4.Flink实时项目之数据拆分
1. 摘要
我们前面采集的日志数据已经保存到 Kafka 中，作为日志数据的 ODS 层，从 kafka 的ODS 层读取的日志数据分为 3 类, 页面日志、启动日志和曝光日志。这三类数据虽然都是用户行为数据，但是有着完全不一样的数据结构，所以要拆分处理。将拆分后的不同的日志写回 Kafka 不同主题中，作为日志 DWD 层。
页面日志输出到主流，启动日志输出到启动侧输出流，曝光日志输出到曝光侧输出流
2. 识别新老用户
本身客户端业务有新老用户的标识，但是不够准确，需要用实时计算再次确认(不涉及业务操作，只是单纯的做个状态确认)。
利用侧输出流实现数据拆分
根据日志数据内容,将日志数据分为 3 类：页面日志、启动日志和曝光日志。将不同流的数据推送下游的 kafka 的不同 Topic 中
3. 代码实现
在包app下创建flink任务BaseLogTask.java，
通过flink消费kafka 的数据，然后记录消费的checkpoint存到hdfs中，记得要手动创建路径，然后给权限
checkpoint可选择性使用，测试时可以关掉。
package com.zhangbao.gmall.realtime.app;
import com.alibaba.fastjson.JSONObject;
import com.zhangbao.gmall.realtime.utils.MyKafkaUtil;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.runtime.state.filesystem.FsStateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
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.kafka.FlinkKafkaConsumer;
/**
* @author: zhangbao
* @date: 2021/6/18 23:29
* @desc:
**/
public class BaseLogTask {
public static void main(String[] args) {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//设置并行度，即kafka分区数
env.setParallelism(4);
//添加checkpoint，每5秒执行一次
env.enableCheckpointing(5000, CheckpointingMode.EXACTLY_ONCE);
env.getCheckpointConfig().setCheckpointTimeout(60000);
env.setStateBackend(new FsStateBackend("hdfs://hadoop101:9000/gmall/flink/checkpoint/baseLogAll"));
//指定哪个用户读取hdfs文件
System.setProperty("HADOOP_USER_NAME","zhangbao");
//添加数据源
String topic = "ods_base_log";
String group = "base_log_app_group";
FlinkKafkaConsumer<String> kafkaSource = MyKafkaUtil.getKafkaSource(topic, group);
DataStreamSource<String> kafkaDs = env.addSource(kafkaSource);
//对格式进行转换
SingleOutputStreamOperator<JSONObject> jsonDs = kafkaDs.map(new MapFunction<String, JSONObject>() {
@Override
public JSONObject map(String s) throws Exception {
return JSONObject.parseObject(s);
}
});
jsonDs.print("json >>> --- ");
try {
//执行
env.execute();
} catch (Exception e) {
e.printStackTrace();
}
}
}MyKafkaUtil.java工具类
package com.zhangbao.gmall.realtime.utils;
import org.apache.flink.api.common.serialization.SimpleStringSchema;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.kafka.clients.consumer.ConsumerConfig;
import java.util.Properties;
/**
* @author: zhangbao
* @date: 2021/6/18 23:41
* @desc:
**/
public class MyKafkaUtil {
private static String kafka_host = "hadoop101:9092,hadoop102:9092,hadoop103:9092";
/**
* kafka消费者
*/
public static FlinkKafkaConsumer<String> getKafkaSource(String topic,String group){
Properties props = new Properties();
props.setProperty(ConsumerConfig.GROUP_ID_CONFIG,group);
props.setProperty(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG,kafka_host);
return new FlinkKafkaConsumer<String>(topic, new SimpleStringSchema(),props);
}
}4. 新老访客状态修复
识别新老客户规则
识别新老访客，前端会对新老客状态进行记录，可能不准，这里再次确认，保存mid某天状态情况（将首次访问日期作为状态保存），等后面设备在有日志过来，从状态中获取日期和日志产生日期比较，如果状态不为空，并且状态日期和当前日期不相等，说明是老访客，如果is_new标记是1，则对其状态进行修复。
import com.alibaba.fastjson.JSONObject;
import com.zhangbao.gmall.realtime.utils.MyKafkaUtil;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.runtime.state.filesystem.FsStateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import java.text.SimpleDateFormat;
import java.util.Date;
/**
* @author: zhangbao
* @date: 2021/6/18 23:29
* @desc:
**/
public class BaseLogTask {
public static void main(String[] args) {
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
//设置并行度，即kafka分区数
env.setParallelism(4);
//添加checkpoint，每5秒执行一次
