lance_datafusion/

exec.rs

// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The Lance Authors

//! Utilities for working with datafusion execution plans

use std::{
    collections::HashMap,
    fmt::{self, Formatter},
    sync::{Arc, LazyLock, Mutex},
    time::Duration,
};

use arrow_array::RecordBatch;
use arrow_schema::Schema as ArrowSchema;
use datafusion::{
    catalog::streaming::StreamingTable,
    dataframe::DataFrame,
    execution::{
        context::{SessionConfig, SessionContext},
        disk_manager::DiskManagerBuilder,
        memory_pool::FairSpillPool,
        runtime_env::RuntimeEnvBuilder,
        TaskContext,
    },
    physical_plan::{
        analyze::AnalyzeExec,
        display::DisplayableExecutionPlan,
        execution_plan::{Boundedness, CardinalityEffect, EmissionType},
        stream::RecordBatchStreamAdapter,
        streaming::PartitionStream,
        DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties, SendableRecordBatchStream,
    },
};
use datafusion_common::{DataFusionError, Statistics};
use datafusion_physical_expr::{EquivalenceProperties, Partitioning};

use futures::{stream, StreamExt};
use lance_arrow::SchemaExt;
use lance_core::{
    utils::{
        futures::FinallyStreamExt,
        tracing::{StreamTracingExt, EXECUTION_PLAN_RUN, TRACE_EXECUTION},
    },
    Error, Result,
};
use log::{debug, info, warn};
use snafu::location;
use tracing::Span;

use crate::udf::register_functions;
use crate::{
    chunker::StrictBatchSizeStream,
    utils::{
        MetricsExt, BYTES_READ_METRIC, INDEX_COMPARISONS_METRIC, INDICES_LOADED_METRIC,
        IOPS_METRIC, PARTS_LOADED_METRIC, REQUESTS_METRIC,
    },
};

/// An source execution node created from an existing stream
///
/// It can only be used once, and will return the stream.  After that the node
/// is exhausted.
///
/// Note: the stream should be finite, otherwise we will report datafusion properties
/// incorrectly.
pub struct OneShotExec {
    stream: Mutex<Option<SendableRecordBatchStream>>,
    // We save off a copy of the schema to speed up formatting and so ExecutionPlan::schema & display_as
    // can still function after exhausted
    schema: Arc<ArrowSchema>,
    properties: PlanProperties,
}

impl OneShotExec {
    /// Create a new instance from a given stream
    pub fn new(stream: SendableRecordBatchStream) -> Self {
        let schema = stream.schema();
        Self {
            stream: Mutex::new(Some(stream)),
            schema: schema.clone(),
            properties: PlanProperties::new(
                EquivalenceProperties::new(schema),
                Partitioning::RoundRobinBatch(1),
                EmissionType::Incremental,
                Boundedness::Bounded,
            ),
        }
    }

    pub fn from_batch(batch: RecordBatch) -> Self {
        let schema = batch.schema();
        let stream = Box::pin(RecordBatchStreamAdapter::new(
            schema,
            stream::iter(vec![Ok(batch)]),
        ));
        Self::new(stream)
    }
}

impl std::fmt::Debug for OneShotExec {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        let stream = self.stream.lock().unwrap();
        f.debug_struct("OneShotExec")
            .field("exhausted", &stream.is_none())
            .field("schema", self.schema.as_ref())
            .finish()
    }
}

impl DisplayAs for OneShotExec {
    fn fmt_as(
        &self,
        t: datafusion::physical_plan::DisplayFormatType,
        f: &mut std::fmt::Formatter,
    ) -> std::fmt::Result {
        let stream = self.stream.lock().unwrap();
        let exhausted = if stream.is_some() { "" } else { "EXHAUSTED" };
        let columns = self
            .schema
            .field_names()
            .iter()
            .cloned()
            .cloned()
            .collect::<Vec<_>>();
        match t {
            DisplayFormatType::Default | DisplayFormatType::Verbose => {
                write!(
                    f,
                    "OneShotStream: {}columns=[{}]",
                    exhausted,
                    columns.join(",")
                )
            }
            DisplayFormatType::TreeRender => {
                write!(
                    f,
                    "OneShotStream\nexhausted={}\ncolumns=[{}]",
                    exhausted,
                    columns.join(",")
                )
            }
        }
    }
}

impl ExecutionPlan for OneShotExec {
    fn name(&self) -> &str {
        "OneShotExec"
    }

