promql/extension_plan/

series_divide.rs

// Copyright 2023 Greptime Team
//
// Licensed 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::pin::Pin;
use std::sync::Arc;
use std::task::{Context, Poll};

use datafusion::arrow::array::{Array, StringArray};
use datafusion::arrow::datatypes::SchemaRef;
use datafusion::arrow::record_batch::RecordBatch;
use datafusion::common::{DFSchema, DFSchemaRef};
use datafusion::error::Result as DataFusionResult;
use datafusion::execution::context::TaskContext;
use datafusion::logical_expr::{EmptyRelation, Expr, LogicalPlan, UserDefinedLogicalNodeCore};
use datafusion::physical_expr::{LexRequirement, PhysicalSortRequirement};
use datafusion::physical_plan::expressions::Column as ColumnExpr;
use datafusion::physical_plan::metrics::{
    BaselineMetrics, Count, ExecutionPlanMetricsSet, MetricBuilder, MetricValue, MetricsSet,
};
use datafusion::physical_plan::{
    DisplayAs, DisplayFormatType, Distribution, ExecutionPlan, PlanProperties, RecordBatchStream,
    SendableRecordBatchStream,
};
use datatypes::arrow::compute;
use datatypes::compute::SortOptions;
use futures::{ready, Stream, StreamExt};
use greptime_proto::substrait_extension as pb;
use prost::Message;
use snafu::ResultExt;

use crate::error::{DeserializeSnafu, Result};
use crate::extension_plan::METRIC_NUM_SERIES;
use crate::metrics::PROMQL_SERIES_COUNT;

#[derive(Debug, PartialEq, Eq, Hash, PartialOrd)]
pub struct SeriesDivide {
    tag_columns: Vec<String>,
    /// `SeriesDivide` requires `time_index` column's name to generate ordering requirement
    /// for input data. But this plan itself doesn't depend on the ordering of time index
    /// column. This is for follow on plans like `RangeManipulate`. Because requiring ordering
    /// here can avoid unnecessary sort in follow on plans.
    time_index_column: String,
    input: LogicalPlan,
}

impl UserDefinedLogicalNodeCore for SeriesDivide {
    fn name(&self) -> &str {
        Self::name()
    }

    fn inputs(&self) -> Vec<&LogicalPlan> {
        vec![&self.input]
    }

    fn schema(&self) -> &DFSchemaRef {
        self.input.schema()
    }

    fn expressions(&self) -> Vec<Expr> {
        vec![]
    }

    fn fmt_for_explain(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
        write!(f, "PromSeriesDivide: tags={:?}", self.tag_columns)
    }

    fn with_exprs_and_inputs(
        &self,
        _exprs: Vec<Expr>,
        inputs: Vec<LogicalPlan>,
    ) -> DataFusionResult<Self> {
        if inputs.is_empty() {
            return Err(datafusion::error::DataFusionError::Internal(
                "SeriesDivide must have at least one input".to_string(),
            ));
        }

        Ok(Self {
            tag_columns: self.tag_columns.clone(),
            time_index_column: self.time_index_column.clone(),
            input: inputs[0].clone(),
        })
    }
}

impl SeriesDivide {
    pub fn new(tag_columns: Vec<String>, time_index_column: String, input: LogicalPlan) -> Self {
        Self {
            tag_columns,
            time_index_column,
            input,
        }
    }

    pub const fn name() -> &'static str {
        "SeriesDivide"
    }

    pub fn to_execution_plan(&self, exec_input: Arc<dyn ExecutionPlan>) -> Arc<dyn ExecutionPlan> {
        Arc::new(SeriesDivideExec {
            tag_columns: self.tag_columns.clone(),
            time_index_column: self.time_index_column.clone(),
            input: exec_input,
            metric: ExecutionPlanMetricsSet::new(),
        })
    }

    pub fn tags(&self) -> &[String] {
        &self.tag_columns
    }

    pub fn serialize(&self) -> Vec<u8> {
        pb::SeriesDivide {
            tag_columns: self.tag_columns.clone(),
            time_index_column: self.time_index_column.clone(),
        }
        .encode_to_vec()
    }

