jkasireddy commented on code in PR #3810:
URL: https://github.com/apache/iceberg-python/pull/3810#discussion_r3854462871


##########
pyiceberg/io/pyarrow.py:
##########
@@ -2349,15 +2423,44 @@ class 
PyArrowStatisticsCollector(PreOrderSchemaVisitor[list[StatisticsCollector]
     _schema: Schema
     _properties: dict[str, str]
     _default_mode: str
+    _inferred_field_ids: set[int] | None
 
     def __init__(self, schema: Schema, properties: dict[str, str]):
         from pyiceberg.table import TableProperties
 
         self._schema = schema
         self._properties = properties
-        self._default_mode = self._properties.get(
-            TableProperties.DEFAULT_WRITE_METRICS_MODE, 
TableProperties.DEFAULT_WRITE_METRICS_MODE_DEFAULT
-        )
+
+        configured_default_mode = 
self._properties.get(TableProperties.DEFAULT_WRITE_METRICS_MODE)
+
+        if configured_default_mode is not None:
+            # An explicitly configured default applies to all columns.
+            self._default_mode = configured_default_mode
+            self._inferred_field_ids = None

Review Comment:
   @greenlaw 
   Good question. I had considered that as well, but I intentionally made an 
explicitly configured `write.metadata.metrics.default` bypass 
`write.metadata.metrics.max-inferred-column-defaults` to match the current Java 
MetricsConfig behavior.
   
   In the Java implementation, max-inferred-column-defaults is applied only 
when there is no explicitly configured default. If 
`write.metadata.metrics.default=full` is set, that is treated as an explicit 
user choice and the mode applies to all columns. Explicit per-column overrides 
similarly take precedence over the inferred limit.
   
   Since the property is specifically max-inferred-column-defaults, I 
interpreted the limit as protecting the automatically inferred default behavior 
rather than overriding an explicit global configuration.
   
   That said, I agree that limiting an explicit full default could also be 
useful for controlling manifest size. If we want PyIceberg to behave 
differently here, I’m happy to adjust it, but that would diverge from the 
current Java behavior. Would you prefer that we preserve Java parity for this 
PR?



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