[scala] Joining Spark dataframes on the key

I have constructed two dataframes. How can we join multiple Spark dataframes ?

For Example :

PersonDf, ProfileDf with a common column as personId as (key). Now how can we have one Dataframe combining PersonDf and ProfileDf?

This question is related to scala apache-spark dataframe apache-spark-sql

The answer is


Apart from my above answer I tried to demonstrate all the spark joins with same case classes using spark 2.x here is my linked in article with full examples and explanation .

All join types : Default inner. Must be one of: inner, cross, outer, full, full_outer, left, left_outer, right, right_outer, left_semi, left_anti.

import org.apache.spark.sql._
import org.apache.spark.sql.functions._


 /**
  * @author : Ram Ghadiyaram
  */
object SparkJoinTypesDemo extends App {
  private[this] implicit val spark = SparkSession.builder().master("local[*]").getOrCreate()
  spark.sparkContext.setLogLevel("ERROR")
  case class Person(name: String, age: Int, personid: Int)
  case class Profile(profileName: String, personid: Int, profileDescription: String)
  /**
    * * @param joinType Type of join to perform. Default `inner`. Must be one of:
    * *                 `inner`, `cross`, `outer`, `full`, `full_outer`, `left`, `left_outer`,
    * *                 `right`, `right_outer`, `left_semi`, `left_anti`.
    */
  val joinTypes = Seq(
    "inner"
    , "outer"
    , "full"
    , "full_outer"
    , "left"
    , "left_outer"
    , "right"
    , "right_outer"
    , "left_semi"
    , "left_anti"
    //, "cross"
  )
  val df1 = spark.sqlContext.createDataFrame(
    Person("Nataraj", 45, 2)
      :: Person("Srinivas", 45, 5)
      :: Person("Ashik", 22, 9)
      :: Person("Deekshita", 22, 8)
      :: Person("Siddhika", 22, 4)
      :: Person("Madhu", 22, 3)
      :: Person("Meghna", 22, 2)
      :: Person("Snigdha", 22, 2)
      :: Person("Harshita", 22, 6)
      :: Person("Ravi", 42, 0)
      :: Person("Ram", 42, 9)
      :: Person("Chidananda Raju", 35, 9)
      :: Person("Sreekanth Doddy", 29, 9)
      :: Nil)
  val df2 = spark.sqlContext.createDataFrame(
    Profile("Spark", 2, "SparkSQLMaster")
      :: Profile("Spark", 5, "SparkGuru")
      :: Profile("Spark", 9, "DevHunter")
      :: Profile("Spark", 3, "Evangelist")
      :: Profile("Spark", 0, "Committer")
      :: Profile("Spark", 1, "All Rounder")
      :: Nil
  )
  val df_asPerson = df1.as("dfperson")
  val df_asProfile = df2.as("dfprofile")
  val joined_df = df_asPerson.join(
    df_asProfile
    , col("dfperson.personid") === col("dfprofile.personid")
    , "inner")

  println("First example inner join  ")


  // you can do alias to refer column name with aliases to  increase readability
  joined_df.select(
    col("dfperson.name")
    , col("dfperson.age")
    , col("dfprofile.profileName")
    , col("dfprofile.profileDescription"))
    .show
  println("all joins in a loop")
  joinTypes foreach { joinType =>
    println(s"${joinType.toUpperCase()} JOIN")
    df_asPerson.join(right = df_asProfile, usingColumns = Seq("personid"), joinType = joinType)
      .orderBy("personid")
      .show()
  }
  println(
    """
      |Till 1.x  cross join is :  df_asPerson.join(df_asProfile)
      |
      | Explicit Cross Join in 2.x :
      | http://blog.madhukaraphatak.com/migrating-to-spark-two-part-4/
      | Cartesian joins are very expensive without an extra filter that can be pushed down.
      |
      | cross join or cartesian product
      |
      |
    """.stripMargin)

  val crossJoinDf = df_asPerson.crossJoin(right = df_asProfile)
  crossJoinDf.show(200, false)
  println(crossJoinDf.explain())
  println(crossJoinDf.count)

  println("createOrReplaceTempView example ")
  println(
    """
      |Creates a local temporary view using the given name. The lifetime of this
      |   temporary view is tied to the [[SparkSession]] that was used to create this Dataset.
    """.stripMargin)




