query/

analyze.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.

//! Customized `ANALYZE` plan that aware of [MergeScanExec].
//!
//! The code skeleton is taken from `datafusion/physical-plan/src/analyze.rs`

use std::any::Any;
use std::fmt::Display;
use std::sync::Arc;

use ahash::HashMap;
use arrow::array::{StringBuilder, UInt32Builder};
use arrow_schema::{DataType, Field, Schema, SchemaRef};
use common_recordbatch::adapter::{MetricCollector, PlanMetrics, RecordBatchMetrics};
use common_recordbatch::{DfRecordBatch, DfSendableRecordBatchStream};
use datafusion::error::Result as DfResult;
use datafusion::execution::TaskContext;
use datafusion::physical_plan::coalesce_partitions::CoalescePartitionsExec;
use datafusion::physical_plan::stream::RecordBatchStreamAdapter;
use datafusion::physical_plan::{
    DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, accept,
};
use datafusion_common::tree_node::{TreeNode, TreeNodeRecursion};
use datafusion_common::{DataFusionError, internal_err};
use datafusion_physical_expr::{Distribution, EquivalenceProperties, Partitioning};
use futures::StreamExt;
use serde::Serialize;
use serde_json::{Value, json};
use sqlparser::ast::AnalyzeFormat;

use crate::dist_plan::MergeScanExec;

const STAGE: &str = "stage";
const NODE: &str = "node";
const PLAN: &str = "plan";

#[derive(Debug)]
pub struct DistAnalyzeExec {
    input: Arc<dyn ExecutionPlan>,
    schema: SchemaRef,
    properties: Arc<PlanProperties>,
    verbose: bool,
    format: AnalyzeFormat,
}

impl DistAnalyzeExec {
    /// Create a new DistAnalyzeExec
    pub fn new(input: Arc<dyn ExecutionPlan>, verbose: bool, format: AnalyzeFormat) -> Self {
        let schema = SchemaRef::new(Schema::new(vec![
            Field::new(STAGE, DataType::UInt32, true),
            Field::new(NODE, DataType::UInt32, true),
            Field::new(PLAN, DataType::Utf8, true),
        ]));
        let properties = Arc::new(Self::compute_properties(&input, schema.clone()));
        Self {
            input,
            schema,
            properties,
            verbose,
            format,
        }
    }

    /// This function creates the cache object that stores the plan properties such as schema, equivalence properties, ordering, partitioning, etc.
    fn compute_properties(input: &Arc<dyn ExecutionPlan>, schema: SchemaRef) -> PlanProperties {
        let eq_properties = EquivalenceProperties::new(schema);
        let output_partitioning = Partitioning::UnknownPartitioning(1);
        let properties = input.properties();
        PlanProperties::new(
            eq_properties,
            output_partitioning,
            properties.emission_type,
            properties.boundedness,
        )
    }

    pub fn input(&self) -> &Arc<dyn ExecutionPlan> {
        &self.input
    }
}

/// Returns verbose analyze metrics as JSON values using the same `JsonMetrics` shape
/// as `EXPLAIN ANALYZE VERBOSE FORMAT JSON`.
///
/// This reads metrics directly from a running physical plan for the experimental
/// HTTP analyze stream. It is a best-effort diagnostic live snapshot, not a
/// transactionally consistent snapshot; metric values may change while this
/// function traverses the plan.
pub fn analyze_plan_metrics_to_json_value(
    plan: &Arc<dyn ExecutionPlan>,
    verbose: bool,
) -> serde_json::Result<Value> {
    let input = plan
        .as_any()
        .downcast_ref::<DistAnalyzeExec>()
        .map(|exec| exec.input().clone())
        .unwrap_or_else(|| plan.clone());

    let mut stages = Vec::new();
    let mut collector = MetricCollector::new(verbose);
    accept(input.as_ref(), &mut collector).unwrap();
    stages.push(json!({
        "stage": 0,
        "node": 0,
        "plan": JsonMetrics::from_record_batch_metrics(collector.record_batch_metrics),
    }));

    let _ = input.apply(|plan| {
        if let Some(merge_scan) = plan.as_any().downcast_ref::<MergeScanExec>() {
            for (node, metric) in merge_scan.sub_stage_metrics().into_iter().enumerate() {
                stages.push(json!({
                    "stage": 1,
                    "node": node,
                    "plan": JsonMetrics::from_record_batch_metrics(metric),
                }));
            }
        }
        Ok(TreeNodeRecursion::Continue)
    });

