Kafka：ZK+Kafka+Spark Streaming集群环境搭建（二十五）Structured Streaming：同一个topic中包含一组数据的多个部分，按照key它们拼接为一条记录（以及遇到的问题）。

需求：

目前kafka的topic上有一批数据，这些数据被分配到9个不同的partition中（就是发布时key:{m1,m2,m3,m4...m9},value:{records items}），mx(m1,m2...m9)这些数据的唯一键值：int_id+start_time，其中int_id和start_time是topic record中的记录。这9组数据按照唯一键值可以拼接（m1.primarykey1,m2.primarykey1,m3.primarykey1.....m9.primarykey1）。

伪代码：

m组成字段包含：

public class MS_PLRULQX {
    private String key;
    private String int_id;
    private String start_time;
    private long MS_PLRULQX_00;
    private long MS_PLRULQX_01;
    
    public String getPrimaryKey() {
　　    return this.int_id + "_" + this.scan_start_time;
    }
}

完整MS_PLRULQX类定义：

import java.io.Serializable;
import org.apache.spark.sql.Row;

public class MS_PLRULQX implements Serializable, Comparable<MS_PLRULQX> {
    private static final long serialVersionUID = -2873721171908282946L;

    public MS_PLRULQX() {
    }
    
    public MS_PLRULQX(Row row) {
        this.key = row.getAs("key");
        this.int_id = row.getAs("int_id");
        this.start_time = row.getAs("start_time");
        this.MS_PLRULQX_00 = row.getAs("MS_PLRULQX_00");
        this.MS_PLRULQX_01 = row.getAs("MS_PLRULQX_01");
    }

    private String key;
    private String int_id;
    private String start_time;
    private long MS_PLRULQX_00;
    private long MS_PLRULQX_01;
    
    public String getKey() {
        return key;
    }

    public void setKey(String key) {
        this.key = key;
    }

    public String getInt_id() {
        return int_id;
    }

    public void setInt_id(String int_id) {
        this.int_id = int_id;
    }

    public String getStart_time() {
        return start_time;
    }

    public void setStart_time(String start_time) {
        this.start_time = start_time;
    }

    public long getMS_PLRULQX_00() {
        return MS_PLRULQX_00;
    }

    public void setMS_PLRULQX_00(long MS_PLRULQX_00) {
        this.MS_PLRULQX_00 = MS_PLRULQX_00;
    }

    public long getMS_PLRULQX_01() {
        return MS_PLRULQX_01;
    }

    public void setMS_PLRULQX_01(long MS_PLRULQX_01) {
        this.MS_PLRULQX_01 = MS_PLRULQX_01;
    }

    public String getPrimaryKey() {
        return this.int_id + "_" + this.scan_start_time;
    }

    @Override
    public int compareTo(MS_PLRULQX other) {
        // key format:MS_PLRULQX1,MS_PLRULQX2,..MS_PLRULQX9
        if (this.getKey().toLowerCase().indexOf("MS_PLRULQX".toLowerCase()) != -1) {
            NumberUtils numberUtils = new NumberUtils();
            String thisKeyStr = this.getKey().toLowerCase().replace("MS_PLRULQX".toLowerCase(), "");
            String otherKeyStr = other.getKey().toLowerCase().replace("MS_PLRULQX".toLowerCase(), "");
            if (numberUtils.isNumber(thisKeyStr)) {
                int thisKeyValue = Integer.valueOf(thisKeyStr);
                int otherKeyValue = Integer.valueOf(otherKeyStr);
                if (thisKeyValue > otherKeyValue) {
                    return 1;
                } else if (thisKeyStr == otherKeyStr) {
                    return 0;
                } else {
                    return -1;
                }
            }
        }

        return this.key.compareTo(other.key);
    }

}

View Code

MS_PLRULQX在9个topic中各有一份，把它们拼接起来，拼接条件primarykey相同的数据才能一起拼接，拼接后保留实体字段如下：

public class MS_PLRULQX_Combine implements Serializable {
    private String key;
    private String int_id;
    private String start_time;

    private long mr_packetlossrateulqci_1_00;
    private long mr_packetlossrateulqci_1_01;

    private long mr_packetlossrateulqci_2_00;
    private long mr_packetlossrateulqci_2_01;

    private long mr_packetlossrateulqci_3_00;
    private long mr_packetlossrateulqci_3_01;

    private long mr_packetlossrateulqci_4_00;
    private long mr_packetlossrateulqci_4_01;

    private long mr_packetlossrateulqci_5_00;
    private long mr_packetlossrateulqci_5_01;

    private long mr_packetlossrateulqci_6_00;
    private long mr_packetlossrateulqci_6_01;

    private long mr_packetlossrateulqci_7_00;
    private long mr_packetlossrateulqci_7_01;

    private long mr_packetlossrateulqci_8_00;
    private long mr_packetlossrateulqci_8_01;
    
    private long mr_packetlossrateulqci_9_00;
    private long mr_packetlossrateulqci_9_01;
}