  df_asPerson.createOrReplaceTempView("dfperson");
  df_asProfile.createOrReplaceTempView("dfprofile")
  val sql =
    s"""
       |SELECT dfperson.name
       |, dfperson.age
       |, dfprofile.profileDescription
       |  FROM  dfperson JOIN  dfprofile
       | ON dfperson.personid == dfprofile.personid
    """.stripMargin
  println(s"createOrReplaceTempView  sql $sql")
  val sqldf = spark.sql(sql)
  sqldf.show


  println(
    """
      |
      |**** EXCEPT DEMO ***
      |
  """.stripMargin)
  println(" df_asPerson.except(df_asProfile) Except demo")
  df_asPerson.except(df_asProfile).show


  println(" df_asProfile.except(df_asPerson) Except demo")
  df_asProfile.except(df_asPerson).show
}

Result :

First example inner join  
+---------------+---+-----------+------------------+
|           name|age|profileName|profileDescription|
+---------------+---+-----------+------------------+
|        Nataraj| 45|      Spark|    SparkSQLMaster|
|       Srinivas| 45|      Spark|         SparkGuru|
|          Ashik| 22|      Spark|         DevHunter|
|          Madhu| 22|      Spark|        Evangelist|
|         Meghna| 22|      Spark|    SparkSQLMaster|
|        Snigdha| 22|      Spark|    SparkSQLMaster|
|           Ravi| 42|      Spark|         Committer|
|            Ram| 42|      Spark|         DevHunter|
|Chidananda Raju| 35|      Spark|         DevHunter|
|Sreekanth Doddy| 29|      Spark|         DevHunter|
+---------------+---+-----------+------------------+

all joins in a loop
INNER JOIN
+--------+---------------+---+-----------+------------------+
|personid|           name|age|profileName|profileDescription|
+--------+---------------+---+-----------+------------------+
|       0|           Ravi| 42|      Spark|         Committer|
|       2|        Snigdha| 22|      Spark|    SparkSQLMaster|
|       2|         Meghna| 22|      Spark|    SparkSQLMaster|
|       2|        Nataraj| 45|      Spark|    SparkSQLMaster|
|       3|          Madhu| 22|      Spark|        Evangelist|
|       5|       Srinivas| 45|      Spark|         SparkGuru|
|       9|            Ram| 42|      Spark|         DevHunter|
|       9|          Ashik| 22|      Spark|         DevHunter|
|       9|Chidananda Raju| 35|      Spark|         DevHunter|
|       9|Sreekanth Doddy| 29|      Spark|         DevHunter|
+--------+---------------+---+-----------+------------------+

OUTER JOIN
+--------+---------------+----+-----------+------------------+
|personid|           name| age|profileName|profileDescription|
+--------+---------------+----+-----------+------------------+
|       0|           Ravi|  42|      Spark|         Committer|
|       1|           null|null|      Spark|       All Rounder|
|       2|        Nataraj|  45|      Spark|    SparkSQLMaster|
|       2|        Snigdha|  22|      Spark|    SparkSQLMaster|
|       2|         Meghna|  22|      Spark|    SparkSQLMaster|
|       3|          Madhu|  22|      Spark|        Evangelist|
|       4|       Siddhika|  22|       null|              null|
|       5|       Srinivas|  45|      Spark|         SparkGuru|
|       6|       Harshita|  22|       null|              null|
|       8|      Deekshita|  22|       null|              null|
|       9|          Ashik|  22|      Spark|         DevHunter|
|       9|            Ram|  42|      Spark|         DevHunter|
|       9|Chidananda Raju|  35|      Spark|         DevHunter|
|       9|Sreekanth Doddy|  29|      Spark|         DevHunter|
+--------+---------------+----+-----------+------------------+

