PySpark UDF - 读写其他数据帧

Question

在 UDF 中，我想将增量表读入数据帧，根据其内容更新应用 UDF 的实际数据帧的行，然后更新增量表。我将在结构化流 foreachbatch 中使用 UDF。这怎么可能？

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"

Answer 1

使用以下UDF读取并更新增量表：

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

将会成功读取并更新dataframe，如下图：

Delta表：

名字	年龄
AA	25
抄送	35
BB	30

更新了增量表：

名字	年龄	已处理_列
AA	25	AA_25
抄送	35	CC_35
BB	30	BB_30

以下是完整代码供您参考：

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()

您可以将 this 引用到结构化流中的 UDF。