完整MS_PLRULQX_Combine 类定义：

import java.io.Serializable;
import java.util.ArrayList;
import java.util.List;

public class MS_PLRULQX_Combine implements Serializable {
    private static final long serialVersionUID = -944128402186054489L;

    public MS_PLRULQX_Combine() {
    }

    public MS_PLRULQX_Combine(List<MS_PLRULQX> list) {
        int sizeOfList = list.size();
        if (sizeOfList > 9) {
            throw new RuntimeException("the measurement group items's length(" + list.size() + ") over than 9");
        }

        if (sizeOfList >= 1) {
            setItem1(list.get(0));
        }
        if (sizeOfList >= 2) {
            setItem2(list.get(1));
        }
        if (sizeOfList >= 3) {
            setItem3(list.get(2));
        }
        if (sizeOfList >= 4) {
            setItem4(list.get(3));
        }
        if (sizeOfList >= 5) {
            setItem5(list.get(4));
        }
        if (sizeOfList >= 6) {
            setItem6(list.get(5));
        }
        if (sizeOfList >= 7) {
            setItem7(list.get(6));
        }
        if (sizeOfList >= 8) {
            setItem8(list.get(7));
        }
        if (sizeOfList >= 9) {
            setItem9(list.get(8));
        }
    }

    private void setItem9(MS_PLRULQX item9) {
        if (item9 != null) {
            this.mr_packetlossrateulqci_9_00 = item9.getMr_packetlossrateulqci_00();
            this.mr_packetlossrateulqci_9_01 = item9.getMr_packetlossrateulqci_01();
        }
    }

    private void setItem8(MS_PLRULQX item8) {
        if (item8 != null) {
            this.mr_packetlossrateulqci_8_00 = item8.getMr_packetlossrateulqci_00();
            this.mr_packetlossrateulqci_8_01 = item8.getMr_packetlossrateulqci_01();
        }
    }

    private void setItem7(MS_PLRULQX item7) {
        if (item7 != null) {
            this.mr_packetlossrateulqci_7_00 = item7.getMr_packetlossrateulqci_00();
            this.mr_packetlossrateulqci_7_01 = item7.getMr_packetlossrateulqci_01();
        }
    }

    private void setItem6(MS_PLRULQX item6) {
        if (item6 != null) {
            this.mr_packetlossrateulqci_6_00 = item6.getMr_packetlossrateulqci_00();
            this.mr_packetlossrateulqci_6_01 = item6.getMr_packetlossrateulqci_01();
        }
    }

    private void setItem5(MS_PLRULQX item5) {
        if (item5 != null) {
            this.mr_packetlossrateulqci_5_00 = item5.getMr_packetlossrateulqci_00();
            this.mr_packetlossrateulqci_5_01 = item5.getMr_packetlossrateulqci_01();
        }
    }

    private void setItem4(MS_PLRULQX item4) {
        if (item4 != null) {
            this.mr_packetlossrateulqci_4_00 = item4.getMr_packetlossrateulqci_00();
            this.mr_packetlossrateulqci_4_01 = item4.getMr_packetlossrateulqci_01();
        }
    }

    private void setItem3(MS_PLRULQX item3) {
        if (item3 != null) {
            this.mr_packetlossrateulqci_3_00 = item3.getMr_packetlossrateulqci_00();
            this.mr_packetlossrateulqci_3_01 = item3.getMr_packetlossrateulqci_01();
        }
    }

    private void setItem2(MS_PLRULQX item2) {
        if (item2 != null) {
            this.mr_packetlossrateulqci_2_00 = item2.getMr_packetlossrateulqci_00();
            this.mr_packetlossrateulqci_2_01 = item2.getMr_packetlossrateulqci_01();
        }
    }

    private void setItem1(MS_PLRULQX item1) {
        if (item1 != null) {
            this.key = item1.getKey();
            this.int_id = item1.getInt_id();
            this.start_time = item1.getStart_time();

