Flattening skills for multi-layer nested Array Struct in PySpark

This article explores in depth how to efficiently flatten complex multi-layer nested array(struct(array(struct)))) structures into array(struct) in PySpark. By combining Spark SQL's transform higher-order functions and flatten functions, we can gracefully extract inner structure fields and merge them with outer fields, ultimately achieving simplification of the target pattern, avoiding the complexity of traditional exploit and groupBy combinations, and providing a more declarative and scalable solution.

Understand complex nested structures and goals

When dealing with big data, we often encounter DataFrames containing complex nested data types. A common scenario is a column containing a structure of type array(struct(array(struct)))) such as:

 root
 |-- a: integer (nullable = true)
 |-- list: array (nullable = true)
 | |-- element: struct (containsNull = true)
 | | |-- b: integer (nullable = true)
 | | |-- sub_list: array (nullable = true)
 | | | |-- element: struct (containsNull = true)
 | | | | |-- c: integer (nullable = true)
 | | | | |-- foo: string (nullable = true)

Our goal is to simplify this multi-layer nesting structure into an array(struct) form, that is, to promote the c and foo fields in sub_list to the struct inside the list, and eliminate the nesting level of sub_list:

 root
 |-- a: integer (nullable = true)
 |-- list: array (nullable = true)
 | |-- element: struct (containsNull = true)
 | | |-- b: integer (nullable = true)
 | | |-- c: integer (nullable = true)
 | | |-- foo: string (nullable true)

This flattening process is crucial for subsequent data analysis and processing.

Challenges and limitations of traditional approaches

Traditional flattening methods usually involve the exploit function, which expands each element in the array into a separate row. For the above structure, if you use exploit directly, it may take multiple exploit operations and then reaggregate through groupBy and collect_list, which can become extremely complex and inefficient in the face of deeper nesting. For example, although the following methods are effective, they are expensive to maintain in complex scenarios:

 from pyspark.sql import SparkSession
from pyspark.sql.functions import inline, expr, collect_list, struct

# Assume df is your DataFrame
# df.select("a", inline("list")) \
# .select(expr("*"), inline("sub_list")) \
# .drop("sub_list") \
# .groupBy("a") \
# .agg(collect_list(struct("b", "c", "foo")).alias("list"))

This approach requires us to "elevate" all nesting levels to the row level and then aggregate, which is contrary to our expected "bottom-up" or "in-place" transformation philosophy. We prefer a solution that can operate directly inside an array without changing the number of DataFrame rows.

PySpark Solution: The combination of Transform and Flatten

PySpark 3.x introduces higher-order functions such as transform, which greatly enhances the processing power of complex data types (especially arrays). Combining transform and flatten, we can solve the above problems gracefully.

The transform function allows us to apply a custom transformation logic to each element in the array and return a new array. When it comes to multi-layer nesting, we can use nested transforms to process layer by layer.

Core logic :

1. Inner layer conversion : First, transform the innermost sub_list. For each element in sub_list (i.e., a struct containing c and foo), we combine it with the b field in the outer struct to create a new flattened struct.
2. Outer conversion : This step of transform will generate an array(array(struct)) structure.
3. Flatten : Finally, use the flatten function to merge the array(array(struct)) structure into a single array(struct).

Sample code :

First, we create a simulated DataFrame to demonstrate:

 from pyspark.sql import SparkSession
from pyspark.sql.functions import col, transform, flatten, struct
from pyspark.sql.types import StructType, StructField, ArrayType, IntegerType, StringType

# Initialize SparkSession
spark = SparkSession.builder.appName("FlattenNestedArrayStruct").getOrCreate()

# Define the initial schema
inner_struct_schema = StructType([
    StructField("c", IntegerType(), True),
    StructField("foo", StringType(), True)
])
outer_struct_schema = StructType([
    StructField("b", IntegerType(), True),
    StructField("sub_list", ArrayType(inner_struct_schema), True)
])
df_schema = StructType([
    StructField("a", IntegerType(), True),
    StructField("list", ArrayType(outer_struct_schema), True)
])

# Create sample data = [
    (1, [
        {"b": 10, "sub_list": [{"c": 100, "foo": "x"}, {"c": 101, "foo": "y"}]},
        {"b": 20, "sub_list": [{"c": 200, "foo": "z"}]}
    ]),
    (2, [
        {"b": 30, "sub_list": [{"c": 300, "foo": "w"}]}
    ])
]

df = spark.createDataFrame(data, schema=df_schema)
df.printSchema()
df.show(truncate=False)

# Apply flattened logic df_flattened = df.withColumn(
    "list",
    flatten(
        transform(
            col("list"), # outer array(array of structs)
            lambda x: transform( # operate on each struct x of the outer array x.getField("sub_list"), # Get sub_list (array of structs) in struct x
                lambda y: struct(x.getField("b").alias("b"), y.getField("c").alias("c"), y.getField("foo").alias("foo")),
            ),
        )
    ),
)

df_flattened.printSchema()
df_flattened.show(truncate=False)

# Stop SparkSession
spark.stop()

Code parsing

1. df.withColumn("list", ...): We choose to modify the list column to contain the flattened results.
2. transform(col("list"), lambda x: ...): This is the outer transform. It iterates over each struct element in the list column, which we name as x. The type of x is struct(b: int, sub_list: array(struct(c: int, foo: string))).
3. transform(x.getField("sub_list"), lambda y: ...): This is the inner transform. It acts on the sub_list field in x. sub_list is an array whose elements (a struct(c: int, foo: string)) are named y.
4. struct(x.getField("b").alias("b"), y.getField("c").alias("c"), y.getField("foo").alias("foo")): Inside the inner transform, we build a new struct.
   • x.getField("b"): Get the b field from outer struct x.
   • y.getField("c"): Get the c field from the inner struct y.
   • y.getField("foo"): Get the foo field from the inner struct y.
   • alias("b"), alias("c"), alias("foo") are used to ensure that the newly generated struct field name is correct. This struct function generates a flattened struct for each y element in sub_list. Therefore, the result of the inner transform is an array(struct(b: int, c: int, foo: string)).
5. Intermediate result : The outer transform collects all these arrays(struct), so its final output is an array(array(struct(b: int, c: int, foo: string))).
6. flatten(...): Finally, the flatten function flattens the array(array(struct)) structure into a single array(struct(b: int, c: int, foo: string)), which is exactly what we expect to target schema.

Notes and best practices

• Field Name : Make sure that the field names used in getField() and struct() exactly match the name in the actual schema.
• Null value processing : The transform function will naturally process empty elements in the array. If sub_list is empty, the inner transform returns an empty array; if list is empty, the outer transform also returns an empty array. flatten handles empty arrays also safe.
• Performance : transform is a built-in higher-order function for Spark SQL, which usually has better performance than custom UDFs (user-defined functions) because it can be optimized in Spark Catalyst optimizer.
• Readability : While nested transforms are very powerful, over-necking may reduce the readability of the code. For more complex scenarios, consider splitting the transformation logic into multiple steps or adding detailed comments.
• Universality : This transform-combined flatten pattern can be generalized to deeper nesting structures, just increase the nesting level of transform.

Summarize

By cleverly combining PySpark's transform and flatten functions, we are able to flatten complex multi-layer nested array(struct(array(struct))) structures into easier-to-process array(struct) structures in a declarative and efficient way. This approach avoids the complexity of traditional exploit and groupBy combinations, and is especially suitable for scenarios where finely transforming elements inside arrays is required. It is a very useful trick when dealing with complex semi-structured data in Spark.

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