ARRAY_CONTAINS在pyspark中的多个值

Question

我正在和一个人合作pyspark.sql.dataframe.DataFrame.我想stack基于多个变量而不是单个变量来过滤行{val}.我正在使用Python 2 Jupyter笔记本.目前,我做了以下事情:

stack = hiveContext.sql("""
    SELECT * 
    FROM db.table
    WHERE col_1 != ''
""")

stack.show()
+---+-------+-------+---------+
| id| col_1 | . . . | list    |
+---+-------+-------+---------+
| 1 |   524 | . . . |[1, 2]   |
| 2 |   765 | . . . |[2, 3]   |
.
.
.
| 9 |   765 | . . . |[4, 5, 8]|

for i in len(list):
    filtered_stack = stack.filter("array_contains(list, {val})".format(val=val.append(list[i])))
    (some query on filtered_stack)

如何在Python代码中重写此内容以根据多个值过滤行？即,{val}等于一个或多个元素的某个数组.

我的问题与以下内容有关:ARRAY_CONTAINS在hive中的多个值,但是我试图在Python 2 Jupyter笔记本中实现上述功能.

Answer 1

使用Python UDF:

from pyspark.sql.functions import udf, size
from pyspark.sql.types import *

intersect = lambda type: (udf(
    lambda x, y: (
        list(set(x) & set(y)) if x is not None and y is not None else None),
    ArrayType(type)))

df = sc.parallelize([([1, 2, 3], [1, 2]), ([3, 4], [5, 6])]).toDF(["xs", "ys"])

integer_intersect = intersect(IntegerType())

df.select(
    integer_intersect("xs", "ys"),
    size(integer_intersect("xs", "ys"))).show()

+----------------+----------------------+
|<lambda>(xs, ys)|size(<lambda>(xs, ys))|
+----------------+----------------------+
|          [1, 2]|                     2|
|              []|                     0|
+----------------+----------------------+

随着文字:

from pyspark.sql.functions import array, lit

df.select(integer_intersect("xs", array(lit(1), lit(5)))).show()

+-------------------------+
|<lambda>(xs, array(1, 5))|
+-------------------------+
|                      [1]|
|                       []|
+-------------------------+

要么

df.where(size(integer_intersect("xs", array(lit(1), lit(5)))) > 0).show()

+---------+------+
|       xs|    ys|
+---------+------+
|[1, 2, 3]|[1, 2]|
+---------+------+