pyspark dataframe foreach填充列表
pyspark dataframe foreach to fill a list
我正在使用 Spark 1.6.1 和 Python 2.7，我有这个问题要解决：
- 获取具有 X 行的数据框 A
- 对于 A 中的每一行，根据字段，创建新数据框 B 的一行或多行
- 保存新数据框 B
我现在提出的解决方案是收集数据框 A，检查它，将 B 的行附加到列表，然后从该列表创建数据框 B。
有了这个解决方案，我显然失去了使用数据帧的所有好处，我想使用 foreach，但我找不到一种方法来完成这项工作。到目前为止我已经试过了：
- 将一个空列表传递给 foreach 函数（这只是忽略了 foreach 函数，什么都不做）
- 创建要在 foreach 函数中使用的全局变量（抱怨找不到列表）
有没有人有什么想法？
谢谢
--------------------编辑：
我尝试过的例子：
def f(row, list):
if row.one:
list += [Row(type='one', field='ok')]
else:
list += [Row(type='one', field='ok')]
list += [Row(type='two', field='nok')]
list = []
dfA.foreach(lambda x : f(x, list))
正如我提到的，这什么都不做，它不执行函数
我也试过（在class开头定义的列表）：
global list
def f(row):
if row.one:
list += [Row(type='one', field='ok')]
else:
list += [Row(type='one', field='ok')]
list += [Row(type='two', field='nok')]
dfA.foreach(list)
------------编辑 2：
我现在正在做的是：
list = []
for row in dfA.collect():
string = re.search(a_regex, row['raw'])
if string:
dates = re.findall(date_regex, string.group())
for date in dates:
date_string = datetime.strptime(date, '%Y-%m-%d').date()
list += [Row(event_type='1', event_date=date_string)]
b_string = re.search(b_regex, row['raw'])
if b_string:
dates = re.findall(date_regex, b_string.group())
for date in dates:
scheduled_to = datetime.strptime(date, '%Y-%m-%d').date()
list += [Row(event_type='2', event_date= date_string)]
然后：
dfB = self._sql_context.createDataFrame(list)
dfA 由其他进程提供，我无法更改它，我知道这是使用数据帧的一种非常愚蠢的方式，但我对此无能为力
--------------------编辑 3：
dfA.raw 样本：
{"new":[],"removed":[{"start":"2018-03-10","end":"2018-03-16","scheduled_by_system":null}]}
{"new":[{"start":"2018-03-10","end":"2018-03-16","scheduled_by_system":null}],"removed":[]}
{"new":[{"start":"2017-01-28","end":"2017-02-03"},{"start":"2017-02-04","end":"2017-02-10"},{"start":"2017-02-11","end":"2017-02-17"},{"start":"2017-02-18","end":"2017-02-24"},{"start":"2017-03-04","end":"2017-03-10"},{"start":"2017-03-11","end":"2017-03-17"},{"start":"2017-03-18","end":"2017-03-24"},{"start":"2017-09-02","end":"2017-09-08"},{"start":"2017-09-16","end":"2017-09-22"},{"start":"2017-09-23","end":"2017-09-29"},{"start":"2017-09-30","end":"2017-10-06"},{"start":"2017-10-07","end":"2017-10-13"},{"start":"2017-12-02","end":"2017-12-08"},{"start":"2017-12-09","end":"2017-12-15"},{"start":"2017-12-16","end":"2017-12-22"},{"start":"2017-12-23","end":"2017-12-29"},{"start":"2018-01-06","end":"2018-01-12"}],"removed":[{"start":"2017-02-04","end":"2017-02-10"},{"start":"2017-02-11","end":"2017-02-17"},{"start":"2017-02-18","end":"2017-02-24"},{"start":"2017-03-04","end":"2017-03-10"},{"start":"2017-03-11","end":"2017-03-17"},{"start":"2017-03-18","end":"2017-03-24"},{"start":"2017-01-28","end":"2017-02-03"},{"start":"2017-09-16","end":"2017-09-22"},{"start":"2017-09-02","end":"2017-09-08"},{"start":"2017-09-30","end":"2017-10-06"},{"start":"2017-10-07","end":"2017-10-13"},{"start":"2017-09-23","end":"2017-09-29"},{"start":"2017-12-16","end":"2017-12-22"},{"start":"2017-12-23","end":"2017-12-29"},{"start":"2018-01-06","end":"2018-01-12"},{"start":"2017-12-09","end":"2017-12-15"},{"start":"2017-12-02","end":"2017-12-08"},{"start":"2018-02-10","end":"2018-02-16"}]}|
