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[GitHub] [arrow-datafusion] yjshen opened a new pull request #2132: WIP: Reduce sort memory usage v1
yjshen opened a new pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132
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[GitHub] [arrow-datafusion] yjshen commented on pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen commented on pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#issuecomment-1085799873
A modified version of TPC-H q1:
select
l_returnflag,
l_linestatus,
l_quantity,
l_extendedprice,
l_discount,
l_tax
from
lineitem
order by
l_extendedprice,
l_discount;
Before this PR:
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 8192, path: "/Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 5922.3 ms and returned 6001214 rows
Query 1 iteration 1 took 5699.3 ms and returned 6001214 rows
Query 1 iteration 2 took 6166.0 ms and returned 6001214 rows
Query 1 avg time: 5929.20 ms
With this PR:
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 8192, path: "/Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 4031.2 ms and returned 6001214 rows
Query 1 iteration 1 took 4375.6 ms and returned 6001214 rows
Query 1 iteration 2 took 4318.8 ms and returned 6001214 rows
Query 1 avg time: 4241.89 ms
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[GitHub] [arrow-datafusion] yjshen commented on a change in pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen commented on a change in pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#discussion_r840510928
##########
File path: datafusion/core/src/physical_plan/sorts/sort2.rs
##########
@@ -0,0 +1,1147 @@
+// 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.
+
+//! Sort that deals with an arbitrary size of the input.
+//! It will do in-memory sorting if it has enough memory budget
+//! but spills to disk if needed.
+
+use crate::error::{DataFusionError, Result};
+use crate::execution::context::TaskContext;
+use crate::execution::memory_manager::{
+ human_readable_size, ConsumerType, MemoryConsumer, MemoryConsumerId, MemoryManager,
+};
+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, CompositeMetricsSet, MemTrackingMetrics, MetricsSet,
+};
+use crate::physical_plan::sorts::sort_preserving_merge::SortPreservingMergeStream;
+use crate::physical_plan::sorts::SortedStream;
+use crate::physical_plan::stream::RecordBatchReceiverStream;
+use crate::physical_plan::{
+ DisplayFormatType, Distribution, EmptyRecordBatchStream, ExecutionPlan, Partitioning,
+ RecordBatchStream, SendableRecordBatchStream, Statistics,
+};
+use crate::prelude::SessionConfig;
+use arrow::array::{make_array, Array, ArrayRef, MutableArrayData, UInt32Array};
+pub use arrow::compute::SortOptions;
+use arrow::compute::{concat, lexsort_to_indices, take, SortColumn, TakeOptions};
+use arrow::datatypes::SchemaRef;
+use arrow::error::Result as ArrowResult;
+use arrow::ipc::reader::FileReader;
+use arrow::record_batch::RecordBatch;
+use async_trait::async_trait;
+use futures::lock::Mutex;
+use futures::{Stream, StreamExt};
+use log::{debug, error};
+use std::any::Any;
+use std::cmp::min;
+use std::fmt;
+use std::fmt::{Debug, Formatter};
+use std::fs::File;
+use std::io::BufReader;
+use std::path::{Path, PathBuf};
+use std::sync::Arc;
+use std::task::{Context, Poll};
+use tempfile::NamedTempFile;
+use tokio::sync::mpsc::{Receiver, Sender};
+use tokio::task;
+
+/// Sort arbitrary size of data to get a total order (may spill several times during sorting based on free memory available).
+///
+/// The basic architecture of the algorithm:
+/// 1. get a non-empty new batch from input
+/// 2. check with the memory manager if we could buffer the batch in memory
+/// 2.1 if memory sufficient, then buffer batch in memory, go to 1.
+/// 2.2 if the memory threshold is reached, sort all buffered batches and spill to file.
+/// buffer the batch in memory, go to 1.
+/// 3. when input is exhausted, merge all in memory batches and spills to get a total order.
+struct ExternalSorter2 {
+ id: MemoryConsumerId,
+ schema: SchemaRef,
+ in_mem_batches: Mutex<Vec<RecordBatch>>,
+ spills: Mutex<Vec<NamedTempFile>>,
+ /// Sort expressions
+ expr: Vec<PhysicalSortExpr>,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ metrics_set: CompositeMetricsSet,
+ metrics: BaselineMetrics,
+}
+
+impl ExternalSorter2 {
+ pub fn new(
+ partition_id: usize,
+ schema: SchemaRef,
+ expr: Vec<PhysicalSortExpr>,
+ metrics_set: CompositeMetricsSet,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ ) -> Self {
+ let metrics = metrics_set.new_intermediate_baseline(partition_id);
+ Self {
+ id: MemoryConsumerId::new(partition_id),
+ schema,
+ in_mem_batches: Mutex::new(vec![]),
+ spills: Mutex::new(vec![]),
+ expr,
+ session_config,
+ runtime,
+ metrics_set,
+ metrics,
+ }
+ }
+
+ async fn insert_batch(
+ &self,
+ input: RecordBatch,
+ tracking_metrics: &MemTrackingMetrics,
+ ) -> Result<()> {
+ if input.num_rows() > 0 {
+ let size = batch_byte_size(&input);
+ self.try_grow(size).await?;
+ self.metrics.mem_used().add(size);
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ let partial = sort_batch(input, self.schema.clone(), &self.expr)?;
Review comment:
The other change: sort each batch before buffering it in memory.
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[GitHub] [arrow-datafusion] yjshen commented on a change in pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen commented on a change in pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#discussion_r840510928
##########
File path: datafusion/core/src/physical_plan/sorts/sort2.rs
##########
@@ -0,0 +1,1147 @@
+// 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.
+
+//! Sort that deals with an arbitrary size of the input.
+//! It will do in-memory sorting if it has enough memory budget
+//! but spills to disk if needed.
+
+use crate::error::{DataFusionError, Result};
+use crate::execution::context::TaskContext;
+use crate::execution::memory_manager::{
+ human_readable_size, ConsumerType, MemoryConsumer, MemoryConsumerId, MemoryManager,
+};
+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, CompositeMetricsSet, MemTrackingMetrics, MetricsSet,
+};
+use crate::physical_plan::sorts::sort_preserving_merge::SortPreservingMergeStream;
+use crate::physical_plan::sorts::SortedStream;
+use crate::physical_plan::stream::RecordBatchReceiverStream;
+use crate::physical_plan::{
+ DisplayFormatType, Distribution, EmptyRecordBatchStream, ExecutionPlan, Partitioning,
+ RecordBatchStream, SendableRecordBatchStream, Statistics,
+};
+use crate::prelude::SessionConfig;
+use arrow::array::{make_array, Array, ArrayRef, MutableArrayData, UInt32Array};
+pub use arrow::compute::SortOptions;
+use arrow::compute::{concat, lexsort_to_indices, take, SortColumn, TakeOptions};
+use arrow::datatypes::SchemaRef;
+use arrow::error::Result as ArrowResult;
+use arrow::ipc::reader::FileReader;
+use arrow::record_batch::RecordBatch;
+use async_trait::async_trait;
+use futures::lock::Mutex;
+use futures::{Stream, StreamExt};
+use log::{debug, error};
+use std::any::Any;
+use std::cmp::min;
+use std::fmt;
+use std::fmt::{Debug, Formatter};
+use std::fs::File;
+use std::io::BufReader;
+use std::path::{Path, PathBuf};
+use std::sync::Arc;
+use std::task::{Context, Poll};
+use tempfile::NamedTempFile;
+use tokio::sync::mpsc::{Receiver, Sender};
+use tokio::task;
+
+/// Sort arbitrary size of data to get a total order (may spill several times during sorting based on free memory available).
+///
+/// The basic architecture of the algorithm:
+/// 1. get a non-empty new batch from input
+/// 2. check with the memory manager if we could buffer the batch in memory
+/// 2.1 if memory sufficient, then buffer batch in memory, go to 1.
+/// 2.2 if the memory threshold is reached, sort all buffered batches and spill to file.
+/// buffer the batch in memory, go to 1.
+/// 3. when input is exhausted, merge all in memory batches and spills to get a total order.
+struct ExternalSorter2 {
+ id: MemoryConsumerId,
+ schema: SchemaRef,
+ in_mem_batches: Mutex<Vec<RecordBatch>>,
+ spills: Mutex<Vec<NamedTempFile>>,
+ /// Sort expressions
+ expr: Vec<PhysicalSortExpr>,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ metrics_set: CompositeMetricsSet,
+ metrics: BaselineMetrics,
+}
+
+impl ExternalSorter2 {
+ pub fn new(
+ partition_id: usize,
+ schema: SchemaRef,
+ expr: Vec<PhysicalSortExpr>,
+ metrics_set: CompositeMetricsSet,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ ) -> Self {
+ let metrics = metrics_set.new_intermediate_baseline(partition_id);
+ Self {
+ id: MemoryConsumerId::new(partition_id),
+ schema,
+ in_mem_batches: Mutex::new(vec![]),
+ spills: Mutex::new(vec![]),
+ expr,
+ session_config,
+ runtime,
+ metrics_set,
+ metrics,
+ }
+ }
+
+ async fn insert_batch(
+ &self,
+ input: RecordBatch,
+ tracking_metrics: &MemTrackingMetrics,
+ ) -> Result<()> {
+ if input.num_rows() > 0 {
+ let size = batch_byte_size(&input);
+ self.try_grow(size).await?;
+ self.metrics.mem_used().add(size);
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ let partial = sort_batch(input, self.schema.clone(), &self.expr)?;
Review comment:
Another change: sort each batch before buffering it in memory.
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[GitHub] [arrow-datafusion] yjshen removed a comment on pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen removed a comment on pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#issuecomment-1085799873
A modified version of TPC-H q1:
```sql
select
l_returnflag,
l_linestatus,
l_quantity,
l_extendedprice,
l_discount,
l_tax
from
lineitem
order by
l_extendedprice,
l_discount;
```
```shell
./target/release/tpch benchmark datafusion --path /Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet -f parquet -q 1
```
Before this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 8192, path: "/Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 5922.3 ms and returned 6001214 rows
Query 1 iteration 1 took 5699.3 ms and returned 6001214 rows
Query 1 iteration 2 took 6166.0 ms and returned 6001214 rows
Query 1 avg time: 5929.20 ms
```
With this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 8192, path: "/Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 4031.2 ms and returned 6001214 rows
Query 1 iteration 1 took 4375.6 ms and returned 6001214 rows
Query 1 iteration 2 took 4318.8 ms and returned 6001214 rows
Query 1 avg time: 4241.89 ms
```
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[GitHub] [arrow-datafusion] yjshen commented on pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen commented on pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#issuecomment-1085806095
A modified version of TPC-H q1:
```sql
select
l_returnflag,
l_linestatus,
l_quantity,
l_extendedprice,
l_discount,
l_tax
from
lineitem
order by
l_extendedprice,
l_discount;
```
```shell
./target/release/tpch benchmark datafusion --path /Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet -f parquet -q 1
```
Before this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 8192, path: "/Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 5922.3 ms and returned 6001214 rows
Query 1 iteration 1 took 5699.3 ms and returned 6001214 rows
Query 1 iteration 2 took 6166.0 ms and returned 6001214 rows
Query 1 avg time: 5929.20 ms
```
With this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 8192, path: "/Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 4031.2 ms and returned 6001214 rows
Query 1 iteration 1 took 4375.6 ms and returned 6001214 rows
Query 1 iteration 2 took 4318.8 ms and returned 6001214 rows
Query 1 avg time: 4241.89 ms
```
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[GitHub] [arrow-datafusion] yjshen commented on a change in pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen commented on a change in pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#discussion_r840496480
##########
File path: benchmarks/queries/q1.sql
##########
@@ -1,21 +1,12 @@
select
l_returnflag,
l_linestatus,
- sum(l_quantity) as sum_qty,
- sum(l_extendedprice) as sum_base_price,
- sum(l_extendedprice * (1 - l_discount)) as sum_disc_price,
- sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge,
- avg(l_quantity) as avg_qty,
- avg(l_extendedprice) as avg_price,
- avg(l_discount) as avg_disc,
- count(*) as count_order
+ l_quantity,
+ l_extendedprice,
+ l_discount,
+ l_tax
from
lineitem
-where
Review comment:
tmp change to bench sort
##########
File path: datafusion/core/src/physical_plan/sorts/sort2.rs
##########
@@ -0,0 +1,1147 @@
+// 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.