    pub fn deserialize(bytes: &[u8]) -> Result<Self> {
        let pb_series_divide = pb::SeriesDivide::decode(bytes).context(DeserializeSnafu)?;
        let placeholder_plan = LogicalPlan::EmptyRelation(EmptyRelation {
            produce_one_row: false,
            schema: Arc::new(DFSchema::empty()),
        });
        Ok(Self {
            tag_columns: pb_series_divide.tag_columns,
            time_index_column: pb_series_divide.time_index_column,
            input: placeholder_plan,
        })
    }
}

#[derive(Debug)]
pub struct SeriesDivideExec {
    tag_columns: Vec<String>,
    time_index_column: String,
    input: Arc<dyn ExecutionPlan>,
    metric: ExecutionPlanMetricsSet,
}

impl ExecutionPlan for SeriesDivideExec {
    fn as_any(&self) -> &dyn Any {
        self
    }

    fn schema(&self) -> SchemaRef {
        self.input.schema()
    }

    fn properties(&self) -> &PlanProperties {
        self.input.properties()
    }

    fn required_input_distribution(&self) -> Vec<Distribution> {
        let schema = self.input.schema();
        vec![Distribution::HashPartitioned(
            self.tag_columns
                .iter()
                // Safety: the tag column names is verified in the planning phase
                .map(|tag| Arc::new(ColumnExpr::new_with_schema(tag, &schema).unwrap()) as _)
                .collect(),
        )]
    }

    fn required_input_ordering(&self) -> Vec<Option<LexRequirement>> {
        let input_schema = self.input.schema();
        let mut exprs: Vec<PhysicalSortRequirement> = self
            .tag_columns
            .iter()
            .map(|tag| PhysicalSortRequirement {
                // Safety: the tag column names is verified in the planning phase
                expr: Arc::new(ColumnExpr::new_with_schema(tag, &input_schema).unwrap()),
                options: Some(SortOptions {
                    descending: false,
                    nulls_first: true,
                }),
            })
            .collect();

        exprs.push(PhysicalSortRequirement {
            expr: Arc::new(
                ColumnExpr::new_with_schema(&self.time_index_column, &input_schema).unwrap(),
            ),
            options: Some(SortOptions {
                descending: false,
                nulls_first: true,
            }),
        });
        vec![Some(LexRequirement::new(exprs))]
    }

    fn maintains_input_order(&self) -> Vec<bool> {
        vec![true; self.children().len()]
    }

    fn children(&self) -> Vec<&Arc<dyn ExecutionPlan>> {
        vec![&self.input]
    }

    fn with_new_children(
        self: Arc<Self>,
        children: Vec<Arc<dyn ExecutionPlan>>,
    ) -> DataFusionResult<Arc<dyn ExecutionPlan>> {
        assert!(!children.is_empty());
        Ok(Arc::new(Self {
            tag_columns: self.tag_columns.clone(),
            time_index_column: self.time_index_column.clone(),
            input: children[0].clone(),
            metric: self.metric.clone(),
        }))
    }

    fn execute(
        &self,
        partition: usize,
        context: Arc<TaskContext>,
    ) -> DataFusionResult<SendableRecordBatchStream> {
        let baseline_metric = BaselineMetrics::new(&self.metric, partition);
        let metrics_builder = MetricBuilder::new(&self.metric);
        let num_series = Count::new();
        metrics_builder
            .with_partition(partition)
            .build(MetricValue::Count {
                name: METRIC_NUM_SERIES.into(),
                count: num_series.clone(),
            });

        let input = self.input.execute(partition, context)?;
        let schema = input.schema();
        let tag_indices = self
            .tag_columns
            .iter()
            .map(|tag| {
                schema
                    .column_with_name(tag)
                    .unwrap_or_else(|| panic!("tag column not found {tag}"))
                    .0
            })
            .collect();
        Ok(Box::pin(SeriesDivideStream {
            tag_indices,
            buffer: vec![],
            schema,
            input,
            metric: baseline_metric,
            num_series,
            inspect_start: 0,
        }))
    }