FULL JOIN
+--------+---------------+----+-----------+------------------+
|personid|           name| age|profileName|profileDescription|
+--------+---------------+----+-----------+------------------+
|       0|           Ravi|  42|      Spark|         Committer|
|       1|           null|null|      Spark|       All Rounder|
|       2|        Nataraj|  45|      Spark|    SparkSQLMaster|
|       2|         Meghna|  22|      Spark|    SparkSQLMaster|
|       2|        Snigdha|  22|      Spark|    SparkSQLMaster|
|       3|          Madhu|  22|      Spark|        Evangelist|
|       4|       Siddhika|  22|       null|              null|
|       5|       Srinivas|  45|      Spark|         SparkGuru|
|       6|       Harshita|  22|       null|              null|
|       8|      Deekshita|  22|       null|              null|
|       9|          Ashik|  22|      Spark|         DevHunter|
|       9|            Ram|  42|      Spark|         DevHunter|
|       9|Sreekanth Doddy|  29|      Spark|         DevHunter|
|       9|Chidananda Raju|  35|      Spark|         DevHunter|
+--------+---------------+----+-----------+------------------+

FULL_OUTER JOIN
+--------+---------------+----+-----------+------------------+
|personid|           name| age|profileName|profileDescription|
+--------+---------------+----+-----------+------------------+
|       0|           Ravi|  42|      Spark|         Committer|
|       1|           null|null|      Spark|       All Rounder|
|       2|        Nataraj|  45|      Spark|    SparkSQLMaster|
|       2|         Meghna|  22|      Spark|    SparkSQLMaster|
|       2|        Snigdha|  22|      Spark|    SparkSQLMaster|
|       3|          Madhu|  22|      Spark|        Evangelist|
|       4|       Siddhika|  22|       null|              null|
|       5|       Srinivas|  45|      Spark|         SparkGuru|
|       6|       Harshita|  22|       null|              null|
|       8|      Deekshita|  22|       null|              null|
|       9|          Ashik|  22|      Spark|         DevHunter|
|       9|            Ram|  42|      Spark|         DevHunter|
|       9|Chidananda Raju|  35|      Spark|         DevHunter|
|       9|Sreekanth Doddy|  29|      Spark|         DevHunter|
+--------+---------------+----+-----------+------------------+

LEFT JOIN
+--------+---------------+---+-----------+------------------+
|personid|           name|age|profileName|profileDescription|
+--------+---------------+---+-----------+------------------+
|       0|           Ravi| 42|      Spark|         Committer|
|       2|        Snigdha| 22|      Spark|    SparkSQLMaster|
|       2|         Meghna| 22|      Spark|    SparkSQLMaster|
|       2|        Nataraj| 45|      Spark|    SparkSQLMaster|
|       3|          Madhu| 22|      Spark|        Evangelist|
|       4|       Siddhika| 22|       null|              null|
|       5|       Srinivas| 45|      Spark|         SparkGuru|
|       6|       Harshita| 22|       null|              null|
|       8|      Deekshita| 22|       null|              null|
|       9|            Ram| 42|      Spark|         DevHunter|
|       9|          Ashik| 22|      Spark|         DevHunter|
|       9|Chidananda Raju| 35|      Spark|         DevHunter|
|       9|Sreekanth Doddy| 29|      Spark|         DevHunter|
+--------+---------------+---+-----------+------------------+

LEFT_OUTER JOIN
+--------+---------------+---+-----------+------------------+
|personid|           name|age|profileName|profileDescription|
+--------+---------------+---+-----------+------------------+
|       0|           Ravi| 42|      Spark|         Committer|
|       2|        Nataraj| 45|      Spark|    SparkSQLMaster|
|       2|         Meghna| 22|      Spark|    SparkSQLMaster|
|       2|        Snigdha| 22|      Spark|    SparkSQLMaster|
|       3|          Madhu| 22|      Spark|        Evangelist|
|       4|       Siddhika| 22|       null|              null|
|       5|       Srinivas| 45|      Spark|         SparkGuru|
|       6|       Harshita| 22|       null|              null|
|       8|      Deekshita| 22|       null|              null|
|       9|Chidananda Raju| 35|      Spark|         DevHunter|
|       9|Sreekanth Doddy| 29|      Spark|         DevHunter|
|       9|          Ashik| 22|      Spark|         DevHunter|
|       9|            Ram| 42|      Spark|         DevHunter|
+--------+---------------+---+-----------+------------------+