+
+//! Sort that deals with an arbitrary size of the input.
+//! It will do in-memory sorting if it has enough memory budget
+//! but spills to disk if needed.
+
+use crate::error::{DataFusionError, Result};
+use crate::execution::context::TaskContext;
+use crate::execution::memory_manager::{
+ human_readable_size, ConsumerType, MemoryConsumer, MemoryConsumerId, MemoryManager,
+};
+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, CompositeMetricsSet, MemTrackingMetrics, MetricsSet,
+};
+use crate::physical_plan::sorts::sort_preserving_merge::SortPreservingMergeStream;
+use crate::physical_plan::sorts::SortedStream;
+use crate::physical_plan::stream::RecordBatchReceiverStream;
+use crate::physical_plan::{
+ DisplayFormatType, Distribution, EmptyRecordBatchStream, ExecutionPlan, Partitioning,
+ RecordBatchStream, SendableRecordBatchStream, Statistics,
+};
+use crate::prelude::SessionConfig;
+use arrow::array::{make_array, Array, ArrayRef, MutableArrayData, UInt32Array};
+pub use arrow::compute::SortOptions;
+use arrow::compute::{concat, lexsort_to_indices, take, SortColumn, TakeOptions};
+use arrow::datatypes::SchemaRef;
+use arrow::error::Result as ArrowResult;
+use arrow::ipc::reader::FileReader;
+use arrow::record_batch::RecordBatch;
+use async_trait::async_trait;
+use futures::lock::Mutex;
+use futures::{Stream, StreamExt};
+use log::{debug, error};
+use std::any::Any;
+use std::cmp::min;
+use std::fmt;
+use std::fmt::{Debug, Formatter};
+use std::fs::File;
+use std::io::BufReader;
+use std::path::{Path, PathBuf};
+use std::sync::Arc;
+use std::task::{Context, Poll};
+use tempfile::NamedTempFile;
+use tokio::sync::mpsc::{Receiver, Sender};
+use tokio::task;
+
+/// Sort arbitrary size of data to get a total order (may spill several times during sorting based on free memory available).
+///
+/// The basic architecture of the algorithm:
+/// 1. get a non-empty new batch from input
+/// 2. check with the memory manager if we could buffer the batch in memory
+/// 2.1 if memory sufficient, then buffer batch in memory, go to 1.
+/// 2.2 if the memory threshold is reached, sort all buffered batches and spill to file.
+/// buffer the batch in memory, go to 1.
+/// 3. when input is exhausted, merge all in memory batches and spills to get a total order.
+struct ExternalSorter2 {
+ id: MemoryConsumerId,
+ schema: SchemaRef,
+ in_mem_batches: Mutex<Vec<RecordBatch>>,
+ spills: Mutex<Vec<NamedTempFile>>,
+ /// Sort expressions
+ expr: Vec<PhysicalSortExpr>,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ metrics_set: CompositeMetricsSet,
+ metrics: BaselineMetrics,
+}
+
+impl ExternalSorter2 {
+ pub fn new(
+ partition_id: usize,
+ schema: SchemaRef,
+ expr: Vec<PhysicalSortExpr>,
+ metrics_set: CompositeMetricsSet,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ ) -> Self {
+ let metrics = metrics_set.new_intermediate_baseline(partition_id);
+ Self {
+ id: MemoryConsumerId::new(partition_id),
+ schema,
+ in_mem_batches: Mutex::new(vec![]),
+ spills: Mutex::new(vec![]),
+ expr,
+ session_config,
+ runtime,
+ metrics_set,
+ metrics,
+ }
+ }
+
+ async fn insert_batch(
+ &self,
+ input: RecordBatch,
+ tracking_metrics: &MemTrackingMetrics,
+ ) -> Result<()> {
+ if input.num_rows() > 0 {
+ let size = batch_byte_size(&input);
+ self.try_grow(size).await?;
+ self.metrics.mem_used().add(size);
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ let partial = sort_batch(input, self.schema.clone(), &self.expr)?;
+ in_mem_batches.push(partial);
+ }
+ Ok(())
+ }
+
+ async fn spilled_before(&self) -> bool {
+ let spills = self.spills.lock().await;
+ !spills.is_empty()
+ }
+
+ /// MergeSort in mem batches as well as spills into total order with `SortPreservingMergeStream`.
+ async fn sort(&self) -> Result<SendableRecordBatchStream> {
+ let partition = self.partition_id();
+ let batch_size = self.session_config.batch_size;
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+
+ if self.spilled_before().await {
+ let tracking_metrics = self
+ .metrics_set
+ .new_intermediate_tracking(partition, self.runtime.clone());
+ let mut streams: Vec<SortedStream> = vec![];
+ if in_mem_batches.len() > 0 {
+ let in_mem_stream = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &self.expr,
+ batch_size,
+ tracking_metrics,
+ )?;
+ let prev_used = self.metrics.mem_used().set(0);
+ streams.push(SortedStream::new(in_mem_stream, prev_used));
+ }
+
+ let mut spills = self.spills.lock().await;
+
+ for spill in spills.drain(..) {
+ let stream = read_spill_as_stream(spill, self.schema.clone())?;
+ streams.push(SortedStream::new(stream, 0));
+ }
+ let tracking_metrics = self
+ .metrics_set
+ .new_final_tracking(partition, self.runtime.clone());
+ Ok(Box::pin(SortPreservingMergeStream::new_from_streams(
+ streams,
+ self.schema.clone(),
+ &self.expr,
+ tracking_metrics,
+ self.session_config.batch_size,
+ )))
+ } else if in_mem_batches.len() > 0 {
+ let tracking_metrics = self
+ .metrics_set
+ .new_final_tracking(partition, self.runtime.clone());
+ let result = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &self.expr,
+ batch_size,
+ tracking_metrics,
+ );
+ // Report to the memory manager we are no longer using memory
+ self.metrics.mem_used().set(0);
+ result
+ } else {
+ Ok(Box::pin(EmptyRecordBatchStream::new(self.schema.clone())))
+ }
+ }
+
+ fn used(&self) -> usize {
+ self.metrics.mem_used().value()
+ }
+
+ fn spilled_bytes(&self) -> usize {
+ self.metrics.spilled_bytes().value()
+ }
+
+ fn spill_count(&self) -> usize {
+ self.metrics.spill_count().value()
+ }
+}
+
+impl Debug for ExternalSorter2 {
+ fn fmt(&self, f: &mut Formatter) -> fmt::Result {
+ f.debug_struct("ExternalSorter2")
+ .field("id", &self.id())
+ .field("memory_used", &self.used())
+ .field("spilled_bytes", &self.spilled_bytes())
+ .field("spill_count", &self.spill_count())
+ .finish()
+ }
+}
+
+#[derive(Debug, Copy, Clone)]
+struct CombinedIndex {
+ batch_idx: usize,
+ row_idx: usize,
+}
+
+impl Drop for ExternalSorter2 {
+ fn drop(&mut self) {
+ self.runtime.drop_consumer(self.id(), self.used());
+ }
+}
+
+#[async_trait]
+impl MemoryConsumer for ExternalSorter2 {
+ fn name(&self) -> String {
+ "ExternalSorter2".to_owned()
+ }
+
+ fn id(&self) -> &MemoryConsumerId {
+ &self.id
+ }
+
+ fn memory_manager(&self) -> Arc<MemoryManager> {
+ self.runtime.memory_manager.clone()
+ }
+
+ fn type_(&self) -> &ConsumerType {
+ &ConsumerType::Requesting
+ }
+
+ async fn spill(&self) -> Result<usize> {
+ debug!(
+ "{}[{}] spilling sort data of {} to disk while inserting ({} time(s) so far)",
+ self.name(),
+ self.id(),
+ self.used(),
+ self.spill_count()
+ );
+
+ let partition = self.partition_id();
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+ // we could always get a chance to free some memory as long as we are holding some
+ if in_mem_batches.len() == 0 {
+ return Ok(0);
+ }
+
+ let tracking_metrics = self
+ .metrics_set
+ .new_intermediate_tracking(partition, self.runtime.clone());
+
+ let spillfile = self.runtime.disk_manager.create_tmp_file()?;
+ let stream = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &*self.expr,
+ self.session_config.batch_size,
+ tracking_metrics,
+ );
+
+ spill_partial_sorted_stream(&mut stream?, spillfile.path(), self.schema.clone())
+ .await?;
+ let mut spills = self.spills.lock().await;
+ let used = self.metrics.mem_used().set(0);
+ self.metrics.record_spill(used);
+ spills.push(spillfile);
+ Ok(used)
+ }
+
+ fn mem_used(&self) -> usize {
+ self.metrics.mem_used().value()
+ }
+}
+
+/// consume the non-empty `sorted_bathes` and do in_mem_sort
+fn in_mem_partial_sort(
+ buffered_batches: &mut Vec<RecordBatch>,
+ schema: SchemaRef,
+ expressions: &[PhysicalSortExpr],
+ batch_size: usize,
+ tracking_metrics: MemTrackingMetrics,
+) -> Result<SendableRecordBatchStream> {
+ assert_ne!(buffered_batches.len(), 0);
+ if buffered_batches.len() == 1 {
+ let result = buffered_batches.pop();
+ Ok(Box::pin(SizedRecordBatchStream::new(
+ schema,
+ vec![Arc::new(result.unwrap())],
+ tracking_metrics,
+ )))
+ } else {
+ let batches = buffered_batches.drain(..).collect::<Vec<_>>();
+ let sorted_iter = {
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ get_sorted_iter(&batches, expressions, batch_size)?
+ };
+ Ok(Box::pin(SortedSizedRecordBatchStream::new(
+ schema,
+ batches,
+ sorted_iter,
+ tracking_metrics,
+ )))
+ }
+}
+
+fn get_sorted_iter(
Review comment:
The main changes:
1. concrete all sort-columns
2. sort to get the index array
3. use `CombinedIndex` to avoid huge batch construction
4. construct a small batch at a time.
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[GitHub] [arrow-datafusion] yjshen edited a comment on pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen edited a comment on pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#issuecomment-1085966913
On my Linux Desktop:
```
H/W path Device Class Description
====================================================================
system MS-7D53 (To be filled by O.E.M.)
