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3.8.基于Flink将数据写入到ClickHouse
编写Flink完成数据写入到ClickHouse操作, 后续基于CK完成指标统计操作
3.8.1.ClickHouse基本介绍
ClickHouse 是俄罗斯的Yandex于2016年开源的列式存储数据库（DBMS），使用C++语言编写，主要用于在线分析处理查询（OLAP），能够使用SQL查询实时生成分析数据报告。
结论: ClickHouse像很多OLAP数据库一样，单表查询速度由于关联查询，而且ClickHouse的两者差距更为明显。
3.8.2.ClickHouse安装步骤
本项目中,我们仅需要安装单机测试版本即可使用(node2安装), 在实际生产中, 大家可以直接将分布式集群版本
- 1-设置yum源
sudo yum install yum-utils
sudo rpm --import https://repo.clickhouse.com/CLICKHOUSE-KEY.GPG
sudo yum-config-manager --add-repo https://repo.clickhouse.com/rpm/stable/x86_64
- 2- 直接基于yum安装即可
sudo yum install clickhouse-server clickhouse-client
- 3-修改配置文件
vim /etc/clickhouse-server/config.xml
修改178行: 打开这一行的注释
<listen_host>::</listen_host>
- 4-启动clickhouse的server
systemctl start clickhouse-server
停止:
systemctl stop clickhouse-server
重启
systemctl restart clickhouse-server
- 5-进入客户端
3.8.3.在ClickHouse中创建目标表
create database itcast_ck;
use itcast_ck;
create table itcast_ck.itcast_ck_ems(
id int,
sid varchar(128),
ip varchar(128),
create_time varchar(128),
session_id varchar(128),
yearInfo varchar(128),
monthInfo varchar(128),
dayInfo varchar(128),
hourInfo varchar(128),
seo_source varchar(128),
area varchar(128),
origin_channel varchar(128),
msg_count int(128),
from_url varchar(128),
PRIMARY KEY (`id`)
) ENGINE=ReplacingMergeTree();
3.8.4.编写Flink代码完成写入到CK操作
import com.itheima.pojo.PulsarTopicPojo;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.io.jdbc.JDBCAppendTableSink;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.pulsar.FlinkPulsarSource;
import org.apache.flink.streaming.connectors.pulsar.internal.JsonDeser;
import org.apache.flink.types.Row;import java.sql.Types;
import java.util.Properties;// 基于Flink完成读取Pulsar中数据将消息数据写入到clickhouse中
public class ItcastFlinkToClickHouse {public static void main(String[] args) throws Exception {//1. 创建Flinnk流式处理核心环境类对象 和 Table API 核心环境类对象StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();//2. 添加Source组件, 从Pulsar中读取消息数据Properties props = new Properties();props.setProperty("topic","persistent://public/default/itcast_ems_tab");props.setProperty("partition.discovery.interval-millis","5000");FlinkPulsarSource<PulsarTopicPojo> pulsarSource = new FlinkPulsarSource<PulsarTopicPojo>("pulsar://node1:6650,node2:6650,node3:6650","http://node1:8080,node2:8080,node3:8080",JsonDeser.of(PulsarTopicPojo.class),props);//2.1 设置pulsarSource组件在消费数据的时候, 默认从什么位置开始消费pulsarSource.setStartFromLatest();DataStreamSource<PulsarTopicPojo> dataStreamSource = env.addSource(pulsarSource);//2.2 转换数据操作: 将 PulsarTopicPojo 转换为ROW对象SingleOutputStreamOperator<Row> rowDataSteam = dataStreamSource.map(new MapFunction<PulsarTopicPojo, Row>() {@Overridepublic Row map(PulsarTopicPojo pulsarTopicPojo) throws Exception {return Row.of(pulsarTopicPojo.getId(), pulsarTopicPojo.getSid(), pulsarTopicPojo.getIp(), pulsarTopicPojo.getCreate_time(),pulsarTopicPojo.getSession_id(), pulsarTopicPojo.getYearInfo(), pulsarTopicPojo.getMonthInfo(), pulsarTopicPojo.getDayInfo(),pulsarTopicPojo.getHourInfo(), pulsarTopicPojo.getSeo_source(), pulsarTopicPojo.getArea(), pulsarTopicPojo.getOrigin_channel(),pulsarTopicPojo.getMsg_count(), pulsarTopicPojo.getFrom_url());}});//2.3: 设置sink操作写入到CK操作String insertSql = "insert into itcast_ck.itcast_ck_ems (id,sid,ip,create_time,session_id,yearInfo,monthInfo,dayInfo,hourInfo,seo_source,area,origin_channel,msg_count,from_url) values(?,?,?,?,?,?,?,?,?,?,?,?,?,?)";JDBCAppendTableSink tableSink = JDBCAppendTableSink.builder().setDrivername("ru.yandex.clickhouse.ClickHouseDriver").setDBUrl("jdbc:clickhouse://node2:8123/itcast_ck").setQuery(insertSql).setBatchSize(1).setParameterTypes(Types.INTEGER,Types.VARCHAR,Types.VARCHAR,Types.VARCHAR,Types.VARCHAR,Types.VARCHAR,Types.VARCHAR,Types.VARCHAR,Types.VARCHAR,Types.VARCHAR,Types.VARCHAR,Types.VARCHAR,Types.INTEGER,Types.VARCHAR).build();tableSink.emitDataStream(rowDataSteam);//3. 提交执行env.execute("itcast_to_ck");}
}
3.9.HBase对接Phoenix实现即席查询
3.9.1.Phoenix安装操作
Phoenix是属于apache旗下的一款基于hbase的工具, 此工具提供一种全新的方式来操作hbase中数据(SQL),
同时Phoenix对hbase进行大量的优化工作, 能够让我们更加有效的操作hbase
整个安装操作, 大家可以参考资料中安装手册, 进行安装即可
3.9.2.在Phoenix中创建表
create view "itcast_h_ems" (
"id" integer primary key,
"f1"."sid" varchar,
"f1"."ip" varchar,
"f1"."create_time" varchar,
"f1"."session_id" varchar,
"f1"."yearInfo" varchar,
"f1"."monthInfo" varchar,
"f1"."dayInfo" varchar,
"f1"."hourInfo" varchar,
"f1"."seo_source" varchar,
"f1"."area" varchar,
"f1"."origin_channel" varchar,
"f1"."msg_count" integer,
"f1"."from_url" varchar
);
3.9.3.在Phoenix中类型说明