RIGHT JOIN
+--------+---------------+----+-----------+------------------+
|personid|           name| age|profileName|profileDescription|
+--------+---------------+----+-----------+------------------+
|       0|           Ravi|  42|      Spark|         Committer|
|       1|           null|null|      Spark|       All Rounder|
|       2|        Snigdha|  22|      Spark|    SparkSQLMaster|
|       2|         Meghna|  22|      Spark|    SparkSQLMaster|
|       2|        Nataraj|  45|      Spark|    SparkSQLMaster|
|       3|          Madhu|  22|      Spark|        Evangelist|
|       5|       Srinivas|  45|      Spark|         SparkGuru|
|       9|Sreekanth Doddy|  29|      Spark|         DevHunter|
|       9|Chidananda Raju|  35|      Spark|         DevHunter|
|       9|            Ram|  42|      Spark|         DevHunter|
|       9|          Ashik|  22|      Spark|         DevHunter|
+--------+---------------+----+-----------+------------------+

RIGHT_OUTER JOIN
+--------+---------------+----+-----------+------------------+
|personid|           name| age|profileName|profileDescription|
+--------+---------------+----+-----------+------------------+
|       0|           Ravi|  42|      Spark|         Committer|
|       1|           null|null|      Spark|       All Rounder|
|       2|         Meghna|  22|      Spark|    SparkSQLMaster|
|       2|        Snigdha|  22|      Spark|    SparkSQLMaster|
|       2|        Nataraj|  45|      Spark|    SparkSQLMaster|
|       3|          Madhu|  22|      Spark|        Evangelist|
|       5|       Srinivas|  45|      Spark|         SparkGuru|
|       9|Sreekanth Doddy|  29|      Spark|         DevHunter|
|       9|          Ashik|  22|      Spark|         DevHunter|
|       9|Chidananda Raju|  35|      Spark|         DevHunter|
|       9|            Ram|  42|      Spark|         DevHunter|
+--------+---------------+----+-----------+------------------+

LEFT_SEMI JOIN
+--------+---------------+---+
|personid|           name|age|
+--------+---------------+---+
|       0|           Ravi| 42|
|       2|        Nataraj| 45|
|       2|         Meghna| 22|
|       2|        Snigdha| 22|
|       3|          Madhu| 22|
|       5|       Srinivas| 45|
|       9|Chidananda Raju| 35|
|       9|Sreekanth Doddy| 29|
|       9|            Ram| 42|
|       9|          Ashik| 22|
+--------+---------------+---+

LEFT_ANTI JOIN
+--------+---------+---+
|personid|     name|age|
+--------+---------+---+
|       4| Siddhika| 22|
|       6| Harshita| 22|
|       8|Deekshita| 22|
+--------+---------+---+


Till 1.x  Cross join is :  `df_asPerson.join(df_asProfile)`

 Explicit Cross Join in 2.x :
 http://blog.madhukaraphatak.com/migrating-to-spark-two-part-4/
 Cartesian joins are very expensive without an extra filter that can be pushed down.