/0 bus MPG X570S EDGE MAX WIFI (MS-7D53)
/0/0 memory 64KiB BIOS
/0/11 memory 32GiB System Memory
/0/11/0 memory 3600 MHz (0.3 ns) [empty]
/0/11/1 memory 16GiB DIMM DDR4 Synchronous Unbuffered (Unregistered) 3600 MHz (0.3
/0/11/2 memory 3600 MHz (0.3 ns) [empty]
/0/11/3 memory 16GiB DIMM DDR4 Synchronous Unbuffered (Unregistered) 3600 MHz (0.3
/0/14 memory 1MiB L1 cache
/0/15 memory 8MiB L2 cache
/0/16 memory 64MiB L3 cache
/0/17 processor AMD Ryzen 9 5950X 16-Core Processor
```
## A modified version of TPC-H q1:
```sql
select
l_returnflag,
l_linestatus,
l_quantity,
l_extendedprice,
l_discount,
l_tax
from
lineitem
order by
l_extendedprice,
l_discount;
```
```
cargo run --release --features "mimalloc" --bin tpch -- benchmark datafusion --iterations 3 --path ../../tpch-parquet/ --format parquet --query 1 --batch-size 4096
```
Without this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 4096, path: "../../tpch-parquet/", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 2851.7 ms and returned 6001214 rows
Query 1 iteration 1 took 2817.7 ms and returned 6001214 rows
Query 1 iteration 2 took 2735.9 ms and returned 6001214 rows
Query 1 avg time: 2801.75 ms
```
With this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 4096, path: "../../tpch-parquet/", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 2843.6 ms and returned 6001214 rows
Query 1 iteration 1 took 2824.4 ms and returned 6001214 rows
Query 1 iteration 2 took 2753.0 ms and returned 6001214 rows
Query 1 avg time: 2807.00 ms
```
Performances are similar.
## cargo criterion --bench sort_limit_query_sql
Without this PR:
```
sort_and_limit_by_int time: [869.27 us 870.72 us 872.19 us]
sort_and_limit_by_float time: [845.95 us 847.36 us 848.77 us]
sort_and_limit_lex_by_int
time: [875.07 us 876.80 us 878.59 us]
sort_and_limit_lex_by_string
time: [878.78 us 880.40 us 882.06 us]
```
With this PR:
```
sort_and_limit_by_int time: [856.71 us 858.58 us 860.41 us]
sort_and_limit_by_float time: [836.55 us 838.40 us 840.29 us]
sort_and_limit_lex_by_int
time: [864.83 us 866.33 us 867.83 us]
sort_and_limit_lex_by_string
time: [866.89 us 868.25 us 869.62 us]
```
Similar performance as well.
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[GitHub] [arrow-datafusion] yjshen commented on a change in pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen commented on a change in pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#discussion_r840501793
##########
File path: datafusion/core/src/physical_plan/sorts/sort2.rs
##########
@@ -0,0 +1,1147 @@
+// 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.
+
+//! Sort that deals with an arbitrary size of the input.
+//! It will do in-memory sorting if it has enough memory budget
+//! but spills to disk if needed.
+
+use crate::error::{DataFusionError, Result};
+use crate::execution::context::TaskContext;
+use crate::execution::memory_manager::{
+ human_readable_size, ConsumerType, MemoryConsumer, MemoryConsumerId, MemoryManager,
+};
+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, CompositeMetricsSet, MemTrackingMetrics, MetricsSet,
+};
+use crate::physical_plan::sorts::sort_preserving_merge::SortPreservingMergeStream;
+use crate::physical_plan::sorts::SortedStream;
+use crate::physical_plan::stream::RecordBatchReceiverStream;
+use crate::physical_plan::{
+ DisplayFormatType, Distribution, EmptyRecordBatchStream, ExecutionPlan, Partitioning,
+ RecordBatchStream, SendableRecordBatchStream, Statistics,
+};
+use crate::prelude::SessionConfig;
+use arrow::array::{make_array, Array, ArrayRef, MutableArrayData, UInt32Array};
+pub use arrow::compute::SortOptions;
+use arrow::compute::{concat, lexsort_to_indices, take, SortColumn, TakeOptions};
+use arrow::datatypes::SchemaRef;
+use arrow::error::Result as ArrowResult;
+use arrow::ipc::reader::FileReader;
+use arrow::record_batch::RecordBatch;
+use async_trait::async_trait;
+use futures::lock::Mutex;
+use futures::{Stream, StreamExt};
+use log::{debug, error};
+use std::any::Any;
+use std::cmp::min;
+use std::fmt;
+use std::fmt::{Debug, Formatter};
+use std::fs::File;
+use std::io::BufReader;
+use std::path::{Path, PathBuf};
+use std::sync::Arc;
+use std::task::{Context, Poll};
+use tempfile::NamedTempFile;
+use tokio::sync::mpsc::{Receiver, Sender};
+use tokio::task;
+
+/// Sort arbitrary size of data to get a total order (may spill several times during sorting based on free memory available).
+///
+/// The basic architecture of the algorithm:
+/// 1. get a non-empty new batch from input
+/// 2. check with the memory manager if we could buffer the batch in memory
+/// 2.1 if memory sufficient, then buffer batch in memory, go to 1.
+/// 2.2 if the memory threshold is reached, sort all buffered batches and spill to file.
+/// buffer the batch in memory, go to 1.
+/// 3. when input is exhausted, merge all in memory batches and spills to get a total order.
+struct ExternalSorter2 {
+ id: MemoryConsumerId,
+ schema: SchemaRef,
+ in_mem_batches: Mutex<Vec<RecordBatch>>,
+ spills: Mutex<Vec<NamedTempFile>>,
+ /// Sort expressions
+ expr: Vec<PhysicalSortExpr>,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ metrics_set: CompositeMetricsSet,
+ metrics: BaselineMetrics,
+}
+
+impl ExternalSorter2 {
+ pub fn new(
+ partition_id: usize,
+ schema: SchemaRef,
+ expr: Vec<PhysicalSortExpr>,
+ metrics_set: CompositeMetricsSet,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ ) -> Self {
+ let metrics = metrics_set.new_intermediate_baseline(partition_id);
+ Self {
+ id: MemoryConsumerId::new(partition_id),
+ schema,
+ in_mem_batches: Mutex::new(vec![]),
+ spills: Mutex::new(vec![]),
+ expr,
+ session_config,
+ runtime,
+ metrics_set,
+ metrics,
+ }
+ }
+
+ async fn insert_batch(
+ &self,
+ input: RecordBatch,
+ tracking_metrics: &MemTrackingMetrics,
+ ) -> Result<()> {
+ if input.num_rows() > 0 {
+ let size = batch_byte_size(&input);
+ self.try_grow(size).await?;
+ self.metrics.mem_used().add(size);
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ let partial = sort_batch(input, self.schema.clone(), &self.expr)?;
+ in_mem_batches.push(partial);
+ }
+ Ok(())
+ }
+
+ async fn spilled_before(&self) -> bool {
+ let spills = self.spills.lock().await;
+ !spills.is_empty()
+ }
+
+ /// MergeSort in mem batches as well as spills into total order with `SortPreservingMergeStream`.
+ async fn sort(&self) -> Result<SendableRecordBatchStream> {
+ let partition = self.partition_id();
+ let batch_size = self.session_config.batch_size;
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+
+ if self.spilled_before().await {
+ let tracking_metrics = self
+ .metrics_set
+ .new_intermediate_tracking(partition, self.runtime.clone());
+ let mut streams: Vec<SortedStream> = vec![];
+ if in_mem_batches.len() > 0 {
+ let in_mem_stream = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &self.expr,
+ batch_size,
+ tracking_metrics,
+ )?;
+ let prev_used = self.metrics.mem_used().set(0);
+ streams.push(SortedStream::new(in_mem_stream, prev_used));
+ }
+
+ let mut spills = self.spills.lock().await;
+
+ for spill in spills.drain(..) {
+ let stream = read_spill_as_stream(spill, self.schema.clone())?;
+ streams.push(SortedStream::new(stream, 0));
+ }
+ let tracking_metrics = self
+ .metrics_set
+ .new_final_tracking(partition, self.runtime.clone());
+ Ok(Box::pin(SortPreservingMergeStream::new_from_streams(
+ streams,
+ self.schema.clone(),
+ &self.expr,
+ tracking_metrics,
+ self.session_config.batch_size,
+ )))
+ } else if in_mem_batches.len() > 0 {
+ let tracking_metrics = self
+ .metrics_set
+ .new_final_tracking(partition, self.runtime.clone());
+ let result = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &self.expr,
+ batch_size,
+ tracking_metrics,
+ );
+ // Report to the memory manager we are no longer using memory
+ self.metrics.mem_used().set(0);
+ result
+ } else {
+ Ok(Box::pin(EmptyRecordBatchStream::new(self.schema.clone())))
+ }
+ }
+
+ fn used(&self) -> usize {
+ self.metrics.mem_used().value()
+ }
+
+ fn spilled_bytes(&self) -> usize {
+ self.metrics.spilled_bytes().value()
+ }
+
+ fn spill_count(&self) -> usize {
+ self.metrics.spill_count().value()
+ }
+}
+
+impl Debug for ExternalSorter2 {
+ fn fmt(&self, f: &mut Formatter) -> fmt::Result {
+ f.debug_struct("ExternalSorter2")
+ .field("id", &self.id())
+ .field("memory_used", &self.used())
+ .field("spilled_bytes", &self.spilled_bytes())
+ .field("spill_count", &self.spill_count())
+ .finish()
+ }
+}
+
+#[derive(Debug, Copy, Clone)]
+struct CombinedIndex {
+ batch_idx: usize,
+ row_idx: usize,
+}
+
+impl Drop for ExternalSorter2 {
+ fn drop(&mut self) {
+ self.runtime.drop_consumer(self.id(), self.used());
+ }
+}
+
+#[async_trait]
+impl MemoryConsumer for ExternalSorter2 {
+ fn name(&self) -> String {
+ "ExternalSorter2".to_owned()
+ }
+
+ fn id(&self) -> &MemoryConsumerId {
+ &self.id
+ }
+
+ fn memory_manager(&self) -> Arc<MemoryManager> {
+ self.runtime.memory_manager.clone()
+ }
+
+ fn type_(&self) -> &ConsumerType {
+ &ConsumerType::Requesting
+ }
+
+ async fn spill(&self) -> Result<usize> {
+ debug!(
+ "{}[{}] spilling sort data of {} to disk while inserting ({} time(s) so far)",
+ self.name(),
+ self.id(),
+ self.used(),
+ self.spill_count()
+ );
+
+ let partition = self.partition_id();
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+ // we could always get a chance to free some memory as long as we are holding some
+ if in_mem_batches.len() == 0 {
+ return Ok(0);
+ }
+
+ let tracking_metrics = self
+ .metrics_set
+ .new_intermediate_tracking(partition, self.runtime.clone());
+
+ let spillfile = self.runtime.disk_manager.create_tmp_file()?;
+ let stream = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &*self.expr,
+ self.session_config.batch_size,
+ tracking_metrics,
+ );
+
+ spill_partial_sorted_stream(&mut stream?, spillfile.path(), self.schema.clone())
+ .await?;
+ let mut spills = self.spills.lock().await;
+ let used = self.metrics.mem_used().set(0);
+ self.metrics.record_spill(used);
+ spills.push(spillfile);
+ Ok(used)
+ }
+
+ fn mem_used(&self) -> usize {
+ self.metrics.mem_used().value()
+ }
+}
+
+/// consume the non-empty `sorted_bathes` and do in_mem_sort
+fn in_mem_partial_sort(
+ buffered_batches: &mut Vec<RecordBatch>,
+ schema: SchemaRef,
+ expressions: &[PhysicalSortExpr],
+ batch_size: usize,
+ tracking_metrics: MemTrackingMetrics,
+) -> Result<SendableRecordBatchStream> {
+ assert_ne!(buffered_batches.len(), 0);
+ if buffered_batches.len() == 1 {
+ let result = buffered_batches.pop();
+ Ok(Box::pin(SizedRecordBatchStream::new(
+ schema,
+ vec![Arc::new(result.unwrap())],
+ tracking_metrics,
+ )))
+ } else {
+ let batches = buffered_batches.drain(..).collect::<Vec<_>>();
+ let sorted_iter = {
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ get_sorted_iter(&batches, expressions, batch_size)?
