scala – 如何针对另一个数据帧过滤一个spark数据帧

scala> val df1 = sc.parallelize((1 to 100).map(a=>(s"user $a",a*0.123,a))).toDF("name","score","user_id")
scala> val df2 = sc.parallelize(List(2,3,4,5,6)).toDF("valid_id")

现在我想过滤df1并返回一个包含df1中所有行的数据帧,其中user_id在df2(“valid_id”)中.换句话说,我想要df1中的所有行,其中user_id是2,5或6

scala> df1.select("user_id").filter($"user_id" in df2("valid_id"))
warning: there were 1 deprecation warning(s); re-run with -deprecation for details
org.apache.spark.sql.AnalysisException: resolved attribute(s) valid_id#20 missing from user_id#18 in operator !Filter user_id#18 IN (valid_id#20);

另一方面,当我尝试对函数进行过滤时,一切看起来都很棒：

scala> df1.select("user_id").filter(($"user_id" % 2) === 0)
res1: org.apache.spark.sql.DataFrame = [user_id: int]

为什么我收到此错误？我的语法有问题吗？

以下评论我试图做左外连接：

scala> df1.show
+-------+------------------+-------+
|   name|             score|user_id|
+-------+------------------+-------+
| user 1|             0.123|      1|
| user 2|             0.246|      2|
| user 3|             0.369|      3|
| user 4|             0.492|      4|
| user 5|             0.615|      5|
| user 6|             0.738|      6|
| user 7|             0.861|      7|
| user 8|             0.984|      8|
| user 9|             1.107|      9|
|user 10|              1.23|     10|
|user 11|             1.353|     11|
|user 12|             1.476|     12|
|user 13|             1.599|     13|
|user 14|             1.722|     14|
|user 15|             1.845|     15|
|user 16|             1.968|     16|
|user 17|             2.091|     17|
|user 18|             2.214|     18|
|user 19|2.3369999999999997|     19|
|user 20|              2.46|     20|
+-------+------------------+-------+
only showing top 20 rows

scala> df2.show
+--------+
|valid_id|
+--------+
|       2|
|       3|
|       4|
|       5|
|       6|
+--------+

scala> df1.join(df2,df1("user_id") === df2("valid_id"))
res6: org.apache.spark.sql.DataFrame = [name: string,score: double,user_id: int,valid_id: int]
scala> res6.collect
res7: Array[org.apache.spark.sql.Row] = Array()

scala> df1.join(df2,df1("user_id") === df2("valid_id"),"left_outer")
res8: org.apache.spark.sql.DataFrame = [name: string,valid_id: int]
scala> res8.count
res9: Long = 0

我用scala 2.10.5运行spark 1.5.0