使用 pyspark 从 JSON DF 数组中删除选择性 JSON

Question

我想从 json 数组中删除多个 json，我在下面的源格式中有一个 json，如下所示。我有一个列表，其中包含需要保存在 json 数组中的设备 ID 列表，其余的需要删除。例如，如我的来源 json 所示，我有 3 dev_id 100010100 , 200020200 and 300030300.

我有 python 列表 device_id_list=[200020200,300030300]，我的最终 JSON 数组应该只包含 json 数组中的 2 json，json dev_id= 100010100 将被删除，如输出 json.

所示

我尝试了一个可能不是最佳的选项，我的方法是将 json 读取为字符串而不是 json，如下所示。

df = spark.read.text("path\iot-sensor.json")
df:pyspark.sql.dataframe.DataFrame
value:string

我写了一个 udf 来删除 json device_id_list 中不存在的那些。它正在删除不存在的 dev_id 并将 json 作为字符串返回。

我希望将此字符串即 dataframe df2 转换为 JSON ，具有相同的源 json 架构 (df2:pyspark.sql.dataframe.DataFrame = [iot_station: array] (Sorce Schema) ) 因为源和输出的架构 json 应该是一样的，如果有更好的解决方案请分享。

UDF:

def drop_dev_id(jsonResponse,dict_keys):
    try:
        data = json.loads(jsonResponse)
        i = 0
        n = len(data['iot_station'])
        while (i < n):
            if data['iot_station'][i]["dev_id"] not in dict_keys:
                data['iot_station'].pop(i)       
                n -= 1
            else:
                i += 1
        return data
           
    except Exception as e:
        print('Exception --> ' + str(e))


def drop_dev_id_udf(dict_keys):
     return udf(lambda row: drop_dev_id(row,dict_keys), StringType())
     
df2 = df.select('value',drop_dev_id_udf(dict_keys)('value')).select('<lambda>(value)')
df2:pyspark.sql.dataframe.DataFrame
<lambda>(value):string

来源JSON

{
  "iot_station": [
    {
      "dev_id": 100010100,
      "device1": dev_val1,
      "device2": "dev_val2",
      "device3": dev_val3,
      "device4": "dev_val4",
      "stationid": [
        {
          "id": id_val,
          "idrs": idrs_val,
          "idrq": "idrq_val",
          "idrx": "idrx_val"
        }
      ],
      "geospat": {
          "id": id_val,
          "idrs": idrs_val,
          "idrq": "idrq_val",
          "idrx": "idrx_val"
      }
    },
    {
      "dev_id": 200020200,      
      "device1": dev_val1,
      "device2": "dev_val2",
      "device3": dev_val3,
      "device4": "dev_val4",
      "stationid": [
        {
          "id": id_val,
          "idrs": idrs_val,
          "idrq": "idrq_val",
          "idrx": "idrx_val"
        }
      ],
      "geospat": {
          "id": id_val,
          "idrs": idrs_val,
          "idrq": "idrq_val",
          "idrx": "idrx_val"
    }
    },
    {
      "dev_id": 300030300,      
      "device1": dev_val1,
      "device2": "dev_val2",
      "device3": dev_val3,
      "device4": "dev_val4",
      "stationid": [
        {
          "id": id_val,
          "idrs": idrs_val,
          "idrq": "idrq_val",
          "idrx": "idrx_val"
        }
      ],
      "geospat": {
          "id": id_val,
          "idrs": idrs_val,
          "idrq": "idrq_val",
          "idrx": "idrx_val"
    }
    }
    ]
}

输出JSON:

{
  "iot_station": [
    {
      "dev_id": 200020200,      
      "device1": dev_val1,
      "device2": "dev_val2",
      "device3": dev_val3,
      "device4": "dev_val4",
      "stationid": [
        {
          "id": id_val,
          "idrs": idrs_val,
          "idrq": "idrq_val",
          "idrx": "idrx_val"
        }
      ],
      "geospat": {
          "id": id_val,
          "idrs": idrs_val,
          "idrq": "idrq_val",
          "idrx": "idrx_val"
    }
    },
    {
      "dev_id": 300030300,      
      "device1": dev_val1,
      "device2": "dev_val2",
      "device3": dev_val3,
      "device4": "dev_val4",
      "stationid": [
        {
          "id": id_val,
          "idrs": idrs_val,
          "idrq": "idrq_val",
          "idrx": "idrx_val"
        }
      ],
      "geospat": {
          "id": id_val,
          "idrs": idrs_val,
          "idrq": "idrq_val",
          "idrx": "idrx_val"
    }
    }
    ]
}

Answer 1

您不需要 UDF 来实现您想要在此处实现的目标。只需正常加载它 JSON 而不是文本并使用 filter 函数过滤数组列 iot_station :

from pyspark.sql import functions as F


df = spark.read.json("path/iot-sensor.json", multiLine=True)

device_id_list = [str(i) for i in [200020200, 300030300]]

df1 = df.withColumn(
    "iot_station",
    F.expr(f"""
        filter(
            iot_station, 
            x -> x.dev_id in ({','.join(device_id_list)})
        )
    """)
)

# check filtered json
df1.select(F.col("iot_station").getItem("dev_id").alias("dev_id")).show(truncate=False)

#+----------------------+
#|dev_id                |
#+----------------------+
#|[200020200, 300030300]|
#+----------------------+

使用 pyspark 从 JSON DF 数组中删除选择性 JSON

Remove selective JSON's from JSON DF array using pyspark

python

apache-spark

apache-spark-sql

pyspark

pyspark-dataframes