    fn metrics(&self) -> Option<MetricsSet> {
        Some(self.metric.clone_inner())
    }

    fn name(&self) -> &str {
        "SeriesDivideExec"
    }
}

impl DisplayAs for SeriesDivideExec {
    fn fmt_as(&self, t: DisplayFormatType, f: &mut std::fmt::Formatter) -> std::fmt::Result {
        match t {
            DisplayFormatType::Default | DisplayFormatType::Verbose => {
                write!(f, "PromSeriesDivideExec: tags={:?}", self.tag_columns)
            }
        }
    }
}

/// Assume the input stream is ordered on the tag columns.
pub struct SeriesDivideStream {
    tag_indices: Vec<usize>,
    buffer: Vec<RecordBatch>,
    schema: SchemaRef,
    input: SendableRecordBatchStream,
    metric: BaselineMetrics,
    /// Index of buffered batches to start inspect next time.
    inspect_start: usize,
    /// Number of series processed.
    num_series: Count,
}

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

impl Stream for SeriesDivideStream {
    type Item = DataFusionResult<RecordBatch>;

    fn poll_next(mut self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll<Option<Self::Item>> {
        loop {
            if !self.buffer.is_empty() {
                let timer = std::time::Instant::now();
                let cut_at = match self.find_first_diff_row() {
                    Ok(cut_at) => cut_at,
                    Err(e) => return Poll::Ready(Some(Err(e))),
                };
                if let Some((batch_index, row_index)) = cut_at {
                    // slice out the first time series and return it.
                    let half_batch_of_first_series =
                        self.buffer[batch_index].slice(0, row_index + 1);
                    let half_batch_of_second_series = self.buffer[batch_index].slice(
                        row_index + 1,
                        self.buffer[batch_index].num_rows() - row_index - 1,
                    );
                    let result_batches = self
                        .buffer
                        .drain(0..batch_index)
                        .chain([half_batch_of_first_series])
                        .collect::<Vec<_>>();
                    if half_batch_of_second_series.num_rows() > 0 {
                        self.buffer[0] = half_batch_of_second_series;
                    } else {
                        self.buffer.remove(0);
                    }
                    let result_batch = compute::concat_batches(&self.schema, &result_batches)?;

                    self.inspect_start = 0;
                    self.num_series.add(1);
                    self.metric.elapsed_compute().add_elapsed(timer);
                    return Poll::Ready(Some(Ok(result_batch)));
                } else {
                    self.metric.elapsed_compute().add_elapsed(timer);
                    // continue to fetch next batch as the current buffer only contains one time series.
                    let next_batch = ready!(self.as_mut().fetch_next_batch(cx)).transpose()?;
                    let timer = std::time::Instant::now();
                    if let Some(next_batch) = next_batch {
                        if next_batch.num_rows() != 0 {
                            self.buffer.push(next_batch);
                        }
                        continue;
                    } else {
                        // input stream is ended
                        let result = compute::concat_batches(&self.schema, &self.buffer)?;
                        self.buffer.clear();
                        self.inspect_start = 0;
                        self.num_series.add(1);
                        self.metric.elapsed_compute().add_elapsed(timer);
                        return Poll::Ready(Some(Ok(result)));
                    }
                }
            } else {
                let batch = match ready!(self.as_mut().fetch_next_batch(cx)) {
                    Some(Ok(batch)) => batch,
                    None => {
                        PROMQL_SERIES_COUNT.observe(self.num_series.value() as f64);
                        return Poll::Ready(None);
                    }
                    error => return Poll::Ready(error),
                };
                self.buffer.push(batch);
                continue;
            }
        }
    }
}

impl SeriesDivideStream {
    fn fetch_next_batch(
        mut self: Pin<&mut Self>,
        cx: &mut Context<'_>,
    ) -> Poll<Option<DataFusionResult<RecordBatch>>> {
        let poll = self.input.poll_next_unpin(cx);
        self.metric.record_poll(poll)
    }

