将 python 字典转换为 pyspark dataframe

Question

I have a json file which contains a dictionary in the following format:

{"a1":{"b1":["c1","c2"], "b2":["c4","c3"]}, "a2":{"b3":["c1","c4"]}}

Is it possible to convert this dictionary into a PySpark dataframe as the following?

 col1 |  col2 |  col3
----------------------
| a1  |   b1  |  c1  |
----------------------
| a1  |   b1  |  c2  |
----------------------
| a1  |   b2  |  c4  |
----------------------
| a1  |   b2  |  c3  |
----------------------
| a2  |   b3  |  c1  |
----------------------
| a2  |   b3  |  c4  |

I have seen the standard format of converting json to PySpark dataframe (example in this link ) but was wondering about nested dictionaries that contain lists as well.

Answer 1

Interesting problem, The main struggle I realized with this problem is your when reading from JSON, your schema is likely has struct type, making it harder to solve, because basically a1 has different type than a2 .

My idea is using somehow converting your struct type to map type, then stack them together, then apply a few explode s:

This is your df

+----------------------------------+
|data                              |
+----------------------------------+
|{{[c1, c2], [c4, c3]}, {[c1, c4]}}|
+----------------------------------+

root
 |-- data: struct (nullable = true)
 |    |-- a1: struct (nullable = true)
 |    |    |-- b1: array (nullable = true)
 |    |    |    |-- element: string (containsNull = true)
 |    |    |-- b2: array (nullable = true)
 |    |    |    |-- element: string (containsNull = true)
 |    |-- a2: struct (nullable = true)
 |    |    |-- b3: array (nullable = true)
 |    |    |    |-- element: string (containsNull = true)

Create a temporary df to handle JSON's first level

first_level_df = df.select('data.*')
first_level_df.show()
first_level_cols = first_level_df.columns # ['a1', 'a2']

+--------------------+----------+
|                  a1|        a2|
+--------------------+----------+
|{[c1, c2], [c4, c3]}|{[c1, c4]}|
+--------------------+----------+

Some helper variables

map_cols = [F.from_json(F.to_json(c), T.MapType(T.StringType(), T.StringType())).alias(c) for c in first_level_cols]
# [Column<'entries AS a1'>, Column<'entries AS a2'>]

stack_cols = ', '.join([f"'{c}', {c}" for c in first_level_cols])
# 'a1', a1, 'a2', a2

Main transformation

(first_level_df
    .select(map_cols)
    .select(F.expr(f'stack(2, {stack_cols})').alias('AA', 'temp'))
    .select('AA', F.explode('temp').alias('BB', 'temp'))
    .select('AA', 'BB', F.explode(F.from_json('temp', T.ArrayType(T.StringType()))).alias('CC'))
    .show(10, False)
)

+---+---+---+
|AA |BB |CC |
+---+---+---+
|a1 |b1 |c1 |
|a1 |b1 |c2 |
|a1 |b2 |c4 |
|a1 |b2 |c3 |
|a2 |b3 |c1 |
|a2 |b3 |c4 |
+---+---+---+