    fn as_any(&self) -> &dyn std::any::Any {
        self
    }

    fn schema(&self) -> arrow_schema::SchemaRef {
        self.schema.clone()
    }

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

    fn with_new_children(
        self: Arc<Self>,
        children: Vec<Arc<dyn ExecutionPlan>>,
    ) -> datafusion_common::Result<Arc<dyn ExecutionPlan>> {
        // OneShotExec has no children, so this should only be called with an empty vector
        if !children.is_empty() {
            return Err(datafusion_common::DataFusionError::Internal(
                "OneShotExec does not support children".to_string(),
            ));
        }
        Ok(self)
    }

    fn execute(
        &self,
        _partition: usize,
        _context: Arc<datafusion::execution::TaskContext>,
    ) -> datafusion_common::Result<SendableRecordBatchStream> {
        let stream = self
            .stream
            .lock()
            .map_err(|err| DataFusionError::Execution(err.to_string()))?
            .take();
        if let Some(stream) = stream {
            Ok(stream)
        } else {
            Err(DataFusionError::Execution(
                "OneShotExec has already been executed".to_string(),
            ))
        }
    }

    fn statistics(&self) -> datafusion_common::Result<datafusion_common::Statistics> {
        Ok(Statistics::new_unknown(&self.schema))
    }

    fn properties(&self) -> &datafusion::physical_plan::PlanProperties {
        &self.properties
    }
}

struct TracedExec {
    input: Arc<dyn ExecutionPlan>,
    properties: PlanProperties,
    span: Span,
}

impl TracedExec {
    pub fn new(input: Arc<dyn ExecutionPlan>, span: Span) -> Self {
        Self {
            properties: input.properties().clone(),
            input,
            span,
        }
    }
}

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

impl std::fmt::Debug for TracedExec {
    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
        write!(f, "TracedExec")
    }
}
impl ExecutionPlan for TracedExec {
    fn name(&self) -> &str {
        "TracedExec"
    }

    fn as_any(&self) -> &dyn std::any::Any {
        self
    }

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

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

    fn with_new_children(
        self: Arc<Self>,
        children: Vec<Arc<dyn ExecutionPlan>>,
    ) -> datafusion_common::Result<Arc<dyn ExecutionPlan>> {
        Ok(Arc::new(Self {
            input: children[0].clone(),
            properties: self.properties.clone(),
            span: self.span.clone(),
        }))
    }

    fn execute(
        &self,
        partition: usize,
        context: Arc<TaskContext>,
    ) -> datafusion_common::Result<SendableRecordBatchStream> {
        let _guard = self.span.enter();
        let stream = self.input.execute(partition, context)?;
        let schema = stream.schema();
        let stream = stream.stream_in_span(self.span.clone());
        Ok(Box::pin(RecordBatchStreamAdapter::new(schema, stream)))
    }
}

/// Callback for reporting statistics after a scan
pub type ExecutionStatsCallback = Arc<dyn Fn(&ExecutionSummaryCounts) + Send + Sync>;

#[derive(Default, Clone)]
pub struct LanceExecutionOptions {
    pub use_spilling: bool,
    pub mem_pool_size: Option<u64>,
    pub batch_size: Option<usize>,
    pub target_partition: Option<usize>,
    pub execution_stats_callback: Option<ExecutionStatsCallback>,
}

impl std::fmt::Debug for LanceExecutionOptions {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("LanceExecutionOptions")
            .field("use_spilling", &self.use_spilling)
            .field("mem_pool_size", &self.mem_pool_size)
            .field("batch_size", &self.batch_size)
            .field("target_partition", &self.target_partition)
            .field(
                "execution_stats_callback",
                &self.execution_stats_callback.is_some(),
            )
            .finish()
    }
}

const DEFAULT_LANCE_MEM_POOL_SIZE: u64 = 100 * 1024 * 1024;

impl LanceExecutionOptions {
    pub fn mem_pool_size(&self) -> u64 {
        self.mem_pool_size.unwrap_or_else(|| {
            std::env::var("LANCE_MEM_POOL_SIZE")
                .map(|s| match s.parse::<u64>() {
                    Ok(v) => v,
                    Err(e) => {
                        warn!("Failed to parse LANCE_MEM_POOL_SIZE: {}, using default", e);
                        DEFAULT_LANCE_MEM_POOL_SIZE
                    }
                })
                .unwrap_or(DEFAULT_LANCE_MEM_POOL_SIZE)
        })
    }

