ODIn/spark-instrumented-optimizer
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity

[SPARK-31853][DOCS] Mention removal of params mixins setter in migration guide

Browse source 
### What changes were proposed in this pull request?
The Pyspark Migration Guide needs to mention a breaking change of the Pyspark ML API.

### Why are the changes needed?
In SPARK-29093, all setters have been removed from `Params` mixins in `pyspark.ml.param.shared`. Those setters had been part of the public pyspark ML API, hence this is a breaking change.

### Does this PR introduce _any_ user-facing change?
Only documentation.

### How was this patch tested?
Visually.

Closes #28663 from EnricoMi/branch-pyspark-migration-guide-setters.

Authored-by: Enrico Minack <github@enrico.minack.dev>
Signed-off-by: Sean Owen <srowen@gmail.com>
This commit is contained in:
Enrico Minack 2020-06-03 18:06:13 -05:00 committed by Sean Owen
parent dc0709fa0c
commit 4bbe3c2bb4
1 changed files with 2 additions and 0 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

2
docs/pyspark-migration-guide.md
View file

@ -45,6 +45,8 @@ Please refer [Migration Guide: SQL, Datasets and DataFrame](sql-migration-guide.
- 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](https://issues.apache.org/jira/browse/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`.