    /// Return the position to cut buffer.
    /// None implies the current buffer only contains one time series.
    fn find_first_diff_row(&mut self) -> DataFusionResult<Option<(usize, usize)>> {
        // fast path: no tag columns means all data belongs to the same series.
        if self.tag_indices.is_empty() {
            return Ok(None);
        }

        let mut resumed_batch_index = self.inspect_start;

        for batch in &self.buffer[resumed_batch_index..] {
            let num_rows = batch.num_rows();
            let mut result_index = num_rows;

            // check if the first row is the same with last batch's last row
            if resumed_batch_index > self.inspect_start.checked_sub(1).unwrap_or_default() {
                let last_batch = &self.buffer[resumed_batch_index - 1];
                let last_row = last_batch.num_rows() - 1;
                for index in &self.tag_indices {
                    let current_array = batch.column(*index);
                    let last_array = last_batch.column(*index);
                    let current_string_array = current_array
                        .as_any()
                        .downcast_ref::<StringArray>()
                        .ok_or_else(|| {
                            datafusion::error::DataFusionError::Internal(
                                "Failed to downcast tag column to StringArray".to_string(),
                            )
                        })?;
                    let last_string_array = last_array
                        .as_any()
                        .downcast_ref::<StringArray>()
                        .ok_or_else(|| {
                            datafusion::error::DataFusionError::Internal(
                                "Failed to downcast tag column to StringArray".to_string(),
                            )
                        })?;
                    let current_value = current_string_array.value(0);
                    let last_value = last_string_array.value(last_row);
                    if current_value != last_value {
                        return Ok(Some((resumed_batch_index - 1, last_batch.num_rows() - 1)));
                    }
                }
            }

            // quick check if all rows are the same by comparing the first and last row in this batch
            let mut all_same = true;
            for index in &self.tag_indices {
                let array = batch.column(*index);
                let string_array =
                    array
                        .as_any()
                        .downcast_ref::<StringArray>()
                        .ok_or_else(|| {
                            datafusion::error::DataFusionError::Internal(
                                "Failed to downcast tag column to StringArray".to_string(),
                            )
                        })?;
                if string_array.value(0) != string_array.value(num_rows - 1) {
                    all_same = false;
                    break;
                }
            }
            if all_same {
                resumed_batch_index += 1;
                continue;
            }

            // check column by column
            for index in &self.tag_indices {
                let array = batch.column(*index);
                let string_array =
                    array
                        .as_any()
                        .downcast_ref::<StringArray>()
                        .ok_or_else(|| {
                            datafusion::error::DataFusionError::Internal(
                                "Failed to downcast tag column to StringArray".to_string(),
                            )
                        })?;
                // the first row number that not equal to the next row.
                let mut same_until = 0;
                while same_until < num_rows - 1 {
                    if string_array.value(same_until) != string_array.value(same_until + 1) {
                        break;
                    }
                    same_until += 1;
                }
                result_index = result_index.min(same_until);
            }

            if result_index + 1 >= num_rows {
                // all rows are the same, inspect next batch
                resumed_batch_index += 1;
            } else {
                return Ok(Some((resumed_batch_index, result_index)));
            }
        }

        self.inspect_start = resumed_batch_index;
        Ok(None)
    }
}

#[cfg(test)]
mod test {
    use datafusion::arrow::datatypes::{DataType, Field, Schema};
    use datafusion::physical_plan::memory::MemoryExec;
    use datafusion::prelude::SessionContext;

    use super::*;

    fn prepare_test_data() -> MemoryExec {
        let schema = Arc::new(Schema::new(vec![
            Field::new("host", DataType::Utf8, true),
            Field::new("path", DataType::Utf8, true),
            Field::new(
                "time_index",
                DataType::Timestamp(datafusion::arrow::datatypes::TimeUnit::Millisecond, None),
                false,
            ),
        ]));