+ };
+ Ok(Box::pin(SortedSizedRecordBatchStream::new(
+ schema,
+ batches,
+ sorted_iter,
+ tracking_metrics,
+ )))
+ }
+}
+
+fn get_sorted_iter(
Review comment:
The main changes:
1. concrete all sort-columns (instead of all columns)
2. sort to get the index array (same with original sort)
3. use `CombinedIndex` to avoid huge batch construction (to access records scattered in different batches)
4. construct a small batch at a time.
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[GitHub] [arrow-datafusion] yjshen commented on a change in pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen commented on a change in pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#discussion_r840501793
##########
File path: datafusion/core/src/physical_plan/sorts/sort2.rs
##########
@@ -0,0 +1,1147 @@
+// 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.
+
+//! Sort that deals with an arbitrary size of the input.
+//! It will do in-memory sorting if it has enough memory budget
+//! but spills to disk if needed.
+
+use crate::error::{DataFusionError, Result};
+use crate::execution::context::TaskContext;
+use crate::execution::memory_manager::{
+ human_readable_size, ConsumerType, MemoryConsumer, MemoryConsumerId, MemoryManager,
+};
+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, CompositeMetricsSet, MemTrackingMetrics, MetricsSet,
+};
+use crate::physical_plan::sorts::sort_preserving_merge::SortPreservingMergeStream;
+use crate::physical_plan::sorts::SortedStream;
+use crate::physical_plan::stream::RecordBatchReceiverStream;
+use crate::physical_plan::{
+ DisplayFormatType, Distribution, EmptyRecordBatchStream, ExecutionPlan, Partitioning,
+ RecordBatchStream, SendableRecordBatchStream, Statistics,
+};
+use crate::prelude::SessionConfig;
+use arrow::array::{make_array, Array, ArrayRef, MutableArrayData, UInt32Array};
+pub use arrow::compute::SortOptions;
+use arrow::compute::{concat, lexsort_to_indices, take, SortColumn, TakeOptions};
+use arrow::datatypes::SchemaRef;
+use arrow::error::Result as ArrowResult;
+use arrow::ipc::reader::FileReader;
+use arrow::record_batch::RecordBatch;
+use async_trait::async_trait;
+use futures::lock::Mutex;
+use futures::{Stream, StreamExt};
+use log::{debug, error};
+use std::any::Any;
+use std::cmp::min;
+use std::fmt;
+use std::fmt::{Debug, Formatter};
+use std::fs::File;
+use std::io::BufReader;
+use std::path::{Path, PathBuf};
+use std::sync::Arc;
+use std::task::{Context, Poll};
+use tempfile::NamedTempFile;
+use tokio::sync::mpsc::{Receiver, Sender};
+use tokio::task;
+
+/// Sort arbitrary size of data to get a total order (may spill several times during sorting based on free memory available).
+///
+/// The basic architecture of the algorithm:
+/// 1. get a non-empty new batch from input
+/// 2. check with the memory manager if we could buffer the batch in memory
+/// 2.1 if memory sufficient, then buffer batch in memory, go to 1.
+/// 2.2 if the memory threshold is reached, sort all buffered batches and spill to file.
+/// buffer the batch in memory, go to 1.
+/// 3. when input is exhausted, merge all in memory batches and spills to get a total order.
+struct ExternalSorter2 {
+ id: MemoryConsumerId,
+ schema: SchemaRef,
+ in_mem_batches: Mutex<Vec<RecordBatch>>,
+ spills: Mutex<Vec<NamedTempFile>>,
+ /// Sort expressions
+ expr: Vec<PhysicalSortExpr>,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ metrics_set: CompositeMetricsSet,
+ metrics: BaselineMetrics,
+}
+
+impl ExternalSorter2 {
+ pub fn new(
+ partition_id: usize,
+ schema: SchemaRef,
+ expr: Vec<PhysicalSortExpr>,
+ metrics_set: CompositeMetricsSet,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ ) -> Self {
+ let metrics = metrics_set.new_intermediate_baseline(partition_id);
+ Self {
+ id: MemoryConsumerId::new(partition_id),
+ schema,
+ in_mem_batches: Mutex::new(vec![]),
+ spills: Mutex::new(vec![]),
+ expr,
+ session_config,
+ runtime,
+ metrics_set,
+ metrics,
+ }
+ }
+
+ async fn insert_batch(
+ &self,
+ input: RecordBatch,
+ tracking_metrics: &MemTrackingMetrics,
+ ) -> Result<()> {
+ if input.num_rows() > 0 {
+ let size = batch_byte_size(&input);
+ self.try_grow(size).await?;
+ self.metrics.mem_used().add(size);
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ let partial = sort_batch(input, self.schema.clone(), &self.expr)?;
+ in_mem_batches.push(partial);
+ }
+ Ok(())
+ }
+
+ async fn spilled_before(&self) -> bool {
+ let spills = self.spills.lock().await;
+ !spills.is_empty()
+ }
+
+ /// MergeSort in mem batches as well as spills into total order with `SortPreservingMergeStream`.
+ async fn sort(&self) -> Result<SendableRecordBatchStream> {
+ let partition = self.partition_id();
+ let batch_size = self.session_config.batch_size;
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+
+ if self.spilled_before().await {
+ let tracking_metrics = self
+ .metrics_set
+ .new_intermediate_tracking(partition, self.runtime.clone());
+ let mut streams: Vec<SortedStream> = vec![];
+ if in_mem_batches.len() > 0 {
+ let in_mem_stream = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &self.expr,
+ batch_size,
+ tracking_metrics,
+ )?;
+ let prev_used = self.metrics.mem_used().set(0);
+ streams.push(SortedStream::new(in_mem_stream, prev_used));
+ }
+
+ let mut spills = self.spills.lock().await;
+
+ for spill in spills.drain(..) {
+ let stream = read_spill_as_stream(spill, self.schema.clone())?;
+ streams.push(SortedStream::new(stream, 0));
+ }
+ let tracking_metrics = self
+ .metrics_set
+ .new_final_tracking(partition, self.runtime.clone());
+ Ok(Box::pin(SortPreservingMergeStream::new_from_streams(
+ streams,
+ self.schema.clone(),
+ &self.expr,
+ tracking_metrics,
+ self.session_config.batch_size,
+ )))
+ } else if in_mem_batches.len() > 0 {
+ let tracking_metrics = self
+ .metrics_set
+ .new_final_tracking(partition, self.runtime.clone());
+ let result = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &self.expr,
+ batch_size,
+ tracking_metrics,
+ );
+ // Report to the memory manager we are no longer using memory
+ self.metrics.mem_used().set(0);
+ result
+ } else {
+ Ok(Box::pin(EmptyRecordBatchStream::new(self.schema.clone())))
+ }
+ }
+
+ fn used(&self) -> usize {
+ self.metrics.mem_used().value()
+ }
+
+ fn spilled_bytes(&self) -> usize {
+ self.metrics.spilled_bytes().value()
+ }
+
+ fn spill_count(&self) -> usize {
+ self.metrics.spill_count().value()
+ }
+}
+
+impl Debug for ExternalSorter2 {
+ fn fmt(&self, f: &mut Formatter) -> fmt::Result {
+ f.debug_struct("ExternalSorter2")
+ .field("id", &self.id())
+ .field("memory_used", &self.used())
+ .field("spilled_bytes", &self.spilled_bytes())
+ .field("spill_count", &self.spill_count())
+ .finish()
+ }
+}
+
+#[derive(Debug, Copy, Clone)]
+struct CombinedIndex {
+ batch_idx: usize,
+ row_idx: usize,
+}
+
+impl Drop for ExternalSorter2 {
+ fn drop(&mut self) {
+ self.runtime.drop_consumer(self.id(), self.used());
+ }
+}
+
+#[async_trait]
+impl MemoryConsumer for ExternalSorter2 {
+ fn name(&self) -> String {
+ "ExternalSorter2".to_owned()
+ }
+
+ fn id(&self) -> &MemoryConsumerId {
+ &self.id
+ }
+
+ fn memory_manager(&self) -> Arc<MemoryManager> {
+ self.runtime.memory_manager.clone()
+ }
+
+ fn type_(&self) -> &ConsumerType {
+ &ConsumerType::Requesting
+ }
+
+ async fn spill(&self) -> Result<usize> {
+ debug!(
+ "{}[{}] spilling sort data of {} to disk while inserting ({} time(s) so far)",
+ self.name(),
+ self.id(),
+ self.used(),
+ self.spill_count()
+ );
+
+ let partition = self.partition_id();
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+ // we could always get a chance to free some memory as long as we are holding some
+ if in_mem_batches.len() == 0 {
+ return Ok(0);
+ }
+
+ let tracking_metrics = self
+ .metrics_set
+ .new_intermediate_tracking(partition, self.runtime.clone());
+
+ let spillfile = self.runtime.disk_manager.create_tmp_file()?;
+ let stream = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &*self.expr,
+ self.session_config.batch_size,
+ tracking_metrics,
+ );
+
+ spill_partial_sorted_stream(&mut stream?, spillfile.path(), self.schema.clone())
+ .await?;
+ let mut spills = self.spills.lock().await;
+ let used = self.metrics.mem_used().set(0);
+ self.metrics.record_spill(used);
+ spills.push(spillfile);
+ Ok(used)
+ }
+
+ fn mem_used(&self) -> usize {
+ self.metrics.mem_used().value()
+ }
+}
+
+/// consume the non-empty `sorted_bathes` and do in_mem_sort
+fn in_mem_partial_sort(
+ buffered_batches: &mut Vec<RecordBatch>,
+ schema: SchemaRef,
+ expressions: &[PhysicalSortExpr],
+ batch_size: usize,
+ tracking_metrics: MemTrackingMetrics,
+) -> Result<SendableRecordBatchStream> {
+ assert_ne!(buffered_batches.len(), 0);
+ if buffered_batches.len() == 1 {
+ let result = buffered_batches.pop();
+ Ok(Box::pin(SizedRecordBatchStream::new(
+ schema,
+ vec![Arc::new(result.unwrap())],
+ tracking_metrics,
+ )))
+ } else {
+ let batches = buffered_batches.drain(..).collect::<Vec<_>>();
+ let sorted_iter = {
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ get_sorted_iter(&batches, expressions, batch_size)?
+ };
+ Ok(Box::pin(SortedSizedRecordBatchStream::new(
+ schema,
+ batches,
+ sorted_iter,
+ tracking_metrics,
+ )))
+ }
+}
+
+fn get_sorted_iter(
Review comment:
The main changes:
1. concrete all sort-columns (instead of all columns)
2. sort to get the index array (same as the original sort)
3. use `CombinedIndex` to avoid huge batch construction (to access records scattered in different batches)
4. construct a small batch at a time.
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[GitHub] [arrow-datafusion] yjshen edited a comment on pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen edited a comment on pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#issuecomment-1085966913
A modified version of TPC-H q1:
```sql
select
l_returnflag,
l_linestatus,
l_quantity,
l_extendedprice,
l_discount,
l_tax
from
lineitem
order by
l_extendedprice,
l_discount;
```
On a newer Linux Desktop:
```
H/W path Device Class Description
====================================================================
system MS-7D53 (To be filled by O.E.M.)