和正则表达式：
a_regex = r'\"new\":{(.*?)}{2}|\"new\":\[(.*?)\]'
b_regex = r'\"removed\":{(.*?)}{2}|removed\":\[(.*?)\]'
date_regex = r'\"start\":\"(\d{4}-\d{2}-\d{2})\"'
dfA.select('raw').show(2,False)
+-------------------------------------------------------------------------------------------------------+
|raw |
+-------------------------------------------------------------------------------------------------------+
|{"new":[{"start":"2018-03-24","end":"2018-03-30","scheduled_by_system":null}],"removed":[]}|
|{"new":[{"start":"2018-03-10","end":"2018-03-16","scheduled_by_system":null}],"removed":[]}|
+-------------------------------------------------------------------------------------------------------+
only showing top 2 rows
df.select('raw').printSchema()
root
|-- raw: string (nullable = true)
在选择所需的 raw 列后，您需要为 return event_type 和 event_date 字符串编写一个 udf 函数。
import re
def searchUdf(regex, dateRegex, x):
list_return = []
string = re.search(regex, x)
if string:
dates = re.findall(dateRegex, string.group())
for date in dates:
date_string = datetime.strptime(date, '%Y-%m-%d').date()
list_return.append(date_string)
return list_return
from pyspark.sql import functions as F
udfFunctionCall = F.udf(searchUdf, T.ArrayType(T.DateType()))
udf 函数将使用作为参数传递的 regex 和 dateRegex 解析原始列字符串，并且 return eventType 和 data_string 作为 arrayType 列
您应该调用定义的 udf 函数并 filter 空行，然后 separate 列作为 event_type 和 event_date 列
df = df.select("raw")
adf = df.select(F.lit(1).alias("event_type"), udfFunctionCall(F.lit(a_regex), F.lit(date_regex), df.raw).alias("event_date"))\
.filter(F.size(F.col("event_date")) > 0)
bdf = df.select(F.lit(2).alias("event_type"), udfFunctionCall(F.lit(a_regex), F.lit(date_regex), df.raw).alias("event_date")) \
.filter(F.size(F.col("event_date")) > 0)
问题中提供的正则表达式
a_regex = r'\"new\":{(.*?)}{2}|\"new\":\[(.*?)\]'
b_regex = r'\"removed\":{(.*?)}{2}|removed\":\[(.*?)\]'
date_regex = r'\"start\":\"(\d{4}-\d{2}-\d{2})\"'
现在您有两个 dataframes 用于两个 event_type，最后一步是 将它们合并 在一起
adf.unionAll(bdf)
就是这样。你的困惑都解决了。
具有以下原始列
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|raw |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|{"new":[],"removed":[{"start":"2018-03-10","end":"2018-03-16","scheduled_by_system":null}]} |
|{"new":[{"start":"2018-03-10","end":"2018-03-16","scheduled_by_system":null}],"removed":[]} |
|{"new":[{"start":"2017-01-28","end":"2017-02-03"},{"start":"2017-02-04","end":"2017-02-10"},{"start":"2017-02-11","end":"2017-02-17"},{"start":"2017-02-18","end":"2017-02-24"},{"start":"2017-03-04","end":"2017-03-10"},{"start":"2017-03-11","end":"2017-03-17"},{"start":"2017-03-18","end":"2017-03-24"},{"start":"2017-09-02","end":"2017-09-08"},{"start":"2017-09-16","end":"2017-09-22"},{"start":"2017-09-23","end":"2017-09-29"},{"start":"2017-09-30","end":"2017-10-06"},{"start":"2017-10-07","end":"2017-10-13"},{"start":"2017-12-02","end":"2017-12-08"},{"start":"2017-12-09","end":"2017-12-15"},{"start":"2017-12-16","end":"2017-12-22"},{"start":"2017-12