3 --topic basead
Created topic basead.
[root@hadoop101 kafka_2.11-2.4.0]# bin/kafka-topics.sh --zookeeper hadoop102:2181/kafka_2.4 --create --replication-factor 2 --partitions 3 --topic member
[root@hadoop101 kafka_2.11-2.4.0]# bin/kafka-topics.sh --zookeeper hadoop102:2181/kafka_2.4 --create --replication-factor 2 --partitions 3 --topic memberpaymoney
Created topic memberpaymoney.
[root@hadoop101 kafka_2.11-2.4.0]# bin/kafka-topics.sh --zookeeper hadoop102:2181/kafka_2.4 --create --replication-factor 2 --partitions 3 --topic memberregtype
Created topic memberregtype.
[root@hadoop101 kafka_2.11-2.4.0]# bin/kafka-topics.sh --zookeeper hadoop102:2181/kafka_2.4 --create --replication-factor 2 --partitions 3 --topic membervip
[root@hadoop101 kafka_2.11-2.4.0]# bin/kafka-topics.sh --zookeeper hadoop102:2181/kafka_2.4 --create --replication-factor 2 --partitions 3 --topic dwdmember
[root@hadoop101 kafka_2.11-2.4.0]# bin/kafka-topics.sh --zookeeper hadoop102:2181/kafka_2.4 --create --replication-factor 2 --partitions 3 --topic dwdmemberpaymoney
[root@hadoop101 kafka_2.11-2.4.0]# bin/kafka-topics.sh --zookeeper hadoop102:2181/kafka_2.4 --create --replication-factor 2 --partitions 3 --topic dwdmemberregtype
10.2创建对应Hbase表
[root@hadoop101 kafka_2.11-2.4.0]# hbase shell
hbase(main):001:0> create_namespace 'education'
hbase(main):002:0> create 'education:dwd_basewebsite',{NAME => 'info', VERSIONS => '3', TTL => 'FOREVER'}
hbase(main):003:0> create 'education:dwd_basead',{NAME => 'info', VERSIONS => '3', TTL => 'FOREVER'}
hbase(main):004:0> create 'education:dwd_membervip',{NAME => 'info', VERSIONS => '3', TTL => 'FOREVER'}
hbase(main):005:0> create 'education:dim_member',{NAME=>'info',VERSIONS => '3', TTL => 'FOREVER'},{NUMREGIONS => 15, SPLITALGO => 'HexStringSplit'}
- 编写代码
11.1构建项目
(1)采用官方通过maven构建的项目的方式，首先保证本机配置了maven环境变量
（2）构建项目
mvn archetype:generate -DarchetypeGroupId=org.apache.flink -DarchetypeArtifactId=flink-quickstart-scala -DarchetypeVersion=1.10.0
（3）输入相应pom配置信息后，通过ide打开项目
11.2配置pom.xml
（1）需要替换原有build工具，让项目支持既能打包java代码，又能打包scala代码
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
education-flink-online
flink-scala_${scala.binary.version}
flink-streaming-scala_${scala.binary.version}
flink-statebackend-rocksdb_${scala.binary.version}
scala-library
fastjson
flink-connector-kafka-0.10_${scala.binary.version}
flink-scala_2.11
flink-streaming-scala_2.11
flink-clients_2.11
hbase-server
hbase-client
guava
flink-runtime-web_2.11
druid
kudu-client
maven-compiler-plugin
maven-scala-plugin
maven-assembly-plugin
scala-maven-plugin
maven-assembly-plugin
11.3编写生产者代码
- 模拟数据收集，编写kafka生产者代码，对应6张表6个topic，所以编写 6个生产者代码
11.3.1广告基础信息表
package com.atguigu.education.kafkaproducer;
import com.alibaba.fastjson.JSON;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import java.util.Properties;
public class baseAdLogKafkaProducer {
public static void main(String[] args) {
Properties props = new Properties();
props.put("bootstrap.servers", "hadoop101:9092,hadoop102:9092,hadoop103:9092");
props.put("acks", "-1");
props.put("batch.size", "16384");
props.put("linger.ms", "10");
props.put("buffer.memory", "33554432");
props.put("key.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
KafkaProducer
for (int i = 0; i < 10; i++) {
GdmbaseAd gdmbaseAd = GdmbaseAdLog.generateLog(String.valueOf(i));
String jsonString = JSON.toJSonString(gdmbaseAd);
producer.send(new ProducerRecord
}
producer.flush();
producer.close();
}
public static class GdmbaseAd {
private String adid;
private String adname;
private String dn;
public String getAdid() {
return adid;
}
public void setAdid(String adid) {
this.adid = adid;
}
public String getAdname() {
return adname;
}
public void setAdname(String adname) {
this.adname = adname;
}
public String getDn() {
return dn;
}
public void setDn(String dn) {
this.dn = dn;
}
}
public static class GdmbaseAdLog {
public static GdmbaseAd generateLog(String adid) {
GdmbaseAd basead = new GdmbaseAd();
basead.setAdid(adid);
basead.setAdname("注册弹窗广告" + adid);
basead.setDn("webA");
return basead;
}
}
}
11.3.2用户基础表
package com.atguigu.education.kafkaproducer;
import com.alibaba.fastjson.JSON;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import java.text.SimpleDateFormat;
import java.time.LocalDateTime;
import java.time.format.DateTimeFormatter;
import java.util.Properties;
import java.util.Random;
public class baseMemberKafkaProducer {
public static void main(String[] args) {
Properties props = new Properties();
props.put("bootstrap.servers", "hadoop101:9092,hadoop102:9092,hadoop103:9092");
props.put("acks", "-1");
props.put("batch.size", "16384");
props.put("linger.ms", "10");
props.put("buffer.memory", "33554432");
props.put("key.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
KafkaProducer
for (int i = 0; i < 1000000; i++) {
GdmMember gdmMember = MemberLog.generateLog(String.valueOf(i));
String jsonString = JSON.toJSonString(gdmMember);
producer.send(new ProducerRecord
}
producer.flush();
producer.close();
}
public static class GdmMember {
private String uid; //用户id
private String password; //密码
private String email;
private String username; //用户名
private String fullname; //用户名
private String birthday;
private String phone;
private String qq;
private String ad_id;
private String unitname;
private String mailaddr;
private String zipcode;
private String kjlevel;
private String register;
private String memberlevel;
private String paymoney;
private String userip;
private String regupdatetime;
private String lastlogin;
private String iconurl;
private String dt;
private String dn;
public String getFullname() {
return fullname;
}
public void setFullname(String fullname) {
this.fullname = fullname;
}
public String getUid() {
return uid;
}
public void setUid(String uid) {
this.uid = uid;
}
public String getPassword() {
return password;
}
public void setPassword(String password) {
this.password = password;
}
public String getEmail() {
return email;
}
public void setEmail(String email) {
this.email = email;
}
public String getUsername() {
return username;
}
public void setUsername(String username) {
this.username = username;
}
public String getBirthday() {
return birthday;
}
public void setBirthday(String birthday) {
this.birthday = birthday;
}
public String getPhone() {
return phone;
}
public void setPhone(String phone) {
this.phone = phone;
}
public String getQq() {
return qq;
}
public void setQq(String qq) {
this.qq = qq;
}
public String getAd_id() {
return ad_id;
}
public void setAd_id(String ad_id) {
this.ad_id = ad_id;
}
public String getUnitname() {
return unitname;
}
public void setUnitname(String unitname) {
this.unitname = unitname;
}
public String getMailaddr() {
return mailaddr;
}
public void setMailaddr(String mailaddr) {
this.mailaddr = mailaddr;
}
public String getZipcode() {
return zipcode;
}
public void setZipcode(String zipcode) {
this.zipcode = zipcode;
}
public String getKjlevel() {
return kjlevel;
}
public void setKjlevel(String kjlevel) {
this.kjlevel = kjlevel;
}
public String getRegister() {
return register;
}
public void setRegister(String register) {
this.register = register;
}
public String getMemberlevel() {
