datafusion 31.0.0

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// to you under the Apache License, Version 2.0 (the
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//
//   http://www.apache.org/licenses/LICENSE-2.0
//
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// software distributed under the License is distributed on an
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// specific language governing permissions and limitations
// under the License.

//! Execution plan for reading in-memory batches of data

use super::expressions::PhysicalSortExpr;
use super::{
    common, DisplayAs, DisplayFormatType, ExecutionPlan, Partitioning, RecordBatchStream,
    SendableRecordBatchStream, Statistics,
};
use arrow::datatypes::SchemaRef;
use arrow::record_batch::RecordBatch;
use core::fmt;
use datafusion_common::{internal_err, project_schema, Result};
use std::any::Any;
use std::sync::Arc;
use std::task::{Context, Poll};

use crate::physical_plan::ordering_equivalence_properties_helper;
use datafusion_common::DataFusionError;
use datafusion_execution::TaskContext;
use datafusion_physical_expr::{LexOrdering, OrderingEquivalenceProperties};
use futures::Stream;

/// Execution plan for reading in-memory batches of data
pub struct MemoryExec {
    /// The partitions to query
    partitions: Vec<Vec<RecordBatch>>,
    /// Schema representing the data before projection
    schema: SchemaRef,
    /// Schema representing the data after the optional projection is applied
    projected_schema: SchemaRef,
    /// Optional projection
    projection: Option<Vec<usize>>,
    // Sort information: one or more equivalent orderings
    sort_information: Vec<LexOrdering>,
}

impl fmt::Debug for MemoryExec {
    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
        write!(f, "partitions: [...]")?;
        write!(f, "schema: {:?}", self.projected_schema)?;
        write!(f, "projection: {:?}", self.projection)?;
        if let Some(sort_info) = &self.sort_information.get(0) {
            write!(f, ", output_ordering: {:?}", sort_info)?;
        }
        Ok(())
    }
}

impl DisplayAs for MemoryExec {
    fn fmt_as(
        &self,
        t: DisplayFormatType,
        f: &mut std::fmt::Formatter,
    ) -> std::fmt::Result {
        match t {
            DisplayFormatType::Default | DisplayFormatType::Verbose => {
                let partition_sizes: Vec<_> =
                    self.partitions.iter().map(|b| b.len()).collect();

                let output_ordering = self
                    .sort_information
                    .first()
                    .map(|output_ordering| {
                        let order_strings: Vec<_> =
                            output_ordering.iter().map(|e| e.to_string()).collect();
                        format!(", output_ordering={}", order_strings.join(","))
                    })
                    .unwrap_or_else(|| "".to_string());

                write!(
                    f,
                    "MemoryExec: partitions={}, partition_sizes={partition_sizes:?}{output_ordering}",
                    partition_sizes.len(),
                )
            }
        }
    }
}

impl ExecutionPlan for MemoryExec {
    /// Return a reference to Any that can be used for downcasting
    fn as_any(&self) -> &dyn Any {
        self
    }

    /// Get the schema for this execution plan
    fn schema(&self) -> SchemaRef {
        self.projected_schema.clone()
    }

    fn children(&self) -> Vec<Arc<dyn ExecutionPlan>> {
        // this is a leaf node and has no children
        vec![]
    }

    /// Get the output partitioning of this plan
    fn output_partitioning(&self) -> Partitioning {
        Partitioning::UnknownPartitioning(self.partitions.len())
    }

    fn output_ordering(&self) -> Option<&[PhysicalSortExpr]> {
        self.sort_information
            .first()
            .map(|ordering| ordering.as_slice())
    }

    fn ordering_equivalence_properties(&self) -> OrderingEquivalenceProperties {
        ordering_equivalence_properties_helper(self.schema(), &self.sort_information)
    }

    fn with_new_children(
        self: Arc<Self>,
        _: Vec<Arc<dyn ExecutionPlan>>,
    ) -> Result<Arc<dyn ExecutionPlan>> {
        internal_err!("Children cannot be replaced in {self:?}")
    }

    fn execute(
        &self,
        partition: usize,
        _context: Arc<TaskContext>,
    ) -> Result<SendableRecordBatchStream> {
        Ok(Box::pin(MemoryStream::try_new(
            self.partitions[partition].clone(),
            self.projected_schema.clone(),
            self.projection.clone(),
        )?))
    }

