Transform complex data types
While working with nested data types, Azure Databricks optimizes certain transformations out-of-the-box. The following code examples demonstrate patterns for working with complex and nested data types in Azure Databricks.
Dot notation for accessing nested data
You can use dot notation (.) to access a nested field.
Python
df.select("column_name.nested_field")
SQL
SELECT column_name.nested_field FROM table_name
Select all nested fields
Use the star operator (*) to select all fields within a given field.
Note
This only unpacks nested fields at the specified depth.
Python
df.select("column_name.*")
SQL
SELECT column_name.* FROM table_name
Create a new nested field
Use the struct() function to create a new nested field.
Python
from pyspark.sql.functions import struct, col
df.select(struct(col("field_to_nest").alias("nested_field")).alias("column_name"))
SQL
SELECT struct(field_to_nest AS nested_field) AS column_name FROM table_name
Nest all fields into a column
Use the star operator (*) to nest all fields from a data source as a single column.
Python
from pyspark.sql.functions import struct
df.select(struct("*").alias("column_name"))
SQL
SELECT struct(*) AS column_name FROM table_name
Select a named field from a nested column
Use square brackets [] to select nested fields from a column.
Python
from pyspark.sql.functions import col
df.select(col("column_name")["field_name"])
SQL
SELECT column_name["field_name"] FROM table_name
Explode nested elements from a map or array
Use the explode() function to unpack values from ARRAY and MAP type columns.
ARRAY columns store values as a list. When unpacked with explode(), each value becomes a row in the output.
Python
from pyspark.sql.functions import explode
df.select(explode("array_name").alias("column_name"))
SQL
SELECT explode(array_name) AS column_name FROM table_name
MAP columns store values as ordered key-value pairs. When unpacked with explode(), each key becomes a column and values become rows.
Python
from pyspark.sql.functions import explode
df.select(explode("map_name").alias("column1_name", "column2_name"))
SQL
SELECT explode(map_name) AS (column1_name, column2_name) FROM table_name
Create an array from a list or set
Use the functions collect_list() or collect_set() to transform the values of a column into an array. collect_list() collects all values in the column, while collect_set() collects only unique values.
Note
Spark does not guarantee the order of items in the array resulting from either operation.
Python
from pyspark.sql.functions import collect_list, collect_set
df.select(collect_list("column_name").alias("array_name"))
df.select(collect_set("column_name").alias("set_name"))
SQL
SELECT collect_list(column_name) AS array_name FROM table_name;
SELECT collect_set(column_name) AS set_name FROM table_name;
Select a column from a map in an array
You can also use dot notation (.) to access fields in maps that are contained within an array. This returns an array of all values for the specified field.
Consider the following data structure:
{
"column_name": [
{"field1": 1, "field2":"a"},
{"field1": 2, "field2":"b"}
]
}
You can return the values from field1 as an array with the following query:
Python
df.select("column_name.field1")
SQL
SELECT column_name.field1 FROM table_name
Transform nested data to JSON
Use the to_json function to convert a complex data type to JSON.
Python
from pyspark.sql.functions import to_json
df.select(to_json("column_name").alias("json_name"))
SQL
SELECT to_json(column_name) AS json_name FROM table_name
To encode all contents of a query or DataFrame, combine this with struct(*).
Python
from pyspark.sql.functions import to_json, struct
df.select(to_json(struct("*")).alias("json_name"))
SQL
SELECT to_json(struct(*)) AS json_name FROM table_name
Note
Azure Databricks also supports to_avro and to_protobuf for transforming complex data types for interoperability with integrated systems.
Transform JSON data to complex data
Use the from_json function to convert JSON data to native complex data types.
Note
You must specify the schema for the JSON data.
Python
from pyspark.sql.functions import from_json
schema = "column1 STRING, column2 DOUBLE"
df.select(from_json("json_name", schema).alias("column_name"))
SQL
SELECT from_json(json_name, "column1 STRING, column2 DOUBLE") AS column_name FROM table_name
Notebook: transform complex data types
The following notebooks provide examples for working with complex data types for Python, Scala, and SQL.