done

Author: DrakeLin

kernel/examples/checkpoint-table/src/main.rs

@@ -7,9 +7,10 @@ use futures::future::{BoxFuture, FutureExt};
 use parquet::arrow::async_writer::{AsyncFileWriter, ParquetObjectWriter};
 use parquet::arrow::AsyncArrowWriter;
 
+use delta_kernel::checkpoint::CheckpointDataIterator;
 use delta_kernel::engine::arrow_data::EngineDataArrowExt;
 use delta_kernel::engine::default::DefaultEngineBuilder;
-use delta_kernel::{ActionReconciliationIterator, DeltaResult, Error, FileMeta, Snapshot};
+use delta_kernel::{DeltaResult, Error, FileMeta, Snapshot};
 
 /// An example program that checkpoints a table.
 /// !!!WARNING!!!: This doesn't use put-if-absent, or a catalog based commit, so it is UNSAFE.
@@ -44,7 +45,7 @@ async fn main() -> ExitCode {
 
 async fn write_data<W: AsyncFileWriter>(
     first_batch: &RecordBatch,
-    batch_iter: &mut ActionReconciliationIterator,
+    batch_iter: &mut impl CheckpointDataIterator,
     parquet_writer: &mut AsyncArrowWriter<W>,
 ) -> DeltaResult<()> {
     parquet_writer.write(first_batch).await?;

kernel/src/checkpoint/mod.rs

@@ -16,7 +16,7 @@
 //! ## Architecture
 //!
 //! - [`CheckpointWriter`] - Core component that manages the checkpoint creation workflow
-//! - [`ActionReconciliationIterator`] - Iterator over the checkpoint data to be written
+//! - [`CheckpointDataIterator`] - Trait for iterators over checkpoint data to be written
 //!
 //! ## Usage
 //!
@@ -33,14 +33,15 @@
 //! # use std::sync::Arc;
 //! # use delta_kernel::ActionReconciliationIterator;
 //! # use delta_kernel::checkpoint::CheckpointWriter;
+//! # use delta_kernel::checkpoint::CheckpointDataIterator;
 //! # use delta_kernel::Engine;
 //! # use delta_kernel::Snapshot;
 //! # use delta_kernel::SnapshotRef;
 //! # use delta_kernel::DeltaResult;
 //! # use delta_kernel::Error;
 //! # use delta_kernel::FileMeta;
 //! # use url::Url;
-//! fn write_checkpoint_file(path: Url, data: &ActionReconciliationIterator) -> DeltaResult<FileMeta> {
+//! fn write_checkpoint_file(path: Url, data: &impl CheckpointDataIterator) -> DeltaResult<FileMeta> {
 //!     todo!() /* engine-specific logic to write data to object storage*/
 //! }
 //!
@@ -109,6 +110,11 @@ use url::Url;
 
 mod stats_transform;
 
+use stats_transform::{
+    build_checkpoint_output_schema, build_checkpoint_read_schema_with_stats, build_stats_transform,
+    StatsTransformConfig,
+};
+
 #[cfg(test)]
 mod tests;
 
@@ -126,8 +132,8 @@ static LAST_CHECKPOINT_SCHEMA: LazyLock<SchemaRef> = LazyLock::new(|| {
     .into()
 });
 
-/// Schema for extracting relevant actions from log files for checkpoint creation
-static CHECKPOINT_ACTIONS_SCHEMA: LazyLock<SchemaRef> = LazyLock::new(|| {
+/// Schema for V1 checkpoints (without checkpointMetadata action)
+static CHECKPOINT_ACTIONS_SCHEMA_V1: LazyLock<SchemaRef> = LazyLock::new(|| {
     Arc::new(StructType::new_unchecked([
         StructField::nullable(ADD_NAME, Add::to_schema()),
         StructField::nullable(REMOVE_NAME, Remove::to_schema()),
@@ -138,16 +144,131 @@ static CHECKPOINT_ACTIONS_SCHEMA: LazyLock<SchemaRef> = LazyLock::new(|| {
     ]))
 });
 