env.enableCheckpointing(5000, CheckpointingMode.EXACTLY_ONCE);
env.getCheckpointConfig().setCheckpointTimeout(60000);
env.setStateBackend(new FsStateBackend("hdfs://hadoop101:9000/gmall/flink/checkpoint/baseLogAll"));
//指定哪个用户读取hdfs文件
System.setProperty("HADOOP_USER_NAME","zhangbao");
//添加数据源，来至kafka的数据
String topic = "ods_base_log";
String group = "base_log_app_group";
FlinkKafkaConsumer<String> kafkaSource = MyKafkaUtil.getKafkaSource(topic, group);
DataStreamSource<String> kafkaDs = env.addSource(kafkaSource);
//对格式进行转换
SingleOutputStreamOperator<JSONObject> jsonDs = kafkaDs.map(new MapFunction<String, JSONObject>() {
@Override
public JSONObject map(String s) throws Exception {
return JSONObject.parseObject(s);
}
});
jsonDs.print("json >>> --- ");
/**
* 识别新老访客，前端会对新老客状态进行记录，可能不准，这里再次确认
* 保存mid某天状态情况（将首次访问日期作为状态保存），等后面设备在有日志过来，从状态中获取日期和日志产生日期比较，
* 如果状态不为空，并且状态日期和当前日期不相等，说明是老访客，如果is_new标记是1，则对其状态进行修复
*/
//根据id对日志进行分组
KeyedStream<JSONObject, String> midKeyedDs = jsonDs.keyBy(data -> data.getJSONObject("common").getString("mid"));
//新老访客状态修复，状态分为算子状态和键控状态，我们这里记录某一个设备状态，使用键控状态比较合适
SingleOutputStreamOperator<JSONObject> midWithNewFlagDs = midKeyedDs.map(new RichMapFunction<JSONObject, JSONObject>() {
//定义mid状态
private ValueState<String> firstVisitDateState;
//定义日期格式化
private SimpleDateFormat sdf;
//初始化方法
@Override
public void open(Configuration parameters) throws Exception {
firstVisitDateState = getRuntimeContext().getState(new ValueStateDescriptor<String>("newMidDateState", String.class));
sdf = new SimpleDateFormat("yyyyMMdd");
}
@Override
public JSONObject map(JSONObject jsonObject) throws Exception {
//获取当前mid状态
String is_new = jsonObject.getJSONObject("common").getString("is_new");
//获取当前日志时间戳
Long ts = jsonObject.getLong("ts");
if ("1".equals(is_new)) {
//访客日期状态
String stateDate = firstVisitDateState.value();
String nowDate = sdf.format(new Date());
if (stateDate != null && stateDate.length() != 0 && !stateDate.equals(nowDate)) {
//是老客
is_new = "0";
jsonObject.getJSONObject("common").put("is_new", is_new);
} else {
//新访客
firstVisitDateState.update(nowDate);
}
}
return jsonObject;
}
});
midWithNewFlagDs.print();
try {
//执行
env.execute();
} catch (Exception e) {
e.printStackTrace();
}
}
}5. 利用侧输出流实现数据拆分
经过上面的新老客户修复后，再将日志数据分为 3 类
启动日志标签定义：OutputTag<String> startTag = new OutputTag<String>("start"){};
和曝光日志标签定义：OutputTag<String> displayTag = new OutputTag<String>("display"){};
页面日志输出到主流，启动日志输出到启动侧输出流，曝光日志输出到曝光日志侧输出流。
数据拆分后发送到kafka
dwd_start_log：启动日志
dwd_display_log：曝光日志
dwd_page_log：页面日志
package com.zhangbao.gmall.realtime.app;
import com.alibaba.fastjson.JSONArray;
import com.alibaba.fastjson.JSONObject;
import com.zhangbao.gmall.realtime.utils.MyKafkaUtil;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.RichMapFunction;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.runtime.state.filesystem.FsStateBackend;
import org.apache.flink.streaming.api.CheckpointingMode;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.util.Collector;
import org.apache.flink.util.OutputTag;
import java.text.SimpleDateFormat;
import java.util.Date;
/**- @author: zhangbao
- @date: 2021/6/18 23:29
- @desc:
