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[英]Nested JSON to Flat PySpark Dataframe on Azure DataBricks
[英]nested json to tsv in databricks pyspark
想要使用 pysoark 在 databricks 笔记本中将嵌套的 json 转换为 tsv。
下面是可以更改列的 json 结构。
{"tables":[{"name":"Result","columns":[{"name":"JobTime","type":"datetime"},{"name":"Status","type":"string"}]
,"rows":[
["2020-04-19T13:45:12.528Z","Failed"]
,["2020-04-19T14:05:40.098Z","Failed"]
,["2020-04-19T13:46:31.655Z","Failed"]
,["2020-04-19T14:01:16.275Z","Failed"],
["2020-04-19T14:03:16.073Z","Failed"],
["2020-04-19T14:01:16.672Z","Failed"],
["2020-04-19T14:02:13.958Z","Failed"],
["2020-04-19T14:04:41.099Z","Failed"],
["2020-04-19T14:04:41.16Z","Failed"],
["2020-04-19T14:05:14.462Z","Failed"]
]}
]}
我是databricks的新手请帮忙
你有两种方法来处理这个问题。 您可以使用json
库(或等效库)在python
中进行一些预处理,或者直接加载到pyspark
并进行以下操作:
from pyspark.sql import SparkSession
import pyspark.sql.functions as f
spark = SparkSession.builder.getOrCreate()
# your json
so_json = """
{"tables":[{"name":"Result","columns":[{"name":"JobTime","type":"datetime"},{"name":"Status","type":"string"}]
,"rows":[
["2020-04-19T13:45:12.528Z","Failed"]
,["2020-04-19T14:05:40.098Z","Failed"]
,["2020-04-19T13:46:31.655Z","Failed"]
,["2020-04-19T14:01:16.275Z","Failed"],
["2020-04-19T14:03:16.073Z","Failed"],
["2020-04-19T14:01:16.672Z","Failed"],
["2020-04-19T14:02:13.958Z","Failed"],
["2020-04-19T14:04:41.099Z","Failed"],
["2020-04-19T14:04:41.16Z","Failed"],
["2020-04-19T14:05:14.462Z","Failed"]
]}
]}
"""
# load in directly using read.json(), you'll see that this becomes
# a nested ArrayType/StructType wombo combo
json_df = spark.read.json(spark._sc.parallelize([so_json]))
json_df.printSchema()
root
|-- tables: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- columns: array (nullable = true)
| | | |-- element: struct (containsNull = true)
| | | | |-- name: string (nullable = true)
| | | | |-- type: string (nullable = true)
| | |-- name: string (nullable = true)
| | |-- rows: array (nullable = true)
| | | |-- element: array (containsNull = true)
| | | | |-- element: string (containsNull = true)
# select nested columns "tables" and "rows" and explode
array_df = json_df.select(f.explode(f.col('tables')['rows'][0]))
Exploding 获取ArrayType
rows
并将其拆分为实际行。 然后您可以通过点或切片表示法进行子选择
array_df.printSchema()
root
|-- col: array (nullable = true)
| |-- element: string (containsNull = true)
tabular_df = array_df.select(
array_df.col[0].alias("JobTime"),
array_df.col[1].alias("Status")
)
tabular_df.show()
+--------------------+------+
| JobTime|Status|
+--------------------+------+
|2020-04-19T13:45:...|Failed|
|2020-04-19T14:05:...|Failed|
|2020-04-19T13:46:...|Failed|
|2020-04-19T14:01:...|Failed|
|2020-04-19T14:03:...|Failed|
|2020-04-19T14:01:...|Failed|
|2020-04-19T14:02:...|Failed|
|2020-04-19T14:04:...|Failed|
|2020-04-19T14:04:...|Failed|
|2020-04-19T14:05:...|Failed|
+--------------------+------+
最后,您希望使用自定义分隔符 ( \t
) 保存为 CSV。 因此:
tabular_df.write.csv("path/to/file.tsv", sep="\t")
注意:您可能需要手动控制类型,例如将JobTime
转换为TimestampType
,但我将由您决定。 希望这可以帮助。
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