 Cross join or Cartesian product



+---------------+---+--------+-----------+--------+------------------+
|name           |age|personid|profileName|personid|profileDescription|
+---------------+---+--------+-----------+--------+------------------+
|Nataraj        |45 |2       |Spark      |2       |SparkSQLMaster    |
|Nataraj        |45 |2       |Spark      |5       |SparkGuru         |
|Nataraj        |45 |2       |Spark      |9       |DevHunter         |
|Nataraj        |45 |2       |Spark      |3       |Evangelist        |
|Nataraj        |45 |2       |Spark      |0       |Committer         |
|Nataraj        |45 |2       |Spark      |1       |All Rounder       |
|Srinivas       |45 |5       |Spark      |2       |SparkSQLMaster    |
|Srinivas       |45 |5       |Spark      |5       |SparkGuru         |
|Srinivas       |45 |5       |Spark      |9       |DevHunter         |
|Srinivas       |45 |5       |Spark      |3       |Evangelist        |
|Srinivas       |45 |5       |Spark      |0       |Committer         |
|Srinivas       |45 |5       |Spark      |1       |All Rounder       |
|Ashik          |22 |9       |Spark      |2       |SparkSQLMaster    |
|Ashik          |22 |9       |Spark      |5       |SparkGuru         |
|Ashik          |22 |9       |Spark      |9       |DevHunter         |
|Ashik          |22 |9       |Spark      |3       |Evangelist        |
|Ashik          |22 |9       |Spark      |0       |Committer         |
|Ashik          |22 |9       |Spark      |1       |All Rounder       |
|Deekshita      |22 |8       |Spark      |2       |SparkSQLMaster    |
|Deekshita      |22 |8       |Spark      |5       |SparkGuru         |
|Deekshita      |22 |8       |Spark      |9       |DevHunter         |
|Deekshita      |22 |8       |Spark      |3       |Evangelist        |
|Deekshita      |22 |8       |Spark      |0       |Committer         |
|Deekshita      |22 |8       |Spark      |1       |All Rounder       |
|Siddhika       |22 |4       |Spark      |2       |SparkSQLMaster    |
|Siddhika       |22 |4       |Spark      |5       |SparkGuru         |
|Siddhika       |22 |4       |Spark      |9       |DevHunter         |
|Siddhika       |22 |4       |Spark      |3       |Evangelist        |
|Siddhika       |22 |4       |Spark      |0       |Committer         |
|Siddhika       |22 |4       |Spark      |1       |All Rounder       |
|Madhu          |22 |3       |Spark      |2       |SparkSQLMaster    |
|Madhu          |22 |3       |Spark      |5       |SparkGuru         |
|Madhu          |22 |3       |Spark      |9       |DevHunter         |
|Madhu          |22 |3       |Spark      |3       |Evangelist        |
|Madhu          |22 |3       |Spark      |0       |Committer         |
|Madhu          |22 |3       |Spark      |1       |All Rounder       |
|Meghna         |22 |2       |Spark      |2       |SparkSQLMaster    |
|Meghna         |22 |2       |Spark      |5       |SparkGuru         |
|Meghna         |22 |2       |Spark      |9       |DevHunter         |
|Meghna         |22 |2       |Spark      |3       |Evangelist        |
|Meghna         |22 |2       |Spark      |0       |Committer         |
|Meghna         |22 |2       |Spark      |1       |All Rounder       |
|Snigdha        |22 |2       |Spark      |2       |SparkSQLMaster    |
|Snigdha        |22 |2       |Spark      |5       |SparkGuru         |
|Snigdha        |22 |2       |Spark      |9       |DevHunter         |
|Snigdha        |22 |2       |Spark      |3       |Evangelist        |
|Snigdha        |22 |2       |Spark      |0       |Committer         |
|Snigdha        |22 |2       |Spark      |1       |All Rounder       |
|Harshita       |22 |6       |Spark      |2       |SparkSQLMaster    |
|Harshita       |22 |6       |Spark      |5       |SparkGuru         |
|Harshita       |22 |6       |Spark      |9       |DevHunter         |
|Harshita       |22 |6       |Spark      |3       |Evangelist        |
|Harshita       |22 |6       |Spark      |0       |Committer         |
|Harshita       |22 |6       |Spark      |1       |All Rounder       |
|Ravi           |42 |0       |Spark      |2       |SparkSQLMaster    |
|Ravi           |42 |0       |Spark      |5       |SparkGuru         |
|Ravi           |42 |0       |Spark      |9       |DevHunter         |
|Ravi           |42 |0       |Spark      |3       |Evangelist        |
|Ravi           |42 |0       |Spark      |0       |Committer         |
|Ravi           |42 |0       |Spark      |1       |All Rounder       |
|Ram            |42 |9       |Spark      |2       |SparkSQLMaster    |
|Ram            |42 |9       |Spark      |5       |SparkGuru         |
|Ram            |42 |9       |Spark      |9       |DevHunter         |
|Ram            |42 |9       |Spark      |3       |Evangelist        |
|Ram            |42 |9       |Spark      |0       |Committer         |
|Ram            |42 |9       |Spark      |1       |All Rounder       |
|Chidananda Raju|35 |9       |Spark      |2       |SparkSQLMaster    |
|Chidananda Raju|35 |9       |Spark      |5       |SparkGuru         |
|Chidananda Raju|35 |9       |Spark      |9       |DevHunter         |
|Chidananda Raju|35 |9       |Spark      |3       |Evangelist        |
|Chidananda Raju|35 |9       |Spark      |0       |Committer         |
|Chidananda Raju|35 |9       |Spark      |1       |All Rounder       |
|Sreekanth Doddy|29 |9       |Spark      |2       |SparkSQLMaster    |
|Sreekanth Doddy|29 |9       |Spark      |5       |SparkGuru         |
|Sreekanth Doddy|29 |9       |Spark      |9       |DevHunter         |
|Sreekanth Doddy|29 |9       |Spark      |3       |Evangelist        |
|Sreekanth Doddy|29 |9       |Spark      |0       |Committer         |
|Sreekanth Doddy|29 |9       |Spark      |1       |All Rounder       |
+---------------+---+--------+-----------+--------+------------------+

