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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.
You can use dot notation (.
) to access a nested field.
df.select("column_name.nested_field")
SELECT column_name.nested_field FROM table_name
Use the star operator (*
) to select all fields within a given field.
Note
This only unpacks nested fields at the specified depth.
df.select("column_name.*")
SELECT column_name.* FROM table_name
Use the struct()
function to create a new nested field.
from pyspark.sql.functions import struct, col
df.select(struct(col("field_to_nest").alias("nested_field")).alias("column_name"))
SELECT struct(field_to_nest AS nested_field) AS column_name FROM table_name
Use the star operator (*
) to nest all fields from a data source as a single column.
from pyspark.sql.functions import struct
df.select(struct("*").alias("column_name"))
SELECT struct(*) AS column_name FROM table_name
Use square brackets []
to select nested fields from a column.
from pyspark.sql.functions import col
df.select(col("column_name")["field_name"])
SELECT column_name["field_name"] FROM table_name
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.
from pyspark.sql.functions import explode
df.select(explode("array_name").alias("column_name"))
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.
from pyspark.sql.functions import explode
df.select(explode("map_name").alias("column1_name", "column2_name"))
SELECT explode(map_name) AS (column1_name, column2_name) FROM table_name
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.
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"))
SELECT collect_list(column_name) AS array_name FROM table_name;
SELECT collect_set(column_name) AS set_name FROM table_name;
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:
df.select("column_name.field1")
SELECT column_name.field1 FROM table_name
Use the to_json
function to convert a complex data type to JSON.
from pyspark.sql.functions import to_json
df.select(to_json("column_name").alias("json_name"))
SELECT to_json(column_name) AS json_name FROM table_name
To encode all contents of a query or DataFrame, combine this with struct(*)
.
from pyspark.sql.functions import to_json, struct
df.select(to_json(struct("*")).alias("json_name"))
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.
Use the from_json
function to convert JSON data to native complex data types.
Note
You must specify the schema for the JSON data.
from pyspark.sql.functions import from_json
schema = "column1 STRING, column2 DOUBLE"
df.select(from_json("json_name", schema).alias("column_name"))
SELECT from_json(json_name, "column1 STRING, column2 DOUBLE") AS column_name FROM table_name
The following notebooks provide examples for working with complex data types for Python, Scala, and SQL.