    pub fn use_spilling(&self) -> bool {
        if !self.use_spilling {
            return false;
        }
        std::env::var("LANCE_BYPASS_SPILLING")
            .map(|_| {
                info!("Bypassing spilling because LANCE_BYPASS_SPILLING is set");
                false
            })
            .unwrap_or(true)
    }
}

pub fn new_session_context(options: &LanceExecutionOptions) -> SessionContext {
    let mut session_config = SessionConfig::new();
    let mut runtime_env_builder = RuntimeEnvBuilder::new();
    if let Some(target_partition) = options.target_partition {
        session_config = session_config.with_target_partitions(target_partition);
    }
    if options.use_spilling() {
        runtime_env_builder = runtime_env_builder
            .with_disk_manager_builder(DiskManagerBuilder::default())
            .with_memory_pool(Arc::new(FairSpillPool::new(
                options.mem_pool_size() as usize
            )));
    }
    let runtime_env = runtime_env_builder.build_arc().unwrap();

    let ctx = SessionContext::new_with_config_rt(session_config, runtime_env);
    register_functions(&ctx);

    ctx
}

static DEFAULT_SESSION_CONTEXT: LazyLock<SessionContext> =
    LazyLock::new(|| new_session_context(&LanceExecutionOptions::default()));

static DEFAULT_SESSION_CONTEXT_WITH_SPILLING: LazyLock<SessionContext> = LazyLock::new(|| {
    new_session_context(&LanceExecutionOptions {
        use_spilling: true,
        ..Default::default()
    })
});

pub fn get_session_context(options: &LanceExecutionOptions) -> SessionContext {
    if options.mem_pool_size() == DEFAULT_LANCE_MEM_POOL_SIZE && options.target_partition.is_none()
    {
        return if options.use_spilling() {
            DEFAULT_SESSION_CONTEXT_WITH_SPILLING.clone()
        } else {
            DEFAULT_SESSION_CONTEXT.clone()
        };
    }
    new_session_context(options)
}

fn get_task_context(
    session_ctx: &SessionContext,
    options: &LanceExecutionOptions,
) -> Arc<TaskContext> {
    let mut state = session_ctx.state();
    if let Some(batch_size) = options.batch_size.as_ref() {
        state.config_mut().options_mut().execution.batch_size = *batch_size;
    }

    state.task_ctx()
}

#[derive(Default, Clone, Debug, PartialEq, Eq)]
pub struct ExecutionSummaryCounts {
    /// The number of I/O operations performed
    pub iops: usize,
    /// The number of requests made to the storage layer (may be larger or smaller than iops
    /// depending on coalescing configuration)
    pub requests: usize,
    /// The number of bytes read during the execution of the plan
    pub bytes_read: usize,
    /// The number of top-level indices loaded
    pub indices_loaded: usize,
    /// The number of index partitions loaded
    pub parts_loaded: usize,
    /// The number of index comparisons performed (the exact meaning depends on the index type)
    pub index_comparisons: usize,
    /// Additional metrics for more detailed statistics.  These are subject to change in the future
    /// and should only be used for debugging purposes.
    pub all_counts: HashMap<String, usize>,
}

fn visit_node(node: &dyn ExecutionPlan, counts: &mut ExecutionSummaryCounts) {
    if let Some(metrics) = node.metrics() {
        for (metric_name, count) in metrics.iter_counts() {
            match metric_name.as_ref() {
                IOPS_METRIC => counts.iops += count.value(),
                REQUESTS_METRIC => counts.requests += count.value(),
                BYTES_READ_METRIC => counts.bytes_read += count.value(),
                INDICES_LOADED_METRIC => counts.indices_loaded += count.value(),
                PARTS_LOADED_METRIC => counts.parts_loaded += count.value(),
                INDEX_COMPARISONS_METRIC => counts.index_comparisons += count.value(),
                _ => {
                    let existing = counts
                        .all_counts
                        .entry(metric_name.as_ref().to_string())
                        .or_insert(0);
                    *existing += count.value();
                }
            }
        }
    }
    for child in node.children() {
        visit_node(child.as_ref(), counts);
    }
}

fn report_plan_summary_metrics(plan: &dyn ExecutionPlan, options: &LanceExecutionOptions) {
    let output_rows = plan
        .metrics()
        .map(|m| m.output_rows().unwrap_or(0))
        .unwrap_or(0);
    let mut counts = ExecutionSummaryCounts::default();
    visit_node(plan, &mut counts);
    tracing::info!(
        target: TRACE_EXECUTION,
        r#type = EXECUTION_PLAN_RUN,
        output_rows,
        iops = counts.iops,
        requests = counts.requests,
        bytes_read = counts.bytes_read,
        indices_loaded = counts.indices_loaded,
        parts_loaded = counts.parts_loaded,
        index_comparisons = counts.index_comparisons,
    );
    if let Some(callback) = options.execution_stats_callback.as_ref() {
        callback(&counts);
    }
}

