我的資料框中有一列包含 JSON 串列，但型別是字串。我需要explode在這個列上運行，所以首先我需要將它轉換成一個串列。我找不到對這個用例的太多參考。
樣本資料：
columnName: "[{"name":"a","info":{"age":"1","grade":"b"},"other":7},{"random":"x"}, {...}]"
以上是資料的樣子，欄位不是固定的（索引 0 可能包含帶有某些欄位的 JSON，而索引 1 將包含帶有其他欄位的欄位）。在串列中可以有更多嵌套的 JSON 或一些額外的欄位。我目前正在使用這個 -
"""explode(split(regexp_replace(regexp_replace(colName, '(\\\},)','}},'), '(\\\[|\\\])',''), "},")) as colName"""我只是用“}}”替換“}”，然后洗掉“[]”，然后在“}”上呼叫拆分，但這種方法不起作用，因為有嵌套的 JSON。
如何從字串中提取陣列？
uj5u.com熱心網友回復：
你可以這樣試試：
// 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 |
------ ---- ----- ------
編輯：
如果您可以使用get_json_object功能，您可以嘗試這種方式
// 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 |
----------------------- ---- ----- ------
請注意，info列不是結構型別。您可能必須按照類似的方式來提取嵌套 json 的列
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