为什么联接后选择在java spark数据帧中引发异常？

我有两个数据帧：左数据帧和右数据帧。它们由三列组成：src relation、dest和具有相同的值

1-我尝试连接这些数据帧，条件是左侧的dst=右侧的src。但它不起作用。错误在哪里

Dataset<Row> r = left
  .join(right, left.col("dst").equalTo(right.col("src")));

2-如果我将左侧的dst重命名为dst，将右侧的src列重命名为dst2，那么我应用了联接，它就可以工作了。但是如果我尝试从optained数据帧中选择某个列。这引发了一个例外。我的错误在哪里

 Dataset<Row> left = input_df.withColumnRenamed("dst", "dst2");
 Dataset<Row> right = input_df.withColumnRenamed("src", "dst2");  
 Dataset<Row> r = left.join(right, left.col("dst2").equalTo(right.col("dst2")));

给出：

+---+---------+----+
|src|predicate|dst2|
+---+---------+----+
|  a|       r1| :b1|
|  a|       r2|   k|
|:b1|       r3| :b4|
|:b1|      r10|   d|
|:b4|       r4|   f|
|:b4|       r5| :b5|
|:b5|       r9|   t|
|:b5|      r10|   e|
+---+---------+----+

+----+---------+---+
|dst2|predicate|dst|
+----+---------+---+
|   a|       r1|:b1|
|   a|       r2|  k|
| :b1|       r3|:b4|
| :b1|      r10|  d|
| :b4|       r4|  f|
| :b4|       r5|:b5|
| :b5|       r9|  t|
| :b5|      r10|  e|
+----+---------+---+

及

给出：

+---+---------+----+
|src|predicate|dst2|
+---+---------+----+
|  a|       r1| :b1|
|  a|       r2|   k|
|:b1|       r3| :b4|
|:b1|      r10|   d|
|:b4|       r4|   f|
|:b4|       r5| :b5|
|:b5|       r9|   t|
|:b5|      r10|   e|
+---+---------+----+

+----+---------+---+
|dst2|predicate|dst|
+----+---------+---+
|   a|       r1|:b1|
|   a|       r2|  k|
| :b1|       r3|:b4|
| :b1|      r10|  d|
| :b4|       r4|  f|
| :b4|       r5|:b5|
| :b5|       r9|  t|
| :b5|      r10|  e|
+----+---------+---+

结果:

+---+---------+----+----+---------+---+
|src|predicate|dst2|dst2|predicate|dst|
+---+---------+----+----+---------+---+
|  a|       r1| b1| b1  |      r10|  d|
|  a|       r1| b1| b1  |       r3| b4|
|b1 |       r3| b4| b4  |       r5| b5|
|b1 |       r3| b4| b4  |       r4|  f|
+---+---------+----+----+---------+---+


Dataset<Row> r = left
  .join(right, left.col("dst2").equalTo(right.col("dst2")))
  .select(left.col("src"),right.col("dst"));

Exception in thread "main" org.apache.spark.sql.AnalysisException: resolved attribute(s) dst#45 missing from dst2#177,src#43,predicate#197,predicate#44,dst2#182,dst#198 in operator !Project [src#43, dst#45];

3-假设所选数据帧工作，如何将获得的数据帧添加到左侧数据帧

我用Java工作。

您使用的是：

r = r.select(left.col("src"), right.col("dst"));

Spark似乎没有找到正确数据帧的血统。这并不令人震惊，因为它经过了大量优化

假设您期望的输出是：

+---+---+
|src|dst|
+---+---+
| b1|:b5|
| b1|  f|
|:b4|  e|
|:b4|  t|
+---+---+

您可以使用以下3个选项之一：

使用col方法 使用列名 以下是完整的源代码：

package net.jgp.books.spark.ch12.lab990_others;

import static org.apache.spark.sql.functions.col;

import java.util.ArrayList;
import java.util.List;

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

/**
 * Self join.
 * 
 * @author jgp
 */
public class SelfJoinAndSelectApp {

  /**
   * main() is your entry point to the application.
   * 
   * @param args
   */
  public static void main(String[] args) {
    SelfJoinAndSelectApp app = new SelfJoinAndSelectApp();
    app.start();
  }

  /**
   * The processing code.
   */
  private void start() {
    // Creates a session on a local master
    SparkSession spark = SparkSession.builder()
        .appName("Self join")
        .master("local[*]")
        .getOrCreate();

    Dataset<Row> inputDf = createDataframe(spark);
    inputDf.show(false);

    Dataset<Row> left = inputDf.withColumnRenamed("dst", "dst2");
    left.show();

    Dataset<Row> right = inputDf.withColumnRenamed("src", "dst2");
    right.show();

    Dataset<Row> r = left.join(
        right,
        left.col("dst2").equalTo(right.col("dst2")));
    r.show();

    Dataset<Row> resultOption1Df = r.select(left.col("src"), r.col("dst"));
    resultOption1Df.show();

    Dataset<Row> resultOption2Df = r.select(col("src"), col("dst"));
    resultOption2Df.show();

