【问题标题】:Spark - Mapping columns to variables in JAVA from an RDD or DataFrameSpark - 从 RDD 或 DataFrame 将列映射到 JAVA 中的变量
【发布时间】:2016-02-24 00:59:06
【问题描述】:

我正在尝试将 Spark RDD 中的 cassandra 行列映射到我可以交互以在 spark 中进行操作但似乎无法将它们放入变量的变量。我有以下代码:

JavaRDD<MeasuredValue> rdd = javaFunctions(sc).cassandraTable("model", "reports", mapRowTo (MeasuredValue.class))
   .select("start_frequency","bandwidth", "power");


    JavaRDD<Value> valueRdd = rdd.flatMap(row-> {
        double start_frequency = row.getStartFrequency();
        float power = row.getPower();
        double bandwidth = row.getBandwidth(); 


        List<Value> list = new ArrayList<Value>();
     // Create Channel Power Buckets    
        for(channel = 1.6000E8; channel <= channel_end;  ){ 
            if( (channel >= start_frequency) && (channel <= (start_frequency + bandwidth)) ) {     
             list.add(new Value(channel, power));
            }  // end if
            channel+=increment;
        }  // end for      

    }) 

我的课程如下所示:

public class Value implements Serializable {
    public Value(Double channel, Float power) {
        this.channel = channel;
        this.power = power;
    }
    Double channel;
    Float power;

    public void setChannel(Double channel) {
        this.channel = channel;
    }
    public void setPower(Float power) {
        this.power = power;
    }
    public Double getChannel() {
        return channel;
    }
    public Float getPower() {
        return power;
    }

    @Override
    public String toString() {
        return "[" +channel +","+power+"]";
    }
}

public static class MeasuredValue implements Serializable {

        public MeasuredValue() { }

        private double start_frequency;
        public double getStart_frequency() { return start_frequency; }
        public void setStart_frequency(double start_frequency) { this.start_frequency = start_frequency; }

        private double bandwidth ;
        public double getBandwidth() { return bandwidth; }
        public void setBandwidth(double bandwidth) { this.bandwidth = bandwidth; }

        private float power;    
        public float getPower() { return power; }
        public void setPower(float power) { this.power = power; }

    }

我尝试使用 lambda 对行进行平面映射的尝试似乎是错误的,因为我收到以下错误:

AbstractJavaRDDlike 类中的方法 flatMap 无法应用 给定类型;必需:找到 FlatMapFunction: (row)->{d[...];}} 原因:无法推断类型变量 U(参数 不匹配; lambda 表达式中的错误返回类型缺少返回值)

我在“创建通道电源桶”循环中遇到了关于

的错误

"从 lambda 表达式引用的局部变量必须是 final 或实际上是最终的”

如果我可以使用 DataFrame 来做到这一点,我会对查看代码来促进这一点感兴趣。

【问题讨论】:

  • 我应该使用DataFrame而不是RDD吗?
  • 第二条错误消息表明 lambda 中使用的某些变量未声明为 final - incrementchannel_end 变量是什么?他们是final吗?
  • 它们的定义如下:// Define Variable double channel,channel_end,start_frequency, increment, bandwidth; float power; long time_key; // Initialize Variables channel_end = 1.6159E8; increment = 5000;
  • 好吧,就是这样(或其中的一部分)——它们必须是最终的,例如final double channel_end = 1.6159E8;
  • 主要问题是能够将行列值映射到变量。我可以从火花中操纵。

标签: java apache-spark cassandra datastax-java-driver


【解决方案1】:

发现的答案是:

JavaRDD<MeasuredValue> rdd = javaFunctions(sc).cassandraTable("SB1000_47130646", "Measured_Value", mapRowTo(MeasuredValue.class));
JavaRDD<Value> valueRdd = rdd.flatMap(new FlatMapFunction<MeasuredValue, Value>(){
@Override 
public Iterable<Value> call(MeasuredValue row) throws Exception { 
double start_frequency = row.getStart_frequency(); 
float power = row.getPower(); 
double bandwidth = row.getBandwidth(); 

// Define Variable 
double channel,channel_end, increment;  

// Initialize Variables 
channel_end = 1.6159E8; 
increment = 5000; 

List<Value> list = new ArrayList<Value>(); 
// Create Channel Power Buckets 
for(channel = 1.6000E8; channel <= channel_end; ){ 
if( (channel >= start_frequency) && (channel <= (start_frequency + bandwidth)) ) { 
list.add(new Value(channel, power)); 
} // end if 
channel+=increment; 
} // end for 

return list; 
}    
    });

    sqlContext.createDataFrame(valueRdd, Value.class).groupBy(col("channel"))
    .agg(min("power"), max("power"), avg("power"))
    .write().mode(SaveMode.Append)      
    .option("table", "results")
    .option("keyspace", "model")
    .format("org.apache.spark.sql.cassandra").save();

} // end session
} // End Compute 

public class Value implements Serializable {
    public Value(Double channel, Float power) {
        this.channel = channel;
        this.power = power;
    }
    Double channel;
    Float power;

    public void setChannel(Double channel) {
        this.channel = channel;
    }
    public void setPower(Float power) {
        this.power = power;
    }
    public Double getChannel() {
        return channel;
    }
    public Float getPower() {
        return power;
    }

    @Override
    public String toString() {
        return "[" +channel +","+power+"]";
    }
}

public static class MeasuredValue implements Serializable {

        public MeasuredValue() { }

        private double start_frequency;
        public double getStart_frequency() { return start_frequency; }
        public void setStart_frequency(double start_frequency) { this.start_frequency = start_frequency; }

        private double bandwidth ;
        public double getBandwidth() { return bandwidth; }
        public void setBandwidth(double bandwidth) { this.bandwidth = bandwidth; }

        private float power;    
        public float getPower() { return power; }
        public void setPower(float power) { this.power = power; }

    }

【讨论】:

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