    /// We recompute the statistics dynamically from the arrow metadata as it is pretty cheap to do so
    fn statistics(&self) -> Statistics {
        common::compute_record_batch_statistics(
            &self.partitions,
            &self.schema,
            self.projection.clone(),
        )
    }
}

impl MemoryExec {
    /// Create a new execution plan for reading in-memory record batches
    /// The provided `schema` should not have the projection applied.
    pub fn try_new(
        partitions: &[Vec<RecordBatch>],
        schema: SchemaRef,
        projection: Option<Vec<usize>>,
    ) -> Result<Self> {
        let projected_schema = project_schema(&schema, projection.as_ref())?;
        Ok(Self {
            partitions: partitions.to_vec(),
            schema,
            projected_schema,
            projection,
            sort_information: vec![],
        })
    }

    /// A memory table can be ordered by multiple expressions simultaneously.
    /// `OrderingEquivalenceProperties` keeps track of expressions that describe the
    /// global ordering of the schema. These columns are not necessarily same; e.g.
    /// ```text
    /// ┌-------┐
    /// | a | b |
    /// |---|---|
    /// | 1 | 9 |
    /// | 2 | 8 |
    /// | 3 | 7 |
    /// | 5 | 5 |
    /// └---┴---┘
    /// ```
    /// where both `a ASC` and `b DESC` can describe the table ordering. With
    /// `OrderingEquivalenceProperties`, we can keep track of these equivalences
    /// and treat `a ASC` and `b DESC` as the same ordering requirement
    /// by outputting the `a ASC` from output_ordering API
    /// and add `b DESC` into `OrderingEquivalenceProperties`
    pub fn with_sort_information(mut self, sort_information: Vec<LexOrdering>) -> Self {
        self.sort_information = sort_information;
        self
    }
}

/// Iterator over batches
pub struct MemoryStream {
    /// Vector of record batches
    data: Vec<RecordBatch>,
    /// Schema representing the data
    schema: SchemaRef,
    /// Optional projection for which columns to load
    projection: Option<Vec<usize>>,
    /// Index into the data
    index: usize,
}

impl MemoryStream {
    /// Create an iterator for a vector of record batches
    pub fn try_new(
        data: Vec<RecordBatch>,
        schema: SchemaRef,
        projection: Option<Vec<usize>>,
    ) -> Result<Self> {
        Ok(Self {
            data,
            schema,
            projection,
            index: 0,
        })
    }
}

impl Stream for MemoryStream {
    type Item = Result<RecordBatch>;

    fn poll_next(
        mut self: std::pin::Pin<&mut Self>,
        _: &mut Context<'_>,
    ) -> Poll<Option<Self::Item>> {
        Poll::Ready(if self.index < self.data.len() {
            self.index += 1;
            let batch = &self.data[self.index - 1];

            // return just the columns requested
            let batch = match self.projection.as_ref() {
                Some(columns) => batch.project(columns)?,
                None => batch.clone(),
            };

            Some(Ok(batch))
        } else {
            None
        })
    }

    fn size_hint(&self) -> (usize, Option<usize>) {
        (self.data.len(), Some(self.data.len()))
    }
}

impl RecordBatchStream for MemoryStream {
    /// Get the schema
    fn schema(&self) -> SchemaRef {
        self.schema.clone()
    }
}

#[cfg(test)]
mod tests {
    use crate::physical_plan::memory::MemoryExec;
    use crate::physical_plan::ExecutionPlan;
    use arrow_schema::{DataType, Field, Schema, SortOptions};
    use datafusion_physical_expr::expressions::col;
    use datafusion_physical_expr::PhysicalSortExpr;
    use std::sync::Arc;

    #[test]
    fn test_memory_order_eq() -> datafusion_common::Result<()> {
        let schema = Arc::new(Schema::new(vec![
            Field::new("a", DataType::Int64, false),
            Field::new("b", DataType::Int64, false),
            Field::new("c", DataType::Int64, false),
        ]));
        let expected_output_order = vec![
            PhysicalSortExpr {
                expr: col("a", &schema)?,
                options: SortOptions::default(),
            },
            PhysicalSortExpr {
                expr: col("b", &schema)?,
                options: SortOptions::default(),
            },
        ];
        let expected_order_eq = vec![PhysicalSortExpr {
            expr: col("c", &schema)?,
            options: SortOptions::default(),
        }];
        let sort_information =
            vec![expected_output_order.clone(), expected_order_eq.clone()];
        let mem_exec = MemoryExec::try_new(&[vec![]], schema, None)?
            .with_sort_information(sort_information);

        assert_eq!(mem_exec.output_ordering().unwrap(), expected_output_order);
        let order_eq = mem_exec.ordering_equivalence_properties();
        assert!(order_eq
            .classes()
            .iter()
            .any(|class| class.contains(&expected_order_eq)));
        Ok(())
    }
}