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How to flatten a nested recursive JSON struct to a list of struct

Wayne
New Contributor III

This is from Spark Event log on Event SparkListenerSQLExecutionStart.
How to flatten the sparkPlanInfo struct into an array of the same struct, then later explode it. Note that the element children is an array containing the parent struct, and the level of nesting could be 0 to any random number.

 
 
2 ACCEPTED SOLUTIONS

Accepted Solutions

Kaniz
Community Manager
Community Manager

Hi @Wayne, To flatten the sparkPlanInfo struct into an array of the same struct and then explode it, you can follow these steps:

 

Flatten the Struct:

  • Use the select function to extract the fields from the sparkPlanInfo struct.
  • Create a new column for each field in the struct.
  • For example, if your sparkPlanInfo struct has fields like field1, field2, and field3, you can create new columns with those names.

Create an Array of the Flattened Struct:

  • Use the array function to create an array containing the flattened struct columns.
  • For example:from pyspark.sql.functions import col, struct, array flattened_struct = struct(col("field1"), col("field2"), col("field3")) df_with_array = df.withColumn("flattened_array", array(flattened_struct))

Explode the Array:

  • Use the explode function to create a separate record for each element of the array.
  • This will repeat the value(s) of the other column(s) for each element in the array.
  • For example:exploded_df = df_with_array.select("id", explode("flattened_array").alias("exploded_struct"))

Now you have an exploded DataFrame where each row corresponds to an element of the original sparkPlanInfo struct. You can access the fields of the struct using dot notation, such as exploded_df.exploded_struct.field1.

 

Remember to adjust the column names and struct fields according to your actual data. The level of nesting can vary, but this approach should work for any random number of nested structs. 🚀

View solution in original post

Kaniz
Community Manager
Community Manager

Thank you for posting your question in our community! We are happy to assist you.

To help us provide you with the most accurate information, could you please take a moment to review the responses and select the one that best answers your question?

This will also help other community members who may have similar questions in the future. Thank you for your participation and let us know if you need any further assistance! 
 

View solution in original post

2 REPLIES 2

Kaniz
Community Manager
Community Manager

Hi @Wayne, To flatten the sparkPlanInfo struct into an array of the same struct and then explode it, you can follow these steps:

 

Flatten the Struct:

  • Use the select function to extract the fields from the sparkPlanInfo struct.
  • Create a new column for each field in the struct.
  • For example, if your sparkPlanInfo struct has fields like field1, field2, and field3, you can create new columns with those names.

Create an Array of the Flattened Struct:

  • Use the array function to create an array containing the flattened struct columns.
  • For example:from pyspark.sql.functions import col, struct, array flattened_struct = struct(col("field1"), col("field2"), col("field3")) df_with_array = df.withColumn("flattened_array", array(flattened_struct))

Explode the Array:

  • Use the explode function to create a separate record for each element of the array.
  • This will repeat the value(s) of the other column(s) for each element in the array.
  • For example:exploded_df = df_with_array.select("id", explode("flattened_array").alias("exploded_struct"))

Now you have an exploded DataFrame where each row corresponds to an element of the original sparkPlanInfo struct. You can access the fields of the struct using dot notation, such as exploded_df.exploded_struct.field1.

 

Remember to adjust the column names and struct fields according to your actual data. The level of nesting can vary, but this approach should work for any random number of nested structs. 🚀

Kaniz
Community Manager
Community Manager

Thank you for posting your question in our community! We are happy to assist you.

To help us provide you with the most accurate information, could you please take a moment to review the responses and select the one that best answers your question?

This will also help other community members who may have similar questions in the future. Thank you for your participation and let us know if you need any further assistance! 
 

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