使用 Spark 从 json 字符串列表中提取数组

Question

我的数据框中有一列包含 JSON 的列表，但类型是字符串。我需要在此列上运行 explode，因此首先我需要将其转换为列表。我找不到很多关于此用例的参考资料。

示例数据：

columnName: "[{"name":"a","info":{"age":"1","grade":"b"},"other":7},{"random":"x"}, {...}]"

以上是数据的样子，字段不固定（索引 0 可能有 JSON 和一些字段，而索引 1 可能有一些字段和其他字段）。在列表中可以有更多嵌套的 JSON 或一些额外的字段。我目前正在使用这个 -

"""explode(split(regexp_replace(regexp_replace(colName, '(\\},)','}},'), '(\\[|\\])',''), "},")) as colName""" 我只是将“}”替换为“}}”，然后删除“[]”，然后在“}”上调用拆分，但这种方法不起作用，因为有嵌套 JSONs.

如何从字符串中提取数组？

Answer 1

你可以这样试试：

// Initial DataFrame

df.show(false)

+----------------------------------------------------------------------+
|columnName                                                            |
+----------------------------------------------------------------------+
|[{"name":"a","info":{"age":"1","grade":"b"},"other":7},{"random":"x"}]|
+----------------------------------------------------------------------+

df.printSchema()

root
 |-- columnName: string (nullable = true)
 
// toArray is a user defined function that parses an array of json objects which is present as a string
     
import org.json.JSONArray

val toArray = udf { (data: String) => {
    val jsonArray = new JSONArray(data)
    var arr: Array[String] = Array()
    val objects = (0 until jsonArray.length).map(x => jsonArray.getJSONObject(x))
    objects.foreach { elem =>
        arr :+= elem.toString
    }
    arr
}
}

// Using the udf and exploding the resultant array

val df1 = df.withColumn("columnName",explode(toArray(col("columnName"))))

df1.show(false)

+-----------------------------------------------------+
|columnName                                           |
+-----------------------------------------------------+
|{"other":7,"name":"a","info":{"grade":"b","age":"1"}}|
|{"random":"x"}                                       |
+-----------------------------------------------------+

df1.printSchema()

root
 |-- columnName: string (nullable = true)
 
// Parsing the json string by obtaining the schema dynamically

val schema = spark.read.json(df1.select("columnName").rdd.map(x => x(0).toString)).schema
val df2 = df1.withColumn("columnName",from_json(col("columnName"),schema))

df2.show(false)

+---------------+
|columnName     |
+---------------+
|[[1, b], a, 7,]|
|[,,, x]        |
+---------------+

df2.printSchema()

root
 |-- columnName: struct (nullable = true)
 |    |-- info: struct (nullable = true)
 |    |    |-- age: string (nullable = true)
 |    |    |-- grade: string (nullable = true)
 |    |-- name: string (nullable = true)
 |    |-- other: long (nullable = true)
 |    |-- random: string (nullable = true)
 
// Extracting all the fields from the json

df2.select(col("columnName.*")).show(false)

+------+----+-----+------+
|info  |name|other|random|
+------+----+-----+------+
|[1, b]|a   |7    |null  |
|null  |null|null |x     |
+------+----+-----+------+

编辑：

如果可以使用get_json_object功能的话可以试试这个

// Get the list of columns dynamically

val columns = spark.read.json(df1.select("columnName").rdd.map(x => x(0).toString)).columns

// define an empty array of Column type and get_json_object function to extract the columns

var extract_columns: Array[Column] = Array()
    columns.foreach { column =>
    extract_columns :+= get_json_object(col("columnName"), "$." + column).as(column)
}

df1.select(extract_columns: _*).show(false)

+-----------------------+----+-----+------+
|info                   |name|other|random|
+-----------------------+----+-----+------+
|{"grade":"b","age":"1"}|a   |7    |null  |
|null                   |null|null |x     |
+-----------------------+----+-----+------+

请注意，info 列不是结构类型。您可能必须按照类似的方式提取嵌套 json

的列

使用 Spark 从 json 字符串列表中提取数组

Extract array from list of json strings using Spark

json

scala

apache-spark

apache-spark-sql