        let path_column_1 = Arc::new(StringArray::from(vec![
            "foo", "foo", "foo", "bar", "bar", "bar", "bar", "bar", "bar", "bla", "bla", "bla",
        ])) as _;
        let host_column_1 = Arc::new(StringArray::from(vec![
            "000", "000", "001", "002", "002", "002", "002", "002", "003", "005", "005", "005",
        ])) as _;
        let time_index_column_1 = Arc::new(
            datafusion::arrow::array::TimestampMillisecondArray::from(vec![
                1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000,
            ]),
        ) as _;

        let path_column_2 = Arc::new(StringArray::from(vec!["bla", "bla", "bla"])) as _;
        let host_column_2 = Arc::new(StringArray::from(vec!["005", "005", "005"])) as _;
        let time_index_column_2 = Arc::new(
            datafusion::arrow::array::TimestampMillisecondArray::from(vec![13000, 14000, 15000]),
        ) as _;

        let path_column_3 = Arc::new(StringArray::from(vec![
            "bla", "🥺", "🥺", "🥺", "🥺", "🥺", "🫠", "🫠",
        ])) as _;
        let host_column_3 = Arc::new(StringArray::from(vec![
            "005", "001", "001", "001", "001", "001", "001", "001",
        ])) as _;
        let time_index_column_3 =
            Arc::new(datafusion::arrow::array::TimestampMillisecondArray::from(
                vec![16000, 17000, 18000, 19000, 20000, 21000, 22000, 23000],
            )) as _;

        let data_1 = RecordBatch::try_new(
            schema.clone(),
            vec![path_column_1, host_column_1, time_index_column_1],
        )
        .unwrap();
        let data_2 = RecordBatch::try_new(
            schema.clone(),
            vec![path_column_2, host_column_2, time_index_column_2],
        )
        .unwrap();
        let data_3 = RecordBatch::try_new(
            schema.clone(),
            vec![path_column_3, host_column_3, time_index_column_3],
        )
        .unwrap();

        MemoryExec::try_new(&[vec![data_1, data_2, data_3]], schema, None).unwrap()
    }

    #[tokio::test]
    async fn overall_data() {
        let memory_exec = Arc::new(prepare_test_data());
        let divide_exec = Arc::new(SeriesDivideExec {
            tag_columns: vec!["host".to_string(), "path".to_string()],
            time_index_column: "time_index".to_string(),
            input: memory_exec,
            metric: ExecutionPlanMetricsSet::new(),
        });
        let session_context = SessionContext::default();
        let result = datafusion::physical_plan::collect(divide_exec, session_context.task_ctx())
            .await
            .unwrap();
        let result_literal = datatypes::arrow::util::pretty::pretty_format_batches(&result)
            .unwrap()
            .to_string();

        let expected = String::from(
            "+------+------+---------------------+\
            \n| host | path | time_index          |\
            \n+------+------+---------------------+\
            \n| foo  | 000  | 1970-01-01T00:00:01 |\
            \n| foo  | 000  | 1970-01-01T00:00:02 |\
            \n| foo  | 001  | 1970-01-01T00:00:03 |\
            \n| bar  | 002  | 1970-01-01T00:00:04 |\
            \n| bar  | 002  | 1970-01-01T00:00:05 |\
            \n| bar  | 002  | 1970-01-01T00:00:06 |\
            \n| bar  | 002  | 1970-01-01T00:00:07 |\
            \n| bar  | 002  | 1970-01-01T00:00:08 |\
            \n| bar  | 003  | 1970-01-01T00:00:09 |\
            \n| bla  | 005  | 1970-01-01T00:00:10 |\
            \n| bla  | 005  | 1970-01-01T00:00:11 |\
            \n| bla  | 005  | 1970-01-01T00:00:12 |\
            \n| bla  | 005  | 1970-01-01T00:00:13 |\
            \n| bla  | 005  | 1970-01-01T00:00:14 |\
            \n| bla  | 005  | 1970-01-01T00:00:15 |\
            \n| bla  | 005  | 1970-01-01T00:00:16 |\
            \n| 🥺   | 001  | 1970-01-01T00:00:17 |\
            \n| 🥺   | 001  | 1970-01-01T00:00:18 |\
            \n| 🥺   | 001  | 1970-01-01T00:00:19 |\
            \n| 🥺   | 001  | 1970-01-01T00:00:20 |\
            \n| 🥺   | 001  | 1970-01-01T00:00:21 |\
            \n| 🫠   | 001  | 1970-01-01T00:00:22 |\
            \n| 🫠   | 001  | 1970-01-01T00:00:23 |\
            \n+------+------+---------------------+",
        );
        assert_eq!(result_literal, expected);
    }