/// Executes a plan using default session & runtime configuration
///
/// Only executes a single partition.  Panics if the plan has more than one partition.
pub fn execute_plan(
    plan: Arc<dyn ExecutionPlan>,
    options: LanceExecutionOptions,
) -> Result<SendableRecordBatchStream> {
    debug!(
        "Executing plan:\n{}",
        DisplayableExecutionPlan::new(plan.as_ref()).indent(true)
    );

    let session_ctx = get_session_context(&options);

    // NOTE: we are only executing the first partition here. Therefore, if
    // the plan has more than one partition, we will be missing data.
    assert_eq!(plan.properties().partitioning.partition_count(), 1);
    let stream = plan.execute(0, get_task_context(&session_ctx, &options))?;

    let schema = stream.schema();
    let stream = stream.finally(move || {
        report_plan_summary_metrics(plan.as_ref(), &options);
    });
    Ok(Box::pin(RecordBatchStreamAdapter::new(schema, stream)))
}

pub async fn analyze_plan(
    plan: Arc<dyn ExecutionPlan>,
    options: LanceExecutionOptions,
) -> Result<String> {
    // This is needed as AnalyzeExec launches a thread task per
    // partition, and we want these to be connected to the parent span
    let plan = Arc::new(TracedExec::new(plan, Span::current()));

    let schema = plan.schema();
    let analyze = Arc::new(AnalyzeExec::new(true, true, plan, schema));

    let session_ctx = get_session_context(&options);
    assert_eq!(analyze.properties().partitioning.partition_count(), 1);
    let mut stream = analyze
        .execute(0, get_task_context(&session_ctx, &options))
        .map_err(|err| {
            Error::io(
                format!("Failed to execute analyze plan: {}", err),
                location!(),
            )
        })?;

    // fully execute the plan
    while (stream.next().await).is_some() {}

    let result = format_plan(analyze);
    Ok(result)
}

pub fn format_plan(plan: Arc<dyn ExecutionPlan>) -> String {
    /// A visitor which calculates additional metrics for all the plans.
    struct CalculateVisitor {
        highest_index: usize,
        index_to_cumulative_cpu: HashMap<usize, usize>,
    }
    impl CalculateVisitor {
        fn calculate_cumulative_cpu(&mut self, plan: &Arc<dyn ExecutionPlan>) -> usize {
            self.highest_index += 1;
            let plan_index = self.highest_index;
            let elapsed_cpu: usize = match plan.metrics() {
                Some(metrics) => metrics.elapsed_compute().unwrap_or_default(),
                None => 0,
            };
            let mut cumulative_cpu = elapsed_cpu;
            for child in plan.children() {
                cumulative_cpu += self.calculate_cumulative_cpu(child);
            }
            self.index_to_cumulative_cpu
                .insert(plan_index, cumulative_cpu);
            cumulative_cpu
        }
    }

    /// A visitor which prints out all the plans.
    struct PrintVisitor {
        highest_index: usize,
        indent: usize,
    }
    impl PrintVisitor {
        fn write_output(
            &mut self,
            plan: &Arc<dyn ExecutionPlan>,
            f: &mut Formatter,
            calcs: &CalculateVisitor,
        ) -> std::fmt::Result {
            self.highest_index += 1;
            write!(f, "{:indent$}", "", indent = self.indent * 2)?;
            plan.fmt_as(datafusion::physical_plan::DisplayFormatType::Verbose, f)?;
            if let Some(metrics) = plan.metrics() {
                let metrics = metrics
                    .aggregate_by_name()
                    .sorted_for_display()
                    .timestamps_removed();

