Migration Guide: PySpark (Python on Spark)
- Upgrading from PySpark 2.4 to 3.0
- Upgrading from PySpark 2.3 to 2.4
- Upgrading from PySpark 2.3.0 to 2.3.1 and above
- Upgrading from PySpark 2.2 to 2.3
- Upgrading from PySpark 1.4 to 1.5
- Upgrading from PySpark 1.0-1.2 to 1.3
Note that this migration guide describes the items specific to PySpark. Many items of SQL migration can be applied when migrating PySpark to higher versions. Please refer Migration Guide: SQL, Datasets and DataFrame.
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
,createDataFrame
from 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
,toPandas
andcreateDataFrame
with “spark.sql.execution.arrow.enabled=true”, etc. -
In PySpark, when creating a
SparkSession
withSparkSession.builder.getOrCreate()
, if there is an existingSparkContext
, the builder was trying to update theSparkConf
of the existingSparkContext
with configurations specified to the builder, but theSparkContext
is shared by allSparkSession
s, 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 settingspark.sql.execution.pandas.convertToArrowArraySafely
totrue
. The default setting isfalse
. 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)
validatesLongType
as well in PySpark. Previously,LongType
was not verified and resulted inNone
in case the value overflows. To restore this behavior,verifySchema
can be set toFalse
to disable the validation. -
As of Spark 3.0,
Row
field 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_ENABLED
totrue
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.
Upgrading from PySpark 2.3 to 2.4
- In PySpark, when Arrow optimization is enabled, previously
toPandas
just failed when Arrow optimization is unable to be used whereascreateDataFrame
from Pandas DataFrame allowed the fallback to non-optimization. Now, bothtoPandas
andcreateDataFrame
from Pandas DataFrame allow the fallback by default, which can be switched off byspark.sql.execution.arrow.fallback.enabled
.
Upgrading from PySpark 2.3.0 to 2.3.1 and above
- As of version 2.3.1 Arrow functionality, including
pandas_udf
andtoPandas()
/createDataFrame()
withspark.sql.execution.arrow.enabled
set toTrue
, has been marked as experimental. These are still evolving and not currently recommended for use in production.
Upgrading from PySpark 2.2 to 2.3
-
In PySpark, now we need Pandas 0.19.2 or upper if you want to use Pandas related functionalities, such as
toPandas
,createDataFrame
from Pandas DataFrame, etc. -
In PySpark, the behavior of timestamp values for Pandas related functionalities was changed to respect session timezone. If you want to use the old behavior, you need to set a configuration
spark.sql.execution.pandas.respectSessionTimeZone
toFalse
. See SPARK-22395 for details. -
In PySpark,
na.fill()
orfillna
also accepts boolean and replaces nulls with booleans. In prior Spark versions, PySpark just ignores it and returns the original Dataset/DataFrame. -
In PySpark,
df.replace
does not allow to omitvalue
whento_replace
is not a dictionary. Previously,value
could be omitted in the other cases and hadNone
by default, which is counterintuitive and error-prone.
Upgrading from PySpark 1.4 to 1.5
-
Resolution of strings to columns in Python now supports using dots (
.
) to qualify the column or access nested values. For exampledf['table.column.nestedField']
. However, this means that if your column name contains any dots you must now escape them using backticks (e.g.,table.`column.with.dots`.nested
). -
DataFrame.withColumn method in PySpark supports adding a new column or replacing existing columns of the same name.
Upgrading from PySpark 1.0-1.2 to 1.3
Python DataTypes No Longer Singletons
When using DataTypes in Python you will need to construct them (i.e. StringType()
) instead of
referencing a singleton.