-// Schema of the [`CheckpointMetadata`] action that is included in V2 checkpoints
-// We cannot use `CheckpointMetadata::to_schema()` as it would include the 'tags' field which
-// we're not supporting yet due to the lack of map support TODO(#880).
-static CHECKPOINT_METADATA_ACTION_SCHEMA: LazyLock<SchemaRef> = LazyLock::new(|| {
-    Arc::new(StructType::new_unchecked([StructField::nullable(
+/// Schema for the checkpointMetadata field in V2 checkpoints.
+/// We cannot use `CheckpointMetadata::to_schema()` as it would include the 'tags' field which
+/// we're not supporting yet due to the lack of map support TODO(#880).
+fn checkpoint_metadata_field() -> StructField {
+    StructField::nullable(
         CHECKPOINT_METADATA_NAME,
         DataType::struct_type_unchecked([StructField::not_null("version", DataType::LONG)]),
-    )]))
+    )
+}
+
+/// Schema for V2 checkpoints (includes checkpointMetadata action)
+static CHECKPOINT_ACTIONS_SCHEMA_V2: LazyLock<SchemaRef> = LazyLock::new(|| {
+    Arc::new(StructType::new_unchecked([
+        StructField::nullable(ADD_NAME, Add::to_schema()),
+        StructField::nullable(REMOVE_NAME, Remove::to_schema()),
+        StructField::nullable(METADATA_NAME, Metadata::to_schema()),
+        StructField::nullable(PROTOCOL_NAME, Protocol::to_schema()),
+        StructField::nullable(SET_TRANSACTION_NAME, SetTransaction::to_schema()),
+        StructField::nullable(SIDECAR_NAME, Sidecar::to_schema()),
+        checkpoint_metadata_field(),
+    ]))
 });
 
+/// Trait for iterators that yield checkpoint data batches.
+///
+/// This trait abstracts over checkpoint data iterators, allowing the concrete implementation
+/// to change without breaking the public API. Implementations yield [`FilteredEngineData`]
+/// batches that should be written to the checkpoint file.
+///
+/// # Yielded Data
+///
+/// All batches conform to [`output_schema()`][Self::output_schema], which is determined by:
+/// - **V1 checkpoints**: Schema based on [`CHECKPOINT_ACTIONS_SCHEMA_V1`] (add, remove, metadata,
+///   protocol, txn, sidecar)
+/// - **V2 checkpoints**: Schema based on [`CHECKPOINT_ACTIONS_SCHEMA_V2`] (same as V1 plus
+///   checkpointMetadata)
+///
+/// The `add.stats` and `add.stats_parsed` fields are included or excluded based on table
+/// properties (`delta.checkpoint.writeStatsAsJson` and `delta.checkpoint.writeStatsAsStruct`).
+///
+/// For V2 checkpoints, the final batch contains the checkpoint metadata action with all other
+/// action fields set to null.
+pub trait CheckpointDataIterator: Iterator<Item = DeltaResult<FilteredEngineData>> {
+    /// Returns the schema for writing checkpoint data.
+    ///
+    /// All batches from this iterator conform to this schema. The schema reflects:
+    /// - V1 vs V2 checkpoint format (V2 includes `checkpointMetadata` field)
+    /// - Stats configuration (`stats` and/or `stats_parsed` fields)
+    fn output_schema(&self) -> &SchemaRef;
+
+    /// Returns true if the iterator has been fully consumed.
+    fn is_exhausted(&self) -> bool;
+
+    /// Returns the total count of actions processed.
+    fn actions_count(&self) -> i64;
+
+    /// Returns the count of add actions processed.
+    fn add_actions_count(&self) -> i64;
+}
+
+/// Iterator that applies stats transforms to checkpoint data batches.
+///
+/// This is the concrete implementation of [`CheckpointDataIterator`] that wraps an
+/// [`ActionReconciliationIterator`] and applies an expression evaluator to each batch
+/// to populate stats fields.
+///
+/// All batches (including the checkpoint metadata batch for V2 checkpoints) share the
+/// same schema and go through the same transform pipeline. The stats transform only
+/// operates on the `add` field, so other fields (including `checkpointMetadata`) pass
+/// through unchanged.
+pub struct TransformingCheckpointIterator {
+    inner: ActionReconciliationIterator,
+    evaluator: Arc<dyn crate::ExpressionEvaluator>,
+    /// Schema for writing checkpoint data (includes/excludes stats fields based on config)
+    output_schema: SchemaRef,
+}
+
+impl TransformingCheckpointIterator {
+    /// Creates a new transforming iterator.
+    pub(crate) fn new(
