scala 如何在 spark 过滤条件中使用 NOT IN 子句
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How to use NOT IN clause in filter condition in spark
提问by Vicky
I want to filter a column of an RDD source :
我想过滤 RDD 源的一列：
val source = sql("SELECT * from sample.source").rdd.map(_.mkString(","))
val destination = sql("select * from sample.destination").rdd.map(_.mkString(","))
val source_primary_key = source.map(rec => (rec.split(",")(0)))
val destination_primary_key = destination.map(rec => (rec.split(",")(0)))
val src = source_primary_key.subtractByKey(destination_primary_key)
I want to use IN clause in filter condition to filter out only the values present in src from source, something like below(EDITED):
我想在过滤条件中使用 IN 子句来仅从源中过滤掉 src 中存在的值，如下所示（已编辑）：
val source = spark.read.csv(inputPath + "/source").rdd.map(_.mkString(","))
val destination = spark.read.csv(inputPath + "/destination").rdd.map(_.mkString(","))
val source_primary_key = source.map(rec => (rec.split(",")(0)))
val destination_primary_key = destination.map(rec => (rec.split(",")(0)))
val extra_in_source = source_primary_key.filter(rec._1 != destination_primary_key._1)
equivalent SQL code is
等效的 SQL 代码是
SELECT * FROM SOURCE WHERE ID IN (select ID from src)
Thank you
谢谢
回答by eliasah
Since your code isn't reproducible, here is a small example using spark-sqlon how to select * from t where id in (...):
由于您的代码不可重现，这里有一个spark-sql关于如何使用的小示例select * from t where id in (...)：
// create a DataFrame for a range 'id' from 1 to 9.
scala> val df = spark.range(1,10).toDF
df: org.apache.spark.sql.DataFrame = [id: bigint]
// values to exclude
scala> val f = Seq(5,6,7)
f: Seq[Int] = List(5, 6, 7)
// select * from df where id is not in the values to exclude
scala> df.filter(!col("id").isin(f : _*)).show
+---+
| id|
+---+
| 1|
| 2|
| 3|
| 4|
| 8|
| 9|
+---+
// select * from df where id is in the values to exclude
scala> df.filter(col("id").isin(f : _*)).show
Here is the RDD version of the not isin:
这是 RDD 版本的not isin：
scala> val rdd = sc.parallelize(1 to 10)
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[2] at parallelize at <console>:24
scala> val f = Seq(5,6,7)
f: Seq[Int] = List(5, 6, 7)
scala> val rdd2 = rdd.filter(x => !f.contains(x))
rdd2: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[3] at filter at <console>:28
Nevertheless, I still believe this is an overkill since you are already using spark-sql.
尽管如此，我仍然认为这是一种矫枉过正，因为您已经在使用spark-sql.
It seems in your case that you are actually dealing with DataFrames, thus the solutions mentioned above don't work.
