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Sparkのさまざまな結合タイプは何ですか?

私はドキュメントを見て、次の結合タイプがサポートされていると言っています:

実行する結合のタイプ。デフォルトのインナー。 inner、cross、outer、full、full_outer、left、left_outer、right、right_outer、left_semi、left_antiのいずれかでなければなりません。

私は StackOverflow answer をSQLジョインで見ましたが、トップ2の回答では上記のジョインの一部に言及していません。 left_semiおよびleft_anti。 Sparkではどういう意味ですか?

27
pathikrit

以下に簡単な実験例を示します。

import org.Apache.spark._
import org.Apache.spark.sql._
import org.Apache.spark.sql.expressions._
import org.Apache.spark.sql.functions._

object SparkSandbox extends App {

  case class Row(id: Int, value: String)

  private[this] implicit val spark = SparkSession.builder().master("local[*]").getOrCreate()
  import spark.implicits._
  spark.sparkContext.setLogLevel("ERROR")

  val r1 = Seq(Row(1, "A1"), Row(2, "A2"), Row(3, "A3"), Row(4, "A4")).toDS()
  val r2 = Seq(Row(3, "A3"), Row(4, "A4"), Row(4, "A4_1"), Row(5, "A5"), Row(6, "A6")).toDS()

  val joinTypes = Seq("inner", "outer", "full", "full_outer", "left", "left_outer", "right", "right_outer", "left_semi", "left_anti")

  joinTypes foreach {joinType =>
    println(s"${joinType.toUpperCase()} JOIN")
    r1.join(right = r2, usingColumns = Seq("id"), joinType = joinType).orderBy("id").show()
  }
}

出力

 INNER JOIN
+---+-----+-----+
| id|value|value|
+---+-----+-----+
|  3|   A3|   A3|
|  4|   A4| A4_1|
|  4|   A4|   A4|
+---+-----+-----+

OUTER JOIN
+---+-----+-----+
| id|value|value|
+---+-----+-----+
|  1|   A1| null|
|  2|   A2| null|
|  3|   A3|   A3|
|  4|   A4|   A4|
|  4|   A4| A4_1|
|  5| null|   A5|
|  6| null|   A6|
+---+-----+-----+

FULL JOIN
+---+-----+-----+
| id|value|value|
+---+-----+-----+
|  1|   A1| null|
|  2|   A2| null|
|  3|   A3|   A3|
|  4|   A4| A4_1|
|  4|   A4|   A4|
|  5| null|   A5|
|  6| null|   A6|
+---+-----+-----+

FULL_OUTER JOIN
+---+-----+-----+
| id|value|value|
+---+-----+-----+
|  1|   A1| null|
|  2|   A2| null|
|  3|   A3|   A3|
|  4|   A4| A4_1|
|  4|   A4|   A4|
|  5| null|   A5|
|  6| null|   A6|
+---+-----+-----+

LEFT JOIN
+---+-----+-----+
| id|value|value|
+---+-----+-----+
|  1|   A1| null|
|  2|   A2| null|
|  3|   A3|   A3|
|  4|   A4| A4_1|
|  4|   A4|   A4|
+---+-----+-----+

LEFT_OUTER JOIN
+---+-----+-----+
| id|value|value|
+---+-----+-----+
|  1|   A1| null|
|  2|   A2| null|
|  3|   A3|   A3|
|  4|   A4| A4_1|
|  4|   A4|   A4|
+---+-----+-----+

RIGHT JOIN
+---+-----+-----+
| id|value|value|
+---+-----+-----+
|  3|   A3|   A3|
|  4|   A4|   A4|
|  4|   A4| A4_1|
|  5| null|   A5|
|  6| null|   A6|
+---+-----+-----+

RIGHT_OUTER JOIN
+---+-----+-----+
| id|value|value|
+---+-----+-----+
|  3|   A3|   A3|
|  4|   A4| A4_1|
|  4|   A4|   A4|
|  5| null|   A5|
|  6| null|   A6|
+---+-----+-----+

LEFT_SEMI JOIN
+---+-----+
| id|value|
+---+-----+
|  3|   A3|
|  4|   A4|
+---+-----+