+        inner: ActionReconciliationIterator,
+        evaluator: Arc<dyn crate::ExpressionEvaluator>,
+        output_schema: SchemaRef,
+    ) -> Self {
+        Self {
+            inner,
+            evaluator,
+            output_schema,
+        }
+    }
+}
+
+impl CheckpointDataIterator for TransformingCheckpointIterator {
+    fn output_schema(&self) -> &SchemaRef {
+        &self.output_schema
+    }
+
+    fn is_exhausted(&self) -> bool {
+        self.inner.is_exhausted()
+    }
+
+    fn actions_count(&self) -> i64 {
+        self.inner.actions_count()
+    }
+
+    fn add_actions_count(&self) -> i64 {
+        self.inner.add_actions_count()
+    }
+}
+
+impl Iterator for TransformingCheckpointIterator {
+    type Item = DeltaResult<FilteredEngineData>;
+
+    fn next(&mut self) -> Option<Self::Item> {
+        let batch = self.inner.next()?;
+
+        // Apply the transform to the batch
+        Some(batch.and_then(|filtered_data| {
+            let (engine_data, selection_vector) = filtered_data.into_parts();
+            let transformed = self.evaluator.evaluate(engine_data.as_ref())?;
+            FilteredEngineData::try_new(transformed, selection_vector)
+        }))
+    }
+}
+
 /// Orchestrates the process of creating a checkpoint for a table.
 ///
 /// The [`CheckpointWriter`] is the entry point for generating checkpoint data for a Delta table.
@@ -213,50 +334,91 @@ impl CheckpointWriter {
     }
     /// Returns the checkpoint data to be written to the checkpoint file.
     ///
-    /// This method reads the actions from the log segment and processes them
-    /// to create the checkpoint data.
+    /// This method reads actions from the log segment, processes them for checkpoint creation,
+    /// and applies stats transforms based on table properties:
+    /// - `delta.checkpoint.writeStatsAsJson` (default: true)
+    /// - `delta.checkpoint.writeStatsAsStruct` (default: false)
     ///
-    /// # Parameters
-    /// - `engine`: Implementation of [`Engine`] APIs.
+    /// The returned iterator (implementing [`CheckpointDataIterator`]) yields batches with stats
+    /// transforms already applied. Use [`CheckpointDataIterator::output_schema`] to get the
+    /// schema for writing the checkpoint file.
     ///
-    /// # Returns: [`ActionReconciliationIterator`] containing the checkpoint data
-    // This method is the core of the checkpoint generation process. It:
-    // 1. Determines whether to write a V1 or V2 checkpoint based on the table's
-    //    `v2Checkpoints` feature support
-    // 2. Reads actions from the log segment using the checkpoint read schema
-    // 3. Filters and deduplicates actions for the checkpoint
-    // 4. Chains the checkpoint metadata action if writing a V2 spec checkpoint
-    //    (i.e., if `v2Checkpoints` feature is supported by table)
-    // 5. Generates the appropriate checkpoint path
-    pub fn checkpoint_data(
-        &self,
-        engine: &dyn Engine,
-    ) -> DeltaResult<ActionReconciliationIterator> {
+    /// # Engine Usage
+    ///
+    /// ```ignore
+    /// let mut checkpoint_data = writer.checkpoint_data(&engine)?;
+    /// let output_schema = checkpoint_data.output_schema().clone();
+    /// while let Some(batch) = checkpoint_data.next() {
+    ///     let data = batch?.apply_selection_vector()?;
+    ///     parquet_writer.write(&data, &output_schema).await?;
+    /// }
+    /// writer.finalize(&engine, &metadata, checkpoint_data)?;
+    /// ```
+    pub fn checkpoint_data(&self, engine: &dyn Engine) -> DeltaResult<impl CheckpointDataIterator> {
+        let config = StatsTransformConfig::from_table_properties(self.snapshot.table_properties());
+
+        // Get stats schema from table configuration.
+        // This already excludes partition columns and applies column mapping.
+        let stats_schema = self
+            .snapshot
+            .table_configuration()
+            .expected_stats_schema()?;
+
+        // Select schema based on V2 checkpoint support
         let is_v2_checkpoints_supported = self
             .snapshot
             .table_configuration()
             .is_feature_supported(&TableFeature::V2Checkpoint);
 