    Ok(Value::Array(stages))
}

impl DisplayAs for DistAnalyzeExec {
    fn fmt_as(&self, t: DisplayFormatType, f: &mut std::fmt::Formatter) -> std::fmt::Result {
        match t {
            DisplayFormatType::Default
            | DisplayFormatType::Verbose
            | DisplayFormatType::TreeRender => {
                write!(f, "DistAnalyzeExec",)
            }
        }
    }
}

impl ExecutionPlan for DistAnalyzeExec {
    fn name(&self) -> &'static str {
        "DistAnalyzeExec"
    }

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

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

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

    /// AnalyzeExec is handled specially so this value is ignored
    fn required_input_distribution(&self) -> Vec<Distribution> {
        vec![]
    }

    fn with_new_children(
        self: Arc<Self>,
        mut children: Vec<Arc<dyn ExecutionPlan>>,
    ) -> DfResult<Arc<dyn ExecutionPlan>> {
        Ok(Arc::new(Self::new(
            children.pop().unwrap(),
            self.verbose,
            self.format,
        )))
    }

    fn execute(
        &self,
        partition: usize,
        context: Arc<TaskContext>,
    ) -> DfResult<DfSendableRecordBatchStream> {
        if 0 != partition {
            return internal_err!("AnalyzeExec invalid partition. Expected 0, got {partition}");
        }

        // Wrap the input plan using `CoalescePartitionsExec` to poll multiple
        // partitions in parallel
        let coalesce_partition_plan = CoalescePartitionsExec::new(self.input.clone());

        // Create future that computes thefinal output
        let captured_input = self.input.clone();
        let captured_schema = self.schema.clone();

        // Finish the input stream and create the output
        let format = self.format;
        let verbose = self.verbose;
        let mut input_stream = coalesce_partition_plan.execute(0, context)?;
        let output = async move {
            let mut total_rows = 0;
            while let Some(batch) = input_stream.next().await.transpose()? {
                total_rows += batch.num_rows();
            }

            create_output_batch(total_rows, captured_input, captured_schema, format, verbose)
        };

        Ok(Box::pin(RecordBatchStreamAdapter::new(
            self.schema.clone(),
            futures::stream::once(output),
        )))
    }
}

/// Build the result [`DfRecordBatch`] of `ANALYZE`
struct AnalyzeOutputBuilder {
    stage_builder: UInt32Builder,
    node_builder: UInt32Builder,
    plan_builder: StringBuilder,
    schema: SchemaRef,
}

impl AnalyzeOutputBuilder {
    fn new(schema: SchemaRef) -> Self {
        Self {
            stage_builder: UInt32Builder::with_capacity(4),
            node_builder: UInt32Builder::with_capacity(4),
            plan_builder: StringBuilder::with_capacity(1, 1024),
            schema,
        }
    }

    fn append_metric(&mut self, stage: u32, node: u32, content: String) {
        self.stage_builder.append_value(stage);
        self.node_builder.append_value(node);
        self.plan_builder.append_value(content);
    }

    fn append_total_rows(&mut self, total_rows: usize) {
        self.stage_builder.append_null();
        self.node_builder.append_null();
        self.plan_builder
            .append_value(format!("Total rows: {}", total_rows));
    }

    fn finish(mut self) -> DfResult<DfRecordBatch> {
        DfRecordBatch::try_new(
            self.schema,
            vec![
                Arc::new(self.stage_builder.finish()),
                Arc::new(self.node_builder.finish()),
                Arc::new(self.plan_builder.finish()),
            ],
        )
        .map_err(DataFusionError::from)
    }
}