/0 bus MPG X570S EDGE MAX WIFI (MS-7D53)
/0/0 memory 64KiB BIOS
/0/11 memory 32GiB System Memory
/0/11/0 memory 3600 MHz (0.3 ns) [empty]
/0/11/1 memory 16GiB DIMM DDR4 Synchronous Unbuffered (Unregistered) 3600 MHz (0.3
/0/11/2 memory 3600 MHz (0.3 ns) [empty]
/0/11/3 memory 16GiB DIMM DDR4 Synchronous Unbuffered (Unregistered) 3600 MHz (0.3
/0/14 memory 1MiB L1 cache
/0/15 memory 8MiB L2 cache
/0/16 memory 64MiB L3 cache
/0/17 processor AMD Ryzen 9 5950X 16-Core Processor
```
```
cargo run --release --features "mimalloc" --bin tpch -- benchmark datafusion --iterations 3 --path ../../tpch-parquet/ --format parquet --query 1 --batch-size 4096
```
Without this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 4096, path: "../../tpch-parquet/", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 2851.7 ms and returned 6001214 rows
Query 1 iteration 1 took 2817.7 ms and returned 6001214 rows
Query 1 iteration 2 took 2735.9 ms and returned 6001214 rows
Query 1 avg time: 2801.75 ms
```
With this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 4096, path: "../../tpch-parquet/", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 2843.6 ms and returned 6001214 rows
Query 1 iteration 1 took 2824.4 ms and returned 6001214 rows
Query 1 iteration 2 took 2753.0 ms and returned 6001214 rows
Query 1 avg time: 2807.00 ms
```
Performances are similar.
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[GitHub] [arrow-datafusion] yjshen commented on a change in pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen commented on a change in pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#discussion_r840501793
##########
File path: datafusion/core/src/physical_plan/sorts/sort2.rs
##########
@@ -0,0 +1,1147 @@
+// 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.
+
+//! Sort that deals with an arbitrary size of the input.
+//! It will do in-memory sorting if it has enough memory budget
+//! but spills to disk if needed.
+
+use crate::error::{DataFusionError, Result};
+use crate::execution::context::TaskContext;
+use crate::execution::memory_manager::{
+ human_readable_size, ConsumerType, MemoryConsumer, MemoryConsumerId, MemoryManager,
+};
+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, CompositeMetricsSet, MemTrackingMetrics, MetricsSet,
+};
+use crate::physical_plan::sorts::sort_preserving_merge::SortPreservingMergeStream;
+use crate::physical_plan::sorts::SortedStream;
+use crate::physical_plan::stream::RecordBatchReceiverStream;
+use crate::physical_plan::{
+ DisplayFormatType, Distribution, EmptyRecordBatchStream, ExecutionPlan, Partitioning,
+ RecordBatchStream, SendableRecordBatchStream, Statistics,
+};
+use crate::prelude::SessionConfig;
+use arrow::array::{make_array, Array, ArrayRef, MutableArrayData, UInt32Array};
+pub use arrow::compute::SortOptions;
+use arrow::compute::{concat, lexsort_to_indices, take, SortColumn, TakeOptions};
+use arrow::datatypes::SchemaRef;
+use arrow::error::Result as ArrowResult;
+use arrow::ipc::reader::FileReader;
+use arrow::record_batch::RecordBatch;
+use async_trait::async_trait;
+use futures::lock::Mutex;
+use futures::{Stream, StreamExt};
+use log::{debug, error};
+use std::any::Any;
+use std::cmp::min;
+use std::fmt;
+use std::fmt::{Debug, Formatter};
+use std::fs::File;
+use std::io::BufReader;
+use std::path::{Path, PathBuf};
+use std::sync::Arc;
+use std::task::{Context, Poll};
+use tempfile::NamedTempFile;
+use tokio::sync::mpsc::{Receiver, Sender};
+use tokio::task;
+
+/// Sort arbitrary size of data to get a total order (may spill several times during sorting based on free memory available).
+///
+/// The basic architecture of the algorithm:
+/// 1. get a non-empty new batch from input
+/// 2. check with the memory manager if we could buffer the batch in memory
+/// 2.1 if memory sufficient, then buffer batch in memory, go to 1.
+/// 2.2 if the memory threshold is reached, sort all buffered batches and spill to file.
+/// buffer the batch in memory, go to 1.
+/// 3. when input is exhausted, merge all in memory batches and spills to get a total order.
+struct ExternalSorter2 {
+ id: MemoryConsumerId,
+ schema: SchemaRef,
+ in_mem_batches: Mutex<Vec<RecordBatch>>,
+ spills: Mutex<Vec<NamedTempFile>>,
+ /// Sort expressions
+ expr: Vec<PhysicalSortExpr>,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ metrics_set: CompositeMetricsSet,
+ metrics: BaselineMetrics,
+}
+
+impl ExternalSorter2 {
+ pub fn new(
+ partition_id: usize,
+ schema: SchemaRef,
+ expr: Vec<PhysicalSortExpr>,
+ metrics_set: CompositeMetricsSet,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ ) -> Self {
+ let metrics = metrics_set.new_intermediate_baseline(partition_id);
+ Self {
+ id: MemoryConsumerId::new(partition_id),
+ schema,
+ in_mem_batches: Mutex::new(vec![]),
+ spills: Mutex::new(vec![]),
+ expr,
+ session_config,
+ runtime,
+ metrics_set,
+ metrics,
+ }
+ }
+
+ async fn insert_batch(
+ &self,
+ input: RecordBatch,
+ tracking_metrics: &MemTrackingMetrics,
+ ) -> Result<()> {
+ if input.num_rows() > 0 {
+ let size = batch_byte_size(&input);
+ self.try_grow(size).await?;
+ self.metrics.mem_used().add(size);
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ let partial = sort_batch(input, self.schema.clone(), &self.expr)?;
+ in_mem_batches.push(partial);
+ }
+ Ok(())
+ }
+
+ async fn spilled_before(&self) -> bool {
+ let spills = self.spills.lock().await;
+ !spills.is_empty()
+ }
+
+ /// MergeSort in mem batches as well as spills into total order with `SortPreservingMergeStream`.
+ async fn sort(&self) -> Result<SendableRecordBatchStream> {
+ let partition = self.partition_id();
+ let batch_size = self.session_config.batch_size;
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+
+ if self.spilled_before().await {
+ let tracking_metrics = self
+ .metrics_set
+ .new_intermediate_tracking(partition, self.runtime.clone());
+ let mut streams: Vec<SortedStream> = vec![];
+ if in_mem_batches.len() > 0 {
+ let in_mem_stream = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &self.expr,
+ batch_size,
+ tracking_metrics,
+ )?;
+ let prev_used = self.metrics.mem_used().set(0);
+ streams.push(SortedStream::new(in_mem_stream, prev_used));
+ }
+
+ let mut spills = self.spills.lock().await;
+
+ for spill in spills.drain(..) {
+ let stream = read_spill_as_stream(spill, self.schema.clone())?;
+ streams.push(SortedStream::new(stream, 0));
+ }
+ let tracking_metrics = self
+ .metrics_set
+ .new_final_tracking(partition, self.runtime.clone());
+ Ok(Box::pin(SortPreservingMergeStream::new_from_streams(
+ streams,
+ self.schema.clone(),
+ &self.expr,
+ tracking_metrics,
+ self.session_config.batch_size,
+ )))
+ } else if in_mem_batches.len() > 0 {
+ let tracking_metrics = self
+ .metrics_set
+ .new_final_tracking(partition, self.runtime.clone());
+ let result = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &self.expr,
+ batch_size,
+ tracking_metrics,
+ );
+ // Report to the memory manager we are no longer using memory
+ self.metrics.mem_used().set(0);
+ result
+ } else {
+ Ok(Box::pin(EmptyRecordBatchStream::new(self.schema.clone())))
+ }
+ }
+
+ fn used(&self) -> usize {
+ self.metrics.mem_used().value()
+ }
+
+ fn spilled_bytes(&self) -> usize {
+ self.metrics.spilled_bytes().value()
+ }
+
+ fn spill_count(&self) -> usize {
+ self.metrics.spill_count().value()
+ }
+}
+
+impl Debug for ExternalSorter2 {
+ fn fmt(&self, f: &mut Formatter) -> fmt::Result {
+ f.debug_struct("ExternalSorter2")
+ .field("id", &self.id())
+ .field("memory_used", &self.used())
+ .field("spilled_bytes", &self.spilled_bytes())
+ .field("spill_count", &self.spill_count())
+ .finish()
+ }
+}
+
+#[derive(Debug, Copy, Clone)]
+struct CombinedIndex {
+ batch_idx: usize,
+ row_idx: usize,
+}
+
+impl Drop for ExternalSorter2 {
+ fn drop(&mut self) {
+ self.runtime.drop_consumer(self.id(), self.used());
+ }
+}
+
+#[async_trait]
+impl MemoryConsumer for ExternalSorter2 {
+ fn name(&self) -> String {
+ "ExternalSorter2".to_owned()
+ }
+
+ fn id(&self) -> &MemoryConsumerId {
+ &self.id
+ }
+
+ fn memory_manager(&self) -> Arc<MemoryManager> {
+ self.runtime.memory_manager.clone()
+ }
+
+ fn type_(&self) -> &ConsumerType {
+ &ConsumerType::Requesting
+ }
+
+ async fn spill(&self) -> Result<usize> {
+ debug!(
+ "{}[{}] spilling sort data of {} to disk while inserting ({} time(s) so far)",
+ self.name(),
+ self.id(),
+ self.used(),
+ self.spill_count()
+ );
+
+ let partition = self.partition_id();
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+ // we could always get a chance to free some memory as long as we are holding some
+ if in_mem_batches.len() == 0 {
+ return Ok(0);
+ }
+
+ let tracking_metrics = self
+ .metrics_set
+ .new_intermediate_tracking(partition, self.runtime.clone());
+
+ let spillfile = self.runtime.disk_manager.create_tmp_file()?;
+ let stream = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &*self.expr,
+ self.session_config.batch_size,
+ tracking_metrics,
+ );
+
+ spill_partial_sorted_stream(&mut stream?, spillfile.path(), self.schema.clone())
+ .await?;
+ let mut spills = self.spills.lock().await;
+ let used = self.metrics.mem_used().set(0);
+ self.metrics.record_spill(used);
+ spills.push(spillfile);
+ Ok(used)
+ }
+
+ fn mem_used(&self) -> usize {
+ self.metrics.mem_used().value()
+ }
+}
+
+/// consume the non-empty `sorted_bathes` and do in_mem_sort
+fn in_mem_partial_sort(
+ buffered_batches: &mut Vec<RecordBatch>,
+ schema: SchemaRef,
+ expressions: &[PhysicalSortExpr],
+ batch_size: usize,
+ tracking_metrics: MemTrackingMetrics,
+) -> Result<SendableRecordBatchStream> {
+ assert_ne!(buffered_batches.len(), 0);
+ if buffered_batches.len() == 1 {
+ let result = buffered_batches.pop();
+ Ok(Box::pin(SizedRecordBatchStream::new(
+ schema,
+ vec![Arc::new(result.unwrap())],
+ tracking_metrics,
+ )))
+ } else {
+ let batches = buffered_batches.drain(..).collect::<Vec<_>>();
+ let sorted_iter = {
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ get_sorted_iter(&batches, expressions, batch_size)?
+ };
+ Ok(Box::pin(SortedSizedRecordBatchStream::new(
+ schema,
+ batches,
+ sorted_iter,
+ tracking_metrics,
+ )))
+ }
+}
+
+fn get_sorted_iter(
Review comment:
The main changes:
1. concrete all sort-columns (instead of all columns)
2. sort to get the index array (same)
3. use `CombinedIndex` to avoid huge batch construction (to access records scattered in different batches)
4. construct a small batch at a time.
