使用 spark 读取和访问 json 文件中的嵌套字段

Question

我有多个 json 文件希望用来创建 spark 数据框。在使用子集进行测试时，当我加载文件时，我会自己获取 json 信息的行，而不是解析的 json 信息。我正在执行以下操作：

    df = spark.read.json('gutenberg/test')
    df.show()
    +--------------------+--------------------+--------------------+
    |                   1|                  10|                   5|
    +--------------------+--------------------+--------------------+
    |                null|[WrappedArray(),W...|                null|
    |                null|                null|[WrappedArray(Uni...|
    |[WrappedArray(Jef...|                null|                null|
    +--------------------+--------------------+--------------------+

当我检查数据框的架构时，它似乎在那里，但无法访问它：

    df.printSchema()
    root
     |-- 1: struct (nullable = true)
     |    |-- author: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- formaturi: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- language: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- rights: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- subject: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- title: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- txt: string (nullable = true)
     |-- 10: struct (nullable = true)
     |    |-- author: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- formaturi: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- language: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- rights: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- subject: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- title: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- txt: string (nullable = true)
     |-- 5: struct (nullable = true)
     |    |-- author: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- formaturi: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- language: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- rights: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- subject: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- title: array (nullable = true)
     |    |    |-- element: string (containsNull = true)
     |    |-- txt: string (nullable = true)

我在尝试访问信息时不断出错，所以任何帮助都会很棒。

具体来说，我正在寻找一个新的数据框，其中的列是（'author'、'formaturi'、'language'、'rights'、'subject'、'title'、'txt'）

我正在使用 pyspark 2.2

Answer 1

由于我不知道json文件到底是什么样的，假设它是一个新行分隔的jsons，这应该可以。

def _construct_key(previous_key, separator, new_key):
    if previous_key:
        return "{}{}{}".format(previous_key, separator, new_key)
    else:
        return new_key

def flatten(nested_dict, separator="_", root_keys_to_ignore=set()):
    assert isinstance(nested_dict, dict)
    assert isinstance(separator, str)
    flattened_dict = dict()

    def _flatten(object_, key):     
        if isinstance(object_, dict):
            for object_key in object_:
                if not (not key and object_key in root_keys_to_ignore):
                    _flatten(object_[object_key], _construct_key(key,\ 
                                       separator, object_key))
        elif isinstance(object_, list) or isinstance(object_, set):
            for index, item in enumerate(object_):
                _flatten(item, _construct_key(key, separator, index))
        else:
            flattened_dict[key] = object_

    _flatten(nested_dict, None)
    return flattened_dict

def flatten(_json):
    return flatt(_json.asDict(True))

df = spark.read.json('gutenberg/test',\
                     primitivesAsString=True,\
                     allowComments=True,\
                     allowUnquotedFieldNames=True,\
                     allowNumericLeadingZero=True,\
                     allowBackslashEscapingAnyCharacter=True,\
                     mode='DROPMALFORMED')\
                     .rdd.map(flatten).toDF()
df.show()