2

I have this kind of JSON data: 

{
 "data": [
    {
      "id": "4619623",
      "team": "452144",
      "created_on": "2018-10-09 02:55:51",
      "links": {
        "edit": "https://some_page",
        "publish": "https://some_publish",
        "default": "https://some_default"
      }
    },
    {
      "id": "4619600",
      "team": "452144",
      "created_on": "2018-10-09 02:42:25",
      "links": {
        "edit": "https://some_page",
        "publish": "https://some_publish",
        "default": "https://some_default"
      }
    }
}

I read this data using Apache spark and I want to write them partition by id column. When I use this: df.write.partitionBy("data.id").json(<path_to_folder>)

I will get error:
Exception in thread "main" org.apache.spark.sql.AnalysisException: Partition column data.id not found in schema

I also tried to use explode function like that:

import org.apache.spark.sql.functions.{col, explode}
val renamedDf= df.withColumn("id", explode(col("data.id")))
renamedDf.write.partitionBy("id").json(<path_to_folder>)

That actually helped, but each id partition folder contained the same original JSON file.

EDIT: schema of df DataFrame:

 |-- data: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- created_on: string (nullable = true)
 |    |    |-- id: string (nullable = true)
 |    |    |-- links: struct (nullable = true)
 |    |    |    |-- default: string (nullable = true)
 |    |    |    |-- edit: string (nullable = true)
 |    |    |    |-- publish: string (nullable = true)

Schema of renamedDf DataFrame:

 |-- data: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- created_on: string (nullable = true)
 |    |    |-- id: string (nullable = true)
 |    |    |-- links: struct (nullable = true)
 |    |    |    |-- default: string (nullable = true)
 |    |    |    |-- edit: string (nullable = true)
 |    |    |    |-- publish: string (nullable = true)
 |-- id: string (nullable = true)

I am using spark 2.1.0

I found this solution: DataFrame partitionBy on nested columns

And this example:http://bigdatums.net/2016/02/12/how-to-extract-nested-json-data-in-spark/

But none of this helped me to solve my problem.

Thanks in andvance for any help.

4

2 回答 2

1

try the following code:

val renamedDf = df
         .select(explode(col("data")) as "x" )
         .select($"x.*")             
renamedDf.write.partitionBy("id").json(<path_to_folder>)
于 2018-10-12T14:34:40.567 回答
0

You are just missing a select statement after the initial explode

val df = spark.read.option("multiLine", true).option("mode", "PERMISSIVE").json("/FileStore/tables/test.json")
df.printSchema

root
 |-- data: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- created_on: string (nullable = true)
 |    |    |-- id: string (nullable = true)
 |    |    |-- links: struct (nullable = true)
 |    |    |    |-- default: string (nullable = true)
 |    |    |    |-- edit: string (nullable = true)
 |    |    |    |-- publish: string (nullable = true)
 |    |    |-- team: string (nullable = true)

import org.apache.spark.sql.functions.{col, explode}
val df1= df.withColumn("data", explode(col("data")))
df1.printSchema

root
 |-- data: struct (nullable = true)
 |    |-- created_on: string (nullable = true)
 |    |-- id: string (nullable = true)
 |    |-- links: struct (nullable = true)
 |    |    |-- default: string (nullable = true)
 |    |    |-- edit: string (nullable = true)
 |    |    |-- publish: string (nullable = true)
 |    |-- team: string (nullable = true)

val df2 = df1.select("data.created_on","data.id","data.team","data.links")
df2.show

+-------------------+-------+------+--------------------+
|         created_on|     id|  team|               links|
+-------------------+-------+------+--------------------+
|2018-10-09 02:55:51|4619623|452144|[https://some_def...|
|2018-10-09 02:42:25|4619600|452144|[https://some_def...|
+-------------------+-------+------+--------------------+

df2.write.partitionBy("id").json("FileStore/tables/test_part.json")
val f = spark.read.json("/FileStore/tables/test_part.json/id=4619600")
f.show

+-------------------+--------------------+------+
|         created_on|               links|  team|
+-------------------+--------------------+------+
|2018-10-09 02:42:25|[https://some_def...|452144|
+-------------------+--------------------+------+

val full = spark.read.json("/FileStore/tables/test_part.json")
full.show

+-------------------+--------------------+------+-------+
|         created_on|               links|  team|     id|
+-------------------+--------------------+------+-------+
|2018-10-09 02:55:51|[https://some_def...|452144|4619623|
|2018-10-09 02:42:25|[https://some_def...|452144|4619600|
+-------------------+--------------------+------+-------+
于 2018-10-14T21:43:19.127 回答