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[GitHub] [arrow-datafusion] yjshen commented on a change in pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen commented on a change in pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#discussion_r841011038
##########
File path: datafusion/core/src/physical_plan/sorts/sort.rs
##########
@@ -271,33 +291,212 @@ fn in_mem_partial_sort(
buffered_batches: &mut Vec<RecordBatch>,
schema: SchemaRef,
expressions: &[PhysicalSortExpr],
+ batch_size: usize,
tracking_metrics: MemTrackingMetrics,
) -> Result<SendableRecordBatchStream> {
assert_ne!(buffered_batches.len(), 0);
+ if buffered_batches.len() == 1 {
+ let result = buffered_batches.pop();
+ Ok(Box::pin(SizedRecordBatchStream::new(
+ schema,
+ vec![Arc::new(result.unwrap())],
+ tracking_metrics,
+ )))
+ } else {
+ let batches = buffered_batches.drain(..).collect::<Vec<_>>();
+ let sorted_iter = {
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ get_sorted_iter(&batches, expressions, batch_size)?
+ };
+ Ok(Box::pin(SortedSizedRecordBatchStream::new(
+ schema,
+ batches,
+ sorted_iter,
+ tracking_metrics,
+ )))
+ }
+}
- let result = {
- // NB timer records time taken on drop, so there are no
- // calls to `timer.done()` below.
- let _timer = tracking_metrics.elapsed_compute().timer();
+fn get_sorted_iter(
Review comment:
The main changes:
1. concrete all sort-columns (instead of all columns)
2. sort to get the index array (same as the original sort)
3. use `CombinedIndex` to avoid huge batch construction (to access records scattered in different batches)
4. construct a small batch at a time.
##########
File path: datafusion/core/src/physical_plan/sorts/sort.rs
##########
@@ -105,13 +107,21 @@ impl ExternalSorter {
}
}
- async fn insert_batch(&self, input: RecordBatch) -> Result<()> {
+ async fn insert_batch(
+ &self,
+ input: RecordBatch,
+ tracking_metrics: &MemTrackingMetrics,
+ ) -> Result<()> {
if input.num_rows() > 0 {
let size = batch_byte_size(&input);
self.try_grow(size).await?;
self.metrics.mem_used().add(size);
let mut in_mem_batches = self.in_mem_batches.lock().await;
- in_mem_batches.push(input);
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ let partial = sort_batch(input, self.schema.clone(), &self.expr)?;
Review comment:
The other change: sort each batch before buffering it in memory.
##########
File path: datafusion/core/src/physical_plan/sorts/sort2.rs
##########
@@ -0,0 +1,1147 @@
+// 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.
+
+//! Sort that deals with an arbitrary size of the input.
+//! It will do in-memory sorting if it has enough memory budget
+//! but spills to disk if needed.
+
+use crate::error::{DataFusionError, Result};
+use crate::execution::context::TaskContext;
+use crate::execution::memory_manager::{
+ human_readable_size, ConsumerType, MemoryConsumer, MemoryConsumerId, MemoryManager,
+};
+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, CompositeMetricsSet, MemTrackingMetrics, MetricsSet,
+};
+use crate::physical_plan::sorts::sort_preserving_merge::SortPreservingMergeStream;
+use crate::physical_plan::sorts::SortedStream;
+use crate::physical_plan::stream::RecordBatchReceiverStream;
+use crate::physical_plan::{
+ DisplayFormatType, Distribution, EmptyRecordBatchStream, ExecutionPlan, Partitioning,
+ RecordBatchStream, SendableRecordBatchStream, Statistics,
+};
+use crate::prelude::SessionConfig;
+use arrow::array::{make_array, Array, ArrayRef, MutableArrayData, UInt32Array};
+pub use arrow::compute::SortOptions;
+use arrow::compute::{concat, lexsort_to_indices, take, SortColumn, TakeOptions};
+use arrow::datatypes::SchemaRef;
+use arrow::error::Result as ArrowResult;
+use arrow::ipc::reader::FileReader;
+use arrow::record_batch::RecordBatch;
+use async_trait::async_trait;
+use futures::lock::Mutex;
+use futures::{Stream, StreamExt};
+use log::{debug, error};
+use std::any::Any;
+use std::cmp::min;
+use std::fmt;
+use std::fmt::{Debug, Formatter};
+use std::fs::File;
+use std::io::BufReader;
+use std::path::{Path, PathBuf};
+use std::sync::Arc;
+use std::task::{Context, Poll};
+use tempfile::NamedTempFile;
+use tokio::sync::mpsc::{Receiver, Sender};
+use tokio::task;
+
+/// Sort arbitrary size of data to get a total order (may spill several times during sorting based on free memory available).
+///
+/// The basic architecture of the algorithm:
+/// 1. get a non-empty new batch from input
+/// 2. check with the memory manager if we could buffer the batch in memory
+/// 2.1 if memory sufficient, then buffer batch in memory, go to 1.
+/// 2.2 if the memory threshold is reached, sort all buffered batches and spill to file.
+/// buffer the batch in memory, go to 1.
+/// 3. when input is exhausted, merge all in memory batches and spills to get a total order.
+struct ExternalSorter2 {
+ id: MemoryConsumerId,
+ schema: SchemaRef,
+ in_mem_batches: Mutex<Vec<RecordBatch>>,
+ spills: Mutex<Vec<NamedTempFile>>,
+ /// Sort expressions
+ expr: Vec<PhysicalSortExpr>,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ metrics_set: CompositeMetricsSet,
+ metrics: BaselineMetrics,
+}
+
+impl ExternalSorter2 {
+ pub fn new(
+ partition_id: usize,
+ schema: SchemaRef,
+ expr: Vec<PhysicalSortExpr>,
+ metrics_set: CompositeMetricsSet,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ ) -> Self {
+ let metrics = metrics_set.new_intermediate_baseline(partition_id);
+ Self {
+ id: MemoryConsumerId::new(partition_id),
+ schema,
+ in_mem_batches: Mutex::new(vec![]),
+ spills: Mutex::new(vec![]),
+ expr,
+ session_config,
+ runtime,
+ metrics_set,
+ metrics,
+ }
+ }
+
+ async fn insert_batch(
+ &self,
+ input: RecordBatch,
+ tracking_metrics: &MemTrackingMetrics,
+ ) -> Result<()> {
+ if input.num_rows() > 0 {
+ let size = batch_byte_size(&input);
+ self.try_grow(size).await?;
+ self.metrics.mem_used().add(size);
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ let partial = sort_batch(input, self.schema.clone(), &self.expr)?;
Review comment:
The other change: sort each batch before buffering it in memory.
##########
File path: datafusion/core/src/physical_plan/sorts/sort2.rs
##########
@@ -0,0 +1,1147 @@
+// 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.
+
+//! Sort that deals with an arbitrary size of the input.
+//! It will do in-memory sorting if it has enough memory budget
+//! but spills to disk if needed.
+
+use crate::error::{DataFusionError, Result};
+use crate::execution::context::TaskContext;
+use crate::execution::memory_manager::{
+ human_readable_size, ConsumerType, MemoryConsumer, MemoryConsumerId, MemoryManager,
+};
+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, CompositeMetricsSet, MemTrackingMetrics, MetricsSet,
+};
+use crate::physical_plan::sorts::sort_preserving_merge::SortPreservingMergeStream;
+use crate::physical_plan::sorts::SortedStream;
+use crate::physical_plan::stream::RecordBatchReceiverStream;
+use crate::physical_plan::{
+ DisplayFormatType, Distribution, EmptyRecordBatchStream, ExecutionPlan, Partitioning,
+ RecordBatchStream, SendableRecordBatchStream, Statistics,
+};
+use crate::prelude::SessionConfig;
+use arrow::array::{make_array, Array, ArrayRef, MutableArrayData, UInt32Array};
+pub use arrow::compute::SortOptions;
+use arrow::compute::{concat, lexsort_to_indices, take, SortColumn, TakeOptions};
+use arrow::datatypes::SchemaRef;
+use arrow::error::Result as ArrowResult;
+use arrow::ipc::reader::FileReader;
+use arrow::record_batch::RecordBatch;
+use async_trait::async_trait;
+use futures::lock::Mutex;
+use futures::{Stream, StreamExt};
+use log::{debug, error};
+use std::any::Any;
+use std::cmp::min;
+use std::fmt;
+use std::fmt::{Debug, Formatter};
+use std::fs::File;
+use std::io::BufReader;
+use std::path::{Path, PathBuf};
+use std::sync::Arc;
+use std::task::{Context, Poll};
+use tempfile::NamedTempFile;
+use tokio::sync::mpsc::{Receiver, Sender};
+use tokio::task;
+
+/// Sort arbitrary size of data to get a total order (may spill several times during sorting based on free memory available).
+///
+/// The basic architecture of the algorithm:
+/// 1. get a non-empty new batch from input
+/// 2. check with the memory manager if we could buffer the batch in memory
+/// 2.1 if memory sufficient, then buffer batch in memory, go to 1.
+/// 2.2 if the memory threshold is reached, sort all buffered batches and spill to file.
+/// buffer the batch in memory, go to 1.
+/// 3. when input is exhausted, merge all in memory batches and spills to get a total order.
+struct ExternalSorter2 {
+ id: MemoryConsumerId,
+ schema: SchemaRef,
+ in_mem_batches: Mutex<Vec<RecordBatch>>,
+ spills: Mutex<Vec<NamedTempFile>>,
+ /// Sort expressions
+ expr: Vec<PhysicalSortExpr>,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ metrics_set: CompositeMetricsSet,
+ metrics: BaselineMetrics,
+}
+
+impl ExternalSorter2 {
+ pub fn new(
+ partition_id: usize,
+ schema: SchemaRef,
+ expr: Vec<PhysicalSortExpr>,
+ metrics_set: CompositeMetricsSet,
+ session_config: Arc<SessionConfig>,
+ runtime: Arc<RuntimeEnv>,
+ ) -> Self {
+ let metrics = metrics_set.new_intermediate_baseline(partition_id);
+ Self {
+ id: MemoryConsumerId::new(partition_id),
+ schema,
+ in_mem_batches: Mutex::new(vec![]),
+ spills: Mutex::new(vec![]),
+ expr,
+ session_config,
+ runtime,
+ metrics_set,
+ metrics,
+ }
+ }
+
+ async fn insert_batch(
+ &self,
+ input: RecordBatch,
+ tracking_metrics: &MemTrackingMetrics,
+ ) -> Result<()> {
+ if input.num_rows() > 0 {
+ let size = batch_byte_size(&input);
+ self.try_grow(size).await?;
+ self.metrics.mem_used().add(size);
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ let partial = sort_batch(input, self.schema.clone(), &self.expr)?;
+ in_mem_batches.push(partial);
+ }
+ Ok(())
+ }
+
+ async fn spilled_before(&self) -> bool {
+ let spills = self.spills.lock().await;
+ !spills.is_empty()
+ }
+
+ /// MergeSort in mem batches as well as spills into total order with `SortPreservingMergeStream`.