-23","end":"2017-12-29"},{"start":"2018-01-06","end":"2018-01-12"}],"removed":[{"start":"2017-02-04","end":"2017-02-10"},{"start":"2017-02-11","end":"2017-02-17"},{"start":"2017-02-18","end":"2017-02-24"},{"start":"2017-03-04","end":"2017-03-10"},{"start":"2017-03-11","end":"2017-03-17"},{"start":"2017-03-18","end":"2017-03-24"},{"start":"2017-01-28","end":"2017-02-03"},{"start":"2017-09-16","end":"2017-09-22"},{"start":"2017-09-02","end":"2017-09-08"},{"start":"2017-09-30","end":"2017-10-06"},{"start":"2017-10-07","end":"2017-10-13"},{"start":"2017-09-23","end":"2017-09-29"},{"start":"2017-12-16","end":"2017-12-22"},{"start":"2017-12-23","end":"2017-12-29"},{"start":"2018-01-06","end":"2018-01-12"},{"start":"2017-12-09","end":"2017-12-15"},{"start":"2017-12-02","end":"2017-12-08"},{"start":"2018-02-10","end":"2018-02-16"}]}|
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
你应该得到
+----------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|event_type|event_date |
+----------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|1 |[2018-03-10] |
|1 |[2017-01-28, 2017-02-04, 2017-02-11, 2017-02-18, 2017-03-04, 2017-03-11, 2017-03-18, 2017-09-02, 2017-09-16, 2017-09-23, 2017-09-30, 2017-10-07, 2017-12-02, 2017-12-09, 2017-12-16, 2017-12-23, 2018-01-06]|
|2 |[2018-03-10] |
|2 |[2017-01-28, 2017-02-04, 2017-02-11, 2017-02-18, 2017-03-04, 2017-03-11, 2017-03-18, 2017-09-02, 2017-09-16, 2017-09-23, 2017-09-30, 2017-10-07, 2017-12-02, 2017-12-09, 2017-12-16, 2017-12-23, 2018-01-06]|
+----------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
我正在使用 Spark 1.6.1 和 Python 2.7，我有这个问题要解决：
- 获取具有 X 行的数据框 A
- 对于 A 中的每一行，根据字段，创建新数据框 B 的一行或多行
- 保存新数据框 B
我现在提出的解决方案是收集数据框 A，检查它，将 B 的行附加到列表，然后从该列表创建数据框 B。
有了这个解决方案，我显然失去了使用数据帧的所有好处，我想使用 foreach，但我找不到一种方法来完成这项工作。到目前为止我已经试过了：
- 将一个空列表传递给 foreach 函数（这只是忽略了 foreach 函数，什么都不做）
- 创建要在 foreach 函数中使用的全局变量（抱怨找不到列表）
有没有人有什么想法？
谢谢
--------------------编辑：
我尝试过的例子：
def f(row, list):
if row.one:
list += [Row(type='one', field='ok')]
else:
list += [Row(type='one', field='ok')]
list += [Row(type='two', field='nok')]
list = []
dfA.foreach(lambda x : f(x, list))
正如我提到的，这什么都不做，它不执行函数
我也试过（在class开头定义的列表）：
global list
def f(row):
if row.one:
list += [Row(type='one', field='ok')]
else:
list += [Row(type='one', field='ok')]
list += [Row(type='two', field='nok')]
dfA.foreach(list)
------------编辑 2：
我现在正在做的是：
list = []
for row in dfA.collect():
string = re.search(a_regex, row['raw'])
if string:
dates = re.findall(date_regex, string.group())
for date in dates:
date_string = datetime.strptime(date, '%Y-%m-%d').date()
list += [Row(event_type='1', event_date=date_string)]
b_string = re.search(b_regex, row['raw'])
if b_string:
dates = re.findall(date_regex, b_string.group())
for date in dates:
scheduled_to = datetime.strptime(date, '%Y-%m-%d').date()
list += [Row(event_type='2', event_date= date_string)]
然后：
dfB = self._sql_context.createDataFrame(list)
dfA 由其他进程提供，我无法更改它，我知道这是使用数据帧的一种非常愚蠢的方式，但我对此无能为力
--------------------编辑 3： dfA.raw 样本：
{"new":[],"removed":[{"start":"2018-03-10","end":"2018-03-16","scheduled_by_system":null}]}
