英文:
Pyspark: How to avoid python UDF as a driver operation?
问题
我有一个需要在pyspark代码中运行的Python UDF，有没有一种调用该UDF的方式，以便我可以使用mappartitions来避免该Python操作仅在驱动节点上运行，并利用整个集群，因为如果我直接在数据框上使用UDF，那将作为驱动程序操作运行，是吗？如何高效地做到这一点？
class Some_class_name:
def pyt_udf(x):
<一些Python操作>
return data
def opr_to_be_done:
df = spark.sql('''select col1, col2 from table_name''')
rdd2 = df.rdd.mapPartitions(lambda x: pyt_udf(x))
英文:
I have a python UDF that needs to be run in a pyspark code, Is there any way of calling that UDF using mappartitions, so that I can avoid that python operation not just run only in the driver node and use the full cluster, because If I just use the UDF directly on the dataframe, that would run as a driver operation, isn't it? What is the efficient way of doing this?
class Some_class_name
def pyt_udf(x):
<some python operation>
return data
def opr_to_be_done:
df = spark.sql(f'''select col1, col2 from table_name''')
rdd2=df.rdd.mappartition(lambda x: pyt_udf(x))
答案1
得分: 0
你可以在PySpark中使用mapPartitions来并行地将Python UDF应用于集群中的节点上的RDD。这可以比直接将UDF应用于DataFrame更高效，后者将在驱动节点上执行。
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.types import StructType, StructField, IntegerType, StringType
# 为输入DataFrame定义模式
schema = StructType([
StructField('col1', IntegerType(), True),
StructField('col2', StringType(), True)
])
# 定义Python UDF
@pandas_udf(schema, PandasUDFType.GROUPED_MAP)
def python_udf(data):
# 对输入数据应用一些Python操作
result = data.apply(<some python operation>)
return result
# 创建一个DataFrame
df = spark.sql("SELECT col1, col2 FROM table_name")
# 将DataFrame转换为RDD并使用mapPartitions应用Python UDF
rdd = df.rdd.mapPartitions(lambda partition: python_udf(partition.toPandas()))
# 将RDD转换回DataFrame
result_df = rdd.toDF(schema)
# 显示结果DataFrame
result_df.show()
请注意，上述代码中的<some python operation>应替换为您要在Python UDF中执行的实际Python操作。
英文:
You can use mapPartitions in PySpark to apply a Python UDF to an RDD in parallel across the nodes in the cluster. This can be more efficient than applying the UDF directly to a DataFrame, which would be executed on the driver node.
from pyspark.sql.functions import pandas_udf, PandasUDFType
from pyspark.sql.types import StructType, StructField, IntegerType, StringType
# Define the schema for the input DataFrame
schema = StructType([
StructField('col1', IntegerType(), True),
StructField('col2', StringType(), True)
])
# Define the Python UDF
@pandas_udf(schema, PandasUDFType.GROUPED_MAP)
def python_udf(data):
# Apply some Python operation to the input data
result = data.apply(<some python operation>)
return result
# Create a DataFrame
df = spark.sql("SELECT col1, col2 FROM table_name")
# Convert the DataFrame to an RDD and apply the Python UDF using mapPartitions
rdd = df.rdd.mapPartitions(lambda partition: python_udf(partition.toPandas()))
# Convert the RDD back to a DataFrame
result_df = rdd.toDF(schema)
# Show the result DataFrame
result_df.show()
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