Spark: Merge 2 dataframes by adding row index/number on both dataframes

rownum + window function i.e solution 1 or zipWithIndex.map i.e solution 2 should help in this case.

Solution 1 : You can use window functions to get this kind of

Then I would suggest you to add rownumber as additional column name to Dataframe say df1.

  DF1              
    C1                    C2                 columnindex                                             
    23397414             20875.7353            1
    5213970              20497.5582            2
    41323308             20935.7956            3
    123276113            18884.0477            4
    76456078             18389.9269            5

the second dataframe

DF2
C3                       C4             columnindex
2008-02-04               262.00            1        
2008-02-05               257.25            2      
2008-02-06               262.75            3      
2008-02-07               237.00            4          
2008-02-08               231.00            5

Now .. do inner join of df1 and df2 that's all... you will get below ouput

something like this

from pyspark.sql.window import Window
from pyspark.sql.functions import rowNumber

w = Window().orderBy()

df1 = ....  // as showed above df1

df2 = ....  // as shown above df2


df11 =  df1.withColumn("columnindex", rowNumber().over(w))
  df22 =  df2.withColumn("columnindex", rowNumber().over(w))

newDF = df11.join(df22, df11.columnindex == df22.columnindex, 'inner').drop(df22.columnindex)
newDF.show()



New DF              
    C1                    C2          C3                                              
    23397414             20875.7353   2008-02-04
    5213970              20497.5582   2008-02-05
    41323308             20935.7956   2008-02-06
    123276113            18884.0477   2008-02-07
    76456078             18389.9269   2008-02-08

Solution 2 : Another good way(probably this is best :)) in scala, which you can translate to pyspark :

/**
* Add Column Index to dataframe 
*/
def addColumnIndex(df: DataFrame) = sqlContext.createDataFrame(
  // Add Column index
  df.rdd.zipWithIndex.map{case (row, columnindex) => Row.fromSeq(row.toSeq :+ columnindex)},
  // Create schema
  StructType(df.schema.fields :+ StructField("columnindex", LongType, false))
)

// Add index now...
val df1WithIndex = addColumnIndex(df1)
val df2WithIndex = addColumnIndex(df2)

 // Now time to join ...
val newone = df1WithIndex
  .join(df2WithIndex , Seq("columnindex"))
  .drop("columnindex")

I thought I would share the python (pyspark) translation for answer #2 above from @Ram Ghadiyaram:

from pyspark.sql.functions import col
def addColumnIndex(df): 
  # Create new column names
  oldColumns = df.schema.names
  newColumns = oldColumns + ["columnindex"]

  # Add Column index
  df_indexed = df.rdd.zipWithIndex().map(lambda (row, columnindex): \
                                         row + (columnindex,)).toDF()

  #Rename all the columns
  new_df = reduce(lambda data, idx: data.withColumnRenamed(oldColumns[idx], 
                  newColumns[idx]), xrange(len(oldColumns)), df_indexed)   
  return new_df

# Add index now...
df1WithIndex = addColumnIndex(df1)
df2WithIndex = addColumnIndex(df2)

#Now time to join ...
newone = df1WithIndex.join(df2WithIndex, col("columnindex"),
                           'inner').drop("columnindex")

I referred to his(@Jed) answer

from pyspark.sql.functions import col
def addColumnIndex(df): 
    # Get old columns names and add a column "columnindex"
    oldColumns = df.columns
    newColumns = oldColumns + ["columnindex"]

    # Add Column index
    df_indexed = df.rdd.zipWithIndex().map(lambda (row, columnindex): \
                                         row + (columnindex,)).toDF()
    #Rename all the columns
    oldColumns = df_indexed.columns  
    new_df = reduce(lambda data, idx:data.withColumnRenamed(oldColumns[idx], 
                  newColumns[idx]), xrange(len(oldColumns)), df_indexed)   
    return new_df

# Add index now...
df1WithIndex = addColumnIndex(df1)
df2WithIndex = addColumnIndex(df2)

#Now time to join ...
newone = df1WithIndex.join(df2WithIndex, col("columnindex"),
                           'inner').drop("columnindex")

for python3 version,

from pyspark.sql.types import StructType, StructField, LongType

def with_column_index(sdf): 
    new_schema = StructType(sdf.schema.fields + [StructField("ColumnIndex", LongType(), False),])
    return sdf.rdd.zipWithIndex().map(lambda row: row[0] + (row[1],)).toDF(schema=new_schema)

df1_ci = with_column_index(df1)
df2_ci = with_column_index(df2)
join_on_index = df1_ci.join(df2_ci, df1_ci.ColumnIndex == df2_ci.ColumnIndex, 'inner').drop("ColumnIndex")