Arrow-optimized Python UDFs in Apache Spark™ 3.5

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In Apache Spark™, Python Person-Outlined Capabilities (UDFs) are among the many hottest options. They empower customers to craft customized code tailor-made to their distinctive knowledge processing wants. Nevertheless, the present Python UDFs, which depend on cloudpickle for serialization and deserialization, encounter efficiency bottlenecks, notably when coping with massive knowledge inputs and outputs.

In Apache Spark 3.5 and Databricks Runtime 14.0, we introduce Arrow-optimized Python UDFs to considerably enhance efficiency. On the core of this optimization lies Apache Arrow, a standardized cross-language columnar in-memory knowledge illustration. By harnessing Arrow, these UDFs bypass the normal, slower strategies of information (de)serialization, resulting in swift knowledge change between JVM and Python processes. With Apache Arrow’s wealthy sort system, these optimized UDFs provide a extra constant and standardized solution to deal with sort coercion.

Arrow optimization for Python UDFs is elective, and customers can management whether or not or to not allow Arrow optimization for particular person UDFs through the use of the "useArrow" boolean parameter of "capabilities.udf". An instance is as proven under:

>>> @udf(returnType='int', useArrow=True)  # An Arrow Python UDF
... def arrow_slen(s):
...   return len(s)
... 

As well as, customers can allow Arrow optimization for all UDFs of a whole SparkSession by way of a Spark configuration: "spark.sql.execution.pythonUDF.arrow.enabled", as proven under:

>>> spark.conf.set("spark.sql.execution.pythonUDF.arrow.enabled", True)
>>> 
>>> @udf(returnType='int')  # An Arrow Python UDF
... def arrow_slen(s):
...   return len(s)
...

Quicker (De)serialization

Apache Arrow is a columnar in-memory knowledge format that gives environment friendly knowledge interchange between completely different programs and programming languages. Not like Pickle, which serializes a whole Row as an object, Arrow shops knowledge in a column-oriented format, permitting for higher compression and reminiscence locality, which is extra appropriate for analytical workloads.

The chart under exhibits the efficiency of an Arrow-optimized Python UDF performing a single transformation with a different-sized enter dataset. The cluster consists of three employees and 1 driver, and every machine within the cluster has 16 vCPUs and 122 GiBs reminiscence. The Arrow-optimized Python UDF is ~1.6 instances sooner than the pickled Python UDF.

Arrow-optimized Python UDF

Arrow-optimized Python UDF has a big benefit in chaining UDFs. As proven under, in the identical cluster, an Arrow-optimized Python UDF can execute ~1.9 instances sooner than a pickled Python UDF on a 32 GBs dataset.

Arrow-optimized Python UDF

See right here for an entire benchmark and outcomes.

Standardized Sort Coercion

UDF sort coercion poses challenges when the Python values returned by the UDF don’t align with the user-specified return sort. Sadly, the default, pickled Python UDF’s sort coercion has sure limitations, comparable to counting on None as a fallback for sort mismatches, resulting in potential ambiguity and knowledge loss. Moreover, changing date, datetime, and tuples to strings can yield ambiguous outcomes. Arrow-optimized Python UDFs handle these points by leveraging Arrow’s well-defined algorithm for sort coercion.

As proven under, an Arrow-optimized Python UDF(useArrow=True) efficiently coerces integers saved as a string again to “int” as specified, however a pickled Python UDF (useArrow=False) falls again to “NULL”.

>>> df = spark.createDataFrame(['1', '2'], schema='string')
>>> df.choose(udf(lambda x: x, 'int', useArrow=True)('worth').alias('str_to_int')).present()
+----------+                                                                    
|str_to_int|
+----------+
|         1|
|         2|
+----------+
>>> df.choose(udf(lambda x: x, 'int', useArrow=False)('worth').alias('str_to_int')).present()
+----------+
|str_to_int|
+----------+
|      NULL|
|      NULL|
+----------+

One other instance is proven under, the place an Arrow-optimized Python UDF (useArrow=True) coerced a date to a string accurately whereas a pickled Python UDF (useArrow=False) returns ambiguous outcomes by exposing the underlying Java objects.

>>> df = spark.createDataFrame([datetime.date(1970, 1, 1), datetime.date(1970, 1, 2)], schema='date')
>>> df.choose(udf(lambda x: x, 'string', useArrow=True)('worth').alias('date_in_string')).present()
+--------------+
|date_in_string|
+--------------+
|    1970-01-01|
|    1970-01-02|
+--------------+
>>> df.choose(udf(lambda x: x, 'string', useArrow=False)('worth').alias('date_in_string')).present()
+-----------------------------------------------------------------------+
|date_in_string                                                         |
+-----------------------------------------------------------------------+
|java.util.GregorianCalendar[time=?,areFieldsSet=false,areAllFieldsSet..|
|java.util.GregorianCalendar[time=?,areFieldsSet=false,areAllFieldsSet..|
+-----------------------------------------------------------------------+

Compared to Pickle, Arrow’s type coercion aims to maintain as much information and precision as possible during the conversion process.

See here for a comprehensive comparison between Pickled Python UDFs and Arrow-optimized Python UDFs regarding type coercion.

Conclusion

Arrow-optimized Python UDFs utilize Apache Arrow for (de)serialization of UDF input and output, resulting in significantly faster (de)serialization compared to the default, pickled Python UDF. Additionally, it standardizes type coercion rules according to the Apache Arrow specifications. Arrow-optimized Python UDFs are available starting from Spark 3.5; see SPARK-40307 for more information.

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