/// Creates the output of AnalyzeExec as a RecordBatch
fn create_output_batch(
    total_rows: usize,
    input: Arc<dyn ExecutionPlan>,
    schema: SchemaRef,
    format: AnalyzeFormat,
    verbose: bool,
) -> DfResult<DfRecordBatch> {
    let mut builder = AnalyzeOutputBuilder::new(schema);

    // Treat the current stage as stage 0. Fetch its metrics
    let mut collector = MetricCollector::new(verbose);
    // Safety: metric collector won't return error
    accept(input.as_ref(), &mut collector).unwrap();
    let stage_0_metrics = collector.record_batch_metrics;

    // Append the metrics of the current stage
    builder.append_metric(0, 0, metrics_to_string(stage_0_metrics, format)?);

    // Find merge scan and append its sub_stage_metrics
    input.apply(|plan| {
        if let Some(merge_scan) = plan.as_any().downcast_ref::<MergeScanExec>() {
            let sub_stage_metrics = merge_scan.sub_stage_metrics();
            for (node, metric) in sub_stage_metrics.into_iter().enumerate() {
                builder.append_metric(1, node as _, metrics_to_string(metric, format)?);
            }
            // might have multiple merge scans, so continue
            return Ok(TreeNodeRecursion::Continue);
        }
        Ok(TreeNodeRecursion::Continue)
    })?;

    // Write total rows
    builder.append_total_rows(total_rows);

    builder.finish()
}

fn metrics_to_string(metrics: RecordBatchMetrics, format: AnalyzeFormat) -> DfResult<String> {
    match format {
        AnalyzeFormat::JSON => Ok(JsonMetrics::from_record_batch_metrics(metrics).to_string()),
        AnalyzeFormat::TEXT => Ok(metrics.to_string()),
        format => Err(DataFusionError::NotImplemented(format!(
            "AnalyzeFormat {format}",
        ))),
    }
}

#[derive(Debug, Default, Serialize)]
struct JsonMetrics {
    name: String,
    param: String,

    // well-known metrics
    output_rows: usize,
    // busy time in nanoseconds
    elapsed_compute: usize,

    // other metrics
    metrics: HashMap<String, usize>,
    children: Vec<JsonMetrics>,
}

impl JsonMetrics {
    fn from_record_batch_metrics(record_batch_metrics: RecordBatchMetrics) -> Self {
        let mut layers: HashMap<usize, Vec<Self>> = HashMap::default();

        for plan_metrics in record_batch_metrics.plan_metrics.into_iter().rev() {
            let (level, mut metrics) = Self::from_plan_metrics(plan_metrics);
            if let Some(next_layer) = layers.remove(&(level + 1)) {
                metrics.children = next_layer;
            }
            if level == 0 {
                return metrics;
            }
            layers.entry(level).or_default().push(metrics);
        }

        // Unreachable path. Each metrics should contains at least one level 0.
        Self::default()
    }

    /// Convert a [`PlanMetrics`] to a [`JsonMetrics`] without children.
    ///
    /// Returns the level of the plan and the [`JsonMetrics`].
    fn from_plan_metrics(plan_metrics: PlanMetrics) -> (usize, Self) {
        let raw_name = plan_metrics.plan.trim_end();
        let mut elapsed_compute = 0;
        let mut output_rows = 0;
        let mut other_metrics = HashMap::default();
        let (name, param) = raw_name.split_once(": ").unwrap_or_default();

        for (name, value) in plan_metrics.metrics.into_iter() {
            if name == "elapsed_compute" {
                elapsed_compute = value;
            } else if name == "output_rows" {
                output_rows = value;
            } else {
                other_metrics.insert(name, value);
            }
        }

        (
            plan_metrics.level,
            Self {
                name: name.to_string(),
                param: param.to_string(),
                output_rows,
                elapsed_compute,
                metrics: other_metrics,
                children: vec![],
            },
        )
    }
}

impl Display for JsonMetrics {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        write!(f, "{}", serde_json::to_string(self).unwrap())
    }
}