    #[tokio::test]
    async fn per_batch_data() {
        let memory_exec = Arc::new(prepare_test_data());
        let divide_exec = Arc::new(SeriesDivideExec {
            tag_columns: vec!["host".to_string(), "path".to_string()],
            time_index_column: "time_index".to_string(),
            input: memory_exec,
            metric: ExecutionPlanMetricsSet::new(),
        });
        let mut divide_stream = divide_exec
            .execute(0, SessionContext::default().task_ctx())
            .unwrap();

        let mut expectations = vec![
            String::from(
                "+------+------+---------------------+\
                \n| host | path | time_index          |\
                \n+------+------+---------------------+\
                \n| foo  | 000  | 1970-01-01T00:00:01 |\
                \n| foo  | 000  | 1970-01-01T00:00:02 |\
                \n+------+------+---------------------+",
            ),
            String::from(
                "+------+------+---------------------+\
                \n| host | path | time_index          |\
                \n+------+------+---------------------+\
                \n| foo  | 001  | 1970-01-01T00:00:03 |\
                \n+------+------+---------------------+",
            ),
            String::from(
                "+------+------+---------------------+\
                \n| host | path | time_index          |\
                \n+------+------+---------------------+\
                \n| bar  | 002  | 1970-01-01T00:00:04 |\
                \n| bar  | 002  | 1970-01-01T00:00:05 |\
                \n| bar  | 002  | 1970-01-01T00:00:06 |\
                \n| bar  | 002  | 1970-01-01T00:00:07 |\
                \n| bar  | 002  | 1970-01-01T00:00:08 |\
                \n+------+------+---------------------+",
            ),
            String::from(
                "+------+------+---------------------+\
                \n| host | path | time_index          |\
                \n+------+------+---------------------+\
                \n| bar  | 003  | 1970-01-01T00:00:09 |\
                \n+------+------+---------------------+",
            ),
            String::from(
                "+------+------+---------------------+\
                \n| host | path | time_index          |\
                \n+------+------+---------------------+\
                \n| bla  | 005  | 1970-01-01T00:00:10 |\
                \n| bla  | 005  | 1970-01-01T00:00:11 |\
                \n| bla  | 005  | 1970-01-01T00:00:12 |\
                \n| bla  | 005  | 1970-01-01T00:00:13 |\
                \n| bla  | 005  | 1970-01-01T00:00:14 |\
                \n| bla  | 005  | 1970-01-01T00:00:15 |\
                \n| bla  | 005  | 1970-01-01T00:00:16 |\
                \n+------+------+---------------------+",
            ),
            String::from(
                "+------+------+---------------------+\
                \n| host | path | time_index          |\
                \n+------+------+---------------------+\
                \n| 🥺   | 001  | 1970-01-01T00:00:17 |\
                \n| 🥺   | 001  | 1970-01-01T00:00:18 |\
                \n| 🥺   | 001  | 1970-01-01T00:00:19 |\
                \n| 🥺   | 001  | 1970-01-01T00:00:20 |\
                \n| 🥺   | 001  | 1970-01-01T00:00:21 |\
                \n+------+------+---------------------+",
            ),
            String::from(
                "+------+------+---------------------+\
                \n| host | path | time_index          |\
                \n+------+------+---------------------+\
                \n| 🫠   | 001  | 1970-01-01T00:00:22 |\
                \n| 🫠   | 001  | 1970-01-01T00:00:23 |\
                \n+------+------+---------------------+",
            ),
        ];
        expectations.reverse();

        while let Some(batch) = divide_stream.next().await {
            let formatted =
                datatypes::arrow::util::pretty::pretty_format_batches(&[batch.unwrap()])
                    .unwrap()
                    .to_string();
            let expected = expectations.pop().unwrap();
            assert_eq!(formatted, expected);
        }
    }