+ async fn sort(&self) -> Result<SendableRecordBatchStream> {
+ let partition = self.partition_id();
+ let batch_size = self.session_config.batch_size;
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+
+ if self.spilled_before().await {
+ let tracking_metrics = self
+ .metrics_set
+ .new_intermediate_tracking(partition, self.runtime.clone());
+ let mut streams: Vec<SortedStream> = vec![];
+ if in_mem_batches.len() > 0 {
+ let in_mem_stream = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &self.expr,
+ batch_size,
+ tracking_metrics,
+ )?;
+ let prev_used = self.metrics.mem_used().set(0);
+ streams.push(SortedStream::new(in_mem_stream, prev_used));
+ }
+
+ let mut spills = self.spills.lock().await;
+
+ for spill in spills.drain(..) {
+ let stream = read_spill_as_stream(spill, self.schema.clone())?;
+ streams.push(SortedStream::new(stream, 0));
+ }
+ let tracking_metrics = self
+ .metrics_set
+ .new_final_tracking(partition, self.runtime.clone());
+ Ok(Box::pin(SortPreservingMergeStream::new_from_streams(
+ streams,
+ self.schema.clone(),
+ &self.expr,
+ tracking_metrics,
+ self.session_config.batch_size,
+ )))
+ } else if in_mem_batches.len() > 0 {
+ let tracking_metrics = self
+ .metrics_set
+ .new_final_tracking(partition, self.runtime.clone());
+ let result = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &self.expr,
+ batch_size,
+ tracking_metrics,
+ );
+ // Report to the memory manager we are no longer using memory
+ self.metrics.mem_used().set(0);
+ result
+ } else {
+ Ok(Box::pin(EmptyRecordBatchStream::new(self.schema.clone())))
+ }
+ }
+
+ fn used(&self) -> usize {
+ self.metrics.mem_used().value()
+ }
+
+ fn spilled_bytes(&self) -> usize {
+ self.metrics.spilled_bytes().value()
+ }
+
+ fn spill_count(&self) -> usize {
+ self.metrics.spill_count().value()
+ }
+}
+
+impl Debug for ExternalSorter2 {
+ fn fmt(&self, f: &mut Formatter) -> fmt::Result {
+ f.debug_struct("ExternalSorter2")
+ .field("id", &self.id())
+ .field("memory_used", &self.used())
+ .field("spilled_bytes", &self.spilled_bytes())
+ .field("spill_count", &self.spill_count())
+ .finish()
+ }
+}
+
+#[derive(Debug, Copy, Clone)]
+struct CombinedIndex {
+ batch_idx: usize,
+ row_idx: usize,
+}
+
+impl Drop for ExternalSorter2 {
+ fn drop(&mut self) {
+ self.runtime.drop_consumer(self.id(), self.used());
+ }
+}
+
+#[async_trait]
+impl MemoryConsumer for ExternalSorter2 {
+ fn name(&self) -> String {
+ "ExternalSorter2".to_owned()
+ }
+
+ fn id(&self) -> &MemoryConsumerId {
+ &self.id
+ }
+
+ fn memory_manager(&self) -> Arc<MemoryManager> {
+ self.runtime.memory_manager.clone()
+ }
+
+ fn type_(&self) -> &ConsumerType {
+ &ConsumerType::Requesting
+ }
+
+ async fn spill(&self) -> Result<usize> {
+ debug!(
+ "{}[{}] spilling sort data of {} to disk while inserting ({} time(s) so far)",
+ self.name(),
+ self.id(),
+ self.used(),
+ self.spill_count()
+ );
+
+ let partition = self.partition_id();
+ let mut in_mem_batches = self.in_mem_batches.lock().await;
+ // we could always get a chance to free some memory as long as we are holding some
+ if in_mem_batches.len() == 0 {
+ return Ok(0);
+ }
+
+ let tracking_metrics = self
+ .metrics_set
+ .new_intermediate_tracking(partition, self.runtime.clone());
+
+ let spillfile = self.runtime.disk_manager.create_tmp_file()?;
+ let stream = in_mem_partial_sort(
+ &mut *in_mem_batches,
+ self.schema.clone(),
+ &*self.expr,
+ self.session_config.batch_size,
+ tracking_metrics,
+ );
+
+ spill_partial_sorted_stream(&mut stream?, spillfile.path(), self.schema.clone())
+ .await?;
+ let mut spills = self.spills.lock().await;
+ let used = self.metrics.mem_used().set(0);
+ self.metrics.record_spill(used);
+ spills.push(spillfile);
+ Ok(used)
+ }
+
+ fn mem_used(&self) -> usize {
+ self.metrics.mem_used().value()
+ }
+}
+
+/// consume the non-empty `sorted_bathes` and do in_mem_sort
+fn in_mem_partial_sort(
+ buffered_batches: &mut Vec<RecordBatch>,
+ schema: SchemaRef,
+ expressions: &[PhysicalSortExpr],
+ batch_size: usize,
+ tracking_metrics: MemTrackingMetrics,
+) -> Result<SendableRecordBatchStream> {
+ assert_ne!(buffered_batches.len(), 0);
+ if buffered_batches.len() == 1 {
+ let result = buffered_batches.pop();
+ Ok(Box::pin(SizedRecordBatchStream::new(
+ schema,
+ vec![Arc::new(result.unwrap())],
+ tracking_metrics,
+ )))
+ } else {
+ let batches = buffered_batches.drain(..).collect::<Vec<_>>();
+ let sorted_iter = {
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ get_sorted_iter(&batches, expressions, batch_size)?
+ };
+ Ok(Box::pin(SortedSizedRecordBatchStream::new(
+ schema,
+ batches,
+ sorted_iter,
+ tracking_metrics,
+ )))
+ }
+}
+
+fn get_sorted_iter(
Review comment:
The main changes:
1. concrete all sort-columns (instead of all columns)
2. sort to get the index array (same as the original sort)
3. use `CombinedIndex` to avoid huge batch construction (to access records scattered in different batches)
4. construct a small batch at a time.
##########
File path: benchmarks/queries/q1.sql
##########
@@ -1,21 +1,12 @@
select
l_returnflag,
l_linestatus,
- sum(l_quantity) as sum_qty,
- sum(l_extendedprice) as sum_base_price,
- sum(l_extendedprice * (1 - l_discount)) as sum_disc_price,
- sum(l_extendedprice * (1 - l_discount) * (1 + l_tax)) as sum_charge,
- avg(l_quantity) as avg_qty,
- avg(l_extendedprice) as avg_price,
- avg(l_discount) as avg_disc,
- count(*) as count_order
+ l_quantity,
+ l_extendedprice,
+ l_discount,
+ l_tax
from
lineitem
-where
Review comment:
tmp change to bench sort
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[GitHub] [arrow-datafusion] yjshen edited a comment on pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen edited a comment on pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#issuecomment-1085799873
A modified version of TPC-H q1:
```sql
select
l_returnflag,
l_linestatus,
l_quantity,
l_extendedprice,
l_discount,
l_tax
from
lineitem
order by
l_extendedprice,
l_discount;
```
```shell
./target/release/tpch benchmark datafusion --path /Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet -f parquet -q 1
```
Before this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 8192, path: "/Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 5922.3 ms and returned 6001214 rows
Query 1 iteration 1 took 5699.3 ms and returned 6001214 rows
Query 1 iteration 2 took 6166.0 ms and returned 6001214 rows
Query 1 avg time: 5929.20 ms
```
With this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 8192, path: "/Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 4031.2 ms and returned 6001214 rows
Query 1 iteration 1 took 4375.6 ms and returned 6001214 rows
Query 1 iteration 2 took 4318.8 ms and returned 6001214 rows
Query 1 avg time: 4241.89 ms
```
--
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[GitHub] [arrow-datafusion] yjshen commented on a change in pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen commented on a change in pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#discussion_r841011135
##########
File path: datafusion/core/src/physical_plan/sorts/sort.rs
##########
@@ -105,13 +107,21 @@ impl ExternalSorter {
}
}
- async fn insert_batch(&self, input: RecordBatch) -> Result<()> {
+ async fn insert_batch(
+ &self,
+ input: RecordBatch,
+ tracking_metrics: &MemTrackingMetrics,
+ ) -> Result<()> {
if input.num_rows() > 0 {
let size = batch_byte_size(&input);
self.try_grow(size).await?;
self.metrics.mem_used().add(size);
let mut in_mem_batches = self.in_mem_batches.lock().await;
- in_mem_batches.push(input);
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ let partial = sort_batch(input, self.schema.clone(), &self.expr)?;
Review comment:
A change here: sort each batch before buffering it in memory.
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[GitHub] [arrow-datafusion] yjshen commented on a change in pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen commented on a change in pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#discussion_r841011038
##########
File path: datafusion/core/src/physical_plan/sorts/sort.rs
##########
@@ -271,33 +291,212 @@ fn in_mem_partial_sort(
buffered_batches: &mut Vec<RecordBatch>,
schema: SchemaRef,
expressions: &[PhysicalSortExpr],
+ batch_size: usize,
tracking_metrics: MemTrackingMetrics,
) -> Result<SendableRecordBatchStream> {
assert_ne!(buffered_batches.len(), 0);
+ if buffered_batches.len() == 1 {
+ let result = buffered_batches.pop();
+ Ok(Box::pin(SizedRecordBatchStream::new(
+ schema,
+ vec![Arc::new(result.unwrap())],
+ tracking_metrics,
+ )))
+ } else {
+ let batches = buffered_batches.drain(..).collect::<Vec<_>>();
+ let sorted_iter = {
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ get_sorted_iter(&batches, expressions, batch_size)?
+ };
+ Ok(Box::pin(SortedSizedRecordBatchStream::new(
+ schema,
+ batches,
+ sorted_iter,
+ tracking_metrics,
+ )))
+ }
+}
- let result = {
- // NB timer records time taken on drop, so there are no
- // calls to `timer.done()` below.
- let _timer = tracking_metrics.elapsed_compute().timer();
+fn get_sorted_iter(
Review comment:
The main changes:
1. concat all sort-columns (instead of all columns)
2. sort to get the index array (same as the original sort)
3. use `CompositeIndex` to avoid huge batch construction (to access records scattered in different batches)
4. construct a small batch at a time.
--
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[GitHub] [arrow-datafusion] yjshen removed a comment on pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen removed a comment on pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#issuecomment-1085806095
A modified version of TPC-H q1:
```sql
select
l_returnflag,
l_linestatus,
l_quantity,
l_extendedprice,
l_discount,
l_tax
from
lineitem
order by
l_extendedprice,
l_discount;
```
```shell
./target/release/tpch benchmark datafusion --path /Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet -f parquet -q 1
```
Before this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 8192, path: "/Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 5922.3 ms and returned 6001214 rows
Query 1 iteration 1 took 5699.3 ms and returned 6001214 rows
Query 1 iteration 2 took 6166.0 ms and returned 6001214 rows
Query 1 avg time: 5929.20 ms
```
With this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 8192, path: "/Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 4031.2 ms and returned 6001214 rows
Query 1 iteration 1 took 4375.6 ms and returned 6001214 rows
Query 1 iteration 2 took 4318.8 ms and returned 6001214 rows
Query 1 avg time: 4241.89 ms
```
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[GitHub] [arrow-datafusion] yjshen edited a comment on pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen edited a comment on pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#issuecomment-1085799873
A modified version of TPC-H q1:
select
l_returnflag,
l_linestatus,
l_quantity,
l_extendedprice,
l_discount,
l_tax
from
lineitem
order by
l_extendedprice,
l_discount;
```shell
./target/release/tpch benchmark datafusion --path /Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet -f parquet -q 1
```
Before this PR:
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 8192, path: "/Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 5922.3 ms and returned 6001214 rows
Query 1 iteration 1 took 5699.3 ms and returned 6001214 rows
Query 1 iteration 2 took 6166.0 ms and returned 6001214 rows
Query 1 avg time: 5929.20 ms
With this PR:
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 8192, path: "/Users/shenyijie/code/arrow-datafusion/benchmarks/tpch-parquet", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 4031.2 ms and returned 6001214 rows
Query 1 iteration 1 took 4375.6 ms and returned 6001214 rows
Query 1 iteration 2 took 4318.8 ms and returned 6001214 rows
Query 1 avg time: 4241.89 ms
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[GitHub] [arrow-datafusion] yjshen edited a comment on pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen edited a comment on pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#issuecomment-1085966913
On a newer Linux Desktop:
```
H/W path Device Class Description
====================================================================
system MS-7D53 (To be filled by O.E.M.)