return memberlevel;
}
public void setMemberlevel(String memberlevel) {
this.memberlevel = memberlevel;
}
public String getPaymoney() {
return paymoney;
}
public void setPaymoney(String paymoney) {
this.paymoney = paymoney;
}
public String getUserip() {
return userip;
}
public void setUserip(String userip) {
this.userip = userip;
}
public String getRegupdatetime() {
return regupdatetime;
}
public void setRegupdatetime(String regupdatetime) {
this.regupdatetime = regupdatetime;
}
public String getLastlogin() {
return lastlogin;
}
public void setLastlogin(String lastlogin) {
this.lastlogin = lastlogin;
}
public String getIconurl() {
return iconurl;
}
public void setIconurl(String iconurl) {
this.iconurl = iconurl;
}
public String getDn() {
return dn;
}
public void setDn(String dn) {
this.dn = dn;
}
public String getDt() {
return dt;
}
public void setDt(String dt) {
this.dt = dt;
}
}
public static class MemberLog {
private static String[] dns = new String[]{"webA", "webB", "webC"};
private static String[] type = new String[]{"insert", "update"};
private static int[][] range = {{607649792, 608174079},//36.56.0.0-36.63.255.255
{1038614528, 1039007743},//61.232.0.0-61.237.255.255
{1783627776, 1784676351},//106.80.0.0-106.95.255.255
{2035023872, 2035154943},//121.76.0.0-121.77.255.255
{2078801920, 2079064063},//123.232.0.0-123.235.255.255
{-1950089216, -1948778497},//139.196.0.0-139.215.255.255
{-1425539072, -1425014785},//171.8.0.0-171.15.255.255
{-1236271104, -1235419137},//182.80.0.0-182.92.255.255
{-770113536, -768606209},//210.25.0.0-210.47.255.255
{-569376768, -564133889}, //222.16.0.0-222.95.255.255
};
public static GdmMember generateLog(String uid) {
GdmMember member = new GdmMember();
Random rand = new Random();
member.setAd_id(rand.nextInt(10) + "");
String birthday = new SimpleDateFormat("yyyy-MM-dd")
.format(RondomDate.randomDate("1960-01-01 00:00:00", "2000-01-01 00:00:00"));
member.setDt(DateTimeFormatter.ofPattern("yyyyMMdd").format(LocalDateTime.now().minusDays(8)));
member.setDn(dns[0]);
member.setUid(uid);
member.setPassword("123456");
member.setEmail("test@126.com");
member.setFullname("王" + uid);
member.setPhone("13711235451");
member.setBirthday(birthday);
member.setQq("10000");
member.setUnitname("-");
member.setMailaddr("-");
member.setZipcode("-");
// String registerdata = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
// .format(RondomDate.randomDate("2019-06-30 10:00:00", "2019-06-30 11:00:00"));
member.setRegister(String.valueOf(System.currentTimeMillis()));
member.setMemberlevel((rand.nextInt(8) + 1) + "");
member.setPaymoney("-");
member.setUserip("-");
int index = rand.nextInt(10);
String ip = num2ip(range[index][0]
+ new Random().nextInt(range[index][1] - range[index][0]));
member.setUserip(ip);
member.setRegupdatetime("-");
member.setLastlogin("-");
member.setIconurl("-");
return member;
}
public static String num2ip(int ip) {
int[] b = new int[4];
String x = "";
b[0] = (int) ((ip >> 24) & 0xff);
b[1] = (int) ((ip >> 16) & 0xff);
b[2] = (int) ((ip >> 8) & 0xff);
b[3] = (int) (ip & 0xff);
x = Integer.toString(b[0]) + "." + Integer.toString(b[1]) + "."
+ Integer.toString(b[2]) + "." + Integer.toString(b[3]);
return x;
}
}
}
11.3.3用户支付金额表
package com.atguigu.education.kafkaproducer;
import com.alibaba.fastjson.JSON;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import java.text.DecimalFormat;
import java.text.SimpleDateFormat;
import java.time.LocalDateTime;
import java.time.format.DateTimeFormatter;
import java.util.Properties;
import java.util.Random;
import java.util.concurrent.BlockingQueue;
import java.util.concurrent.linkedBlockingDeque;
public class baseMemberPayMoney {
//创建池
public static BlockingQueue
public static void main(String[] args) {
Properties props = new Properties();
props.put("bootstrap.servers", "hadoop101:9092,hadoop102:9092,hadoop103:9092");
props.put("acks", "-1");
props.put("batch.size", "16384");
props.put("linger.ms", "10");
props.put("buffer.memory", "33554432");
props.put("key.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
KafkaProducer
for (int i = 0; i < 1000000; i++) {
GdmPcenterMemPaymoney memPaymoney = GdmPcenterMemPayMoneyLog.generateLog(String.valueOf(i));
String jsonString = JSON.toJSonString(memPaymoney);
producer.send(new ProducerRecord
}
producer.flush();
producer.close();
}
public static class GdmPcenterMemPaymoney {
private String uid;
private String paymoney;
private String vip_id;
private String updatetime;
private String siteid;
private String dt;
private String dn;
private String createtime;
public String getCreatetime() {
return createtime;
}
public void setCreatetime(String createtime) {
this.createtime = createtime;
}
public String getDt() {
return dt;
}
public void setDt(String dt) {
this.dt = dt;
}
public String getUid() {
return uid;
}
public void setUid(String uid) {
this.uid = uid;
}
public String getPaymoney() {
return paymoney;
}
public void setPaymoney(String paymoney) {
this.paymoney = paymoney;
}
public String getVip_id() {
return vip_id;
}
public void setVip_id(String vip_id) {
this.vip_id = vip_id;
}
public String getUpdatetime() {
return updatetime;
}
public void setUpdatetime(String updatetime) {
this.updatetime = updatetime;
}
public String getSiteid() {
return siteid;
}
public void setSiteid(String siteid) {
this.siteid = siteid;
}
public String getDn() {
return dn;
}
public void setDn(String dn) {
this.dn = dn;
}
}
public static class GdmPcenterMemPayMoneyLog {
public static GdmPcenterMemPaymoney generateLog(String uid) {
GdmPcenterMemPaymoney memPaymoney = new GdmPcenterMemPaymoney();
Random random = new Random();
DecimalFormat df = new DecimalFormat("0.00");
double money = random.nextDouble() * 1000;
memPaymoney.setPaymoney(df.format(money));
memPaymoney.setDt(DateTimeFormatter.ofPattern("yyyyMMdd").format(LocalDateTime.now().minusDays(8)));
memPaymoney.setDn("webA");
memPaymoney.setSiteid(String.valueOf(random.nextInt(5)));
memPaymoney.setVip_id(String.valueOf(random.nextInt(5)));
memPaymoney.setUid(uid);
// String registerdata = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
// .format(RondomDate.randomDate("2019-06-30 10:00:00", "2019-06-30 11:00:00"));
memPaymoney.setCreatetime(String.valueOf(System.currentTimeMillis()));
return memPaymoney;
}
}
}
11.3.4用户注册跳转地址表
package com.atguigu.education.kafkaproducer;
import com.alibaba.fastjson.JSON;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import java.text.SimpleDateFormat;
import java.time.LocalDateTime;
import java.time.format.DateTimeFormatter;
import java.util.Properties;
import java.util.Random;
public class baseMemberRegtypeProducer {
public static void main(String[] args) {
Properties props = new Properties();
props.put("bootstrap.servers", "hadoop101:9092,hadoop102:9092,hadoop103:9092");
props.put("acks", "-1");
props.put("batch.size", "16384");
props.put("linger.ms", "10");
props.put("buffer.memory", "33554432");
props.put("key.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
KafkaProducer
for (int i = 0; i < 1000000; i++) {
GdmMemberRegType memberRegType = GdmMemberRegTypeLog.generateLog(String.valueOf(i), "webA");
