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Azure – PySpark UDF – read and write other dataframe

within a UDF I want to read a delta table into a dataframe, based on its content update the row of the actual dataframe, on which the UDF is applied, and then update the delta table. I would use the UDF within a structured streaming foreachbatch. How is this possible?

df_other = spark.read.format("delta").load(path)

@udf(StringType())
def my_udf(df_other: DataFrame) -> str:
    ...
    # some things to do based on df_other's content.
    ...
    df_new_rows = ...
    df_new_rows.write.format("delta").mode("append").write(path)
    ...
    return "wathever"
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2

Answers


  1. 0

    Use the following UDF to read and update the delta table:

    def read_and_process_delta_table(spark, table_path):
    # Define UDF to perform operations on DataFrame
    @udf(StringType())
    def my_combined_udf(name, age):
        # Perform operations based on name and age
        # Example: Concatenate name and age
        return f"{name}_{age}"

    # Read Delta table
    delta_df = spark.read.format("delta").load(table_path)

    # Apply combined UDF to DataFrame
    processed_df = delta_df.withColumn("processed_column", my_combined_udf(delta_df["name"], delta_df["age"]))
    processed_df.write.format("delta").mode("overwrite").option("mergeSchema", "true").save(table_path)
    return processed_df

    It will read and update the dataframe successfully, as shown below:

    Delta table:

    name age
    AA 25
    CC 35
    BB 30

    Updated delta table:

    name age processed_column
    AA 25 AA_25
    CC 35 CC_35
    BB 30 BB_30

    Here is the complete code for your reference:

    from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType

# Function to read Delta table
def read_and_process_delta_table(spark, table_path):
    # Define UDF to perform operations on DataFrame
    @udf(StringType())
    def my_combined_udf(name, age):
        # Perform operations based on name and age
        # Example: Concatenate name and age
        return f"{name}_{age}"

    # Read Delta table
    delta_df = spark.read.format("delta").load(table_path)

    # Apply combined UDF to DataFrame
    processed_df = delta_df.withColumn("processed_column", my_combined_udf(delta_df["name"], delta_df["age"]))
    processed_df.write.format("delta").mode("overwrite").option("mergeSchema", "true").save(table_path)
    return processed_df

# Usage
spark = SparkSession.builder.appName("DeltaUDFExample").getOrCreate()
table_path = "/mnt/files/delta_table"

# Read and process Delta table
result = read_and_process_delta_table(spark, table_path)

# Show the result
result.show()

    You can refer this to UDF within a structured streaming.

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  2. 0 
    from pyspark.sql import SparkSession
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
# Initialize SparkSession
spark = SparkSession.builder 
    .appName("MyApp") 
    .getOrCreate()

# Load data from Delta Lake format
df_other = spark.read.format("delta").load(path)

# Define UDF
@udf(StringType())
def my_udf(df_other_json):
    # Here you can access df_other's content and perform operations
    # Note: You might need to adjust this function based on what you want to do with df_other
    # For example, if df_other is a DataFrame, you can't directly pass it as a parameter to a UDF.
    # You can consider converting it to JSON or any other suitable format for your use case.
    
    # some things to do based on df_other's content.
    # For demonstration purposes, let's assume we're just converting it to JSON here.
    df_other_json = df_other.toJSON().collect()

    # Do something with df_other_json
    
    # Assuming df_new_rows is computed based on df_other's content
    df_new_rows = ...  # Some processing to generate new DataFrame rows
    
    # Write new rows to Delta Lake
    df_new_rows.write.format("delta").mode("append").save(path)
    
    return "whatever"

# Register UDF
spark.udf.register("my_udf", my_udf)

# Now you can use this UDF in your Spark DataFrame operations
# For example:
# df_with_udf = df_other.withColumn("result", my_udf(df_other["column_name"]))

# Stop SparkSession
spark.stop()
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