Merge remote-tracking branch 'apache/main' into alamb/advanced_parque…

…t_index

Cargo.toml

@@ -52,7 +52,7 @@ homepage = "https://datafusion.apache.org"
 license = "Apache-2.0"
 readme = "README.md"
 repository = "https://github.com/apache/datafusion"
-rust-version = "1.73"
+rust-version = "1.75"
 version = "39.0.0"
 
 [workspace.dependencies]
@@ -107,7 +107,7 @@ doc-comment = "0.3"
 env_logger = "0.11"
 futures = "0.3"
 half = { version = "2.2.1", default-features = false }
-hashbrown = { version = "0.14", features = ["raw"] }
+hashbrown = { version = "0.14.5", features = ["raw"] }
 indexmap = "2.0.0"
 itertools = "0.12"
 log = "^0.4"

datafusion-cli/Cargo.toml

@@ -26,7 +26,7 @@ license = "Apache-2.0"
 homepage = "https://datafusion.apache.org"
 repository = "https://github.com/apache/datafusion"
 # Specify MSRV here as `cargo msrv` doesn't support workspace version
-rust-version = "1.73"
+rust-version = "1.75"
 readme = "README.md"
 
 [dependencies]

datafusion/core/Cargo.toml

@@ -30,7 +30,7 @@ authors = { workspace = true }
 # Specify MSRV here as `cargo msrv` doesn't support workspace version and fails with
 # "Unable to find key 'package.rust-version' (or 'package.metadata.msrv') in 'arrow-datafusion/Cargo.toml'"
 # https://github.com/foresterre/cargo-msrv/issues/590
-rust-version = "1.73"
+rust-version = "1.75"
 
 [lints]
 workspace = true

datafusion/core/src/dataframe/mod.rs

@@ -50,12 +50,11 @@ use datafusion_common::{
 };
 use datafusion_expr::lit;
 use datafusion_expr::{
-    avg, count, max, min, utils::COUNT_STAR_EXPANSION, TableProviderFilterPushDown,
+    avg, max, min, utils::COUNT_STAR_EXPANSION, TableProviderFilterPushDown,
     UNNAMED_TABLE,
 };
 use datafusion_expr::{case, is_null};
-use datafusion_functions_aggregate::expr_fn::sum;
-use datafusion_functions_aggregate::expr_fn::{median, stddev};
+use datafusion_functions_aggregate::expr_fn::{count, median, stddev, sum};
 
 use async_trait::async_trait;
 
@@ -854,10 +853,7 @@ impl DataFrame {
     /// ```
     pub async fn count(self) -> Result<usize> {
         let rows = self
-            .aggregate(
-                vec![],
-                vec![datafusion_expr::count(Expr::Literal(COUNT_STAR_EXPANSION))],
-            )?
+            .aggregate(vec![], vec![count(Expr::Literal(COUNT_STAR_EXPANSION))])?
             .collect()
             .await?;
         let len = *rows
@@ -1594,9 +1590,10 @@ mod tests {
     use datafusion_common::{Constraint, Constraints};
     use datafusion_common_runtime::SpawnedTask;
     use datafusion_expr::{
-        array_agg, cast, count_distinct, create_udf, expr, lit, BuiltInWindowFunction,
+        array_agg, cast, create_udf, expr, lit, BuiltInWindowFunction,
         ScalarFunctionImplementation, Volatility, WindowFrame, WindowFunctionDefinition,
     };
+    use datafusion_functions_aggregate::expr_fn::count_distinct;
     use datafusion_physical_expr::expressions::Column;
     use datafusion_physical_plan::{get_plan_string, ExecutionPlanProperties};
 

datafusion/core/src/datasource/file_format/parquet.rs

@@ -455,13 +455,26 @@ async fn fetch_schema(
 }
 
 /// Read and parse the statistics of the Parquet file at location `path`
+///
+/// See [`statistics_from_parquet_meta`] for more details
 async fn fetch_statistics(
     store: &dyn ObjectStore,
     table_schema: SchemaRef,
     file: &ObjectMeta,
     metadata_size_hint: Option<usize>,
 ) -> Result<Statistics> {
     let metadata = fetch_parquet_metadata(store, file, metadata_size_hint).await?;
+    statistics_from_parquet_meta(&metadata, table_schema).await
+}
+
+/// Convert statistics in  [`ParquetMetaData`] into [`Statistics`]
+///
+/// The statistics are calculated for each column in the table schema
+/// using the row group statistics in the parquet metadata.
+pub async fn statistics_from_parquet_meta(
+    metadata: &ParquetMetaData,
+    table_schema: SchemaRef,
+) -> Result<Statistics> {
     let file_metadata = metadata.file_metadata();
 
     let file_schema = parquet_to_arrow_schema(
@@ -1402,6 +1415,66 @@ mod tests {
         Ok(())
     }
 