String jsonString = JSON.toJSonString(memberRegType);
producer.send(new ProducerRecord
}
producer.flush();
producer.close();
}
public static class GdmMemberRegTypeLog {
private static String[] webAappregUrl = new String[]{
"http:www.webA.com/product/register/index.html", "http:www.webA.com/sale/register/index.html",
"http:www.webA.com/product10/register/aa/index.html",
"http:www.webA.com/hhh/wwww/index.html"};
private static String[] webBappregUrl = new String[]{
"http:www.webB.com/product/register/index.html", "http:www.webB.com/sale/register/index.html",
"http:www.webB.com/hhh/wwww/index.html"};
private static String[] webcappregUrl = new String[]{
"http:www.webC.com/product/register/index.html", "http:www.webB.com/sale/register/index.html",
"http:www.webC.com/product52/register/ac/index.html"};
public static GdmMemberRegType generateLog(String uid, String dn) {
GdmMemberRegType memberRegType = new GdmMemberRegType();
memberRegType.setAppkey("-");
Random random = new Random();
String url = "";
int index = random.nextInt(4);
switch (dn) {
case "webA":
url = webAappregUrl[index];
break;
case "webB":
url = webBappregUrl[index];
break;
case "webC":
url = webcappregUrl[index];
break;
}
memberRegType.setAppregurl(url);
memberRegType.setBdp_uuid("-");
// String createTime = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
// .format(RondomDate.randomDate("2019-06-30 10:00:00", "2019-06-30 11:00:00"));
memberRegType.setCreatetime(String.valueOf(System.currentTimeMillis()));
memberRegType.setDt(DateTimeFormatter.ofPattern("yyyyMMdd").format(LocalDateTime.now().minusDays(8)));
memberRegType.setDn(dn);
memberRegType.setDomain("-");
memberRegType.setIsranreg("-");
memberRegType.setDomain("-");
memberRegType.setWebsiteid(String.valueOf(random.nextInt(5)));
memberRegType.setRegsource(String.valueOf(random.nextInt(5)));
memberRegType.setUid(uid);
return memberRegType;
}
}
public static class GdmMemberRegType {
private String reflagid;
private String uid;
private String regsource; //注册来源 1.pc 2.mobile 3.app 4.wechat
private String appkey;
private String appregurl;
private String websiteid;
private String domain;
private String isranreg;
private String bdp_uuid;
private String createtime;
private String dt;
private String dn;
public String getDt() {
return dt;
}
public void setDt(String dt) {
this.dt = dt;
}
public String getReflagid() {
return reflagid;
}
public void setReflagid(String reflagid) {
this.reflagid = reflagid;
}
public String getUid() {
return uid;
}
public void setUid(String uid) {
this.uid = uid;
}
public String getRegsource() {
return regsource;
}
public void setRegsource(String regsource) {
this.regsource = regsource;
}
public String getAppkey() {
return appkey;
}
public void setAppkey(String appkey) {
this.appkey = appkey;
}
public String getAppregurl() {
return appregurl;
}
public void setAppregurl(String appregurl) {
this.appregurl = appregurl;
}
public String getWebsiteid() {
return websiteid;
}
public void setWebsiteid(String websiteid) {
this.websiteid = websiteid;
}
public String getDomain() {
return domain;
}
public void setDomain(String domain) {
this.domain = domain;
}
public String getIsranreg() {
return isranreg;
}
public void setIsranreg(String isranreg) {
this.isranreg = isranreg;
}
public String getBdp_uuid() {
return bdp_uuid;
}
public void setBdp_uuid(String bdp_uuid) {
this.bdp_uuid = bdp_uuid;
}
public String getCreatetime() {
return createtime;
}
public void setCreatetime(String createtime) {
this.createtime = createtime;
}
public String getDn() {
return dn;
}
public void setDn(String dn) {
this.dn = dn;
}
}
}
11.3.5 Vip级别基础表
package com.atguigu.education.kafkaproducer;
import com.alibaba.fastjson.JSON;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import java.text.SimpleDateFormat;
import java.util.Properties;
public class baseMemberVipProducer {
public static void main(String[] args) {
Properties props = new Properties();
props.put("bootstrap.servers", "hadoop101:9092,hadoop102:9092,hadoop103:9092");
props.put("acks", "-1");
props.put("batch.size", "16384");
props.put("linger.ms", "10");
props.put("buffer.memory", "33554432");
props.put("key.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
KafkaProducer
for (int i = 0; i < 5; i++) {
GdmPcenterMemViplevel memViplevel= GdmPcenterMemViplevelLog.generateLog(String.valueOf(i));
String jsonString = JSON.toJSonString(memViplevel);
producer.send(new ProducerRecord
}
producer.flush();
producer.close();
}
public static class GdmPcenterMemViplevelLog {
private static String[] vipLevels = new String[]{"普通会员", "白金", "银卡", "金卡", "钻石"};
public static GdmPcenterMemViplevel generateLog(String vipid) {
GdmPcenterMemViplevel memViplevel = new GdmPcenterMemViplevel();
memViplevel.setDiscountval("-");
memViplevel.setDn("webA");
String time = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
.format(RondomDate.randomDate("2015-01-01 00:00:00", "2016-06-30 00:00:00"));
String time2 = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss")
.format(RondomDate.randomDate("2016-01-01 00:00:00", "2019-06-30 00:00:00"));
memViplevel.setLast_modify_time("");
memViplevel.setStart_time(time);
memViplevel.setEnd_time(time2);
memViplevel.setLast_modify_time(time2);
memViplevel.setMax_free("-");
memViplevel.setMin_free("-");
memViplevel.setNext_level("-");
memViplevel.setVip_id(vipid);
memViplevel.setOperator("update");
memViplevel.setVip_level(vipLevels[Integer.parseInt(vipid)]);
return memViplevel;
}
}
public static class GdmPcenterMemViplevel {
private String vip_id;
private String vip_name;
private String vip_level;
private String min_free;
private String max_free;
private String start_time;
private String end_time;
private String next_level;
private String discountval;
private String last_modify_time;
private String operator;
private String siteid;
private String dn;
public String getVip_id() {
return vip_id;
}
public void setVip_id(String vip_id) {
this.vip_id = vip_id;
}
public String getVip_name() {
return vip_name;
}
public void setVip_name(String vip_name) {
this.vip_name = vip_name;
}
public String getVip_level() {
return vip_level;
}
public void setVip_level(String vip_level) {
this.vip_level = vip_level;
}
public String getMin_free() {
return min_free;
}
public void setMin_free(String min_free) {
this.min_free = min_free;
}
public String getMax_free() {
return max_free;
}
public void setMax_free(String max_free) {
this.max_free = max_free;
}
public String getStart_time() {
return start_time;
}
public void setStart_time(String start_time) {
this.start_time = start_time;
}
public String getEnd_time() {
return end_time;
}
public void setEnd_time(String end_time) {
this.end_time = end_time;
}
public String getNext_level() {
return next_level;
}
public void setNext_level(String next_level) {
this.next_level = next_level;
}
public String getDiscountval() {
return discountval;
}
public void setDiscountval(String discountval) {
this.discountval = discountval;
}
public String getLast_modify_time() {
return last_modify_time;
}
public void setLast_modify_time(String last_modify_time) {
this.last_modify_time = last_modify_time;
}
public String getOperator() {
return operator;
}
public void setOperator(String operator) {
this.operator = operator;
}
public String getSiteid() {
return siteid;
}
public void setSiteid(String siteid) {
this.siteid = siteid;
}
public String getDn() {
return dn;
}
public void setDn(String dn) {
this.dn = dn;
}
}
}
11.3.6网站信息基础表
package com.atguigu.education.kafkaproducer;
import com.alibaba.fastjson.JSON;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import java.util.Properties;
import java.util.Random;
public class baseWebSiteKafkaProducer {
public static void main(String[] args) {
Properties props = new Properties();
props.put("bootstrap.servers", "hadoop101:9092,hadoop102:9092,hadoop103:9092");
props.put("acks", "-1");
props.put("batch.size", "16384");
props.put("linger.ms", "10");
props.put("buffer.memory", "33554432");
props.put("key.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
KafkaProducer
for (int i = 0; i < 5; i++) {
GdmbaseWebsite gdmbaseWebsite = GdmbaseWebSieLog.generateLog(String.valueOf(i), "webA");
String jsonString = JSON.toJSonString(gdmbaseWebsite);
producer.send(new ProducerRecord
}
for (int i = 0; i < 5; i++) {
GdmbaseWebsite gdmbaseWebsite = GdmbaseWebSieLog.generateLog(String.valueOf(i), "webB");
String jsonString = JSON.toJSonString(gdmbaseWebsite);
producer.send(new ProducerRecord
}
for (int i = 0; i < 5; i++) {
GdmbaseWebsite gdmbaseWebsite = GdmbaseWebSieLog.generateLog(String.valueOf(i), "webC");
String jsonString = JSON.toJSonString(gdmbaseWebsite);
producer.send(new ProducerRecord
}
producer.flush();
producer.close();
}
public static class GdmbaseWebsite {
private String siteid;
private String sitename;
private String siteurl;
private String creator;
private String createtime;
private String delete;
private String dn;
public String getSiteid() {
return siteid;
}
public void setSiteid(String siteid) {
this.siteid = siteid;
}
public String getSitename() {
return sitename;
}
public void setSitename(String sitename) {
this.sitename = sitename;
}
public String getSiteurl() {
return siteurl;
}
public void setSiteurl(String siteurl) {
this.siteurl = siteurl;
}
public String getCreator() {
return creator;
}
public void setCreator(String creator) {
this.creator = creator;
}
public String getCreatetime() {
return createtime;
}
public void setCreatetime(String createtime) {
this.createtime = createtime;
}
public String getDelete() {
return delete;
}
public void setDelete(String delete) {
this.delete = delete;
}
public String getDn() {
return dn;
}
public void setDn(String dn) {
this.dn = dn;
}
}
public static class GdmbaseWebSieLog {
private static String[] siteNames = new String[]{"百度", "163", "114", "126", "谷歌"};
private static String[] siteUrls = new String[]{"wwww.baidu.com", "www.163.com", "www.114.com",
"www.126.com", "www.google.com"};
public static GdmbaseWebsite generateLog(String siteid, String dn) {
GdmbaseWebsite baseWebsite = new GdmbaseWebsite();
Random rand = new Random();
baseWebsite.setCreatetime("2000-01-01");
baseWebsite.setDelete("0");
baseWebsite.setSiteid(siteid);
baseWebsite.setCreator("admin");
baseWebsite.setDn(dn);
int index = Integer.parseInt(siteid);
baseWebsite.setSitename(siteNames[index]);
baseWebsite.setSiteurl(siteUrls[index] + "/" + dn);
return baseWebsite;
}
}
}
编写完代码后，点击运行向对应的topic发送数据，注意：用户基础表、用户支付金额表、用户跳转地址表 三表最好同时发送保证延迟误差不是太高
11.4编写case class
- 编写case class用于flink序列化与反序列化
package com.atguigu.education.model
case class DwdMemberPayMoney(uid: Int, var paymoney: String, siteid: Int, vip_id: Int, createtime: String, dt: String, dn: String)
package com.atguigu.education.model
case class baseViplevel(vip_id: Int, vip_level: String, start_time: String,
end_time: String, last_modify_time: String, max_free: String,
min_free: String, next_level: String, operator: String, dn: String)
package com.atguigu.education.model
case class baseWebSite(siteid: Int, sitename: String, siteurl: String, delete: String, createtime: String, creator: String, dn: String)
package com.atguigu.education.model
case class DwdMember(uid: Int, ad_id: Int, birthday: String, email: String, fullname: String, iconurl: String,
lastlogin: String, mailaddr: String, memberlevel: String, password: String, paymoney: String, phone: String,
qq: String, register: String, regupdatetime: String, unitname: String, userip: String, zipcode: String, dt: String,
dn: String)
package com.atguigu.education.model
case class DwdMemberPayMoney(uid: Int, var paymoney: String, siteid: Int, vip_id: Int, createtime: String, dt: String, dn: String)
package com.atguigu.education.model
case class DwdMemberRegtype(uid: Int, appkey: String, appregurl: String, bdp_uuid: String, createtime: String,
isranreg: String, regsource: String, websiteid: String, dt: String, dn: String)
package com.atguigu.education.model
case class TopicAndValue(topic:String,value:String)
11.5编写flink source与sink的反序列化类和序列化类
package com.atguigu.education.model
import java.nio.charset.Charset
import org.apache.flink.streaming.util.serialization.KeyedSerializationSchema
class DwdKafkaProducerSerializationSchema extends KeyedSerializationSchema[TopicAndValue] {
val serialVersionUID = 1351665280744549933L;
override def serializeKey(element: TopicAndValue): Array[Byte] = null
override def serializevalue(element: TopicAndValue): Array[Byte] = {
element.value.getBytes(Charset.forName("utf-8"))
}
override def getTargetTopic(element: TopicAndValue): String = {
"dwd" + element.topic
}
}
package com.atguigu.education.model
import com.google.gson.Gson
import org.apache.flink.api.common.typeinfo.{TypeHint, TypeInformation}
import org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchema
import org.apache.kafka.clients.consumer.ConsumerRecord
class DwdMemberDeserializationSchema extends KafkaDeserializationSchema[DwdMember] {
override def isEndOfStream(nextElement: DwdMember): Boolean = false
override def deserialize(record: ConsumerRecord[Array[Byte], Array[Byte]]): DwdMember = {
val gson = new Gson()
gson.fromJson(new String(record.value(), "utf-8"), classOf[DwdMember])
}
override def getProducedType: TypeInformation[DwdMember] = {
TypeInformation.of(new TypeHint[DwdMember] {})
}
}
package com.atguigu.education.model
import com.google.gson.Gson
import org.apache.flink.api.common.typeinfo.{TypeHint, TypeInformation}
import org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchema
import org.apache.kafka.clients.consumer.ConsumerRecord
class DwdMemberPayMoneyDeserializationSchema extends KafkaDeserializationSchema[DwdMemberPayMoney] {
override def isEndOfStream(nextElement: DwdMemberPayMoney): Boolean = false
override def deserialize(record: ConsumerRecord[Array[Byte], Array[Byte]]): DwdMemberPayMoney = {
val gson = new Gson()
gson.fromJson(new String(record.value(), "utf-8"), classOf[DwdMemberPayMoney])
}
override def getProducedType: TypeInformation[DwdMemberPayMoney] = {
TypeInformation.of(new TypeHint[DwdMemberPayMoney] {})
}
}
package com.atguigu.education.model
import com.google.gson.Gson
import org.apache.flink.api.common.typeinfo.{TypeHint, TypeInformation}
import org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchema
import org.apache.kafka.clients.consumer.ConsumerRecord