                write!(f, ", metrics=[{metrics}]")?;
            } else {
                write!(f, ", metrics=[]")?;
            }
            let cumulative_cpu = calcs
                .index_to_cumulative_cpu
                .get(&self.highest_index)
                .unwrap();
            let cumulative_cpu_duration = Duration::from_nanos((*cumulative_cpu) as u64);
            write!(f, ", cumulative_cpu={cumulative_cpu_duration:?}")?;
            writeln!(f)?;
            self.indent += 1;
            for child in plan.children() {
                self.write_output(child, f, calcs)?;
            }
            self.indent -= 1;
            std::fmt::Result::Ok(())
        }
    }
    // A wrapper which prints out a plan.
    struct PrintWrapper {
        plan: Arc<dyn ExecutionPlan>,
    }
    impl fmt::Display for PrintWrapper {
        fn fmt(&self, f: &mut Formatter) -> std::fmt::Result {
            let mut calcs = CalculateVisitor {
                highest_index: 0,
                index_to_cumulative_cpu: HashMap::new(),
            };
            calcs.calculate_cumulative_cpu(&self.plan);
            let mut prints = PrintVisitor {
                highest_index: 0,
                indent: 0,
            };
            prints.write_output(&self.plan, f, &calcs)
        }
    }
    let wrapper = PrintWrapper { plan };
    format!("{}", wrapper)
}

pub trait SessionContextExt {
    /// Creates a DataFrame for reading a stream of data
    ///
    /// This dataframe may only be queried once, future queries will fail
    fn read_one_shot(
        &self,
        data: SendableRecordBatchStream,
    ) -> datafusion::common::Result<DataFrame>;
}

struct OneShotPartitionStream {
    data: Arc<Mutex<Option<SendableRecordBatchStream>>>,
    schema: Arc<ArrowSchema>,
}

impl std::fmt::Debug for OneShotPartitionStream {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        let data = self.data.lock().unwrap();
        f.debug_struct("OneShotPartitionStream")
            .field("exhausted", &data.is_none())
            .field("schema", self.schema.as_ref())
            .finish()
    }
}

impl OneShotPartitionStream {
    fn new(data: SendableRecordBatchStream) -> Self {
        let schema = data.schema();
        Self {
            data: Arc::new(Mutex::new(Some(data))),
            schema,
        }
    }
}

impl PartitionStream for OneShotPartitionStream {
    fn schema(&self) -> &arrow_schema::SchemaRef {
        &self.schema
    }

    fn execute(&self, _ctx: Arc<TaskContext>) -> SendableRecordBatchStream {
        let mut stream = self.data.lock().unwrap();
        stream
            .take()
            .expect("Attempt to consume a one shot dataframe multiple times")
    }
}

impl SessionContextExt for SessionContext {
    fn read_one_shot(
        &self,
        data: SendableRecordBatchStream,
    ) -> datafusion::common::Result<DataFrame> {
        let schema = data.schema();
        let part_stream = Arc::new(OneShotPartitionStream::new(data));
        let provider = StreamingTable::try_new(schema, vec![part_stream])?;
        self.read_table(Arc::new(provider))
    }
}

#[derive(Clone, Debug)]
pub struct StrictBatchSizeExec {
    input: Arc<dyn ExecutionPlan>,
    batch_size: usize,
}

impl StrictBatchSizeExec {
    pub fn new(input: Arc<dyn ExecutionPlan>, batch_size: usize) -> Self {
        Self { input, batch_size }
    }
}

impl DisplayAs for StrictBatchSizeExec {
    fn fmt_as(
        &self,
        _t: datafusion::physical_plan::DisplayFormatType,
        f: &mut std::fmt::Formatter,
    ) -> std::fmt::Result {
        write!(f, "StrictBatchSizeExec")
    }
}

impl ExecutionPlan for StrictBatchSizeExec {
    fn name(&self) -> &str {
        "StrictBatchSizeExec"
    }

    fn as_any(&self) -> &dyn std::any::Any {
        self
    }

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

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

    fn with_new_children(
        self: Arc<Self>,
        children: Vec<Arc<dyn ExecutionPlan>>,
    ) -> datafusion_common::Result<Arc<dyn ExecutionPlan>> {
        Ok(Arc::new(Self {
            input: children[0].clone(),
            batch_size: self.batch_size,
        }))
    }

    fn execute(
        &self,
        partition: usize,
        context: Arc<TaskContext>,
    ) -> datafusion_common::Result<SendableRecordBatchStream> {
        let stream = self.input.execute(partition, context)?;
        let schema = stream.schema();
        let stream = StrictBatchSizeStream::new(stream, self.batch_size);
        Ok(Box::pin(RecordBatchStreamAdapter::new(schema, stream)))
    }

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

    fn benefits_from_input_partitioning(&self) -> Vec<bool> {
        vec![false]
    }

    fn partition_statistics(
        &self,
        partition: Option<usize>,
    ) -> datafusion_common::Result<Statistics> {
        self.input.partition_statistics(partition)
    }

    fn cardinality_effect(&self) -> CardinalityEffect {
        CardinalityEffect::Equal
    }
}