/ public class BaseLogTask { private static final String TOPIC_START = "dwd_start_log"; private static final String TOPIC_DISPLAY = "dwd_display_log"; private static final String TOPIC_PAGE = "dwd_page_log"; public static void main(String[] args) { StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment(); //设置并行度，即kafka分区数 env.setParallelism(4); //添加checkpoint，每5秒执行一次 env.enableCheckpointing(5000, CheckpointingMode.EXACTLY_ONCE); env.getCheckpointConfig().setCheckpointTimeout(60000); env.setStateBackend(new FsStateBackend("hdfs://hadoop101:9000/gmall/flink/checkpoint/baseLogAll")); //指定哪个用户读取hdfs文件 System.setProperty("HADOOP_USER_NAME","zhangbao"); //添加数据源，来至kafka的数据 String topic = "ods_base_log"; String group = "base_log_app_group"; FlinkKafkaConsumerkafkaSource = MyKafkaUtil.getKafkaSource(topic, group); DataStreamSource kafkaDs = env.addSource(kafkaSource); //对格式进行转换 SingleOutputStreamOperator jsonDs = kafkaDs.map(new MapFunction
* 识别新老访客，前端会对新老客状态进行记录，可能不准，这里再次确认
* 保存mid某天状态情况（将首次访问日期作为状态保存），等后面设备在有日志过来，从状态中获取日期和日志产生日期比较，
* 如果状态不为空，并且状态日期和当前日期不相等，说明是老访客，如果is_new标记是1，则对其状态进行修复
/ //根据id对日志进行分组 KeyedStreammidWithNewFlagDs = midKeyedDs.map(new RichMapFunction firstVisitDateState; //定义日期格式化 private SimpleDateFormat sdf; //初始化方法 @Override public void open(Configuration parameters) throws Exception { firstVisitDateState = getRuntimeContext().getState(new ValueStateDescriptor ("newMidDateState", String.class)); sdf = new SimpleDateFormat("yyyyMMdd"); } @Override public JSONObject map(JSONObject jsonObject) throws Exception { //获取当前mid状态 String is_new = jsonObject.getJSONObject("common").getString("is_new"); //获取当前日志时间戳 Long ts = jsonObject.getLong("ts"); if ("1".equals(is_new)) { //访客日期状态 String stateDate = firstVisitDateState.value(); String nowDate = sdf.format(new Date()); if (stateDate != null && stateDate.length() != 0 && !stateDate.equals(nowDate)) { //是老客 is_new = "0"; jsonObject.getJSONObject("common").put("is_new", is_new); } else { //新访客 firstVisitDateState.update(nowDate); } } return jsonObject; } }); // midWithNewFlagDs.print(); /
* 根据日志数据内容,将日志数据分为 3 类, 页面日志、启动日志和曝光日志。页面日志
* 输出到主流,启动日志输出到启动侧输出流,曝光日志输出到曝光日志侧输出流
* 侧输出流：1接收迟到数据，2分流
/ //定义启动侧输出流标签，加大括号为了生成相应类型 OutputTagstartTag = new OutputTag *("start"){}; //定义曝光侧输出流标签 OutputTag displayTag = new OutputTag ("display"){}; SingleOutputStreamOperator pageDs = midWithNewFlagDs.process( new ProcessFunction collector) throws Exception { String dataStr = jsonObject.toString(); JSONObject startJson = jsonObject.getJSONObject("start"); //判断是否启动日志 if (startJson != null && startJson.size() > 0) { context.output(startTag, dataStr); } else { //判断是否曝光日志 JSONArray jsonArray = jsonObject.getJSONArray("displays"); if (jsonArray != null && jsonArray.size() > 0) { //给每一条曝光事件加pageId String pageId = jsonObject.getJSONObject("page").getString("page_id"); //遍历输出曝光日志 for (int i = 0; i < jsonArray.size(); i++) { JSONObject disPlayObj = jsonArray.getJSONObject(i); disPlayObj.put("page_id", pageId); context.output(displayTag, disPlayObj.toString()); } } else { //如果不是曝光日志，则是页面日志，输出到主流 collector.collect(dataStr); } } } } ); //获取侧输出流 DataStream startDs = pageDs.getSideOutput(startTag); DataStream disPlayDs = pageDs.getSideOutput(displayTag); //打印输出 startDs.print("start>>>"); disPlayDs.print("display>>>"); pageDs.print("page>>>"); /
* 将不同流的日志数据发送到指定的kafka主题
*/
startDs.addSink(MyKafkaUtil.getKafkaSink(TOPIC_START));
disPlayDs.addSink(MyKafkaUtil.getKafkaSink(TOPIC_DISPLAY));
pageDs.addSink(MyKafkaUtil.getKafkaSink(TOPIC_PAGE));
try {
//执行
env.execute();
} catch (Exception e) {
e.printStackTrace();
}
}
}
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