    #[tokio::test]
    async fn test_all_batches_same_combination() {
        // Create a schema with host and path columns, same as prepare_test_data
        let schema = Arc::new(Schema::new(vec![
            Field::new("host", DataType::Utf8, true),
            Field::new("path", DataType::Utf8, true),
            Field::new(
                "time_index",
                DataType::Timestamp(datafusion::arrow::datatypes::TimeUnit::Millisecond, None),
                false,
            ),
        ]));

        // Create batches with three different combinations
        // Each batch contains only one combination
        // Batches with the same combination are adjacent

        // First combination: "server1", "/var/log"
        let batch1 = RecordBatch::try_new(
            schema.clone(),
            vec![
                Arc::new(StringArray::from(vec!["server1", "server1", "server1"])) as _,
                Arc::new(StringArray::from(vec!["/var/log", "/var/log", "/var/log"])) as _,
                Arc::new(datafusion::arrow::array::TimestampMillisecondArray::from(
                    vec![1000, 2000, 3000],
                )) as _,
            ],
        )
        .unwrap();

        let batch2 = RecordBatch::try_new(
            schema.clone(),
            vec![
                Arc::new(StringArray::from(vec!["server1", "server1"])) as _,
                Arc::new(StringArray::from(vec!["/var/log", "/var/log"])) as _,
                Arc::new(datafusion::arrow::array::TimestampMillisecondArray::from(
                    vec![4000, 5000],
                )) as _,
            ],
        )
        .unwrap();

        // Second combination: "server2", "/var/data"
        let batch3 = RecordBatch::try_new(
            schema.clone(),
            vec![
                Arc::new(StringArray::from(vec!["server2", "server2", "server2"])) as _,
                Arc::new(StringArray::from(vec![
                    "/var/data",
                    "/var/data",
                    "/var/data",
                ])) as _,
                Arc::new(datafusion::arrow::array::TimestampMillisecondArray::from(
                    vec![6000, 7000, 8000],
                )) as _,
            ],
        )
        .unwrap();

        let batch4 = RecordBatch::try_new(
            schema.clone(),
            vec![
                Arc::new(StringArray::from(vec!["server2"])) as _,
                Arc::new(StringArray::from(vec!["/var/data"])) as _,
                Arc::new(datafusion::arrow::array::TimestampMillisecondArray::from(
                    vec![9000],
                )) as _,
            ],
        )
        .unwrap();

        // Third combination: "server3", "/opt/logs"
        let batch5 = RecordBatch::try_new(
            schema.clone(),
            vec![
                Arc::new(StringArray::from(vec!["server3", "server3"])) as _,
                Arc::new(StringArray::from(vec!["/opt/logs", "/opt/logs"])) as _,
                Arc::new(datafusion::arrow::array::TimestampMillisecondArray::from(
                    vec![10000, 11000],
                )) as _,
            ],
        )
        .unwrap();

        let batch6 = RecordBatch::try_new(
            schema.clone(),
            vec![
                Arc::new(StringArray::from(vec!["server3", "server3", "server3"])) as _,
                Arc::new(StringArray::from(vec![
                    "/opt/logs",
                    "/opt/logs",
                    "/opt/logs",
                ])) as _,
                Arc::new(datafusion::arrow::array::TimestampMillisecondArray::from(
                    vec![12000, 13000, 14000],
                )) as _,
            ],
        )
        .unwrap();

        // Create MemoryExec with these batches, keeping same combinations adjacent
        let memory_exec = Arc::new(
            MemoryExec::try_new(
                &[vec![batch1, batch2, batch3, batch4, batch5, batch6]],
                schema.clone(),
                None,
            )
            .unwrap(),
        );

