我试图爆炸一个名为phone的列，如下面的架构和内容：
(customer_external_id,StringType
phones,StringType)
customer_id phones
x8x46x5 [{"phone" : "(xx) 35xx4x80"},{"phone" : "(xx) xxxx46605"}]
xx44xx5 [{"phone" : "(xx) xxx3x8443"}]
4xxxxx5 [{"phone" : "(xx) x6xx083x3"},{"areaCode" : "xx"},{"phone" : "(xx) 3xxx83x3"}]
xx6564x [{"phone" : "(x3) x88xx344x"}]
xx8x4x0 [{"phone" : "(xx) x83x5x8xx"}]
xx0434x [{"phone" : "(0x) 3x6x4080"},{"areaCode" : "xx"}]
x465x40 [{"phone" : "(6x) x6x445xx"}]
x0684x8 [{"phone" : "(xx) x8x88x4x4"},{"phone" : "(xx) x8x88x4x4"}]
x84x850 [{"phone" : "(xx) 55x56xx4"}]
x0604xx [{"phone" : "(xx) x8x4xxx68"}]
4x6xxx0 [{"phone" : "(xx) x588x43xx"},{"phone" : "(xx) 5x6465xx"},{"phone" : "(xx) x835xxxx8"},{"phone" : "(xx) x5x6465xx"}]
x6x000x [{"phone" : "(xx) xxx044xx4"}]
5x65533 [{"phone" : "(xx) xx686x0xx"}]
x3668xx [{"phone" : "(5x) 33x8x3x4"},{"phone" : "(5x) 8040x8x6"}]
所以我尝试运行此代码并得到后续错误：
df.select('customer_external_id', explode(df.phones))
AnalysisException: u"cannot resolve 'explode(`phones`)' due to data type mismatch: input to function explode should be array or map type, not StringType;;
'Project [customer_external_id#293, explode(phones#296) AS List()]\n+- Relation[order_id#292,customer_external_id#293,name#294,email#295,phones#296,phones_version#297,classification#298,locale#299] parquet\n"
通过这个错误，我发现我的列是一个StringType，所以我运行此代码删除括号并转换为json：
phones = df.select('customer_external_id', 'phones').rdd\
.map(lambda x: str(x).replace('[','')\
.replace(']','')\
.replace('},{', ','))\
.map(lambda x: json.loads(x).get('phone')\
.map(lambda x: Row(x))\
.toDF(df.select('customer_external_id','phones').schema)
phones.show()
org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 38.0 failed 4 times, most recent failure: Lost task 0.3 in stage 38.0 (TID 2740, 10.112.80.248, executor 8): org.apache.spark.api.python.PythonException: Traceback (most recent call last)
显然我无法向Json施放，我无法爆炸。那么我怎么能正确处理这种数据来获得这个输出：
+-----------+--------+--------------+
|customer_id|areaCode|phone |
+-----------+--------+--------------+
|x8x46x5 |null |(xx) 35xx4x80 |
|x8x46x5 |null |(xx) xxxx46605|
|xx44xx5 |null |(xx) xxx3x8443|
|4xxxxx5 |null |(xx) x6xx083x3|
|4xxxxx5 |xx |null |
|4xxxxx5 |null |(xx) 3xxx83x3 |
|xx6564x |null |(x3) x88xx344x|
|xx8x4x0 |null |(xx) x83x5x8xx|
|xx0434x |null |(0x) 3x6x4080 |
|xx0434x |xx |null |
|x465x40 |null |(6x) x6x445xx |
|x0684x8 |null |(xx) x8x88x4x4|
|x0684x8 |null |(xx) x8x88x4x4|
|x84x850 |null |(xx) 55x56xx4 |
|x0604xx |null |(xx) x8x4xxx68|
|4x6xxx0 |null |(xx) x588x43xx|
|4x6xxx0 |null |(xx) 5x6465xx |
|4x6xxx0 |null |(xx) x835xxxx8|
|4x6xxx0 |null |(xx) x5x6465xx|
|x6x000x |null |(xx) xxx044xx4|
|5x65533 |null |(xx) xx686x0xx|
|x3668xx |null |(5x) 33x8x3x4 |
|x3668xx |null |(5x) 8040x8x6 |
+-----------+--------+--------------+
答案 0 :(得分：4)
您要做的是使用from_json方法将字符串转换为数组然后爆炸：
from pyspark.sql.functions import *
