Upgrading PySpark

Upgrading from PySpark 3.3 to 3.4

  • In Spark 3.4, the schema of an array column is inferred by merging the schemas of all elements in the array. To restore the previous behavior where the schema is only inferred from the first element, you can set spark.sql.pyspark.legacy.inferArrayTypeFromFirstElement.enabled to true.

  • In Spark 3.4, if Pandas on Spark API Groupby.apply’s func parameter return type is not specified and compute.shortcut_limit is set to 0, the sampling rows will be set to 2 (ensure sampling rows always >= 2) to make sure infer schema is accurate.

  • In Spark 3.4, if Pandas on Spark API Index.insert is out of bounds, will raise IndexError with index {} is out of bounds for axis 0 with size {} to follow pandas 1.4 behavior.

  • In Spark 3.4, the series name will be preserved in Pandas on Spark API Series.mode to follow pandas 1.4 behavior.

  • In Spark 3.4, the Pandas on Spark API Index.__setitem__ will first to check value type is Column type to avoid raising unexpected ValueError in is_list_like like Cannot convert column into bool: please use ‘&’ for ‘and’, ‘|’ for ‘or’, ‘~’ for ‘not’ when building DataFrame boolean expressions..

  • In Spark 3.4, the Pandas on Spark API astype('category') will also refresh categories.dtype according to original data dtype to follow pandas 1.4 behavior.

  • In Spark 3.4, the Pandas on Spark API supports groupby positional indexing in GroupBy.head and GroupBy.tail to follow pandas 1.4. Negative arguments now work correctly and result in ranges relative to the end and start of each group, Previously, negative arguments returned empty frames.

  • In Spark 3.4, the infer schema process of groupby.apply in Pandas on Spark, will first infer the pandas type to ensure the accuracy of the pandas dtype as much as possible.

  • In Spark 3.4, the Series.concat sort parameter will be respected to follow pandas 1.4 behaviors.

  • In Spark 3.4, the DataFrame.__setitem__ will make a copy and replace pre-existing arrays, which will NOT be over-written to follow pandas 1.4 behaviors.

  • In Spark 3.4, the SparkSession.sql and the Pandas on Spark API sql have got new parameter args which provides binding of named parameters to their SQL literals.

  • In Spark 3.4, Pandas API on Spark follows for the pandas 2.0, and some APIs were deprecated or removed in Spark 3.4 according to the changes made in pandas 2.0. Please refer to the [release notes of pandas](https://pandas.pydata.org/docs/dev/whatsnew/) for more details.

  • In Spark 3.4, the custom monkey-patch of collections.namedtuple was removed, and cloudpickle was used by default. To restore the previous behavior for any relevant pickling issue of collections.namedtuple, set PYSPARK_ENABLE_NAMEDTUPLE_PATCH environment variable to 1.

Upgrading from PySpark 3.2 to 3.3

  • In Spark 3.3, the pyspark.pandas.sql method follows [the standard Python string formatter](https://docs.python.org/3/library/string.html#format-string-syntax). To restore the previous behavior, set PYSPARK_PANDAS_SQL_LEGACY environment variable to 1.

  • In Spark 3.3, the drop method of pandas API on Spark DataFrame supports dropping rows by index, and sets dropping by index instead of column by default.

  • In Spark 3.3, PySpark upgrades Pandas version, the new minimum required version changes from 0.23.2 to 1.0.5.

  • In Spark 3.3, the repr return values of SQL DataTypes have been changed to yield an object with the same value when passed to eval.

Upgrading from PySpark 3.1 to 3.2

  • In Spark 3.2, the PySpark methods from sql, ml, spark_on_pandas modules raise the TypeError instead of ValueError when are applied to an param of inappropriate type.

  • In Spark 3.2, the traceback from Python UDFs, pandas UDFs and pandas function APIs are simplified by default without the traceback from the internal Python workers. In Spark 3.1 or earlier, the traceback from Python workers was printed out. To restore the behavior before Spark 3.2, you can set spark.sql.execution.pyspark.udf.simplifiedTraceback.enabled to false.

  • In Spark 3.2, pinned thread mode is enabled by default to map each Python thread to the corresponding JVM thread. Previously, one JVM thread could be reused for multiple Python threads, which resulted in one JVM thread local being shared to multiple Python threads. Also, note that now pyspark.InheritableThread or pyspark.inheritable_thread_target is recommended to use together for a Python thread to properly inherit the inheritable attributes such as local properties in a JVM thread, and to avoid a potential resource leak issue. To restore the behavior before Spark 3.2, you can set PYSPARK_PIN_THREAD environment variable to false.

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 and createDataFrame with “spark.sql.execution.arrow.enabled=true”, etc.

  • In PySpark, when creating a SparkSession with SparkSession.builder.getOrCreate(), if there is an existing SparkContext, the builder was trying to update the SparkConf of the existing SparkContext with configurations specified to the builder, but the SparkContext is shared by all SparkSession 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 a SparkSession.

  • 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.convertToArrowArraySafely to 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, 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 variable PYSPARK_ROW_FIELD_SORTING_ENABLED to 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 any set*(self, value) setter methods anymore, use the respective self.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 whereas createDataFrame from Pandas DataFrame allowed the fallback to non-optimization. Now, both toPandas and createDataFrame from Pandas DataFrame allow the fallback by default, which can be switched off by spark.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 and toPandas()/createDataFrame() with spark.sql.execution.arrow.enabled set to True, 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 to False. See SPARK-22395 for details.

  • In PySpark, na.fill() or fillna 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 omit value when to_replace is not a dictionary. Previously, value could be omitted in the other cases and had None 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 example df['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

  • When using DataTypes in Python you will need to construct them (i.e. StringType()) instead of referencing a singleton.