/0 bus MPG X570S EDGE MAX WIFI (MS-7D53)
/0/0 memory 64KiB BIOS
/0/11 memory 32GiB System Memory
/0/11/0 memory 3600 MHz (0.3 ns) [empty]
/0/11/1 memory 16GiB DIMM DDR4 Synchronous Unbuffered (Unregistered) 3600 MHz (0.3
/0/11/2 memory 3600 MHz (0.3 ns) [empty]
/0/11/3 memory 16GiB DIMM DDR4 Synchronous Unbuffered (Unregistered) 3600 MHz (0.3
/0/14 memory 1MiB L1 cache
/0/15 memory 8MiB L2 cache
/0/16 memory 64MiB L3 cache
/0/17 processor AMD Ryzen 9 5950X 16-Core Processor
```
## A modified version of TPC-H q1:
```sql
select
l_returnflag,
l_linestatus,
l_quantity,
l_extendedprice,
l_discount,
l_tax
from
lineitem
order by
l_extendedprice,
l_discount;
```
```
cargo run --release --features "mimalloc" --bin tpch -- benchmark datafusion --iterations 3 --path ../../tpch-parquet/ --format parquet --query 1 --batch-size 4096
```
Without this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 4096, path: "../../tpch-parquet/", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 2851.7 ms and returned 6001214 rows
Query 1 iteration 1 took 2817.7 ms and returned 6001214 rows
Query 1 iteration 2 took 2735.9 ms and returned 6001214 rows
Query 1 avg time: 2801.75 ms
```
With this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 4096, path: "../../tpch-parquet/", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 2843.6 ms and returned 6001214 rows
Query 1 iteration 1 took 2824.4 ms and returned 6001214 rows
Query 1 iteration 2 took 2753.0 ms and returned 6001214 rows
Query 1 avg time: 2807.00 ms
```
Performances are similar.
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[GitHub] [arrow-datafusion] yjshen edited a comment on pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen edited a comment on pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#issuecomment-1085966913
On a newer Linux Desktop:
```
H/W path Device Class Description
====================================================================
system MS-7D53 (To be filled by O.E.M.)
/0 bus MPG X570S EDGE MAX WIFI (MS-7D53)
/0/0 memory 64KiB BIOS
/0/11 memory 32GiB System Memory
/0/11/0 memory 3600 MHz (0.3 ns) [empty]
/0/11/1 memory 16GiB DIMM DDR4 Synchronous Unbuffered (Unregistered) 3600 MHz (0.3
/0/11/2 memory 3600 MHz (0.3 ns) [empty]
/0/11/3 memory 16GiB DIMM DDR4 Synchronous Unbuffered (Unregistered) 3600 MHz (0.3
/0/14 memory 1MiB L1 cache
/0/15 memory 8MiB L2 cache
/0/16 memory 64MiB L3 cache
/0/17 processor AMD Ryzen 9 5950X 16-Core Processor
```
## A modified version of TPC-H q1:
```sql
select
l_returnflag,
l_linestatus,
l_quantity,
l_extendedprice,
l_discount,
l_tax
from
lineitem
order by
l_extendedprice,
l_discount;
```
```
cargo run --release --features "mimalloc" --bin tpch -- benchmark datafusion --iterations 3 --path ../../tpch-parquet/ --format parquet --query 1 --batch-size 4096
```
Without this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 4096, path: "../../tpch-parquet/", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 2851.7 ms and returned 6001214 rows
Query 1 iteration 1 took 2817.7 ms and returned 6001214 rows
Query 1 iteration 2 took 2735.9 ms and returned 6001214 rows
Query 1 avg time: 2801.75 ms
```
With this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 4096, path: "../../tpch-parquet/", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 2843.6 ms and returned 6001214 rows
Query 1 iteration 1 took 2824.4 ms and returned 6001214 rows
Query 1 iteration 2 took 2753.0 ms and returned 6001214 rows
Query 1 avg time: 2807.00 ms
```
Performances are similar.
## cargo criterion --bench sort_limit_query_sql
Without this PR:
```
sort_and_limit_by_int time: [869.27 us 870.72 us 872.19 us]
sort_and_limit_by_float time: [845.95 us 847.36 us 848.77 us]
sort_and_limit_lex_by_int
time: [875.07 us 876.80 us 878.59 us]
sort_and_limit_lex_by_string
time: [878.78 us 880.40 us 882.06 us]
```
With this PR:
```
sort_and_limit_by_int time: [856.71 us 858.58 us 860.41 us]
sort_and_limit_by_float time: [836.55 us 838.40 us 840.29 us]
sort_and_limit_lex_by_int
time: [864.83 us 866.33 us 867.83 us]
sort_and_limit_lex_by_string
time: [866.89 us 868.25 us 869.62 us]
```
Similar performance as well.
--
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[GitHub] [arrow-datafusion] yjshen commented on pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen commented on pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#issuecomment-1085966913
On a newer Linux Desktop:
```
H/W path Device Class Description
====================================================================
system MS-7D53 (To be filled by O.E.M.)
/0 bus MPG X570S EDGE MAX WIFI (MS-7D53)
/0/0 memory 64KiB BIOS
/0/11 memory 32GiB System Memory
/0/11/0 memory 3600 MHz (0.3 ns) [empty]
/0/11/1 memory 16GiB DIMM DDR4 Synchronous Unbuffered (Unregistered) 3600 MHz (0.3
/0/11/2 memory 3600 MHz (0.3 ns) [empty]
/0/11/3 memory 16GiB DIMM DDR4 Synchronous Unbuffered (Unregistered) 3600 MHz (0.3
/0/14 memory 1MiB L1 cache
/0/15 memory 8MiB L2 cache
/0/16 memory 64MiB L3 cache
/0/17 processor AMD Ryzen 9 5950X 16-Core Processor
```
```
cargo run --release --features "mimalloc" --bin tpch -- benchmark datafusion --iterations 3 --path ../../tpch-parquet/ --format parquet --query 1 --batch-size 4096
```
Without this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 4096, path: "../../tpch-parquet/", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 2851.7 ms and returned 6001214 rows
Query 1 iteration 1 took 2817.7 ms and returned 6001214 rows
Query 1 iteration 2 took 2735.9 ms and returned 6001214 rows
Query 1 avg time: 2801.75 ms
```
With this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 4096, path: "../../tpch-parquet/", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 2843.6 ms and returned 6001214 rows
Query 1 iteration 1 took 2824.4 ms and returned 6001214 rows
Query 1 iteration 2 took 2753.0 ms and returned 6001214 rows
Query 1 avg time: 2807.00 ms
```
Performances are similar.
--
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To respond to the message, please log on to GitHub and use the
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[GitHub] [arrow-datafusion] yjshen edited a comment on pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen edited a comment on pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#issuecomment-1085966913
Hardware Settings:
```
H/W path Device Class Description
====================================================================
system MS-7D53 (To be filled by O.E.M.)
/0 bus MPG X570S EDGE MAX WIFI (MS-7D53)
/0/0 memory 64KiB BIOS
/0/11 memory 32GiB System Memory
/0/11/0 memory 3600 MHz (0.3 ns) [empty]
/0/11/1 memory 16GiB DIMM DDR4 Synchronous Unbuffered (Unregistered) 3600 MHz (0.3
/0/11/2 memory 3600 MHz (0.3 ns) [empty]
/0/11/3 memory 16GiB DIMM DDR4 Synchronous Unbuffered (Unregistered) 3600 MHz (0.3
/0/14 memory 1MiB L1 cache
/0/15 memory 8MiB L2 cache
/0/16 memory 64MiB L3 cache
/0/17 processor AMD Ryzen 9 5950X 16-Core Processor
```
## A modified version of TPC-H q1:
```sql
select
l_returnflag,
l_linestatus,
l_quantity,
l_extendedprice,
l_discount,
l_tax
from
lineitem
order by
l_extendedprice,
l_discount;
```
```
cargo run --release --features "mimalloc" --bin tpch -- benchmark datafusion --iterations 3 --path ../../tpch-parquet/ --format parquet --query 1 --batch-size 4096
```
Without this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 4096, path: "../../tpch-parquet/", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 2851.7 ms and returned 6001214 rows
Query 1 iteration 1 took 2817.7 ms and returned 6001214 rows
Query 1 iteration 2 took 2735.9 ms and returned 6001214 rows
Query 1 avg time: 2801.75 ms
```
With this PR:
```
Running benchmarks with the following options: DataFusionBenchmarkOpt { query: 1, debug: false, iterations: 3, partitions: 2, batch_size: 4096, path: "../../tpch-parquet/", file_format: "parquet", mem_table: false, output_path: None }
Query 1 iteration 0 took 2843.6 ms and returned 6001214 rows
Query 1 iteration 1 took 2824.4 ms and returned 6001214 rows
Query 1 iteration 2 took 2753.0 ms and returned 6001214 rows
Query 1 avg time: 2807.00 ms
```
Performances are similar.
## cargo criterion --bench sort_limit_query_sql
Without this PR:
```
sort_and_limit_by_int time: [869.27 us 870.72 us 872.19 us]
sort_and_limit_by_float time: [845.95 us 847.36 us 848.77 us]
sort_and_limit_lex_by_int
time: [875.07 us 876.80 us 878.59 us]
sort_and_limit_lex_by_string
time: [878.78 us 880.40 us 882.06 us]
```
With this PR:
```
sort_and_limit_by_int time: [856.71 us 858.58 us 860.41 us]
sort_and_limit_by_float time: [836.55 us 838.40 us 840.29 us]
sort_and_limit_lex_by_int
time: [864.83 us 866.33 us 867.83 us]
sort_and_limit_lex_by_string
time: [866.89 us 868.25 us 869.62 us]
```
Similar performance as well.
--
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[GitHub] [arrow-datafusion] yjshen commented on a change in pull request #2132: WIP: Reduce sort memory usage v1
Posted by GitBox <gi...@apache.org>.
yjshen commented on a change in pull request #2132:
URL: https://github.com/apache/arrow-datafusion/pull/2132#discussion_r841011038
##########
File path: datafusion/core/src/physical_plan/sorts/sort.rs
##########
@@ -271,33 +291,212 @@ fn in_mem_partial_sort(
buffered_batches: &mut Vec<RecordBatch>,
schema: SchemaRef,
expressions: &[PhysicalSortExpr],
+ batch_size: usize,
tracking_metrics: MemTrackingMetrics,
) -> Result<SendableRecordBatchStream> {
assert_ne!(buffered_batches.len(), 0);
+ if buffered_batches.len() == 1 {
+ let result = buffered_batches.pop();
+ Ok(Box::pin(SizedRecordBatchStream::new(
+ schema,
+ vec![Arc::new(result.unwrap())],
+ tracking_metrics,
+ )))
+ } else {
+ let batches = buffered_batches.drain(..).collect::<Vec<_>>();
+ let sorted_iter = {
+ // NB timer records time taken on drop, so there are no
+ // calls to `timer.done()` below.
+ let _timer = tracking_metrics.elapsed_compute().timer();
+ get_sorted_iter(&batches, expressions, batch_size)?
+ };
+ Ok(Box::pin(SortedSizedRecordBatchStream::new(
+ schema,
+ batches,
+ sorted_iter,
+ tracking_metrics,
+ )))
+ }
+}
- let result = {
- // NB timer records time taken on drop, so there are no
- // calls to `timer.done()` below.
- let _timer = tracking_metrics.elapsed_compute().timer();
+fn get_sorted_iter(
Review comment:
The main changes:
1. concat all sort-columns (instead of all columns)
2. sort to get the index array (same as the original sort)
3. use `CombinedIndex` to avoid huge batch construction (to access records scattered in different batches)
4. construct a small batch at a time.
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