Flatten Nested Struct in PySpark Array - pyspark

Question

Given a schema like:
root
|-- first_name: string
|-- last_name: string
|-- degrees: array
| |-- element: struct
| | |-- school: string
| | |-- advisors: struct
| | | |-- advisor1: string
| | | |-- advisor2: string
How can I get a schema like:
root
|-- first_name: string
|-- last_name: string
|-- degrees: array
| |-- element: struct
| | |-- school: string
| | |-- advisor1: string
| | |-- advisor2: string
Currently, I explode the array, flatten the structure by selecting advisor.* and then group by first_name, last_name and rebuild the array with collect_list. I'm hoping there's a cleaner/shorter way to do this. Currently, there's a lot of pain renaming some fields and stuff that I don't want to get into here. Thanks!

Answer 1

You can use udf to change the datatype of nested columns in dataframe.
Suppose you have read the dataframe as df1
from pyspark.sql.functions import udf
from pyspark.sql.types import *
def foo(data):
return
(
list(map(
lambda x: (
x["school"],
x["advisors"]["advisor1"],
x["advisors"]["advisor1"]
),
data
))
)
struct = ArrayType(
StructType([
StructField("school", StringType()),
StructField("advisor1", StringType()),
StructField("advisor2", StringType())
])
)
udf_foo = udf(foo, struct)
df2 = df1.withColumn("degrees", udf_foo("degrees"))
df2.printSchema()
output:
root
|-- degrees: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- school: string (nullable = true)
| | |-- advisor1: string (nullable = true)
| | |-- advisor2: string (nullable = true)
|-- first_name: string (nullable = true)
|-- last_name: string (nullable = true)

Answer 2

Here's a more generic solution which can flatten multiple nested struct layers:
def flatten_df(nested_df, layers):
flat_cols = []
nested_cols = []
flat_df = []
flat_cols.append([c[0] for c in nested_df.dtypes if c[1][:6] != 'struct'])
nested_cols.append([c[0] for c in nested_df.dtypes if c[1][:6] == 'struct'])
flat_df.append(nested_df.select(flat_cols[0] +
[col(nc+'.'+c).alias(nc+'_'+c)
for nc in nested_cols[0]
for c in nested_df.select(nc+'.*').columns])
)
for i in range(1, layers):
print (flat_cols[i-1])
flat_cols.append([c[0] for c in flat_df[i-1].dtypes if c[1][:6] != 'struct'])
nested_cols.append([c[0] for c in flat_df[i-1].dtypes if c[1][:6] == 'struct'])
flat_df.append(flat_df[i-1].select(flat_cols[i] +
[col(nc+'.'+c).alias(nc+'_'+c)
for nc in nested_cols[i]
for c in flat_df[i-1].select(nc+'.*').columns])
)
return flat_df[-1]
just call with:
my_flattened_df = flatten_df(my_df_having_structs, 3)
(second parameter is the level of layers to be flattened, in my case it's 3)