{"new":[{"start":"2018-03-10","end":"2018-03-16","scheduled_by_system":null}],"removed":[]}
{"new":[{"start":"2017-01-28","end":"2017-02-03"},{"start":"2017-02-04","end":"2017-02-10"},{"start":"2017-02-11","end":"2017-02-17"},{"start":"2017-02-18","end":"2017-02-24"},{"start":"2017-03-04","end":"2017-03-10"},{"start":"2017-03-11","end":"2017-03-17"},{"start":"2017-03-18","end":"2017-03-24"},{"start":"2017-09-02","end":"2017-09-08"},{"start":"2017-09-16","end":"2017-09-22"},{"start":"2017-09-23","end":"2017-09-29"},{"start":"2017-09-30","end":"2017-10-06"},{"start":"2017-10-07","end":"2017-10-13"},{"start":"2017-12-02","end":"2017-12-08"},{"start":"2017-12-09","end":"2017-12-15"},{"start":"2017-12-16","end":"2017-12-22"},{"start":"2017-12-23","end":"2017-12-29"},{"start":"2018-01-06","end":"2018-01-12"}],"removed":[{"start":"2017-02-04","end":"2017-02-10"},{"start":"2017-02-11","end":"2017-02-17"},{"start":"2017-02-18","end":"2017-02-24"},{"start":"2017-03-04","end":"2017-03-10"},{"start":"2017-03-11","end":"2017-03-17"},{"start":"2017-03-18","end":"2017-03-24"},{"start":"2017-01-28","end":"2017-02-03"},{"start":"2017-09-16","end":"2017-09-22"},{"start":"2017-09-02","end":"2017-09-08"},{"start":"2017-09-30","end":"2017-10-06"},{"start":"2017-10-07","end":"2017-10-13"},{"start":"2017-09-23","end":"2017-09-29"},{"start":"2017-12-16","end":"2017-12-22"},{"start":"2017-12-23","end":"2017-12-29"},{"start":"2018-01-06","end":"2018-01-12"},{"start":"2017-12-09","end":"2017-12-15"},{"start":"2017-12-02","end":"2017-12-08"},{"start":"2018-02-10","end":"2018-02-16"}]}|
和正则表达式：
a_regex = r'\"new\":{(.*?)}{2}|\"new\":\[(.*?)\]'
b_regex = r'\"removed\":{(.*?)}{2}|removed\":\[(.*?)\]'
date_regex = r'\"start\":\"(\d{4}-\d{2}-\d{2})\"'
dfA.select('raw').show(2,False)
+-------------------------------------------------------------------------------------------------------+
|raw |
+-------------------------------------------------------------------------------------------------------+
|{"new":[{"start":"2018-03-24","end":"2018-03-30","scheduled_by_system":null}],"removed":[]}|
|{"new":[{"start":"2018-03-10","end":"2018-03-16","scheduled_by_system":null}],"removed":[]}|
+-------------------------------------------------------------------------------------------------------+
only showing top 2 rows
df.select('raw').printSchema()
root
|-- raw: string (nullable = true)
在选择所需的 raw 列后，您需要为 return event_type 和 event_date 字符串编写一个 udf 函数。
import re
def searchUdf(regex, dateRegex, x):
list_return = []
string = re.search(regex, x)
if string:
dates = re.findall(dateRegex, string.group())
for date in dates:
date_string = datetime.strptime(date, '%Y-%m-%d').date()
list_return.append(date_string)
return list_return
from pyspark.sql import functions as F
udfFunctionCall = F.udf(searchUdf, T.ArrayType(T.DateType()))
udf 函数将使用作为参数传递的 regex 和 dateRegex 解析原始列字符串，并且 return eventType 和 data_string 作为 arrayType 列
您应该调用定义的 udf 函数并 filter 空行，然后 separate 列作为 event_type 和 event_date 列
df = df.select("raw")