        // Create SeriesDivideExec
        let divide_exec = Arc::new(SeriesDivideExec {
            tag_columns: vec!["host".to_string(), "path".to_string()],
            time_index_column: "time_index".to_string(),
            input: memory_exec,
            metric: ExecutionPlanMetricsSet::new(),
        });

        // Execute the division
        let session_context = SessionContext::default();
        let result =
            datafusion::physical_plan::collect(divide_exec.clone(), session_context.task_ctx())
                .await
                .unwrap();

        // Verify that we got 3 batches (one for each combination)
        assert_eq!(result.len(), 3);

        // First batch should have 5 rows (3 + 2 from the "server1" combination)
        assert_eq!(result[0].num_rows(), 5);

        // Second batch should have 4 rows (3 + 1 from the "server2" combination)
        assert_eq!(result[1].num_rows(), 4);

        // Third batch should have 5 rows (2 + 3 from the "server3" combination)
        assert_eq!(result[2].num_rows(), 5);

        // Verify values in first batch (server1, /var/log)
        let host_array1 = result[0]
            .column(0)
            .as_any()
            .downcast_ref::<StringArray>()
            .unwrap();
        let path_array1 = result[0]
            .column(1)
            .as_any()
            .downcast_ref::<StringArray>()
            .unwrap();
        let time_index_array1 = result[0]
            .column(2)
            .as_any()
            .downcast_ref::<datafusion::arrow::array::TimestampMillisecondArray>()
            .unwrap();

        for i in 0..5 {
            assert_eq!(host_array1.value(i), "server1");
            assert_eq!(path_array1.value(i), "/var/log");
            assert_eq!(time_index_array1.value(i), 1000 + (i as i64) * 1000);
        }

        // Verify values in second batch (server2, /var/data)
        let host_array2 = result[1]
            .column(0)
            .as_any()
            .downcast_ref::<StringArray>()
            .unwrap();
        let path_array2 = result[1]
            .column(1)
            .as_any()
            .downcast_ref::<StringArray>()
            .unwrap();
        let time_index_array2 = result[1]
            .column(2)
            .as_any()
            .downcast_ref::<datafusion::arrow::array::TimestampMillisecondArray>()
            .unwrap();

        for i in 0..4 {
            assert_eq!(host_array2.value(i), "server2");
            assert_eq!(path_array2.value(i), "/var/data");
            assert_eq!(time_index_array2.value(i), 6000 + (i as i64) * 1000);
        }

        // Verify values in third batch (server3, /opt/logs)
        let host_array3 = result[2]
            .column(0)
            .as_any()
            .downcast_ref::<StringArray>()
            .unwrap();
        let path_array3 = result[2]
            .column(1)
            .as_any()
            .downcast_ref::<StringArray>()
            .unwrap();
        let time_index_array3 = result[2]
            .column(2)
            .as_any()
            .downcast_ref::<datafusion::arrow::array::TimestampMillisecondArray>()
            .unwrap();

        for i in 0..5 {
            assert_eq!(host_array3.value(i), "server3");
            assert_eq!(path_array3.value(i), "/opt/logs");
            assert_eq!(time_index_array3.value(i), 10000 + (i as i64) * 1000);
        }

        // Also verify streaming behavior
        let mut divide_stream = divide_exec
            .execute(0, SessionContext::default().task_ctx())
            .unwrap();

        // Should produce three batches, one for each combination
        let batch1 = divide_stream.next().await.unwrap().unwrap();
        assert_eq!(batch1.num_rows(), 5); // server1 combination

        let batch2 = divide_stream.next().await.unwrap().unwrap();
        assert_eq!(batch2.num_rows(), 4); // server2 combination

        let batch3 = divide_stream.next().await.unwrap().unwrap();
        assert_eq!(batch3.num_rows(), 5); // server3 combination

        // No more batches should be produced
        assert!(divide_stream.next().await.is_none());
    }
}