[英]Extract few fields from JSON and return rest as a map in Pyspark Dataframe
[英]nested json from rest api to pyspark dataframe
我正在嘗試創建一個數據管道,我在其中從 REST API 請求數據。 輸出是一個很好的嵌套 json 文件。 我想將 json 文件讀入 pyspark 數據幀。 當我在本地保存文件並使用以下代碼時,這很好用:
from pyspark.sql import *
from pyspark.sql.functions import *
spark = SparkSession\
.builder\
.appName("jsontest")\
.getOrCreate()
raw_df = spark.read.json(r"my_json_path", multiLine='true')
但是當我在發出 API 請求后想直接創建一個 pyspark 數據幀時,我收到以下錯誤:
我使用以下代碼進行休息 api 調用並轉換為 pyspark 數據幀:
apiCallHeaders = {'Authorization': 'Bearer ' + bearer_token}
apiCallResponse = requests.get(data_url, headers=apiCallHeaders, verify=True)
json_rdd = spark.sparkContext.parallelize(apiCallResponse.text)
raw_df = spark.read.json(json_rdd)
以下是部分響應輸出
{"networks":[{"href":"/v2/networks/velobike-moscow","id":"velobike-moscow","name":"Velobike"},{"href":"/v2/networks/bycyklen","id":"bycyklen","name":"Bycyklen"},{"href":"/v2/networks/nu-connect","id":"nu-connect","name":"Nu-Connect"},{"href":"/v2/networks/baerum-bysykkel","id":"baerum-bysykkel","name":"Bysykkel"},{"href":"/v2/networks/bysykkelen","id":"bysykkelen","name":"Bysykkelen"},{"href":"/v2/networks/onroll-a-rua","id":"onroll-a-rua","name":"Onroll"},{"href":"/v2/networks/onroll-albacete","id":"onroll-albacete","name":"Onroll"},{"href":"/v2/networks/onroll-alhama-de-murcia","id":"onroll-alhama-de-murcia","name":"Onroll"},{"href":"/v2/networks/onroll-almunecar","id":"onroll-almunecar","name":"Onroll"},{"href":"/v2/networks/onroll-antequera","id":"onroll-antequera","name":"Onroll"},{"href":"/v2/networks/onroll-aranda-de-duero","id":"onroll-aranda-de-duero","name":"Onroll"}
我希望我的問題是有道理的,有人可以提供幫助。
提前致謝!
按照這個答案,您可以添加以下幾行:
import os
import sys
os.environ['PYSPARK_PYTHON'] = sys.executable
os.environ['PYSPARK_DRIVER_PYTHON'] = sys.executable
要運行您的代碼,必須在此處添加[ ]
:
rdd = spark.sparkContext.parallelize([apiCallResponse.text])
看一個例子:
import requests
response = requests.get('http://api.citybik.es/v2/networks?fields=id,name,href')
rdd = spark.sparkContext.parallelize([response.text])
df = spark.read.json(rdd)
df.printSchema()
# root
# |-- networks: array (nullable = true)
# | |-- element: struct (containsNull = true)
# | | |-- href: string (nullable = true)
# | | |-- id: string (nullable = true)
# | | |-- name: string (nullable = true)
(df
.selectExpr('inline(networks)')
.show(n=5, truncate=False))
# +----------------------------+---------------+----------+
# |href |id |name |
# +----------------------------+---------------+----------+
# |/v2/networks/velobike-moscow|velobike-moscow|Velobike |
# |/v2/networks/bycyklen |bycyklen |Bycyklen |
# |/v2/networks/nu-connect |nu-connect |Nu-Connect|
# |/v2/networks/baerum-bysykkel|baerum-bysykkel|Bysykkel |
# |/v2/networks/bysykkelen |bysykkelen |Bysykkelen|
# +----------------------------+---------------+----------+
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