adf = df.select(F.lit(1).alias("event_type"), udfFunctionCall(F.lit(a_regex), F.lit(date_regex), df.raw).alias("event_date"))\
.filter(F.size(F.col("event_date")) > 0)
bdf = df.select(F.lit(2).alias("event_type"), udfFunctionCall(F.lit(a_regex), F.lit(date_regex), df.raw).alias("event_date")) \
.filter(F.size(F.col("event_date")) > 0)
问题中提供的正则表达式
a_regex = r'\"new\":{(.*?)}{2}|\"new\":\[(.*?)\]'
b_regex = r'\"removed\":{(.*?)}{2}|removed\":\[(.*?)\]'
date_regex = r'\"start\":\"(\d{4}-\d{2}-\d{2})\"'
现在您有两个 dataframes 用于两个 event_type，最后一步是 将它们合并 在一起
adf.unionAll(bdf)
就是这样。你的困惑都解决了。
具有以下原始列
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|raw |
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|{"new":[],"removed":[{"start":"2018-03-10","end":"2018-03-16","scheduled_by_system":null}]} |
|{"new":[{"start":"2018-03-10","end":"2018-03-16","scheduled_by_system":null}],"removed":[]} |
|{"new":[{"start":"2017-01-28","end":"2017-02-03"},{"start":"2017-02-04","end":"2017-02-10"},{"start":"2017-02-11","end":"2017-02-17"},{"start":"2017-02-18","end":"2017-02-24"},{"start":"2017-03-04","end":"2017-03-10"},{"start":"2017-03-11","end":"2017-03-17"},{"start":"2017-03-18","end":"2017-03-24"},{"start":"2017-09-02","end":"2017-09-08"},{"start":"2017-09-16","end":"2017-09-22"},{"start":"2017-09-23","end":"2017-09-29"},{"start":"2017-09-30","end":"2017-10-06"},{"start":"2017-10-07","end":"2017-10-13"},{"start":"2017-12-02","end":"2017-12-08"},{"start":"2017-12-09","end":"2017-12-15"},{"start":"2017-12-16","end":"2017-12-22"},{"start":"2017-12-23","end":"2017-12-29"},{"start":"2018-01-06","end":"2018-01-12"}],"removed":[{"start":"2017-02-04","end":"2017-02-10"},{"start":"2017-02-11","end":"2017-02-17"},{"start":"2017-02-18","end":"2017-02-24"},{"start":"2017-03-04","end":"2017-03-10"},{"start":"2017-03-11","end":"2017-03-17"},{"start":"2017-03-18","end":"2017-03-24"},{"start":"2017-01-28","end":"2017-02-03"},{"start":"2017-09-16","end":"2017-09-22"},{"start":"2017-09-02","end":"2017-09-08"},{"start":"2017-09-30","end":"2017-10-06"},{"start":"2017-10-07","end":"2017-10-13"},{"start":"2017-09-23","end":"2017-09-29"},{"start":"2017-12-16","end":"2017-12-22"},{"start":"2017-12-23","end":"2017-12-29"},{"start":"2018-01-06","end":"2018-01-12"},{"start":"2017-12-09","end":"2017-12-15"},{"start":"2017-12-02","end":"2017-12-08"},{"start":"2018-02-10","end":"2018-02-16"}]}|
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
你应该得到
+----------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|event_type|event_date |
+----------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|1 |[2018-03-10] |
|1 |[2017-01-28, 2017-02-04, 2017-02-11, 2017-02-18, 2017-03-04, 2017-03-11, 2017-03-18, 2017-09-02, 2017-09-16, 2017-09-23, 2017-09-30, 2017-10-07, 2017-12-02, 2017-12-09, 2017-12-16, 2017-12-23, 2018-01-06]|
|2 |[2018-03-10] |
|2 |[2017-01-28, 2017-02-04, 2017-02-11, 2017-02-18, 2017-03-04, 2017-03-11, 2017-03-18, 2017-09-02, 2017-09-16, 2017-09-23, 2017-09-30, 2017-10-07, 2017-12-02, 2017-12-09, 2017-12-16, 2017-12-23, 2018-01-06]|
+----------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+