Structured Streaming之Event-Time的Window操作

布满荆棘的人生 2022-12-29 15:28 155阅读 0赞

**基于Event-Time的Window操作**  
详细工作流程：  
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![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80NTM2NjQ5OQ_size_16_color_FFFFFF_t_70 1]  
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方式一：

package com.kfk.spark.structuredstreaming;
    
    import com.kfk.spark.common.CommSparkSession;
    import org.apache.spark.api.java.function.FlatMapFunction;
    import org.apache.spark.sql.*;
    import org.apache.spark.sql.streaming.StreamingQuery;
    import org.apache.spark.sql.streaming.StreamingQueryException;
    import scala.Tuple2;
    
    import java.sql.Timestamp;
    import java.util.ArrayList;
    import java.util.Iterator;
    import java.util.List;
    
    /** * @author : 蔡政洁 * @email :caizhengjie888@icloud.com * @date : 2020/12/22 * @time : 9:44 下午 */
    public class EventTimeWindow { 
        public static void main(String[] args) throws StreamingQueryException { 
            SparkSession spark = CommSparkSession.getSparkSession();
    
            // input table
            Dataset<Row> dflines = spark.readStream()
                    .format("socket")
                    .option("host", "bigdata-pro-m04")
                    .option("port", 9999)
                    .option("includeTimestamp",true)
                    .load();
    
            /** * 输入数据：spark storm hive * 接受到数据模型： * 第一步：words -> spark storm hive, timestamp -> 2019-09-09 12:12:12 * eg: tuple -> (spark storm hive , 2019-09-09 12:12:12) * * 第二步：split()操作 * 对tuple中的key进行split操作 * * 第三步：flatMap()操作 * 返回类型 -> list(tuple(spark,2019-09-09 12:12:12), * tuple(storm,2019-09-09 12:12:12), * tuple(hive,2019-09-09 12:12:12)) */
            Dataset<Row> words =  dflines.as(Encoders.tuple(Encoders.STRING(),Encoders.TIMESTAMP()))
                    .flatMap(new FlatMapFunction<Tuple2<String, Timestamp>, Tuple2<String, Timestamp>>() { 
                        @Override
                        public Iterator<Tuple2<String, Timestamp>> call(Tuple2<String, Timestamp> stringTimestampTuple2) throws Exception { 
                            List<Tuple2<String, Timestamp>> result = new ArrayList<>();
                            for (String word : stringTimestampTuple2._1.split(" ")){ 
                                result.add(new Tuple2<>(word,stringTimestampTuple2._2));
                            }
                            return result.iterator();
                        }
                    },Encoders.tuple(Encoders.STRING(),Encoders.TIMESTAMP())).toDF("word","wordtime");
    
            // result table
            Dataset<Row> windowedCounts = words.groupBy(
                    functions.window(words.col("wordtime"),
                            "10 minutes",
                            "5 minutes"),
                    words.col("word")
            ).count();
    
            StreamingQuery query = windowedCounts.writeStream()
                    .outputMode("update")
                    .format("console")
                    .option("truncate","false")
                    .start();
            query.awaitTermination();
    
        }
    }

方式二：

package com.kfk.spark.structuredstreaming;
    
    import java.sql.Timestamp;
    
    /** * @author : 蔡政洁 * @email :caizhengjie888@icloud.com * @date : 2020/12/22 * @time : 9:25 下午 */
    public class EventData { 
    
        //timestamp: Timestamp,
        //word: String
    
        private Timestamp wordtime;
        private String word;
    
        public Timestamp getWordtime() { 
            return wordtime;
        }
    
        public void setWordtime(Timestamp wordtime) { 
            this.wordtime = wordtime;
        }
    
        public String getWord() { 
            return word;
        }
    
        public void setWord(String word) { 
            this.word = word;
        }
    
        public EventData() { 
        }
    
        public EventData(Timestamp wordtime, String word) { 
            this.wordtime = wordtime;
            this.word = word;
        }
    }

package com.kfk.spark.structuredstreaming;
    
    import com.kfk.spark.common.CommSparkSession;
    import org.apache.spark.api.java.function.MapFunction;
    import org.apache.spark.sql.*;
    import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder;
    import org.apache.spark.sql.streaming.StreamingQuery;
    import org.apache.spark.sql.streaming.StreamingQueryException;
    
    
    import java.sql.Timestamp;
    
    
    /** * @author : 蔡政洁 * @email :caizhengjie888@icloud.com * @date : 2020/12/22 * @time : 9:44 下午 */
    public class EventTimeWindow2 { 
        public static void main(String[] args) throws StreamingQueryException { 
            SparkSession spark = CommSparkSession.getSparkSession();
    
            // input table
            Dataset<String> dflines = spark.readStream()
                    .format("socket")
                    .option("host", "bigdata-pro-m04")
                    .option("port", 9999)
                    .load().as(Encoders.STRING());
    
            /** * 数据源：2019-03-29 16:40:00,hadoop * 2019-03-29 16:40:10,storm * * // 根据javaBean转换为dataset */
            Dataset<EventData> dfeventdata = dflines.map(new MapFunction<String, EventData>() { 
                @Override
                public EventData call(String value) throws Exception { 
    
                    String[] lines  = value.split(",");
                    return new EventData(Timestamp.valueOf(lines[0]),lines[1]);
                }
            }, ExpressionEncoder.javaBean(EventData.class));
    
            // result table
            Dataset<Row> windowedCounts = dfeventdata.groupBy(
                    functions.window(dfeventdata.col("wordtime"),
                            "10 minutes",
                            "5 minutes"),
                    dfeventdata.col("word")
            ).count();
    
            StreamingQuery query = windowedCounts.writeStream()
                    .outputMode("update")
                    .format("console")
                    .option("truncate","false")
                    .start();
            query.awaitTermination();
    
        }
    }

--------------------

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