from pyspark.sql.types import *
phone_schema = ArrayType(StructType([StructField("phone", StringType())]))
converted = inputDF\
.withColumn("areaCode", get_json_object("phones", "$[*].areaCode"))\
.withColumn("phones", explode(from_json("phones", phone_schema)))\
.withColumn("phone", col("phones.phone"))\
.drop("phones")\
.filter(~isnull("phone"))
converted.show()
答案 1 :(得分：1)
我认为您应该可以直接致电json.loads()，而不必使用replace()。
string将ArrayType(MapType())映射到json.loads()。flatMap()为阵列的每个元素创建一个新的Row()。看看下面的例子：
from StringIO import StringIO
from pyspark.sql import Row
import json
import pandas as pd
# mock up some sample data
data = StringIO("""customer_id\tphones
x8x46x5\t[{"phone" : "(xx) 35xx4x80"},{"phone" : "(xx) xxxx46605"}]
xx44xx5\t[{"phone" : "(xx) xxx3x8443"}]
4xxxxx5\t[{"phone" : "(xx) x6xx083x3"},{"areaCode" : "xx"},{"phone" : "(xx) 3xxx83x3"}]
xx6564x\t[{"phone" : "(x3) x88xx344x"}]
xx8x4x0\t[{"phone" : "(xx) x83x5x8xx"}]
xx0434x\t[{"phone" : "(0x) 3x6x4080"},{"areaCode" : "xx"}]
x465x40\t[{"phone" : "(6x) x6x445xx"}]
x0684x8\t[{"phone" : "(xx) x8x88x4x4"},{"phone" : "(xx) x8x88x4x4"}]
x84x850\t[{"phone" : "(xx) 55x56xx4"}]
x0604xx\t[{"phone" : "(xx) x8x4xxx68"}]
4x6xxx0\t[{"phone" : "(xx) x588x43xx"},{"phone" : "(xx) 5x6465xx"},{"phone" : "(xx) x835xxxx8"},{"phone" : "(xx) x5x6465xx"}]
x6x000x\t[{"phone" : "(xx) xxx044xx4"}]
5x65533\t[{"phone" : "(xx) xx686x0xx"}]
x3668xx\t[{"phone" : "(5x) 33x8x3x4"},{"phone" : "(5x) 8040x8x6"}]""")
pandas_df = pd.read_csv(data, sep="\t")
df = sqlCtx.createDataFrame(pandas_df) # convert pandas to spark df
# run the steps outlined above
df.rdd\
.map(lambda x: Row(customer_id=x['customer_id'], phones=json.loads(x['phones'])))\
.flatMap(lambda x: [Row(customer_id=x['customer_id'], phone=phone) for phone in x['phones']])\
.map(lambda x: Row(customer_id=x['customer_id'], phone=x['phone'].get('phone'), areaCode=x['phone'].get('areaCode')))\
.toDF()\
.select('customer_id', 'areaCode', 'phone')\
.show(truncate=False, n=100)
输出：
+-----------+--------+--------------+
|customer_id|areaCode|phone |
+-----------+--------+--------------+
|x8x46x5 |null |(xx) 35xx4x80 |
|x8x46x5 |null |(xx) xxxx46605|
|xx44xx5 |null |(xx) xxx3x8443|
|4xxxxx5 |null |(xx) x6xx083x3|
|4xxxxx5 |xx |null |
|4xxxxx5 |null |(xx) 3xxx83x3 |
|xx6564x |null |(x3) x88xx344x|
|xx8x4x0 |null |(xx) x83x5x8xx|
|xx0434x |null |(0x) 3x6x4080 |
|xx0434x |xx |null |
|x465x40 |null |(6x) x6x445xx |
|x0684x8 |null |(xx) x8x88x4x4|
|x0684x8 |null |(xx) x8x88x4x4|
|x84x850 |null |(xx) 55x56xx4 |
|x0604xx |null |(xx) x8x4xxx68|
|4x6xxx0 |null |(xx) x588x43xx|
|4x6xxx0 |null |(xx) 5x6465xx |
|4x6xxx0 |null |(xx) x835xxxx8|
|4x6xxx0 |null |(xx) x5x6465xx|
|x6x000x |null |(xx) xxx044xx4|
|5x65533 |null |(xx) xx686x0xx|
|x3668xx |null |(5x) 33x8x3x4 |
|x3668xx |null |(5x) 8040x8x6 |
+-----------+--------+--------------+
我不确定这是否是您希望的输出，但这可以帮助您实现目标。