== Physical Plan ==
BroadcastNestedLoopJoin BuildRight, Cross
:- LocalTableScan [name#0, age#1, personid#2]
+- BroadcastExchange IdentityBroadcastMode
   +- LocalTableScan [profileName#7, personid#8, profileDescription#9]
()
78
createOrReplaceTempView example 

Creates a local temporary view using the given name. The lifetime of this
   temporary view is tied to the [[SparkSession]] that was used to create this Dataset.

createOrReplaceTempView  sql 
SELECT dfperson.name
, dfperson.age
, dfprofile.profileDescription
  FROM  dfperson JOIN  dfprofile
 ON dfperson.personid == dfprofile.personid

+---------------+---+------------------+
|           name|age|profileDescription|
+---------------+---+------------------+
|        Nataraj| 45|    SparkSQLMaster|
|       Srinivas| 45|         SparkGuru|
|          Ashik| 22|         DevHunter|
|          Madhu| 22|        Evangelist|
|         Meghna| 22|    SparkSQLMaster|
|        Snigdha| 22|    SparkSQLMaster|
|           Ravi| 42|         Committer|
|            Ram| 42|         DevHunter|
|Chidananda Raju| 35|         DevHunter|
|Sreekanth Doddy| 29|         DevHunter|
+---------------+---+------------------+



**** EXCEPT DEMO ***


 df_asPerson.except(df_asProfile) Except demo
+---------------+---+--------+
|           name|age|personid|
+---------------+---+--------+
|          Ashik| 22|       9|
|       Harshita| 22|       6|
|          Madhu| 22|       3|
|            Ram| 42|       9|
|           Ravi| 42|       0|
|Chidananda Raju| 35|       9|
|       Siddhika| 22|       4|
|       Srinivas| 45|       5|
|Sreekanth Doddy| 29|       9|
|      Deekshita| 22|       8|
|         Meghna| 22|       2|
|        Snigdha| 22|       2|
|        Nataraj| 45|       2|
+---------------+---+--------+

 df_asProfile.except(df_asPerson) Except demo
+-----------+--------+------------------+
|profileName|personid|profileDescription|
+-----------+--------+------------------+
|      Spark|       5|         SparkGuru|
|      Spark|       9|         DevHunter|
|      Spark|       2|    SparkSQLMaster|
|      Spark|       3|        Evangelist|
|      Spark|       0|         Committer|
|      Spark|       1|       All Rounder|
+-----------+--------+------------------+

As discussed above these are the venn diagrams of all the joins. enter image description here


Let me explain with an example

  1. create emp DataFrame

    import spark.sqlContext.implicits._ val emp = Seq((1,"Smith",-1,"2018","10","M",3000), (2,"Rose",1,"2010","20","M",4000), (3,"Williams",1,"2010","10","M",1000), (4,"Jones",2,"2005","10","F",2000), (5,"Brown",2,"2010","40","",-1), (6,"Brown",2,"2010","50","",-1) ) val empColumns = Seq("emp_id","name","superior_emp_id","year_joined", "emp_dept_id","gender","salary")

    val empDF = emp.toDF(empColumns:_*)

  2. Create dept DataFrame

    val dept = Seq(("Finance",10), ("Marketing",20), ("Sales",30), ("IT",40) )

    val deptColumns = Seq("dept_name","dept_id") val deptDF = dept.toDF(deptColumns:_*)

Now let's join emp.emp_dept_id with dept.dept_id

empDF.join(deptDF,empDF("emp_dept_id") ===  deptDF("dept_id"),"inner")
    .show(false)