            this.mr_packetlossrateulqci_1_00 = item1.getMr_packetlossrateulqci_00();
            this.mr_packetlossrateulqci_1_01 = item1.getMr_packetlossrateulqci_01();
        }
    }

    private String key;
    private String int_id;
    private String start_time;

    private long mr_packetlossrateulqci_1_00;
    private long mr_packetlossrateulqci_1_01;

    private long mr_packetlossrateulqci_2_00;
    private long mr_packetlossrateulqci_2_01;

    private long mr_packetlossrateulqci_3_00;
    private long mr_packetlossrateulqci_3_01;

    private long mr_packetlossrateulqci_4_00;
    private long mr_packetlossrateulqci_4_01;

    private long mr_packetlossrateulqci_5_00;
    private long mr_packetlossrateulqci_5_01;

    private long mr_packetlossrateulqci_6_00;
    private long mr_packetlossrateulqci_6_01;

    private long mr_packetlossrateulqci_7_00;
    private long mr_packetlossrateulqci_7_01;

    private long mr_packetlossrateulqci_8_00;
    private long mr_packetlossrateulqci_8_01;
    
    private long mr_packetlossrateulqci_9_00;
    private long mr_packetlossrateulqci_9_01;

    public String getKey() {
        return key;
    }

    public void setKey(String key) {
        this.key = key;
    }

    public String getInt_id() {
        return int_id;
    }

    public void setInt_id(String int_id) {
        this.int_id = int_id;
    }

    public String getStart_time() {
        return start_time;
    }

    public void setStart_time(String start_time) {
        this.start_time = start_time;
    }

    public long getMr_packetlossrateulqci_1_00() {
        return mr_packetlossrateulqci_1_00;
    }

    public void setMr_packetlossrateulqci_1_00(long mr_packetlossrateulqci_1_00) {
        this.mr_packetlossrateulqci_1_00 = mr_packetlossrateulqci_1_00;
    }

    public long getMr_packetlossrateulqci_1_01() {
        return mr_packetlossrateulqci_1_01;
    }

    public void setMr_packetlossrateulqci_1_01(long mr_packetlossrateulqci_1_01) {
        this.mr_packetlossrateulqci_1_01 = mr_packetlossrateulqci_1_01;
    }

    public long getMr_packetlossrateulqci_2_00() {
        return mr_packetlossrateulqci_2_00;
    }

    public void setMr_packetlossrateulqci_2_00(long mr_packetlossrateulqci_2_00) {
        this.mr_packetlossrateulqci_2_00 = mr_packetlossrateulqci_2_00;
    }

    public long getMr_packetlossrateulqci_2_01() {
        return mr_packetlossrateulqci_2_01;
    }

    public void setMr_packetlossrateulqci_2_01(long mr_packetlossrateulqci_2_01) {
        this.mr_packetlossrateulqci_2_01 = mr_packetlossrateulqci_2_01;
    }

    public long getMr_packetlossrateulqci_3_00() {
        return mr_packetlossrateulqci_3_00;
    }

    public void setMr_packetlossrateulqci_3_00(long mr_packetlossrateulqci_3_00) {
        this.mr_packetlossrateulqci_3_00 = mr_packetlossrateulqci_3_00;
    }

    public long getMr_packetlossrateulqci_3_01() {
        return mr_packetlossrateulqci_3_01;
    }

    public void setMr_packetlossrateulqci_3_01(long mr_packetlossrateulqci_3_01) {
        this.mr_packetlossrateulqci_3_01 = mr_packetlossrateulqci_3_01;
    }

    public long getMr_packetlossrateulqci_4_00() {
        return mr_packetlossrateulqci_4_00;
    }

    public void setMr_packetlossrateulqci_4_00(long mr_packetlossrateulqci_4_00) {
        this.mr_packetlossrateulqci_4_00 = mr_packetlossrateulqci_4_00;
    }

    public long getMr_packetlossrateulqci_4_01() {
        return mr_packetlossrateulqci_4_01;
    }

    public void setMr_packetlossrateulqci_4_01(long mr_packetlossrateulqci_4_01) {
        this.mr_packetlossrateulqci_4_01 = mr_packetlossrateulqci_4_01;
    }

    public long getMr_packetlossrateulqci_5_00() {
        return mr_packetlossrateulqci_5_00;
    }

    public void setMr_packetlossrateulqci_5_00(long mr_packetlossrateulqci_5_00) {
        this.mr_packetlossrateulqci_5_00 = mr_packetlossrateulqci_5_00;
    }

