Spark DataframeにはPandaのマージインジケータと同等のオプションがありますか？

Question

のpythonパンダライブラリには、以下の機能が含まれています：パンダのvalue_counts（）関数と組み合わせ

DataFrame.merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, 
       right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, 
       indicator=False) 

インジケータフィールドは、すぐに参加を行ってどれだけかを決定するために使用することができます。

例：

In [48]: df1 = pd.DataFrame({'col1': [0, 1], 'col_left':['a', 'b']}) 

In [49]: df2 = pd.DataFrame({'col1': [1, 2, 2],'col_right':[2, 2, 2]}) 

In [50]: pd.merge(df1, df2, on='col1', how='outer', indicator=True) 
Out[50]: 
    col1 col_left col_right  _merge 
0  0  a  NaN left_only 
1  1  b  2.0  both 
2  2  NaN  2.0 right_only 
3  2  NaN  2.0 right_only 

スパークデータフレーム内の参加のパフォーマンスをチェックするための最良の方法は何ですか？

カスタム関数は、答えの1で提供されていた：それはまだ正しい結果を得られませんが、それは希望の場合、それは素晴らしいことだ：

ASchema = StructType([StructField('id', IntegerType(),nullable=False), 
       StructField('name', StringType(),nullable=False)]) 
BSchema = StructType([StructField('id', IntegerType(),nullable=False), 
       StructField('role', StringType(),nullable=False)]) 
AData = sc.parallelize ([ Row(1,'michel'), Row(2,'diederik'), Row(3,'rok'), Row(4,'piet')]) 
BData = sc.parallelize ([ Row(1,'engineer'), Row(2,'lead'), Row(3,'scientist'), Row(5,'manager')]) 
ADF = hc.createDataFrame(AData,ASchema) 
BDF = hc.createDataFrame(BData,BSchema) 
DFJOIN = ADF.join(BDF, ADF['id'] == BDF['id'], "outer") 
DFJOIN.show() 

Input: 
+----+--------+----+---------+ 
| id| name| id|  role| 
+----+--------+----+---------+ 
| 1| michel| 1| engineer| 
| 2|diederik| 2|  lead| 
| 3|  rok| 3|scientist| 
| 4| piet|null|  null| 
|null| null| 5| manager| 
+----+--------+----+---------+ 

from pyspark.sql.functions import * 
DFJOINMERGE = DFJOIN.withColumn("_merge", when(ADF["id"].isNull(), "right_only").when(BDF["id"].isNull(), "left_only").otherwise("both"))\ 
    .withColumn("id", coalesce(ADF["id"], BDF["id"]))\ 
    .drop(ADF["id"])\ 
    .drop(BDF["id"]) 
DFJOINMERGE.show() 

Output 
+---+--------+---+---------+------+ 
| id| name| id|  role|_merge| 
+---+--------+---+---------+------+ 
| 1| michel| 1| engineer| both| 
| 2|diederik| 2|  lead| both| 
| 3|  rok| 3|scientist| both| 
| 4| piet| 4|  null| both| 
| 5| null| 5| manager| both| 
+---+--------+---+---------+------+ 

==> I would expect id 4 to be left, and id 5 to be right. 

Changing join to "left": 


Input: 
+---+--------+----+---------+ 
| id| name| id|  role| 
+---+--------+----+---------+ 
| 1| michel| 1| engineer| 
| 2|diederik| 2|  lead| 
| 3|  rok| 3|scientist| 
| 4| piet|null|  null| 
+---+--------+----+---------+ 

Output 
+---+--------+---+---------+------+ 
| id| name| id|  role|_merge| 
+---+--------+---+---------+------+ 
| 1| michel| 1| engineer| both| 
| 2|diederik| 2|  lead| both| 
| 3|  rok| 3|scientist| both| 
| 4| piet| 4|  null| both| 
+---+--------+---+---------+------+ 

Answer 1

はこれを試してみてください：

>>> from pyspark.sql.functions import * 
>>> sdf1 = sqlContext.createDataFrame(df1) 
>>> sdf2 = sqlContext.createDataFrame(df2) 
>>> sdf = sdf1.join(sdf2, sdf1["col1"] == sdf2["col1"], "outer") 
>>> sdf.withColumn("_merge", when(sdf1["col1"].isNull(), "right_only").when(sdf2["col1"].isNull(), "left_only").otherwise("both"))\ 
... .withColumn("col1", coalesce(sdf1["col1"], sdf2["col1"]))\ 
... .drop(sdf1["col1"])\ 
... .drop(sdf2["col1"]) 

Answer 2

変更されましたLostInOverflowの答えは、この作業を得ました：

from pyspark.sql import Row 

ASchema = StructType([StructField('ida', IntegerType(),nullable=False), 
       StructField('name', StringType(),nullable=False)]) 
BSchema = StructType([StructField('idb', IntegerType(),nullable=False), 
       StructField('role', StringType(),nullable=False)]) 
AData = sc.parallelize ([ Row(1,'michel'), Row(2,'diederik'), Row(3,'rok'), Row(4,'piet')]) 
BData = sc.parallelize ([ Row(1,'engineer'), Row(2,'lead'), Row(3,'scientist'), Row(5,'manager')]) 
ADF = hc.createDataFrame(AData,ASchema) 
BDF = hc.createDataFrame(BData,BSchema) 
DFJOIN = ADF.join(BDF, ADF['ida'] == BDF['idb'], "outer") 
DFJOIN.show() 


+----+--------+----+---------+ 
| ida| name| idb|  role| 
+----+--------+----+---------+ 
| 1| michel| 1| engineer| 
| 2|diederik| 2|  lead| 
| 3|  rok| 3|scientist| 
| 4| piet|null|  null| 
|null| null| 5| manager| 
+----+--------+----+---------+ 

from pyspark.sql.functions import * 
DFJOINMERGE = DFJOIN.withColumn("_merge", when(DFJOIN["ida"].isNull(), "right_only").when(DFJOIN["idb"].isNull(), "left_only").otherwise("both"))\ 
    .withColumn("id", coalesce(ADF["ida"], BDF["idb"]))\ 
    .drop(DFJOIN["ida"])\ 
    .drop(DFJOIN["idb"]) 
#DFJOINMERGE.show() 
DFJOINMERGE.groupBy("_merge").count().show() 

+----------+-----+ 
| _merge|count| 
+----------+-----+ 
|right_only| 1| 
| left_only| 1| 
|  both| 3| 
+----------+-----+