This results below

+------+--------+---------------+-----------+-----------+------+------+---------+-------+
|emp_id|name    |superior_emp_id|year_joined|emp_dept_id|gender|salary|dept_name|dept_id|
+------+--------+---------------+-----------+-----------+------+------+---------+-------+
|1     |Smith   |-1             |2018       |10         |M     |3000  |Finance  |10     |
|2     |Rose    |1              |2010       |20         |M     |4000  |Marketing|20     |
|3     |Williams|1              |2010       |10         |M     |1000  |Finance  |10     |
|4     |Jones   |2              |2005       |10         |F     |2000  |Finance  |10     |
|5     |Brown   |2              |2010       |40         |      |-1    |IT       |40     |
+------+--------+---------------+-----------+-----------+------+------+---------+-------+

If you are looking in python PySpark Join with example and also find the complete Scala example at Spark Join


you can use

val resultDf = PersonDf.join(ProfileDf, PersonDf("personId") === ProfileDf("personId"))

or shorter and more flexible (as you can easely specify more than 1 columns for joining)

val resultDf = PersonDf.join(ProfileDf,Seq("personId"))

One way

// join type can be inner, left, right, fullouter
val mergedDf = df1.join(df2, Seq("keyCol"), "inner")
// keyCol can be multiple column names seperated by comma
val mergedDf = df1.join(df2, Seq("keyCol1", "keyCol2"), "left")

Another way

import spark.implicits._ 
val mergedDf = df1.as("d1").join(df2.as("d2"), ($"d1.colName" === $"d2.colName"))
// to select specific columns as output
val mergedDf = df1.as("d1").join(df2.as("d2"), ($"d1.colName" === $"d2.colName")).select($"d1.*", $"d2.anotherColName")

Posting a java based solution, incase your team only uses java. The keyword inner will ensure that matching rows only are present in the final dataframe.

            Dataset<Row> joined = PersonDf.join(ProfileDf, 
                    PersonDf.col("personId").equalTo(ProfileDf.col("personId")),
                    "inner");
            joined.show();

From https://spark.apache.org/docs/1.5.1/api/java/org/apache/spark/sql/DataFrame.html, use join:

Inner equi-join with another DataFrame using the given column.

PersonDf.join(ProfileDf,$"personId")

OR

PersonDf.join(ProfileDf,PersonDf("personId") === ProfileDf("personId"))

Update:

You can also save the DFs as temp table using df.registerTempTable("tableName") and you can write sql queries using sqlContext.


inner join with scala

val joinedDataFrame = PersonDf.join(ProfileDf ,"personId")
joinedDataFrame.show

Examples related to scala

Intermediate language used in scalac? Why does calling sumr on a stream with 50 tuples not complete Select Specific Columns from Spark DataFrame Joining Spark dataframes on the key Provide schema while reading csv file as a dataframe how to filter out a null value from spark dataframe Fetching distinct values on a column using Spark DataFrame Can't push to the heroku Spark - Error "A master URL must be set in your configuration" when submitting an app Add jars to a Spark Job - spark-submit

Examples related to apache-spark

Select Specific Columns from Spark DataFrame Select columns in PySpark dataframe What is the difference between spark.sql.shuffle.partitions and spark.default.parallelism? How to find count of Null and Nan values for each column in a PySpark dataframe efficiently? Spark dataframe: collect () vs select () How does createOrReplaceTempView work in Spark? Spark difference between reduceByKey vs groupByKey vs aggregateByKey vs combineByKey Filter df when values matches part of a string in pyspark Filtering a pyspark dataframe using isin by exclusion Convert date from String to Date format in Dataframes

Examples related to dataframe

Trying to merge 2 dataframes but get ValueError How to show all of columns name on pandas dataframe? Python Pandas - Find difference between two data frames Pandas get the most frequent values of a column Display all dataframe columns in a Jupyter Python Notebook How to convert column with string type to int form in pyspark data frame? Display/Print one column from a DataFrame of Series in Pandas Binning column with python pandas Selection with .loc in python Set value to an entire column of a pandas dataframe

Examples related to apache-spark-sql

Select Specific Columns from Spark DataFrame Pyspark: Filter dataframe based on multiple conditions Select columns in PySpark dataframe What is the difference between spark.sql.shuffle.partitions and spark.default.parallelism? How to find count of Null and Nan values for each column in a PySpark dataframe efficiently? Spark dataframe: collect () vs select () How does createOrReplaceTempView work in Spark? Filter df when values matches part of a string in pyspark Convert date from String to Date format in Dataframes Take n rows from a spark dataframe and pass to toPandas()