LEFT_ANTI JOIN
+---+-----+
| id|value|
+---+-----+
|  1|   A1|
|  2|   A2|
+---+-----+
56
pathikrit

以下は、Sparkで利用可能なさまざまな種類の結合タイプの視覚的な説明です。

Spark参加に関する次の素晴らしいブログをご覧ください。 http://kirillpavlov.com/blog/2016/04/23/beyond-traditional-join-with-Apache-spark/

15
Sohum Sachdev

パティクリットの例が大好き。以下は、Java v2とクロス結合を含むデータフレームを使用したSparkの可能な変換です。

package net.jgp.books.sparkInAction.ch12.lab940AllJoins;

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;

/**
 * All joins in a single app, inspired by
 * https://stackoverflow.com/questions/45990633/what-are-the-various-join-types-in-spark.
 * 
 * Used in Spark in Action 2e, http://jgp.net/sia
 * 
 * @author jgp
 */
public class AllJoinsApp {

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

  /**
   * The processing code.
   */
  private void start() {
    // Creates a session on a local master
    SparkSession spark = SparkSession.builder()
        .appName("Processing of invoices")
        .master("local")
        .getOrCreate();

    StructType schema = DataTypes.createStructType(new StructField[] {
        DataTypes.createStructField(
            "id",
            DataTypes.IntegerType,
            false),
        DataTypes.createStructField(
            "value",
            DataTypes.StringType,
            false) });

    List<Row> rows = new ArrayList<Row>();
    rows.add(RowFactory.create(1, "A1"));
    rows.add(RowFactory.create(2, "A2"));
    rows.add(RowFactory.create(3, "A3"));
    rows.add(RowFactory.create(4, "A4"));
    Dataset<Row> dfLeft = spark.createDataFrame(rows, schema);
    dfLeft.show();

    rows = new ArrayList<Row>();
    rows.add(RowFactory.create(3, "A3"));
    rows.add(RowFactory.create(4, "A4"));
    rows.add(RowFactory.create(4, "A4_1"));
    rows.add(RowFactory.create(5, "A5"));
    rows.add(RowFactory.create(6, "A6"));
    Dataset<Row> dfRight = spark.createDataFrame(rows, schema);
    dfRight.show();

    String[] joinTypes = new String[] { 
        "inner", // v2.0.0. default
        "cross", // v2.2.0
        "outer", // v2.0.0
        "full", // v2.1.1
        "full_outer", // v2.1.1
        "left", // v2.1.1
        "left_outer", // v2.0.0
        "right", // v2.1.1
        "right_outer", // v2.0.0
        "left_semi", // v2.0.0, was leftsemi before v2.1.1
        "left_anti" // v2.1.1
        };

    for (String joinType : joinTypes) {
      System.out.println(joinType.toUpperCase() + " JOIN");
      Dataset<Row> df = dfLeft.join(
          dfRight, 
          dfLeft.col("id").equalTo(dfRight.col("id")), 
          joinType);
      df.orderBy(dfLeft.col("id")).show();
    }
  }
}

Spark in Action、2e12章リポジトリ にこの例を配置します。

5
jgp
Spark data frame support following types of joins between two dataframes.
Please find the list of joins and joining string with respect to join types along with scala syntax.
We can use following joining values used for specify the join type in Scala- Spark code. 
***Mathod:*** Leftdataframe.join(Rightdataframe, join_conditions, joinStringName)

Join Name : Join String name in scala -Spark code

1. inner : 'inner'
2. cross: 'cross'
3. outer: 'outer'
4. full: 'full'
5. full outer: 'fullouter'
6. left : 'left'
7. left outer : 'leftouter'
8. right : 'right'
9. right outer : 'rightouter'
10. left semi: 'leftsemi'
11. left anti: 'leftanti'

example: 1. Left Semi join: 
Leftdataframe.join(Rightdataframe, join_conditions, "leftsemi");
2. inner Join Example:
Leftdataframe.join(Rightdataframe, join_conditions, "inner");

Its tested and working well.
2
Rajeev Rathor