pyspark.sql.DataFrame.mapInArrow#

DataFrame.mapInArrow(func, schema, barrier=False, profile=None)[source]#

Maps an iterator of batches in the current DataFrame using a Python native function that is performed on pyarrow.RecordBatchs both as input and output, and returns the result as a DataFrame.

This method applies the specified Python function to an iterator of pyarrow.RecordBatchs, each representing a batch of rows from the original DataFrame. The returned iterator of pyarrow.RecordBatchs are combined as a DataFrame. The size of the function’s input and output can be different. Each pyarrow.RecordBatch size can be controlled by spark.sql.execution.arrow.maxRecordsPerBatch.

Added in version 3.3.0.

Parameters:

funcfunction: a Python native function that takes an iterator of pyarrow.RecordBatchs, and outputs an iterator of pyarrow.RecordBatchs.
schemapyspark.sql.types.DataType or str: the return type of the func in PySpark. The value can be either a pyspark.sql.types.DataType object or a DDL-formatted type string.
barrierbool, optional, default False: Use barrier mode execution, ensuring that all Python workers in the stage will be launched concurrently.

Added in version 3.5.0.
profilepyspark.resource.ResourceProfile. The optional ResourceProfile: to be used for mapInArrow.

Added in version 4.0.0.

See also

pyspark.sql.functions.pandas_udf
DataFrame.mapInPandas

Examples

>>> import pyarrow as pa
>>> df = spark.createDataFrame([(1, 21), (2, 30)], ("id", "age"))
>>> def filter_func(iterator):
...     for batch in iterator:
...         yield batch.filter(pa.compute.field("id") == 1)
>>> df.mapInArrow(filter_func, df.schema).show()
+---+---+
| id|age|
+---+---+
|  1| 21|
+---+---+

Set barrier to True to force the mapInArrow stage running in the barrier mode, it ensures all Python workers in the stage will be launched concurrently.

>>> df.mapInArrow(filter_func, df.schema, barrier=True).collect()
[Row(id=1, age=21)]