Upgrading from PySpark 2.4 to 3.0¶
In Spark 3.0, PySpark requires a pandas version of 0.23.2 or higher to use pandas related functionality, such as
toPandas,createDataFramefrom pandas DataFrame, and so on.In Spark 3.0, PySpark requires a PyArrow version of 0.12.1 or higher to use PyArrow related functionality, such as
pandas_udf,toPandasandcreateDataFramewith “spark.sql.execution.arrow.enabled=true”, etc.In PySpark, when creating a
SparkSessionwithSparkSession.builder.getOrCreate(), if there is an existingSparkContext, the builder was trying to update theSparkConfof the existingSparkContextwith configurations specified to the builder, but theSparkContextis shared by allSparkSessions, so we should not update them. In 3.0, the builder comes to not update the configurations. This is the same behavior as Java/Scala API in 2.3 and above. If you want to update them, you need to update them prior to creating aSparkSession.In PySpark, when Arrow optimization is enabled, if Arrow version is higher than 0.11.0, Arrow can perform safe type conversion when converting pandas.Series to an Arrow array during serialization. Arrow raises errors when detecting unsafe type conversions like overflow. You enable it by setting
spark.sql.execution.pandas.convertToArrowArraySafelyto true. The default setting is false. PySpark behavior for Arrow versions is illustrated in the following table:PyArrow version
Integer overflow
Floating point truncation
0.11.0 and below
Raise error
Silently allows
> 0.11.0, arrowSafeTypeConversion=false
Silent overflow
Silently allows
> 0.11.0, arrowSafeTypeConversion=true
Raise error
Raise error
In Spark 3.0,
createDataFrame(..., verifySchema=True)validates LongType as well in PySpark. Previously, LongType was not verified and resulted in None in case the value overflows. To restore this behavior, verifySchema can be set to False to disable the validation.As of Spark 3.0,
Rowfield names are no longer sorted alphabetically when constructing with named arguments for Python versions 3.6 and above, and the order of fields will match that as entered. To enable sorted fields by default, as in Spark 2.4, set the environment variablePYSPARK_ROW_FIELD_SORTING_ENABLEDto true for both executors and driver - this environment variable must be consistent on all executors and driver; otherwise, it may cause failures or incorrect answers. For Python versions less than 3.6, the field names will be sorted alphabetically as the only option.In Spark 3.0,
pyspark.ml.param.shared.Has*mixins do not provide anyset*(self, value)setter methods anymore, use the respectiveself.set(self.*, value)instead. See SPARK-29093 for details.