在您的情况下，您实际上正在处理数据帧，因此上述解决方案不起作用。
You can use the left anti join approach :
您可以使用左反连接方法：
scala> val source = spark.read.format("csv").load("source.file")
source: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 9 more fields]
scala> val destination = spark.read.format("csv").load("destination.file")
destination: org.apache.spark.sql.DataFrame = [_c0: string, _c1: string ... 9 more fields]
scala> source.show
+---+------------------+--------+----------+---------------+---+---+----------+-----+---------+------------+
|_c0| _c1| _c2| _c3| _c4|_c5|_c6| _c7| _c8| _c9| _c10|
+---+------------------+--------+----------+---------------+---+---+----------+-----+---------+------------+
| 1| Ravi kumar| Ravi | kumar| MSO | 1| M|17-01-1994| 74.5| 24000.78| Alabama |
| 2|Shekhar shudhanshu| Shekhar|shudhanshu| Manulife | 2| M|18-01-1994|76.34| 250000| Alaska |
| 3|Preethi Narasingam| Preethi|Narasingam| Retail | 3| F|19-01-1994|77.45|270000.01| Arizona |
| 4| Abhishek Nair|Abhishek| Nair| Banking | 4| M|20-01-1994|78.65| 345000| Arkansas |
| 5| Ram Sharma| Ram| Sharma|Infrastructure | 5| M|21-01-1994|79.12| 45000| California |
| 6| Chandani Kumari|Chandani| Kumari| BNFS | 6| F|22-01-1994|80.13| 43000.02| Colorado |
| 7| Balaji Kumar| Balaji| Kumar| MSO | 1| M|23-01-1994|81.33| 1234678|Connecticut |
| 8| Naveen Shekrappa| Naveen| Shekrappa| Manulife | 2| M|24-01-1994| 100| 789414| Delaware |
| 9| Milind Chavan| Milind| Chavan| Retail | 3| M|25-01-1994|83.66| 245555| Florida |
| 10| Raghu Rajeev| Raghu| Rajeev| Banking | 4| M|26-01-1994|87.65| 235468| Georgia|
+---+------------------+--------+----------+---------------+---+---+----------+-----+---------+------------+
scala> destination.show
+---+-------------------+--------+----------+---------------+---+---+----------+-----+---------+------------+
|_c0| _c1| _c2| _c3| _c4|_c5|_c6| _c7| _c8| _c9| _c10|
+---+-------------------+--------+----------+---------------+---+---+----------+-----+---------+------------+
| 1| Ravi kumar| Revi | kumar| MSO | 1| M|17-01-1994| 74.5| 24000.78| Alabama |
| 1| Ravi1 kumar| Revi | kumar| MSO | 1| M|17-01-1994| 74.5| 24000.78| Alabama |
| 1| Ravi2 kumar| Revi | kumar| MSO | 1| M|17-01-1994| 74.5| 24000.78| Alabama |
| 2| Shekhar shudhanshu| Shekhar|shudhanshu| Manulife | 2| M|18-01-1994|76.34| 250000| Alaska |
| 3|Preethi Narasingam1| Preethi|Narasingam| Retail | 3| F|19-01-1994|77.45|270000.01| Arizona |
| 4| Abhishek Nair1|Abhishek| Nair| Banking | 4| M|20-01-1994|78.65| 345000| Arkansas |
| 5| Ram Sharma| Ram| Sharma|Infrastructure | 5| M|21-01-1994|79.12| 45000| California |
| 6| Chandani Kumari|Chandani| Kumari| BNFS | 6| F|22-01-1994|80.13| 43000.02| Colorado |
| 7| Balaji Kumar| Balaji| Kumar| MSO | 1| M|23-01-1994|81.33| 1234678|Connecticut |
| 8| Naveen Shekrappa| Naveen| Shekrappa| Manulife | 2| M|24-01-1994| 100| 789414| Delaware |
| 9| Milind Chavan| Milind| Chavan| Retail | 3| M|25-01-1994|83.66| 245555| Florida |
| 10| Raghu Rajeev| Raghu| Rajeev| Banking | 4| M|26-01-1994|87.65| 235468| Georgia|
+---+-------------------+--------+----------+---------------+---+---+----------+-----+---------+------------+
You'll just need to do the following :
您只需要执行以下操作：
scala> val res1 = source.join(destination, Seq("_c0"), "leftanti")
scala> val res2 = destination.join(source, Seq("_c0"), "leftanti")
It's the same logic I mentioned in my answer here.
这与我在此处的回答中提到的逻辑相同。
回答by Blue Bird
You can try like--
你可以试试——
df.filter(~df.Dept.isin("30","20")).show()
//This will list all the columns of df where Dept NOT IN 30 or 20
//这将列出 df 的所有列，其中 Dept NOT IN 30 或 20
回答by J. P
You can try something similar in Java,
你可以在 Java 中尝试类似的东西，
ds = ds.filter(functions.not(functions.col(COLUMN_NAME).isin(exclusionSet)));
where exclusionSet is a set of objects that needs to be removed from your dataset.
其中 excludeSet 是一组需要从数据集中删除的对象。