    public long getMr_packetlossrateulqci_5_01() {
        return mr_packetlossrateulqci_5_01;
    }

    public void setMr_packetlossrateulqci_5_01(long mr_packetlossrateulqci_5_01) {
        this.mr_packetlossrateulqci_5_01 = mr_packetlossrateulqci_5_01;
    }

    public long getMr_packetlossrateulqci_6_00() {
        return mr_packetlossrateulqci_6_00;
    }

    public void setMr_packetlossrateulqci_6_00(long mr_packetlossrateulqci_6_00) {
        this.mr_packetlossrateulqci_6_00 = mr_packetlossrateulqci_6_00;
    }

    public long getMr_packetlossrateulqci_6_01() {
        return mr_packetlossrateulqci_6_01;
    }

    public void setMr_packetlossrateulqci_6_01(long mr_packetlossrateulqci_6_01) {
        this.mr_packetlossrateulqci_6_01 = mr_packetlossrateulqci_6_01;
    }

    public long getMr_packetlossrateulqci_7_00() {
        return mr_packetlossrateulqci_7_00;
    }

    public void setMr_packetlossrateulqci_7_00(long mr_packetlossrateulqci_7_00) {
        this.mr_packetlossrateulqci_7_00 = mr_packetlossrateulqci_7_00;
    }

    public long getMr_packetlossrateulqci_7_01() {
        return mr_packetlossrateulqci_7_01;
    }

    public void setMr_packetlossrateulqci_7_01(long mr_packetlossrateulqci_7_01) {
        this.mr_packetlossrateulqci_7_01 = mr_packetlossrateulqci_7_01;
    }

    public long getMr_packetlossrateulqci_8_00() {
        return mr_packetlossrateulqci_8_00;
    }

    public void setMr_packetlossrateulqci_8_00(long mr_packetlossrateulqci_8_00) {
        this.mr_packetlossrateulqci_8_00 = mr_packetlossrateulqci_8_00;
    }

    public long getMr_packetlossrateulqci_8_01() {
        return mr_packetlossrateulqci_8_01;
    }

    public void setMr_packetlossrateulqci_8_01(long mr_packetlossrateulqci_8_01) {
        this.mr_packetlossrateulqci_8_01 = mr_packetlossrateulqci_8_01;
    }

    public long getMr_packetlossrateulqci_9_00() {
        return mr_packetlossrateulqci_9_00;
    }

    public void setMr_packetlossrateulqci_9_00(long mr_packetlossrateulqci_9_00) {
        this.mr_packetlossrateulqci_9_00 = mr_packetlossrateulqci_9_00;
    }

    public long getMr_packetlossrateulqci_9_01() {
        return mr_packetlossrateulqci_9_01;
    }

    public void setMr_packetlossrateulqci_9_01(long mr_packetlossrateulqci_9_01) {
        this.mr_packetlossrateulqci_9_01 = mr_packetlossrateulqci_9_01;
    }
}

View Code

从topic上获取数据流：

Dataset<Row> dsParsed = this.sparkSession.readStream().format("kafka").options(this.kafkaOptions).option("subscribe", topicName)
                .option("startingOffsets", "earliest").load();

String waterMarkName = "query" + this.getTopicEncodeName(topicName) + "Agg";
int windowDuration = 2 * 60;
int slideDuration = 60;

try {
    dsParsed.withWatermark("timestamp", "2 hour").createTempView(waterMarkName);
} catch (AnalysisException e1) {
    e1.printStackTrace();
    throw new RuntimeException(e1);
}

String aggSQL = "xxx";
Dataset<Row> dsSQL1 = sparkSession.sql(aggSQL);
dsSQL1.printSchema();

对获取的数据流按照key进行数据拼接：

正确的处理方式：按照key对数据进行分组，然后对同一组数据按照key进行排序，之后完成数据合并，把合并结果打印到console上。

KeyValueGroupedDataset<String, Row> tuple2Dataset = dsSQL1.groupByKey((MapFunction<Row, String>) row -> {
    String int_id = row.getAs("int_id");
    String start_time = row.getAs("start_time");
    String key = int_id + "_" + start_time;
    return key;
}, Encoders.STRING());

Dataset<MS_PLRULQX_Combine> tuple2FlatMapDataset = tuple2Dataset.flatMapGroups(
        new FlatMapGroupsFunction<String, Row, MS_PLRULQX_Combine>() {
            private static final long serialVersionUID = 1400167811199763836L;

