Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

[Cleanup] Move AggregatedMetricsSet to metrics for further reuse #1663

Merged
merged 1 commit into from
Jan 24, 2022
Merged

[Cleanup] Move AggregatedMetricsSet to metrics for further reuse #1663

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
149 changes: 149 additions & 0 deletions datafusion/src/physical_plan/metrics/aggregated.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,149 @@
// 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.

//! Metrics common for complex operators with multiple steps.

use crate::physical_plan::metrics::{
BaselineMetrics, Count, ExecutionPlanMetricsSet, MetricsSet, Time,
};
use std::sync::Arc;
use std::time::Duration;

#[derive(Debug, Clone)]
/// Aggregates all metrics during a complex operation, which is composed of multiple steps and
/// each stage reports its statistics separately.
/// Give sort as an example, when the dataset is more significant than available memory, it will report
/// multiple in-mem sort metrics and final merge-sort metrics from `SortPreservingMergeStream`.
/// Therefore, We need a separation of metrics for which are final metrics (for output_rows accumulation),
/// and which are intermediate metrics that we only account for elapsed_compute time.
pub struct AggregatedMetricsSet {
intermediate: Arc<std::sync::Mutex<Vec<ExecutionPlanMetricsSet>>>,
final_: Arc<std::sync::Mutex<Vec<ExecutionPlanMetricsSet>>>,
}

impl AggregatedMetricsSet {
/// Create a new aggregated set
pub(crate) fn new() -> Self {
Self {
intermediate: Arc::new(std::sync::Mutex::new(vec![])),
final_: Arc::new(std::sync::Mutex::new(vec![])),
}
}

/// create a new intermediate baseline
pub(crate) fn new_intermediate_baseline(&self, partition: usize) -> BaselineMetrics {
let ms = ExecutionPlanMetricsSet::new();
let result = BaselineMetrics::new(&ms, partition);
self.intermediate.lock().unwrap().push(ms);
result
}

/// create a new final baseline
pub(crate) fn new_final_baseline(&self, partition: usize) -> BaselineMetrics {
let ms = ExecutionPlanMetricsSet::new();
let result = BaselineMetrics::new(&ms, partition);
self.final_.lock().unwrap().push(ms);
result
}

fn merge_compute_time(&self, dest: &Time) {
let time1 = self
.intermediate
.lock()
.unwrap()
.iter()
.map(|es| {
es.clone_inner()
.elapsed_compute()
.map_or(0u64, |v| v as u64)
})
.sum();
let time2 = self
.final_
.lock()
.unwrap()
.iter()
.map(|es| {
es.clone_inner()
.elapsed_compute()
.map_or(0u64, |v| v as u64)
})
.sum();
dest.add_duration(Duration::from_nanos(time1));
dest.add_duration(Duration::from_nanos(time2));
}

fn merge_spill_count(&self, dest: &Count) {
let count1 = self
.intermediate
.lock()
.unwrap()
.iter()
.map(|es| es.clone_inner().spill_count().map_or(0, |v| v))
.sum();
let count2 = self
.final_
.lock()
.unwrap()
.iter()
.map(|es| es.clone_inner().spill_count().map_or(0, |v| v))
.sum();
dest.add(count1);
dest.add(count2);
}

fn merge_spilled_bytes(&self, dest: &Count) {
let count1 = self
.intermediate
.lock()
.unwrap()
.iter()
.map(|es| es.clone_inner().spilled_bytes().map_or(0, |v| v))
.sum();
let count2 = self
.final_
.lock()
.unwrap()
.iter()
.map(|es| es.clone_inner().spilled_bytes().map_or(0, |v| v))
.sum();
dest.add(count1);
dest.add(count2);
}

fn merge_output_count(&self, dest: &Count) {
let count = self
.final_
.lock()
.unwrap()
.iter()
.map(|es| es.clone_inner().output_rows().map_or(0, |v| v))
.sum();
dest.add(count);
}

/// Aggregate all metrics into a one
pub(crate) fn aggregate_all(&self) -> MetricsSet {
let metrics = ExecutionPlanMetricsSet::new();
let baseline = BaselineMetrics::new(&metrics, 0);
self.merge_compute_time(baseline.elapsed_compute());
self.merge_spill_count(baseline.spill_count());
self.merge_spilled_bytes(baseline.spilled_bytes());
self.merge_output_count(baseline.output_rows());
metrics.clone_inner()
}
}
2 changes: 2 additions & 0 deletions datafusion/src/physical_plan/metrics/mod.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@

//! Metrics for recording information about execution

mod aggregated;
mod baseline;
mod builder;
mod value;
Expand All @@ -30,6 +31,7 @@ use std::{
use hashbrown::HashMap;

