andreacfm commented on code in PR #8528:
URL: https://github.com/apache/iceberg/pull/8528#discussion_r1324335822
##########
docs/spark-writes.md:
##########
@@ -313,6 +313,28 @@ data.writeTo("prod.db.table")
.createOrReplace()
```
+### Schema Merge
+
+While inserting or updating Iceberg is capable of resolving schema mismatch at
runtime. If configured accordingly Iceberg will perform an automatic schema
evolution as following:
+
+* A new column is present in the source but not in the target table. The new
column is added to the target table. Column values are set to NULL in all the
+rows already present in the table
+* A column is present in the target but not in the source. The target column
value is set to NULL when inserting or left unchanged when updating the row.
+
+The target table must be configured to accept-any-schema
+
+```sql
+ALTER TABLE prod.db.sample SET TBLPROPERTIES (
+ 'write.spark.accept-any-schema'='true'
+)
+```
+The writer must enable the `schema-merge` option.
Review Comment:
Ops. I think schema-merge is the equivalent in pyspark. Since the sample is
in Scala we should probably use mergeSchema. Is this correct @RussellSpitzer ?
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