// 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.

//! A memory-conscious aggregation implementation that limits group buckets to a fixed number

use crate::aggregates::group_values::GroupByMetrics;
use crate::aggregates::topk::priority_map::PriorityMap;
#[cfg(debug_assertions)]
use crate::aggregates::topk_types_supported;
use crate::aggregates::{
    AggregateExec, PhysicalGroupBy, aggregate_expressions, evaluate_group_by,
    evaluate_many,
};
use crate::metrics::BaselineMetrics;
use crate::{RecordBatchStream, SendableRecordBatchStream};
use arrow::array::{Array, ArrayRef, RecordBatch};
use arrow::datatypes::SchemaRef;
use arrow::util::pretty::print_batches;
use datafusion_common::Result;
use datafusion_common::internal_datafusion_err;
use datafusion_execution::TaskContext;
use datafusion_physical_expr::PhysicalExpr;
use datafusion_physical_expr_common::metrics::RecordOutput;
use futures::stream::{Stream, StreamExt};
use log::{Level, trace};
use std::pin::Pin;
use std::sync::Arc;
use std::task::{Context, Poll};

pub struct GroupedTopKAggregateStream {
    partition: usize,
    row_count: usize,
    started: bool,
    schema: SchemaRef,
    input: SendableRecordBatchStream,
    baseline_metrics: BaselineMetrics,
    group_by_metrics: GroupByMetrics,
    aggregate_arguments: Vec<Vec<Arc<dyn PhysicalExpr>>>,
    group_by: Arc<PhysicalGroupBy>,
    priority_map: PriorityMap,
}

impl GroupedTopKAggregateStream {
    pub fn new(
        aggr: &AggregateExec,
        context: &Arc<TaskContext>,
        partition: usize,
        limit: usize,
    ) -> Result<Self> {
        let agg_schema = Arc::clone(&aggr.schema);
        let group_by = Arc::clone(&aggr.group_by);
        let input = aggr.input.execute(partition, Arc::clone(context))?;
        let baseline_metrics = BaselineMetrics::new(&aggr.metrics, partition);
        let group_by_metrics = GroupByMetrics::new(&aggr.metrics, partition);
        let aggregate_arguments =
            aggregate_expressions(&aggr.aggr_expr, &aggr.mode, group_by.expr.len())?;

        let (expr, _) = &aggr.group_expr().expr()[0];
        let kt = expr.data_type(&aggr.input().schema())?;

        // Check if this is a MIN/MAX aggregate or a DISTINCT-like operation
        let (vt, desc) = if let Some((val_field, desc)) = aggr.get_minmax_desc() {
            // MIN/MAX case: use the aggregate output type
            (val_field.data_type().clone(), desc)
        } else {
            // DISTINCT case: use the group key type and get ordering from limit_order_descending
            // The ordering direction is set by the optimizer when it pushes down the limit
            let desc = aggr
                .limit_options()
                .and_then(|config| config.descending)
                .ok_or_else(|| {
                    internal_datafusion_err!(
                        "Ordering direction required for DISTINCT with limit"
                    )
                })?;
            (kt.clone(), desc)
        };

        // Type validation is performed by the optimizer and can_use_topk() check.
        // This debug assertion documents the contract without runtime overhead in release builds.
        #[cfg(debug_assertions)]
        {
            debug_assert!(
                topk_types_supported(&kt, &vt),
                "TopK type validation should have been performed by optimizer and can_use_topk(). \
                 Found unsupported types: key={kt:?}, value={vt:?}"
            );
        }

        // Note: Null values in aggregate columns are filtered by the aggregation layer
        // before reaching the heap, so the heap implementations don't need explicit null handling.
        let priority_map = PriorityMap::new(kt, vt, limit, desc)?;