-        let actions = self.snapshot.log_segment().read_actions(
-            engine,
-            CHECKPOINT_ACTIONS_SCHEMA.clone(),
-            None,
-        )?;
+        let base_schema = if is_v2_checkpoints_supported {
+            &CHECKPOINT_ACTIONS_SCHEMA_V2
+        } else {
+            &CHECKPOINT_ACTIONS_SCHEMA_V1
+        };
+
+        // Read schema includes stats_parsed so COALESCE expressions can operate on it.
+        // For commits, stats_parsed will be read as nulls (column doesn't exist in source).
+        let read_schema = build_checkpoint_read_schema_with_stats(base_schema, &stats_schema);
 
-        // Create iterator over actions for checkpoint data
+        // Read actions from log segment
+        let actions =
+            self.snapshot
+                .log_segment()
+                .read_actions(engine, read_schema.clone(), None)?;
+
+        // Process actions through reconciliation
         let checkpoint_data = ActionReconciliationProcessor::new(
             self.deleted_file_retention_timestamp()?,
             self.get_transaction_expiration_timestamp()?,
         )
         .process_actions_iter(actions);
 
-        let checkpoint_metadata =
-            is_v2_checkpoints_supported.then(|| self.create_checkpoint_metadata_batch(engine));
+        // Build output schema based on stats config (determines which fields are included)
+        let output_schema = build_checkpoint_output_schema(&config, base_schema, &stats_schema);
+
+        // Build transform expression and create expression evaluator
+        let transform_expr = build_stats_transform(&config, stats_schema);
+        let evaluator = engine.evaluation_handler().new_expression_evaluator(
+            read_schema.clone(),
+            transform_expr,
+            output_schema.clone().into(),
+        )?;
+
+        // For V2 checkpoints, chain the checkpoint metadata batch to the action stream.
+        // The checkpoint metadata batch uses the read schema (with stats_parsed), so it can
+        // go through the same stats transform pipeline.
+        let checkpoint_metadata = is_v2_checkpoints_supported
+            .then(|| self.create_checkpoint_metadata_batch(engine, &read_schema));
 
-        // Wrap the iterator to track action counts
-        Ok(ActionReconciliationIterator::new(Box::new(
-            checkpoint_data.chain(checkpoint_metadata),
-        )))
+        // Create action reconciliation iterator, chaining checkpoint metadata for V2
+        let inner =
+            ActionReconciliationIterator::new(Box::new(checkpoint_data.chain(checkpoint_metadata)));
+
+        Ok(TransformingCheckpointIterator::new(
+            inner,
+            evaluator,
+            output_schema,
+        ))
     }
 
     /// Finalizes checkpoint creation by saving metadata about the checkpoint.
@@ -280,7 +442,7 @@ impl CheckpointWriter {
         self,
         engine: &dyn Engine,
         metadata: &FileMeta,
-        checkpoint_data: ActionReconciliationIterator,
+        checkpoint_data: impl CheckpointDataIterator,
     ) -> DeltaResult<()> {
         // Ensure the checkpoint data iterator is fully exhausted
         if !checkpoint_data.is_exhausted() {
@@ -325,23 +487,49 @@ impl CheckpointWriter {
     ///
     /// # Implementation Details
     ///
-    /// The function creates a single-row [`EngineData`] batch containing only the
-    /// version field of the [`CheckpointMetadata`] action. Future implementations will
-    /// include the additional metadata field `tags` when map support is added.
+    /// The function creates a single-row [`EngineData`] batch using the full V2 checkpoint
+    /// schema, with all action fields (add, remove, etc.) set to null except for the
+    /// `checkpointMetadata` field. This ensures the checkpoint metadata batch has the same
+    /// schema as other action batches, allowing them to be written to the same Parquet file.
     ///
     /// # Returns:
     /// A [`ActionReconciliationBatch`] batch including the single-row [`EngineData`] batch along with
     /// an accompanying selection vector with a single `true` value, indicating the action in
     /// batch should be included in the checkpoint.
+    /// Creates the checkpoint metadata batch with the given schema.
+    ///
+    /// The schema must be the read schema (with stats_parsed) so the batch can go through
+    /// the same stats transform pipeline as regular action batches.
     fn create_checkpoint_metadata_batch(
         &self,
         engine: &dyn Engine,
+        schema: &SchemaRef,
     ) -> DeltaResult<ActionReconciliationBatch> {
-        let checkpoint_metadata_batch = engine.evaluation_handler().create_one(
-            CHECKPOINT_METADATA_ACTION_SCHEMA.clone(),
-            &[Scalar::from(self.version)],
+        use crate::expressions::{Expression, StructData, Transform};
+
+        // Start with an all-null row
+        let null_row = engine.evaluation_handler().null_row(schema.clone())?;
+
+        // Build the checkpointMetadata struct value
+        let checkpoint_metadata_value = Scalar::Struct(StructData::try_new(
+            vec![StructField::not_null("version", DataType::LONG)],
+            vec![Scalar::from(self.version)],
+        )?);
+
+        // Use a Transform to set just the checkpointMetadata field, keeping others null
+        let transform = Transform::new_top_level().with_replaced_field(
+            CHECKPOINT_METADATA_NAME,
+            Arc::new(Expression::literal(checkpoint_metadata_value)),
+        );
+
+        let evaluator = engine.evaluation_handler().new_expression_evaluator(
+            schema.clone(),
+            Arc::new(Expression::transform(transform)),
+            schema.clone().into(),
         )?;
 
+        let checkpoint_metadata_batch = evaluator.evaluate(null_row.as_ref())?;
+
         let filtered_data = FilteredEngineData::with_all_rows_selected(checkpoint_metadata_batch);
 
         Ok(ActionReconciliationBatch {

kernel/src/checkpoint/stats_transform.rs

@@ -1,4 +1,3 @@
-#![allow(dead_code)] // TODO: Remove when integrated in checkpoint_data()
 //! Transforms for populating stats_parsed and stats fields in checkpoint data.
 //!
 //! When writing checkpoints, statistics can be stored in two formats: