在基于 Spark Dataframe 的 API 中过滤多列

Question

I have a dataframe like:

+--------+-------+--------------------+-------------------+
|     id1|    id2|                body|         created_at|
+--------+-------+--------------------+-------------------+
|1       |      4|....................|2017-10-01 00:00:05|
|2       |      3|....................|2017-10-01 00:00:05|
|3       |      2|....................|2017-10-01 00:00:05|
|4       |      1|....................|2017-10-01 00:00:05|
+--------+-------+--------------------+-------------------+

I would like to filter the table using both id1 and id2 . For example get rows where id1=1, id2=4 and id1=2, id2=3 .

Currently, I'm using loop to generate a giant query string for df.filter() , ie ((id1 = 1) and (id2 = 4)) or ((id1 = 2) and (id2 = 3)) . Just wondering if there is a more properly way to achieve this?

Answer 1

You can generate a helper DF (table):

tmp:

+--------+-------+
|     id1|    id2|
+--------+-------+
|1       |      4|
|2       |      3|
+--------+-------+

and then join them:

SELECT a.*
FROM tab a
JOIN tmp b
  ON (a.id1 = b.id1 and a.id2 = b.id2)

where tab is your original DF, registered as a table