+    #[tokio::test]
+    async fn test_statistics_from_parquet_metadata() -> Result<()> {
+        // Data for column c1: ["Foo", null, "bar"]
+        let c1: ArrayRef =
+            Arc::new(StringArray::from(vec![Some("Foo"), None, Some("bar")]));
+        let batch1 = RecordBatch::try_from_iter(vec![("c1", c1.clone())]).unwrap();
+
+        // Data for column c2: [1, 2, null]
+        let c2: ArrayRef = Arc::new(Int64Array::from(vec![Some(1), Some(2), None]));
+        let batch2 = RecordBatch::try_from_iter(vec![("c2", c2)]).unwrap();
+
+        // Use store_parquet to write each batch to its own file
+        // . batch1 written into first file and includes:
+        //    - column c1 that has 3 rows with one null. Stats min and max of string column is missing for this test even the column has values
+        // . batch2 written into second file and includes:
+        //    - column c2 that has 3 rows with one null. Stats min and max of int are avaialble and 1 and 2 respectively
+        let store = Arc::new(LocalFileSystem::new()) as _;
+        let (files, _file_names) = store_parquet(vec![batch1, batch2], false).await?;
+
+        let state = SessionContext::new().state();
+        let format = ParquetFormat::default();
+        let schema = format.infer_schema(&state, &store, &files).await.unwrap();
+
+        let null_i64 = ScalarValue::Int64(None);
+        let null_utf8 = ScalarValue::Utf8(None);
+
+        // Fetch statistics for first file
+        let pq_meta = fetch_parquet_metadata(store.as_ref(), &files[0], None).await?;
+        let stats = statistics_from_parquet_meta(&pq_meta, schema.clone()).await?;
+        //
+        assert_eq!(stats.num_rows, Precision::Exact(3));
+        // column c1
+        let c1_stats = &stats.column_statistics[0];
+        assert_eq!(c1_stats.null_count, Precision::Exact(1));
+        assert_eq!(c1_stats.max_value, Precision::Absent);
+        assert_eq!(c1_stats.min_value, Precision::Absent);
+        // column c2: missing from the file so the table treats all 3 rows as null
+        let c2_stats = &stats.column_statistics[1];
+        assert_eq!(c2_stats.null_count, Precision::Exact(3));
+        assert_eq!(c2_stats.max_value, Precision::Exact(null_i64.clone()));
+        assert_eq!(c2_stats.min_value, Precision::Exact(null_i64.clone()));
+
+        // Fetch statistics for second file
+        let pq_meta = fetch_parquet_metadata(store.as_ref(), &files[1], None).await?;
+        let stats = statistics_from_parquet_meta(&pq_meta, schema.clone()).await?;
+        assert_eq!(stats.num_rows, Precision::Exact(3));
+        // column c1: missing from the file so the table treats all 3 rows as null
+        let c1_stats = &stats.column_statistics[0];
+        assert_eq!(c1_stats.null_count, Precision::Exact(3));
+        assert_eq!(c1_stats.max_value, Precision::Exact(null_utf8.clone()));
+        assert_eq!(c1_stats.min_value, Precision::Exact(null_utf8.clone()));
+        // column c2
+        let c2_stats = &stats.column_statistics[1];
+        assert_eq!(c2_stats.null_count, Precision::Exact(1));
+        assert_eq!(c2_stats.max_value, Precision::Exact(2i64.into()));
+        assert_eq!(c2_stats.min_value, Precision::Exact(1i64.into()));
+
+        Ok(())
+    }
+
     #[tokio::test]
     async fn read_small_batches() -> Result<()> {
         let config = SessionConfig::new().with_batch_size(2);

datafusion/core/src/datasource/physical_plan/parquet/access_plan.rs

@@ -389,7 +389,7 @@ mod test {
         let access_plan = ParquetAccessPlan::new(vec![
             RowGroupAccess::Scan,
             RowGroupAccess::Selection(
-                // select / skip all 20 rows in row group 1
+                // specifies all 20 rows in row group 1
                 vec![
                     RowSelector::select(5),
                     RowSelector::skip(7),
@@ -468,7 +468,7 @@ mod test {
     fn test_invalid_too_few() {
         let access_plan = ParquetAccessPlan::new(vec![
             RowGroupAccess::Scan,
-            // select 12 rows, but row group 1 has 20
+            // specify only 12 rows in selection, but row group 1 has 20
             RowGroupAccess::Selection(
                 vec![RowSelector::select(5), RowSelector::skip(7)].into(),
             ),
@@ -489,7 +489,7 @@ mod test {
     fn test_invalid_too_many() {
         let access_plan = ParquetAccessPlan::new(vec![
             RowGroupAccess::Scan,
-            // select 22 rows, but row group 1 has only 20
+            // specify 22 rows in selection, but row group 1 has only 20
             RowGroupAccess::Selection(
                 vec![
                     RowSelector::select(10),

datafusion/core/src/datasource/physical_plan/parquet/mod.rs

@@ -156,9 +156,8 @@ pub use writer::plan_to_parquet;
 /// used to implement external indexes on top of parquet files and select only
 /// portions of the files.
 ///
-/// The `ParquetExec` will try and further reduce any provided
-/// `ParquetAccessPlan` further based on the contents of `ParquetMetadata` and
-/// other settings.
+/// The `ParquetExec` will try and reduce any provided `ParquetAccessPlan`
+/// further based on the contents of `ParquetMetadata` and other settings.
 ///
 /// ## Example of providing a ParquetAccessPlan
 ///

datafusion/core/src/datasource/physical_plan/parquet/opener.rs

@@ -238,6 +238,8 @@ fn create_initial_plan(
 
             // check row group count matches the plan
             return Ok(access_plan.clone());
+        } else {
+            debug!("ParquetExec Ignoring unknown extension specified for {file_name}");
         }
     }