class DwdMemberRegtypeDeserializationSchema extends KafkaDeserializationSchema[DwdMemberRegtype] {
override def isEndOfStream(nextElement: DwdMemberRegtype): Boolean = false
override def deserialize(record: ConsumerRecord[Array[Byte], Array[Byte]]): DwdMemberRegtype = {
val gson = new Gson()
gson.fromJson(new String(record.value(), "utf-8"), classOf[DwdMemberRegtype])
}
override def getProducedType: TypeInformation[DwdMemberRegtype] = {
TypeInformation.of(new TypeHint[DwdMemberRegtype] {})
}
}
package com.atguigu.education.model
import org.apache.flink.api.common.typeinfo.{TypeHint, TypeInformation}
import org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchema
import org.apache.kafka.clients.consumer.ConsumerRecord
class TopicAndValueDeserializationSchema extends KafkaDeserializationSchema[TopicAndValue] {
//表是流最后一条元素
override def isEndOfStream(t: TopicAndValue): Boolean = {
false
}
override def deserialize(consumerRecord: ConsumerRecord[Array[Byte], Array[Byte]]): TopicAndValue = {
new TopicAndValue(consumerRecord.topic(), new String(consumerRecord.value(), "utf-8"))
}
//告诉flink 数据类型
override def getProducedType: TypeInformation[TopicAndValue] = {
TypeInformation.of(new TypeHint[TopicAndValue] {})
}
}
11.6编写实时数仓ods层处理
11.6.1工具类
package com.atguigu.education.util;
import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
public class ParseJsonData {
public static JSonObject getJsonData(String data) {
try {
return JSONObject.parseObject(data);
} catch (Exception e) {
return null;
}
}
public static String getJsonString(Object o) {
return JSON.toJSonString(o);
}
}
11.6.2编写ods逻辑
（1）flink监控多个topic，先对topic中数据进行过滤，使用侧输出流将维度表数据和事实表数据区分开，维度表写入Hbase表中，事实表写入第二层kafka topic
package com.atguigu.education.etl
import java.util.Properties
import com.alibaba.fastjson.JSONObject
import com.atguigu.education.model.{DwdKafkaProducerSerializationSchema, GlobalConfig, TopicAndValue, TopicAndValueDeserializationSchema}
import com.atguigu.education.util.ParseJsonData
import org.apache.flink.api.common.restartstrategy.RestartStrategies
import org.apache.flink.api.common.time.Time
import org.apache.flink.api.java.utils.ParameterTool
import org.apache.flink.contrib.streaming.state.RocksDBStateBackend
import org.apache.flink.runtime.state.StateBackend
import org.apache.flink.streaming.api.environment.CheckpointConfig
import org.apache.flink.streaming.api.functions.ProcessFunction
import org.apache.flink.streaming.api.scala.{StreamExecutionEnvironment, _}
import org.apache.flink.streaming.api.{CheckpointingMode, TimeCharacteristic}
import org.apache.flink.streaming.connectors.kafka.{FlinkKafkaConsumer010, FlinkKafkaProducer010}
import org.apache.flink.util.Collector
//flink run -m yarn-cluster -ynm odsetldata -p 12 -ys 4 -yjm 1024 -ytm 2048m -d -c com.atguigu.education.etl.OdsEtlData -yqu flink ./education-flink-online-1.0-SNAPSHOT-jar-with-dependencies.jar --group.id test --bootstrap.servers hadoop101:9092,hadoop102:9092,hadoop103:9092 --topic basewebsite,basead,member,memberpaymoney,memberregtype,membervip
//--group.id test --bootstrap.servers hadoop101:9092,hadoop102:9092,hadoop103:9092 --topic basewebsite,basead,member,memberpaymoney,memberregtype,membervip
object OdsEtlData {
val BOOTSTRAP_SERVERS = "bootstrap.servers"
val GROUP_ID = "group.id"
val RETRIES = "retries"
val TOPIC = "topic"
def main(args: Array[String]): Unit = {
val params = ParameterTool.fromArgs(args)
// val env = StreamExecutionEnvironment.getExecutionEnvironment
val env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI()
env.getConfig.setGlobalJobParameters(params)
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime) //设置时间模式为事件时间
//checkpoint设置
env.enableCheckpointing(60000l) //1分钟做一次checkpoint
val checkpointConfig = env.getCheckpointConfig
checkpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE) //仅仅一次
checkpointConfig.setMinPauseBetweenCheckpoints(30000l) //设置checkpoint间隔时间30秒
checkpointConfig.setCheckpointTimeout(10000l) //设置checkpoint超时时间
checkpointConfig.enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION) //cancel时保留checkpoint
//设置statebackend 为rockdb
// val stateBackend: StateBackend = new RocksDBStateBackend("hdfs://mycluster/flink/checkpoint")
// env.setStateBackend(stateBackend)
//设置重启策略 重启3次 间隔10秒
env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3, Time.seconds(10)))
import scala.collection.JavaConverters._
val topicList = params.get(TOPIC).split(",").toBuffer.asJava
val consumerProps = new Properties()
consumerProps.setProperty(BOOTSTRAP_SERVERS, params.get(BOOTSTRAP_SERVERS))
consumerProps.setProperty(GROUP_ID, params.get(GROUP_ID))
val kafaEventSource = new FlinkKafkaConsumer010[TopicAndValue](topicList, new TopicAndValueDeserializationSchema, consumerProps)
kafaEventSource.setStartFromEarliest()
val dataStream = env.addSource(kafaEventSource).filter(item => {
//先过滤非json数据
val obj = ParseJsonData.getJsonData(item.value)
obj.isInstanceOf[JSONObject]
})
//将dataStream拆成两份 一份维度表写到hbase 另一份事实表数据写到第二层kafka
val sideOutHbaseTag = new OutputTag[TopicAndValue]("hbaseSinkStream")
// val sideOutGreenPlumTag = new OutputTag[TopicAndValue]("greenplumSinkStream")
// val sideOutKuduTag = new OutputTag[TopicAndValue]("kuduSinkStream")
val result = dataStream.process(new ProcessFunction[TopicAndValue, TopicAndValue] {
override def processElement(value: TopicAndValue, ctx: ProcessFunction[TopicAndValue, TopicAndValue]#Context, out: Collector[TopicAndValue]): Unit = {
value.topic match {
case "basead" | "basewebsite" | "membervip" => ctx.output(sideOutHbaseTag, value)
case _ => out.collect(value)
}
}
})
//侧输出流得到 需要写入hbase的数据
result.getSideOutput(sideOutHbaseTag).addSink(new DwdHbaseSink)
// // //事实表数据写入第二层kafka
result.addSink(new FlinkKafkaProducer010[TopicAndValue](GlobalConfig.BOOTSTRAP_SERVERS, "", new DwdKafkaProducerSerializationSchema))
env.execute()
}
}
11.6.3编写hbase sink
（1）维度表为广告基础信息表、Vip级别基础表、网站信息基础表,三表数据分别写入对用hbase表中
package com.atguigu.education.etl
import com.atguigu.education.model.{baseAd, baseViplevel, baseWebSite, GlobalConfig, TopicAndValue}
import com.google.gson.{Gson, JsonObject}
import org.apache.flink.configuration.Configuration
import org.apache.flink.streaming.api.functions.sink.{RichSinkFunction, SinkFunction}
import org.apache.hadoop.hbase.{HbaseConfiguration, TableName}
import org.apache.hadoop.hbase.client.{Connection, ConnectionFactory, Put}
import org.apache.hadoop.hbase.util.Bytes
class DwdHbaseSink extends RichSinkFunction[TopicAndValue] {
var connection: Connection = _
//打开hbase连接
override def open(parameters: Configuration): Unit = {
val conf = HbaseConfiguration.create()
conf.set("hbase.zookeeper.quorum", GlobalConfig.Hbase_ZOOKEEPER_QUORUM)
conf.set("hbase.zookeeper.property.clientPort", GlobalConfig.Hbase_ZOOKEEPER_PROPERTY_CLIENTPORT)
connection = ConnectionFactory.createConnection(conf)
}
//写入hbase
override def invoke(value: TopicAndValue, context: SinkFunction.Context[_]): Unit = {
value.topic match {
case "basewebsite" => invokebaseWebSite("education:dwd_basewebsite", value)
case "basead" => invokebasebaseAd("education:dwd_basead", value)
case _ => invokebaseVipLevel("education:dwd_membervip", value)
}
}
//关闭hbase连接
override def close(): Unit = {
super.close()
connection.close()
}
def invokebaseWebSite(tableNamae: String, value: TopicAndValue): Unit = {
val gson = new Gson()
val basewebsite = gson.fromJson(value.value, classOf[baseWebSite])
val table = connection.getTable(TableName.valueOf(tableNamae))
val put = new Put(Bytes.toBytes(basewebsite.siteid + "_" + basewebsite.dn))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("siteid"), Bytes.toBytes(basewebsite.siteid))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("sitename"), Bytes.toBytes(basewebsite.sitename))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("siteurl"), Bytes.toBytes(basewebsite.siteurl))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("delete"), Bytes.toBytes(basewebsite.delete))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("createtime"), Bytes.toBytes(basewebsite.createtime))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("creator"), Bytes.toBytes(basewebsite.creator))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("dn"), Bytes.toBytes(basewebsite.dn))
table.put(put)
table.close()
}
def invokebasebaseAd(tableName: String, value: TopicAndValue) = {
val gson = new Gson()
val basead = gson.fromJson(value.value, classOf[baseAd])
val table = connection.getTable(TableName.valueOf(tableName))
val put = new Put(Bytes.toBytes(basead.adid + "_" + basead.dn))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("adid"), Bytes.toBytes(basead.adid))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("adname"), Bytes.toBytes(basead.adname))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("dn"), Bytes.toBytes(basead.dn))
table.put(put)
table.close()
}
def invokebaseVipLevel(tableName: String, value: TopicAndValue) = {
val gson = new Gson()
val baseViplevel = gson.fromJson(value.value, classOf[baseViplevel])
val table = connection.getTable(TableName.valueOf(tableName))
val put = new Put(Bytes.toBytes(baseViplevel.vip_id + "_" + baseViplevel.dn))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("vip_id"), Bytes.toBytes(baseViplevel.vip_id))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("vip_level"), Bytes.toBytes(baseViplevel.vip_level))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("start_time"), Bytes.toBytes(baseViplevel.start_time))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("end_time"), Bytes.toBytes(baseViplevel.end_time))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("last_modify_time"), Bytes.toBytes(baseViplevel.last_modify_time))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("max_free"), Bytes.toBytes(baseViplevel.max_free))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("min_free"), Bytes.toBytes(baseViplevel.min_free))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("next_level"), Bytes.toBytes(baseViplevel.next_level))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("operator"), Bytes.toBytes(baseViplevel.operator))
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes("dn"), Bytes.toBytes(baseViplevel.dn))
table.put(put)
table.close()
}
}
11.6.4编写kafka sink
（1）重写TargetTopic方法，此方法就是要发往的topic名称，拼上前缀各ods层topic发往对应dwd层topic
package com.atguigu.education.model
import java.nio.charset.Charset
import org.apache.flink.streaming.util.serialization.KeyedSerializationSchema
class DwdKafkaProducerSerializationSchema extends KeyedSerializationSchema[TopicAndValue] {
val serialVersionUID = 1351665280744549933L;
override def serializeKey(element: TopicAndValue): Array[Byte] = null
override def serializevalue(element: TopicAndValue): Array[Byte] = {
element.value.getBytes(Charset.forName("utf-8"))
}
override def getTargetTopic(element: TopicAndValue): String = {
"dwd" + element.topic
}
}
11.6.5编写dim逻辑
（1）dim层逻辑，flink程序监听dwd层topic，也就是对应的三张事实表数据，用户基础表、用户支付金额表、用户注册跳转地址表，形成三个流进行三流join，以用户表为主表使用flink co group实现left join。三流join完之后再使用flink async异步io关联维度表数据，最终写入hbase宽表
package com.atguigu.education.etl
import java.lang
import java.time.{LocalDateTime, ZoneOffset}
import java.time.format.DateTimeFormatter
import java.util.Properties
import akka.remote.WireFormats.TimeUnit
import com.alibaba.fastjson.{JSON, JSONObject}
import com.atguigu.education.model._
import com.atguigu.education.util.ParseJsonData
import org.apache.flink.api.common.functions.CoGroupFunction
import org.apache.flink.api.common.restartstrategy.RestartStrategies
import org.apache.flink.api.java.utils.ParameterTool
import org.apache.flink.streaming.api.environment.CheckpointConfig
import org.apache.flink.streaming.api.functions.KeyedProcessFunction
import org.apache.flink.streaming.api.functions.timestamps.BoundedOutOfOrdernessTimestampExtractor
import org.apache.flink.streaming.api.scala.{StreamExecutionEnvironment, _}
import org.apache.flink.streaming.api.windowing.assigners.{ProcessingTimeSessionWindows, SlidingEventTimeWindows, TumblingEventTimeWindows}
import org.apache.flink.streaming.api.windowing.time.Time
import org.apache.flink.streaming.api.windowing.triggers.CountTrigger
import org.apache.flink.streaming.api.{CheckpointingMode, TimeCharacteristic}
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010
import org.apache.flink.types.parser.LongParser
import org.apache.flink.util.Collector
import scala.collection.mutable
//flink run -m yarn-cluster -ynm dimetl -p 12 -ys 4 -yjm 1024 -ytm 2048m -d -c com.atguigu.education.etl.DImJoinData -yqu flink ./education-flink-online-1.0-SNAPSHOT-jar-with-dependencies.jar --group.id test --bootstrap.servers hadoop101:9092,hadoop102:9092,hadoop103:9092
//--bootstrap.servers hadoop101:9092,hadoop102:9092,hadoop103:9092 --group.id test
object DImJoinData {
val BOOTSTRAP_SERVERS = "bootstrap.servers"
val GROUP_ID = "group.id"
val RETRIES = "retries"
val TOPIC = "topic"
def main(args: Array[String]): Unit = {
val params = ParameterTool.fromArgs(args)
// val env = StreamExecutionEnvironment.getExecutionEnvironment
val env = StreamExecutionEnvironment.createLocalEnvironmentWithWebUI()
env.getConfig.setGlobalJobParameters(params)
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime) //设置时间模式为事件时间
//checkpoint设置
env.enableCheckpointing(60000l) //1分钟做一次checkpoint
val checkpointConfig = env.getCheckpointConfig
checkpointConfig.setCheckpointingMode(CheckpointingMode.EXACTLY_ONCE) //仅仅一次
checkpointConfig.setMinPauseBetweenCheckpoints(30000l) //设置checkpoint间隔时间30秒
checkpointConfig.setCheckpointTimeout(10000l) //设置checkpoint超时时间
checkpointConfig.enableExternalizedCheckpoints(CheckpointConfig.ExternalizedCheckpointCleanup.RETAIN_ON_CANCELLATION) //cancel时保留checkpoint
//设置statebackend 为rockdb
// val stateBackend: StateBackend = new RocksDBStateBackend("hdfs://mycluster/flink/checkpoint")
// env.setStateBackend(stateBackend)
//设置重启策略 重启3次 间隔10秒
env.setRestartStrategy(RestartStrategies.fixedDelayRestart(3, org.apache.flink.api.common.time.Time.seconds(10)))
val consumerProps = new Properties()
consumerProps.setProperty(BOOTSTRAP_SERVERS, params.get(BOOTSTRAP_SERVERS))
consumerProps.setProperty(GROUP_ID, params.get(GROUP_ID))
val dwdmemberSource = new FlinkKafkaConsumer010[DwdMember]("dwdmember", new DwdMemberDeserializationSchema, consumerProps)
val dwdmemberpaymoneySource = new FlinkKafkaConsumer010[DwdMemberPayMoney]("dwdmemberpaymoney", new DwdMemberPayMoneyDeserializationSchema, consumerProps)
val dwdmemberregtypeSource = new FlinkKafkaConsumer010[DwdMemberRegtype]("dwdmemberregtype", new DwdMemberRegtypeDeserializationSchema, consumerProps)
dwdmemberSource.setStartFromEarliest()
dwdmemberpaymoneySource.setStartFromEarliest()
dwdmemberregtypeSource.setStartFromEarliest()
//注册时间作为 事件时间 水位线设置为10秒
val dwdmemberStream = env.addSource(dwdmemberSource).assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[DwdMember](Time.seconds(10)) {
override def extractTimestamp(element: DwdMember): Long = {
element.register.toLong
}
})
//创建时间作为 事件时间 水位线设置10秒
val dwdmemberpaymoneyStream = env.addSource(dwdmemberpaymoneySource).assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[DwdMemberPayMoney](Time.seconds(10)) {
override def extractTimestamp(element: DwdMemberPayMoney): Long = {
element.createtime.toLong
}
})
//
val dwdmemberregtypeStream = env.addSource(dwdmemberregtypeSource).assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[DwdMemberRegtype](Time.seconds(10)) {
override def extractTimestamp(element: DwdMemberRegtype): Long = {
element.createtime.toLong
}
})
// 用户表先关联注册表 以用户表为主表 用cogroup 实现left join
val dwdmemberLeftJoinRegtyeStream = dwdmemberStream.coGroup(dwdmemberregtypeStream)
.where(item => item.uid + "_" + item.dn).equalTo(item => item.uid + "_" + item.dn)
// .window(SlidingEventTimeWindows.of(Time.seconds(10), Time.seconds(5)))
// .window(ProcessingTimeSessionWindows.withGap(Time.seconds(5)))
.window(TumblingEventTimeWindows.of(Time.minutes(10)))
.trigger(CountTrigger.of(1))
.apply(new MemberLeftJoinRegtype)
.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor[String](Time.seconds(10)) {
override def extractTimestamp(element: String): Long = {
val register = ParseJsonData.getJsonData(element).getString("register")
register.toLong
}
})
//再根据用户信息跟消费金额进行关联 用户表为主表进行 left join 根据uid和dn进行join
val resultStream = dwdmemberLeftJoinRegtyeStream.coGroup(dwdmemberpaymoneyStream)
.where(item => {
val jsonObject = ParseJsonData.getJsonData(item)
val uid = jsonObject.getString("uid")
val dn = jsonObject.getString("dn")
uid + "_" + dn
}).equalTo(item => item.uid + "_" + item.dn)
.window(TumblingEventTimeWindows.of(Time.minutes(10)))
.trigger(CountTrigger.of(1))
.apply(new resultStreamCoGroupFunction)
val resultDStream = AsyncDataStream.unorderedWait(resultStream, new DimHbaseAsyncFunction, 10l, java.util.concurrent.TimeUnit.MINUTES, 12)
// val resultDStream = AsyncDataStream.unorderedWait(resultStream, new DimKuduAsyncFunction, 10l, java.util.concurrent.TimeUnit.MINUTES, 12)
resultDStream.print()
resultDStream.addSink(new DwsMemberKuduSink)
env.execute()
}
class MemberLeftJoinRegtype extends CoGroupFunction[DwdMember, DwdMemberRegtype, String] {
override def coGroup(first: lang.Iterable[DwdMember], second: lang.Iterable[DwdMemberRegtype], out: Collector[String]): Unit = {
var bl = false
val leftIterator = first.iterator()
val rightIterator = second.iterator()
while (leftIterator.hasNext) {
val dwdMember = leftIterator.next()
val jsonObject = new JSonObject()
jsonObject.put("uid", dwdMember.uid)
jsonObject.put("ad_id", dwdMember.ad_id)
jsonObject.put("birthday", dwdMember.birthday)
jsonObject.put("email", dwdMember.email)
jsonObject.put("fullname", dwdMember.fullname)
jsonObject.put("iconurl", dwdMember.iconurl)
jsonObject.put("lastlogin", dwdMember.lastlogin)
jsonObject.put("mailaddr", dwdMember.mailaddr)
jsonObject.put("memberlevel", dwdMember.memberlevel)
jsonObject.put("password", dwdMember.password)
jsonObject.put("phone", dwdMember.phone)
jsonObject.put("qq", dwdMember.qq)
jsonObject.put("register", dwdMember.register)
jsonObject.put("regupdatetime", dwdMember.regupdatetime)
jsonObject.put("unitname", dwdMember.unitname)
jsonObject.put("userip", dwdMember.userip)
jsonObject.put("zipcode", dwdMember.zipcode)
jsonObject.put("dt", dwdMember.dt)
jsonObject.put("dn", dwdMember.dn)
while (rightIterator.hasNext) {
val dwdMemberRegtype = rightIterator.next()
jsonObject.put("appkey", dwdMemberRegtype.appkey)
jsonObject.put("appregurl", dwdMemberRegtype.appregurl)
jsonObject.put("bdp_uuid", dwdMemberRegtype.bdp_uuid)
jsonObject.put("createtime", dwdMemberRegtype.createtime)
jsonObject.put("isranreg", dwdMemberRegtype.isranreg)
jsonObject.put("regsource", dwdMemberRegtype.regsource)
jsonObject.put("websiteid", dwdMemberRegtype.websiteid)
bl = true
out.collect(jsonObject.toJSONString)
}
if (!bl) {
jsonObject.put("appkey", "")
jsonObject.put("appregurl", "")
jsonObject.put("bdp_uuid", "")
jsonObject.put("createtime", "")
jsonObject.put("isranreg", "")
jsonObject.put("regsource", "")
jsonObject.put("websiteid", "")
out.collect(jsonObject.toJSONString)
}
}
}
}
class resultStreamCoGroupFunction extends CoGroupFunction[String, DwdMemberPayMoney, String] {
override def coGroup(first: lang.Iterable[String], second: lang.Iterable[DwdMemberPayMoney], out: Collector[String]): Unit = {
var bl = false
val leftIterator = first.iterator()
val rightIterator = second.iterator()
while (leftIterator.hasNext) {
val jsonObject = ParseJsonData.getJsonData(leftIterator.next())
while (rightIterator.hasNext) {
val dwdMemberPayMoney = rightIterator.next()
jsonObject.put("paymoney", dwdMemberPayMoney.paymoney)
jsonObject.put("siteid", dwdMemberPayMoney.siteid)
jsonObject.put("vip_id", dwdMemberPayMoney.vip_id)
bl = true
out.collect(jsonObject.toJSONString)
}
if (!bl) {
jsonObject.put("paymoney", "")
jsonObject.put("siteid", "")
jsonObject.put("vip_id", "")
out.collect(jsonObject.toJSONString)
}
}
}
}
}
11.6.6编写async io关联hbase维度表表
(1)使用线程池，异步操作数据库，同时使用guava缓存功能，减少对hbase的查询操作,关联维度表数据后，再写入hbase宽表
package com.atguigu.education.etl
import java.math.BigInteger
import java.security.MessageDigest
import java.util.concurrent.{ExecutorService, Executors, TimeUnit}
import com.alibaba.fastjson.JSONObject
import com.atguigu.education.model.{GlobalConfig, ResultMode}
import com.atguigu.education.util.ParseJsonData
import com.google.common.cache.{Cache, CacheBuilder}
import org.apache.flink.configuration.Configuration
import org.apache.flink.streaming.api.scala.async.{ResultFuture, RichAsyncFunction}
import org.apache.flink.util.ExecutorUtils
import org.apache.hadoop.hbase.{CellUtil, HbaseConfiguration, TableName}
import org.apache.hadoop.hbase.client.{Connection, ConnectionFactory, Get, Put}
import org.apache.hadoop.hbase.util.Bytes
class DimHbaseAsyncFunction extends RichAsyncFunction[String, String] {
var executorService: ExecutorService = _
var cache: Cache[String, String] = _
//使用opentsdb 的 异步客户端 http://opentsdb.github.io/asynchbase/javadoc/index.html
override def open(parameters: Configuration): Unit = {
// val hbase_config = new Config()
// hbase_config.overrideConfig("hbase.zookeeper.quorum", GlobalConfig.Hbase_ZOOKEEPER_QUORUM)
// hbase_config.overrideConfig("hbase.zookeeper.property.clientPort", GlobalConfig.Hbase_ZOOKEEPER_PROPERTY_CLIENTPORT)
// //创建连接
// hbaseClient = new HbaseClient(hbase_config)
//初始化线程池 12个线程
executorService = Executors.newFixedThreadPool(12)
//初始化缓存
cache = CacheBuilder.newBuilder()
.concurrencyLevel(12) //设置并发级别 允许12个线程同时访问
.expireAfterAccess(2, TimeUnit.HOURS) //设置缓存 2小时 过期
.maximumSize(10000) //设置缓存大小
.build()
}
override def close(): Unit = {
ExecutorUtils.gracefulShutdown(100, TimeUnit.MILLISECONDS, executorService);
}
override def timeout(input: String, resultFuture: ResultFuture[String]): Unit = {
resultFuture.complete(Array("timeout:" + input))
}
override def asyncInvoke(input: String, resultFuture: ResultFuture[String]): Unit = {
executorService.submit(new Runnable {
override def run(): Unit = {
try {
val conf = HbaseConfiguration.create()
conf.set("hbase.zookeeper.quorum", GlobalConfig.Hbase_ZOOKEEPER_QUORUM)
conf.set("hbase.zookeeper.property.clientPort", GlobalConfig.Hbase_ZOOKEEPER_PROPERTY_CLIENTPORT)
val connection = ConnectionFactory.createConnection(conf)
val resultJsonObject = getHbaseJoinData(input, connection, cache)
resultFuture.complete(Array(resultJsonObject.toJSONString))
writeDataToHbase(connection, resultJsonObject)
} catch {
case e: Exception => resultFuture.complete(Array("error:" + e.printStackTrace()))
}
}
})
}
def getHbaseJoinData(input: String, connection: Connection, cache: Cache[String, String]): JSonObject = {
val jsonObject = ParseJsonData.getJsonData(input)
//通过adid siteid vipid 去hbase维度表查询数据
val ad_id = jsonObject.getString("ad_id")
val siteid = jsonObject.getString("siteid")
val vip_id = jsonObject.getString("vip_id")
val dn = jsonObject.getString("dn")
var adname: String = ""
var sitename: String = ""
var siteurl: String = ""
var delete: String = ""
var site_createtime: String = ""
var site_creator: String = ""
var vip_level: String = ""
var vip_start_time: String = ""
var vip_end_tiem: String = ""
var last_modify_time = ""
var max_free = ""
var min_free = ""
var next_level = ""
var operator = ""
//查询广告关联表 adname 先去缓存中查询 如果缓存中没有再去hbase中查询
adname = cache.getIfPresent("adname:" + ad_id + "_" + dn)
if (adname == null || "".equals(adname)) {
val table = connection.getTable(TableName.valueOf("education:dwd_basead"))
val get = new Get(Bytes.toBytes(ad_id + "_" + dn)).addColumn(Bytes.toBytes("info"), Bytes.toBytes("adname"))
val result = table.get(get)
for (cell <- result.rawCells()) {
adname = Bytes.toString(CellUtil.clonevalue(cell))
}
cache.put("adname:" + ad_id + "_" + dn, adname) //放入缓存
table.close()
}
//查询网站关联表 sitename stiteurl等信息
if (!"".equals(siteid)) {
val siteDetail = cache.getIfPresent("siteDetail:" + siteid + "_" + dn) //先从缓存中取
if (siteDetail == null || "".equals(siteDetail)) {
val table = connection.getTable(TableName.valueOf("education:dwd_basewebsite"))
val get = new Get(Bytes.toBytes(siteid + "_" + dn)).addFamily(Bytes.toBytes("info"))
val result = table.get(get)
for (cell <- result.rawCells()) {
val colName = Bytes.toString(CellUtil.cloneQualifier(cell))
val value = Bytes.toString(CellUtil.clonevalue(cell))
colName match {
case "sitename" => sitename = value
case "siteurl" => siteurl = value
case "delete" => delete = value
case "createtime" => site_createtime = value
case "creator" => site_creator = value
case _ => null
}
}
//将查询到的数据拼装成json格式 存入缓存
val jsonObject = new JSonObject()
jsonObject.put("sitename", sitename)
jsonObject.put("siteurl", siteurl)
jsonObject.put("delete", delete)
jsonObject.put("site_createtime", site_createtime)
jsonObject.put("site_creator", site_creator)
cache.put("siteDetail:" + siteid + "_" + dn, jsonObject.toJSONString)
table.close()
} else {
//如果缓存中有数据 则解析缓存中的json数据
val jsonObject = ParseJsonData.getJsonData(siteDetail)
sitename = jsonObject.getString("sitename")
siteurl = jsonObject.getString("siteurl")
delete = jsonObject.getString("delete")
site_createtime = jsonObject.getString("site_createtime")
site_creator = jsonObject.getString("site_creator")
}
}
//vip表关联的数据
if (!"".equals(vip_id)) {
val vipDetail = cache.getIfPresent("vipDetail:" + vip_id + "_" + dn) //先查询缓存
if (vipDetail == null || "".equals(vipDetail)) {
val table = connection.getTable(TableName.valueOf("education:dwd_membervip"))
val get = new Get(Bytes.toBytes(vip_id + "_" + dn)).addFamily(Bytes.toBytes("info"))
val result = table.get(get)
for (cell <- result.rawCells()) {
val colName = Bytes.toString(CellUtil.cloneQualifier(cell))
val value = Bytes.toString(CellUtil.clonevalue(cell))
colName match {
case "vip_level" => vip_level = value
case "vip_start_time" => vip_start_time = value
case "vip_end_time" => vip_end_tiem = value
case "last_modify_time" => last_modify_time = value
case "max_free" => max_free = value
case "min_free" => min_free = value
case "next_level" => next_level = value
case "operator" => operator = value
case _ => null
}
}
//将查询到的数据拼装成json 存入缓存
val jsonObject = new JSonObject()
jsonObject.put("vip_level", vip_level)
jsonObject.put("vip_start_time", vip_start_time)
jsonObject.put("vip_end_tiem", vip_end_tiem)
jsonObject.put("last_modify_time", last_modify_time)
jsonObject.put("max_free", max_free)
jsonObject.put("min_free", min_free)
jsonObject.put("next_level", next_level)
jsonObject.put("operator", operator)
cache.put("vipDetail:" + vip_id + "_" + dn, jsonObject.toJSONString)
table.close()
} else {
//如果缓存中有值 就解析缓存中的数据
val jsonObject = ParseJsonData.getJsonData(vipDetail)
vip_level = jsonObject.getString("vip_level")
vip_start_time = jsonObject.getString("vip_start_time")
vip_end_tiem = jsonObject.getString("vip_end_tiem")
last_modify_time = jsonObject.getString("last_modify_time")
max_free = jsonObject.getString("max_free")
min_free = jsonObject.getString("min_free")
next_level = jsonObject.getString("next_level")
operator = jsonObject.getString("operator")
}
}
jsonObject.put("adname", adname)
jsonObject.put("sitename", sitename)
jsonObject.put("siteurl", siteurl)
jsonObject.put("delete", delete)
jsonObject.put("site_createtime", site_createtime)
jsonObject.put("site_creator", site_creator)
jsonObject.put("vip_level", vip_level)
jsonObject.put("vip_start_time", vip_start_time)
jsonObject.put("vip_end_tiem", vip_end_tiem)
jsonObject.put("last_modify_time", last_modify_time)
jsonObject.put("max_free", max_free)
jsonObject.put("min_free", min_free)
jsonObject.put("next_level", next_level)
jsonObject.put("operator", operator)
jsonObject
}
def writeDataToHbase(connection: Connection, resultJsonObject: JSONObject) = {
val table = connection.getTable(TableName.valueOf("education:dim_member"))
//rowkey substring(md5(uid),0,5)+uid+dn
val uid = resultJsonObject.getString("uid")
val dn = resultJsonObject.getString("dn")
val rowkey = generateHash(uid).substring(0, 5) + uid + dn
val put = new Put(Bytes.toBytes(rowkey))
val keySet = resultJsonObject.keySet().toArray
for (key <- keySet) {
put.addColumn(Bytes.toBytes("info"), Bytes.toBytes(key.toString),
Bytes.toBytes(resultJsonObject.getString(key.toString)))
}
table.put(put)
table.close()
}
def generateHash(input: String): String = {
try {
if (input == null) {
null
}
val md = MessageDigest.getInstance("MD5")
md.update(input.getBytes());
val digest = md.digest();
val bi = new BigInteger(1, digest);
var hashText = bi.toString(16);
while (hashText.length() < 32) {
hashText = "0" + hashText;
}
hashText
} catch {
case e: Exception => e.printStackTrace(); null
}
}
}