        Ok(GroupedTopKAggregateStream {
            partition,
            started: false,
            row_count: 0,
            schema: agg_schema,
            input,
            baseline_metrics,
            group_by_metrics,
            aggregate_arguments,
            group_by,
            priority_map,
        })
    }
}

impl RecordBatchStream for GroupedTopKAggregateStream {
    fn schema(&self) -> SchemaRef {
        Arc::clone(&self.schema)
    }
}

impl GroupedTopKAggregateStream {
    fn intern(&mut self, ids: &ArrayRef, vals: &ArrayRef) -> Result<()> {
        let _timer = self.group_by_metrics.time_calculating_group_ids.timer();

        let len = ids.len();
        self.priority_map
            .set_batch(Arc::clone(ids), Arc::clone(vals));

        let has_nulls = vals.null_count() > 0;
        for row_idx in 0..len {
            if has_nulls && vals.is_null(row_idx) {
                continue;
            }
            self.priority_map.insert(row_idx)?;
        }
        Ok(())
    }
}

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

    fn poll_next(
        mut self: Pin<&mut Self>,
        cx: &mut Context<'_>,
    ) -> Poll<Option<Self::Item>> {
        let elapsed_compute = self.baseline_metrics.elapsed_compute().clone();
        let emitting_time = self.group_by_metrics.emitting_time.clone();
        while let Poll::Ready(res) = self.input.poll_next_unpin(cx) {
            let _timer = elapsed_compute.timer();
            match res {
                // got a batch, convert to rows and append to our TreeMap
                Some(Ok(batch)) => {
                    self.started = true;
                    trace!(
                        "partition {} has {} rows and got batch with {} rows",
                        self.partition,
                        self.row_count,
                        batch.num_rows()
                    );
                    if log::log_enabled!(Level::Trace) && batch.num_rows() < 20 {
                        print_batches(std::slice::from_ref(&batch))?;
                    }
                    self.row_count += batch.num_rows();
                    let batches = &[batch];
                    let group_by_values =
                        evaluate_group_by(&self.group_by, batches.first().unwrap())?;
                    assert_eq!(
                        group_by_values.len(),
                        1,
                        "Exactly 1 group value required"
                    );
                    assert_eq!(
                        group_by_values[0].len(),
                        1,
                        "Exactly 1 group value required"
                    );
                    let group_by_values = Arc::clone(&group_by_values[0][0]);
                    let input_values = if self.aggregate_arguments.is_empty() {
                        // DISTINCT case: use group key as both key and value
                        Arc::clone(&group_by_values)
                    } else {
                        // MIN/MAX case: evaluate aggregate expressions
                        let _timer =
                            self.group_by_metrics.aggregate_arguments_time.timer();
                        let input_values = evaluate_many(
                            &self.aggregate_arguments,
                            batches.first().unwrap(),
                        )?;
                        assert_eq!(input_values.len(), 1, "Exactly 1 input required");
                        assert_eq!(input_values[0].len(), 1, "Exactly 1 input required");
                        Arc::clone(&input_values[0][0])
                    };

                    // iterate over each column of group_by values
                    (*self).intern(&group_by_values, &input_values)?;
                }
                // inner is done, emit all rows and switch to producing output
                None => {
                    if self.priority_map.is_empty() {
                        trace!("partition {} emit None", self.partition);
                        return Poll::Ready(None);
                    }
                    let batch = {
                        let _timer = emitting_time.timer();
                        let mut cols = self.priority_map.emit()?;
                        // For DISTINCT case (no aggregate expressions), only use the group key column
                        // since the schema only has one field and key/value are the same
                        if self.aggregate_arguments.is_empty() {
                            cols.truncate(1);
                        }
                        RecordBatch::try_new(Arc::clone(&self.schema), cols)?
                    };
                    let batch = batch.record_output(&self.baseline_metrics);
                    trace!(
                        "partition {} emit batch with {} rows",
                        self.partition,
                        batch.num_rows()
                    );
                    if log::log_enabled!(Level::Trace) {
                        print_batches(std::slice::from_ref(&batch))?;
                    }
                    return Poll::Ready(Some(Ok(batch)));
                }
                // inner had error, return to caller
                Some(Err(e)) => {
                    return Poll::Ready(Some(Err(e)));
                }
            }
        }
        Poll::Pending
    }
}
