英文:
How to use a Spark UDF that takes an array of struct as an argument in order to build a new column?
问题
The goal is to create a column named "subscriberPresent" based on the content of the "id" and "profile" columns using the given condition. One way to achieve this without a UDF is to use Spark SQL's built-in functions. You can do this with the when and otherwise functions. Here's how you can do it:
import org.apache.spark.sql.functions._
val result = df.withColumn("subscriberPresent", when(col("contractArray.profile") === "SUBSCRIBER" && col("contractArray.id").isNotNull, true).otherwise(false))
This code will add the "subscriberPresent" column to your DataFrame based on the condition you provided.
Please note that the column references in the code (col("contractArray.profile") and col("contractArray.id")) assume that you are working with a nested structure where "profile" and "id" are nested within the "contractArray" array. You might need to adjust these column references if your DataFrame structure is different.
英文:
I have the following df DataFrame:
df.printSchema()
root
|-- code: string (nullable = true)
|-- contractId: string (nullable = true)
|-- contractArray: array (nullable = false)
| |-- element: struct (containsNull = false)
| | |-- profile: string (nullable = true)
| | |-- id: string (nullable = true)
df.show()
+---------------+----------------------------------------+
|code|contractId| contractArray|
+---------------+----------------------------------------+
| A| 45 8| [{CONSUMER, 789}, {SUBSCRIBER, 789}]|
| AC| 7896 0| [{CONSUMER, null}]|
| BB| 12 7| [{CONSUMER, null}, {SUBSCRIBER, null}]|
| CCC| 753 8| [{SUBSCRIBER, null}, {CONSUMER, 7854}]|
+-----------------+--------------------------------------+
The goal is to create a column named subscriberPresent which would contain a boolean based on the content of the id and profile columns. The condition to respect for the contents of the subscriberPresent column is:
if(col("role") === "SUBSCRIBER" && col("id") != null) true
else false
So, the desired result is the following:
+---------------+----------------------------------------+-----------------+
|code|contractId| contractArray|subscriberPresent|
+---------------+----------------------------------------+-----------------+
| A| 45 8| [{CONSUMER, 789}, {SUBSCRIBER, 789}]| true|
| AC| 7896 0| [{CONSUMER, null}]| false|
| BB| 12 7| [{CONSUMER, null}, {SUBSCRIBER, null}]| false|
| CCC| 753 8| [{SUBSCRIBER, null}, {CONSUMER, 7854}]| false|
+-----------------+--------------------------------------+-----------------+
I was thinking of making a UDF to handle this case but there may be another way to achieve it. Do you have any suggestions ?
答案1
得分: 1
你可以使用Spark中的内置when和exists函数来实现所需的结果，而无需使用UDF。
尝试运行这段代码：
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.{col, expr}
// 创建一个SparkSession（如果尚未创建）
val spark = SparkSession.builder().getOrCreate()
// 假设您的DataFrame已经定义为'df'
val resultDF = df.withColumn(
"subscriberPresent",
expr("exists(contractArray, x -> x.profile = 'SUBSCRIBER' and x.id is not null)")
)
resultDF.show()
英文:
You can achieve the desired result without using a UDF by using built-in
when and exists functions in Spark.
Try out this code:
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.{col, expr}
// Create a SparkSession (if you haven't already)
val spark = SparkSession.builder().getOrCreate()
// Assuming your DataFrame is already defined as 'df'
val resultDF = df.withColumn(
"subscriberPresent",
expr("exists(contractArray, x -> x.profile = 'SUBSCRIBER' and x.id is not null)")
)
resultDF.show()
通过集体智慧和协作来改善编程学习和解决问题的方式。致力于成为全球开发者共同参与的知识库，让每个人都能够通过互相帮助和分享经验来进步。
评论