问题
The python Pandas library contains the following function :
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)
The indicator field combined with Panda's value_counts() function can be used to quickly determine how well a join performed.
Example:
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
What is the best way to check the performance of a join within a Spark Dataframe?
A custom function was provided in 1 of the answers: It does not yet give the correct results but it would be great if it would:
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|
+---+--------+---+---------+------+
回答1:
Try this:
>>> 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"])
回答2:
Altered LostInOverflow 's answer and got this working:
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|
+----------+-----+
来源：https://stackoverflow.com/questions/38721194/does-spark-dataframe-have-an-equivalent-option-of-pandas-merge-indicator