How to remove rows in DataFrame on column based on another DataFrame?

Question

I'm trying to use SQLContext.subtract() in Spark 1.6.1 to remove rows from a dataframe based on a column from another dataframe. Let's use an example:

from pyspark.sql import Row

df1 = sqlContext.createDataFrame([
    Row(name='Alice', age=2),
    Row(name='Bob', age=1),
]).alias('df1')

df2 = sqlContext.createDataFrame([
    Row(name='Bob'),
])

df1_with_df2 = df1.join(df2, 'name').select('df1.*')
df1_without_df2 = df1.subtract(df1_with_df2)

Since I want all rows from df1 which don't include name='Bob' I expect Row(age=2, name='Alice'). But I also retrieve Bob:

print(df1_without_df2.collect())
# [Row(age='1', name='Bob'), Row(age='2', name='Alice')]

After various experiments to get down to this MCVE, I found out that the issue is with the age key. If I omit it:

df1_noage = sqlContext.createDataFrame([
    Row(name='Alice'),
    Row(name='Bob'),
]).alias('df1_noage')

df1_noage_with_df2 = df1_noage.join(df2, 'name').select('df1_noage.*')
df1_noage_without_df2 = df1_noage.subtract(df1_noage_with_df2)
print(df1_noage_without_df2.collect())
# [Row(name='Alice')]

Then I only get Alice as expected. The weirdest observation I made is that it's possible to add keys, as long as they're after (in the lexicographical order sense) the key I use in the join:

df1_zage = sqlContext.createDataFrame([
    Row(zage=2, name='Alice'),
    Row(zage=1, name='Bob'),
]).alias('df1_zage')

df1_zage_with_df2 = df1_zage.join(df2, 'name').select('df1_zage.*')
df1_zage_without_df2 = df1_zage.subtract(df1_zage_with_df2)
print(df1_zage_without_df2.collect())
# [Row(name='Alice', zage=2)]

I correctly get Alice (with her zage)! In my real examples, I'm interested in all columns, not only the ones that are after name.

Jacek Laskowski · Accepted Answer · 2020-04-05 17:20:57Z

3

Well there are some bugs here (the first issue looks like related to to the same problem as SPARK-6231) and JIRA looks like a good idea, but SUBTRACT / EXCEPT is no the right choice for partial matches.

Instead, as of Spark 2.0, you can use anti-join:

df1.join(df1_with_df2, ["name"], "leftanti").show()

In 1.6 you can do pretty much the same thing with standard outer join:

import pyspark.sql.functions as F

ref = df1_with_df2.select("name").alias("ref")

(df1
    .join(ref, ref.name == df1.name, "leftouter")
    .filter(F.isnull("ref.name"))
    .drop(F.col("ref.name")))

edited Apr 5, 2020 at 17:20

Jacek Laskowski

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answered Jul 12, 2016 at 11:26

zero323

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