            @Override
            public Iterator<MS_PLRULQX_Combine> call(String key, Iterator<Row> values) throws Exception {
                List<MS_PLRULQX> list = new ArrayList<MS_PLRULQX>();

                while (values.hasNext()) {
                    Row value = values.next();
                    MS_PLRULQX item = new MS_PLRULQX(value);
                    list.add(item);
                }

                Collections.sort(list, (v1, v2) -> -(v2.compareTo(v1)));

                return Arrays.asList(new MS_PLRULQX_Combine(list)).iterator();
            }
        }, Encoders.bean(MS_PLRULQX_Combine.class));

Dataset<Row> rows = tuple2FlatMapDataset.toDF();
rows.writeStream().format("console").outputMode("complete").trigger(Trigger.ProcessingTime(1, TimeUnit.MINUTES)).start();

对获取的数据流按照key进行数据拼接，另外一种方案遇到的问题：

该方案使用JavaRDD进行分组，排序，合并。

import scala.Tuple2;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.function.Function;
import org.apache.spark.api.java.function.PairFunction;

JavaPairRDD<String, MS_PLRULQX> pairs = dsSQL1.toJavaRDD().mapToPair(new PairFunction<Row, String, MS_PLRULQX>() {
    private static final long serialVersionUID = -5203498264050492910L;

    @Override
    public Tuple2<String, MS_PLRULQX> call(Row row) throws Exception {
        MS_PLRULQX value = new MS_PLRULQX(row);

        return new Tuple2<String, MS_PLRULQX>(value.getPrimaryKey(), value);
    }
});

JavaPairRDD<String, Iterable<MS_PLRULQX>> group = pairs.groupByKey();

JavaPairRDD<String, MS_PLRULQX_Combine> keyVsValuePairRDD = group.mapToPair(tuple -> {
    List<MS_PLRULQX> list = new ArrayList<MS_PLRULQX>();
    Iterator<MS_PLRULQX> it = tuple._2.iterator();
    while (it.hasNext()) {
        MS_PLRULQX score = it.next();
        list.add(score);
    }

    Collections.sort(list, (v1, v2) -> -(v2.compareTo(v1)));

    return new Tuple2<String, MS_PLRULQX_Combine>(tuple._1, new MS_PLRULQX_Combine(list));
});

JavaRDD<MS_PLRULQX_Combine> javaRDD = keyVsValuePairRDD
        .map(new Function<Tuple2<String, MS_PLRULQX_Combine>, MS_PLRULQX_Combine>() {
            private static final long serialVersionUID = -3031600976005716506L;

            @Override
            public MS_PLRULQX_Combine call(Tuple2<String, MS_PLRULQX_Combine> v1) throws Exception {
                return v1._2;
            }
        });

Dataset<Row> rows = this.sparkSession.createDataFrame(javaRDD, MS_PLRULQX_Combine.class);
rows.writeStream().format("console").outputMode("complete").trigger(Trigger.ProcessingTime(1, TimeUnit.MINUTES)).start();
sparkSession.streams().awaitAnyTermination();

抛出错误的位置就是：

Exception in thread "main" org.apache.spark.sql.AnalysisException: Queries with streaming sources must be executed with writeStream.start();;
at com.xx.xx.streaming.drivers.XXXDriver.run(xxxxDriver.java:85) 错误代码执行“JavaPairRDD<String, MS_PLRULQX> pairs = dsSQL1.toJavaRDD().mapToPair(new PairFunction<Row, String, MS_PLRULQX>() {”该行。
该错误代码，看起来像是“执行了.toJavaRDD()和执行dsSQL1.show/dsSQL1.collection.foreach(println(_))一样。”

从spark官网上（spark2.3.1)中只提到了structured spark支持Dataset/DataFrame API，并未提到支持RDD：

posted @ 2018-08-28 23:48 cctext 阅读(1113) 评论(0) 编辑收藏举报

刷新页面返回顶部

yy

基础才是编程人员应该深入研究的问题，警告自己问题解决不了时，多从运行原理底层研究后再考虑方案。

Kafka：ZK+Kafka+Spark Streaming集群环境搭建（二十五）Structured Streaming：同一个topic中包含一组数据的多个部分，按照key它们拼接为一条记录（以及遇到的问题）。

需求：

从topic上获取数据流：

对获取的数据流按照key进行数据拼接：

对获取的数据流按照key进行数据拼接，另外一种方案遇到的问题：

公告