Sql PySpark中的regexp

我试图在pyspark中重现django ORM查询的结果：

social_filter = '(facebook|flipboard|linkedin|pinterest|reddit|twitter)'
Collection.objects.filter(social__iregex=social_filter)

我的主要问题是它应该不区分大小写

我试过这个：

social_filter = "social ILIKE 'facebook' OR social ILIKE 'flipboard' OR social ILIKE 'linkedin' OR social ILIKE 'pinterest' OR social ILIKE 'reddit' OR social ILIKE 'twitter'"
df = sessions.filter(social_filter)

这将导致以下错误：

Py4JJavaError: An error occurred while calling o31.filter.
: java.lang.RuntimeException: [1.22] failure: end of input expected

social ILIKE 'facebook' OR social ILIKE 'flipboard' OR social ILIKE 'linkedin' OR social ILIKE 'pinterest' OR social ILIKE 'reddit' OR social ILIKE 'twitter'

以及以下表达式：

social_filter = "social  ~* (facebook|flipboard|linkedin|pinterest|reddit|twitter)"
df = sessions.filter(social_filter)

与此相关：

Py4JJavaError: An error occurred while calling o31.filter.
: java.lang.RuntimeException: [1.17] failure: identifier expected

social  ~* (facebook|flipboard|linkedin|pinterest|reddit|twitter)
       ^
    at scala.sys.package$.error(package.scala:27)
    at org.apache.spark.sql.catalyst.SqlParser.parseExpression(SqlParser.scala:45)
    at org.apache.spark.sql.DataFrame.filter(DataFrame.scala:652)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)

---

求求你，救命

以下内容如何：

>>> rdd = sc.parallelize([Row(name='bob', social='TWITter'), 
                          Row(name='steve', social='facebook')])
>>> df = sqlContext.createDataFrame(rdd)
>>> df.where("LOWER(social) LIKE 'twitter'").collect()
[Row(name=u'bob', social=u'TWITter')]

如果您需要实际的正则表达式，您可以对所有您想要的社交网络执行此操作。否则，如果匹配是精确的，则可以执行以下操作：

>>> df.where("LOWER(social) IN ('twitter', 'facebook')").collect()
[Row(name=u'bob', social=u'TWITter'), Row(name=u'steve', social=u'facebook')]

---

那么以下内容如何：

>>> rdd = sc.parallelize([Row(name='bob', social='TWITter'), 
                          Row(name='steve', social='facebook')])
>>> df = sqlContext.createDataFrame(rdd)
>>> df.where("LOWER(social) LIKE 'twitter'").collect()
[Row(name=u'bob', social=u'TWITter')]

如果您需要实际的正则表达式，您可以对所有您想要的社交网络执行此操作。否则，如果匹配是精确的，则可以执行以下操作：

>>> df.where("LOWER(social) IN ('twitter', 'facebook')").collect()
[Row(name=u'bob', social=u'TWITter'), Row(name=u'steve', social=u'facebook')]

---

您现在也可以使用自定义项进行此操作：

from pyspark.sql import functions as F
from pyspark.sql.types import BooleanType
import re as re

def filter_fn(s):
     return re.search('(facebook|flipboard|linkedin|pinterest|reddit|twitter)', s, re.IGNORECASE) is not None


filter_udf = F.udf(filter_fn, BooleanType())

sessions_filtered = sessions.filter(filter_udf(sessions['social']))

---

您现在也可以使用自定义项进行此操作：

from pyspark.sql import functions as F
from pyspark.sql.types import BooleanType
import re as re

def filter_fn(s):
     return re.search('(facebook|flipboard|linkedin|pinterest|reddit|twitter)', s, re.IGNORECASE) is not None


filter_udf = F.udf(filter_fn, BooleanType())

sessions_filtered = sessions.filter(filter_udf(sessions['social']))