diff --git a/docs/pyspark-migration-guide.md b/docs/pyspark-migration-guide.md
index 8ea4fec75edf8..f7f20389aa694 100644
--- a/docs/pyspark-migration-guide.md
+++ b/docs/pyspark-migration-guide.md
@@ -87,7 +87,7 @@ Please refer [Migration Guide: SQL, Datasets and DataFrame](sql-migration-guide.
   - Since Spark 3.0, `Column.getItem` is fixed such that it does not call `Column.apply`. Consequently, if `Column` is used as an argument to `getItem`, the indexing operator should be used.
     For example, `map_col.getItem(col('id'))` should be replaced with `map_col[col('id')]`.
 
-  - 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 Python versions less than 3.6, the field names will be sorted alphabetically as the only option.
+  - 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.
 
 ## Upgrading from PySpark 2.3 to 2.4