timsaucer commented on code in PR #2298:
URL: https://github.com/apache/iceberg-rust/pull/2298#discussion_r3132655499


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crates/integrations/datafusion/src/table/partitioned.rs:
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@@ -0,0 +1,315 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+use std::any::Any;
+use std::sync::Arc;
+
+use async_trait::async_trait;
+use datafusion::arrow::datatypes::SchemaRef as ArrowSchemaRef;
+use datafusion::catalog::Session;
+use datafusion::common::DataFusionError;
+use datafusion::datasource::{TableProvider, TableType};
+use datafusion::error::Result as DFResult;
+use datafusion::logical_expr::{Expr, TableProviderFilterPushDown};
+use datafusion::physical_plan::ExecutionPlan;
+use futures::TryStreamExt;
+use iceberg::arrow::schema_to_arrow_schema;
+use iceberg::{Catalog, Error, ErrorKind, NamespaceIdent, Result, TableIdent};
+
+use crate::error::to_datafusion_error;
+use crate::physical_plan::expr_to_predicate::convert_filters_to_predicate;
+use crate::physical_plan::partitioned_scan::IcebergPartitionedScan;
+
+/// Catalog-backed table provider that scans each data file in a separate 
DataFusion partition.
+///
+/// This provider reloads table metadata from the catalog on every 
[`scan`][Self::scan] call
+/// to guarantee freshness, then issues one DataFusion partition per data file 
so that
+/// DataFusion's scheduler can execute file reads in parallel.
+///
+/// Write operations are not supported. Use [`IcebergTableProvider`] for write 
access.
+///
+/// For consistent read-only access to a fixed snapshot without per-scan 
catalog overhead,
+/// use [`IcebergStaticTableProvider`] instead.
+#[derive(Debug, Clone)]
+pub struct IcebergPartitionedTableProvider {
+    catalog: Arc<dyn Catalog>,
+    table_ident: TableIdent,
+    schema: ArrowSchemaRef,
+}
+
+impl IcebergPartitionedTableProvider {
+    pub async fn try_new(
+        catalog: Arc<dyn Catalog>,
+        namespace: NamespaceIdent,
+        name: impl Into<String>,
+    ) -> Result<Self> {
+        let table_ident = TableIdent::new(namespace, name.into());
+        // First load: used only to snapshot the Arrow schema for DataFusion 
planning.
+        // A second load_table is issued at scan time to guarantee the 
freshest snapshot.
+        let table = catalog.load_table(&table_ident).await?;
+        let schema = 
Arc::new(schema_to_arrow_schema(table.metadata().current_schema())?);
+        Ok(Self {
+            catalog,
+            table_ident,
+            schema,
+        })
+    }
+}
+
+#[async_trait]
+impl TableProvider for IcebergPartitionedTableProvider {
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    fn schema(&self) -> ArrowSchemaRef {
+        self.schema.clone()
+    }
+
+    fn table_type(&self) -> TableType {
+        TableType::Base
+    }
+
+    async fn scan(
+        &self,
+        _state: &dyn Session,
+        projection: Option<&Vec<usize>>,
+        filters: &[Expr],
+        _limit: Option<usize>,
+    ) -> DFResult<Arc<dyn ExecutionPlan>> {
+        // Per-partition row limits are not yet implemented for 
IcebergPartitionedScan.
+        // DataFusion will apply a GlobalLimitExec on top of this node when 
needed.
+
+        // Second load: fetch the latest snapshot so scans always reflect 
current table state.
+        let table = self
+            .catalog
+            .load_table(&self.table_ident)
+            .await
+            .map_err(to_datafusion_error)?;
+
+        // Projection indices are resolved against self.schema (captured at 
try_new time),
+        // same as IcebergTableProvider / IcebergTableScan.
+        let col_names = projection.map(|indices| {
+            indices
+                .iter()
+                .map(|&i| self.schema.field(i).name().clone())
+                .collect::<Vec<_>>()
+        });
+
+        let predicate = convert_filters_to_predicate(filters);
+
+        let mut builder = table.scan();
+        builder = match col_names {
+            Some(names) => builder.select(names),
+            None => builder.select_all(),
+        };
+        if let Some(pred) = predicate {
+            builder = builder.with_filter(pred);
+        }
+
+        let tasks = builder
+            .build()
+            .map_err(to_datafusion_error)?
+            .plan_files()
+            .await
+            .map_err(to_datafusion_error)?
+            .try_collect::<Vec<_>>()
+            .await
+            .map_err(to_datafusion_error)?;

Review Comment:
   I suppose I'd need to understand those conversations. I think I mentioned in 
one of the other comments on this PR, but I found the whole discussion 
difficult to track. Maybe I can find some time this weekend to look through 
that sized based partitioning they mention.



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