// public exports
pub use aggregated::AggregatedMetricsSet;
pub use baseline::{BaselineMetrics, RecordOutput};
pub use builder::MetricBuilder;
pub use value::{Count, MetricValue, ScopedTimerGuard, Time, Timestamp};
Expand Down
130 changes: 2 additions & 128 deletions datafusion/src/physical_plan/sorts/sort.rs
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -26,9 +26,7 @@ use crate::execution::memory_manager::{
use crate::execution::runtime_env::RuntimeEnv;
use crate::physical_plan::common::{batch_byte_size, IPCWriter, SizedRecordBatchStream};
use crate::physical_plan::expressions::PhysicalSortExpr;
use crate::physical_plan::metrics::{
BaselineMetrics, Count, ExecutionPlanMetricsSet, MetricsSet, Time,
};
use crate::physical_plan::metrics::{AggregatedMetricsSet, BaselineMetrics, MetricsSet};
use crate::physical_plan::sorts::sort_preserving_merge::SortPreservingMergeStream;
use crate::physical_plan::sorts::SortedStream;
use crate::physical_plan::stream::RecordBatchReceiverStream;
Expand All @@ -54,7 +52,6 @@ use std::fs::File;
use std::io::BufReader;
use std::sync::atomic::{AtomicUsize, Ordering};
use std::sync::Arc;
use std::time::Duration;
use tokio::sync::mpsc::{Receiver as TKReceiver, Sender as TKSender};
use tokio::task;

Expand Down Expand Up @@ -365,121 +362,6 @@ pub struct SortExec {
preserve_partitioning: bool,
}

#[derive(Debug, Clone)]
/// Aggregates all metrics during a complex operation, which is composed of multiple steps and
/// each stage reports its statistics separately.
/// Give sort as an example, when the dataset is more significant than available memory, it will report
/// multiple in-mem sort metrics and final merge-sort metrics from `SortPreservingMergeStream`.
/// Therefore, We need a separation of metrics for which are final metrics (for output_rows accumulation),
/// and which are intermediate metrics that we only account for elapsed_compute time.
struct AggregatedMetricsSet {
intermediate: Arc<std::sync::Mutex<Vec<ExecutionPlanMetricsSet>>>,
final_: Arc<std::sync::Mutex<Vec<ExecutionPlanMetricsSet>>>,
}

impl AggregatedMetricsSet {
fn new() -> Self {
Self {
intermediate: Arc::new(std::sync::Mutex::new(vec![])),
final_: Arc::new(std::sync::Mutex::new(vec![])),
}
}

fn new_intermediate_baseline(&self, partition: usize) -> BaselineMetrics {
let ms = ExecutionPlanMetricsSet::new();
let result = BaselineMetrics::new(&ms, partition);
self.intermediate.lock().unwrap().push(ms);
result
}

fn new_final_baseline(&self, partition: usize) -> BaselineMetrics {
let ms = ExecutionPlanMetricsSet::new();
let result = BaselineMetrics::new(&ms, partition);
self.final_.lock().unwrap().push(ms);
result
}

/// We should accumulate all times from all steps' reports for the total time consumption.
fn merge_compute_time(&self, dest: &Time) {
let time1 = self
.intermediate
.lock()
.unwrap()
.iter()
.map(|es| {
es.clone_inner()
.elapsed_compute()
.map_or(0u64, |v| v as u64)
})
.sum();
let time2 = self
.final_
.lock()
.unwrap()
.iter()
.map(|es| {
es.clone_inner()
.elapsed_compute()
.map_or(0u64, |v| v as u64)
})
.sum();
dest.add_duration(Duration::from_nanos(time1));
dest.add_duration(Duration::from_nanos(time2));
}

/// We should accumulate all count from all steps' reports for the total spill count.
fn merge_spill_count(&self, dest: &Count) {
let count1 = self
.intermediate
.lock()
.unwrap()
.iter()
.map(|es| es.clone_inner().spill_count().map_or(0, |v| v))
.sum();
let count2 = self
.final_
.lock()
.unwrap()
.iter()
.map(|es| es.clone_inner().spill_count().map_or(0, |v| v))
.sum();
dest.add(count1);
dest.add(count2);
}

/// We should accumulate all spilled bytes from all steps' reports for the total spilled bytes.
fn merge_spilled_bytes(&self, dest: &Count) {
let count1 = self
.intermediate
.lock()
.unwrap()
.iter()
.map(|es| es.clone_inner().spilled_bytes().map_or(0, |v| v))
.sum();
let count2 = self
.final_
.lock()
.unwrap()
.iter()
.map(|es| es.clone_inner().spilled_bytes().map_or(0, |v| v))
.sum();
dest.add(count1);
dest.add(count2);
}

/// We should only care about output from the final stage metrics.
fn merge_output_count(&self, dest: &Count) {
let count = self
.final_
.lock()
.unwrap()
.iter()
.map(|es| es.clone_inner().output_rows().map_or(0, |v| v))
.sum();
dest.add(count);
}
}

impl SortExec {
/// Create a new sort execution plan
pub fn try_new(
Expand Down Expand Up @@ -595,15 +477,7 @@ impl ExecutionPlan for SortExec {
}

fn metrics(&self) -> Option<MetricsSet> {
let metrics = ExecutionPlanMetricsSet::new();
let baseline = BaselineMetrics::new(&metrics, 0);
self.all_metrics
.merge_compute_time(baseline.elapsed_compute());
self.all_metrics.merge_spill_count(baseline.spill_count());
self.all_metrics
.merge_spilled_bytes(baseline.spilled_bytes());
self.all_metrics.merge_output_count(baseline.output_rows());
Some(metrics.clone_inner())
Some(self.all_metrics.aggregate_all())
}

fn fmt_as(
Expand Down