    Dataset<Row> resultOption3Df = r.select("src", "dst");
    resultOption3Df.show();
  }

  private static Dataset<Row> createDataframe(SparkSession spark) {
    StructType schema = DataTypes.createStructType(new StructField[] {
        DataTypes.createStructField(
            "src",
            DataTypes.StringType,
            false),
        DataTypes.createStructField(
            "predicate",
            DataTypes.StringType,
            false),
        DataTypes.createStructField(
            "dst",
            DataTypes.StringType,
            false) });

    List<Row> rows = new ArrayList<>();
    rows.add(RowFactory.create("a", "r1", ":b1"));
    rows.add(RowFactory.create("a", "r2", "k"));
    rows.add(RowFactory.create("b1", "r3", ":b4"));
    rows.add(RowFactory.create("b1", "r10", "d"));
    rows.add(RowFactory.create(":b4", "r4", "f"));
    rows.add(RowFactory.create(":b4", "r5", ":b5"));
    rows.add(RowFactory.create(":b5", "r9", "t"));
    rows.add(RowFactory.create(":b5", "r10", "e"));

    return spark.createDataFrame(rows, schema);
  }
}

---

您可以添加您得到的错误和代码片段吗。@Vijay_Shinde请检查更新后的邮件，然后尝试选择Colsrc，coldst或只选择Src，dst；加入后，它应该创建一个不同的数据框，并且可能无法识别左侧和右侧。在转换前后添加show and printSchema或数据框确实有助于理解您的用例。@ApurbaPandey在java中不能只使用colColName。加入左侧和右侧时，我有以下专栏：src | predicate | dst2 | dst2 | predicate | dst，右数据帧的printSchema是root |-dst2:string nullable=true |-predicate:string nullable=true |-dst:string nullable=true那么为什么不识别dst呢？我认为你应该在你的问题中添加一个完整的show和printSchema。。。这里不太可读。可以编辑它。您是否可以在加入之前添加用于左右构建的代码？看看是否有帮助。如果需要的话，很乐意提供更多帮助。谢谢你，太棒了。我的最后一个问题是：如果对数据帧执行sql查询，如：input.RegisterPentableR7；数据集r7=spark.sqlSELECT a.src、a.predicate、b.dst+，其中a.dst=b.src；在这种情况下，作为专家，您更喜欢哪种解决方案？为什么？@Moudi，再问一个问题，在这里标记我，我将在这么小的空间内回答难以回答的问题：。很高兴我能帮助您投票表决我的解决方案：。

Dataset<Row> resultOption1Df = r.select(left.col("src"), r.col("dst"));
resultOption1Df.show();

Dataset<Row> resultOption2Df = r.select(col("src"), col("dst"));
resultOption2Df.show();

Dataset<Row> resultOption3Df = r.select("src", "dst");
resultOption3Df.show();

package net.jgp.books.spark.ch12.lab990_others;

import static org.apache.spark.sql.functions.col;

import java.util.ArrayList;
import java.util.List;

import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;

/**
 * Self join.
 * 
 * @author jgp
 */
public class SelfJoinAndSelectApp {

  /**
   * main() is your entry point to the application.
   * 
   * @param args
   */
  public static void main(String[] args) {
    SelfJoinAndSelectApp app = new SelfJoinAndSelectApp();
    app.start();
  }

  /**
   * The processing code.
   */
  private void start() {
    // Creates a session on a local master
    SparkSession spark = SparkSession.builder()
        .appName("Self join")
        .master("local[*]")
        .getOrCreate();

    Dataset<Row> inputDf = createDataframe(spark);
    inputDf.show(false);

    Dataset<Row> left = inputDf.withColumnRenamed("dst", "dst2");
    left.show();

    Dataset<Row> right = inputDf.withColumnRenamed("src", "dst2");
    right.show();

    Dataset<Row> r = left.join(
        right,
        left.col("dst2").equalTo(right.col("dst2")));
    r.show();

    Dataset<Row> resultOption1Df = r.select(left.col("src"), r.col("dst"));
    resultOption1Df.show();

    Dataset<Row> resultOption2Df = r.select(col("src"), col("dst"));
    resultOption2Df.show();

    Dataset<Row> resultOption3Df = r.select("src", "dst");
    resultOption3Df.show();
  }

  private static Dataset<Row> createDataframe(SparkSession spark) {
    StructType schema = DataTypes.createStructType(new StructField[] {
        DataTypes.createStructField(
            "src",
            DataTypes.StringType,
            false),
        DataTypes.createStructField(
            "predicate",
            DataTypes.StringType,
            false),
        DataTypes.createStructField(
            "dst",
            DataTypes.StringType,
            false) });

    List<Row> rows = new ArrayList<>();
    rows.add(RowFactory.create("a", "r1", ":b1"));
    rows.add(RowFactory.create("a", "r2", "k"));
    rows.add(RowFactory.create("b1", "r3", ":b4"));
    rows.add(RowFactory.create("b1", "r10", "d"));
    rows.add(RowFactory.create(":b4", "r4", "f"));
    rows.add(RowFactory.create(":b4", "r5", ":b5"));
    rows.add(RowFactory.create(":b5", "r9", "t"));
    rows.add(RowFactory.create(":b5", "r10", "e"));

    return spark.createDataFrame(rows, schema);
  }
}