[GitHub] [arrow] jorgecarleitao commented on a change in pull request #8982: ARROW-10582: [Rust] [DataFusion] Implement "repartition" operator

[GitBox <gi...@apache.org>]

jorgecarleitao commented on a change in pull request #8982:
URL: https://github.com/apache/arrow/pull/8982#discussion_r548381582



##########
File path: rust/datafusion/src/dataframe.rs
##########
@@ -172,6 +174,24 @@ pub trait DataFrame {
         right_cols: &[&str],
     ) -> Result<Arc<dyn DataFrame>>;
 
+    /// Repartition a DataFrame based on a logical partitioning scheme.
+    ///
+    /// ```
+    /// # use datafusion::prelude::*;
+    /// # use datafusion::error::Result;
+    /// # fn main() -> Result<()> {
+    /// let mut ctx = ExecutionContext::new();
+    /// let df = ctx.read_csv("tests/example.csv", CsvReadOptions::new())?;
+    /// let df1 = df.repartition(Partitioning::RoundRobinBatch(4))?;
+    /// let df2 = df.repartition(Partitioning::Hash(vec![col("a")], 4))?;

Review comment:
       I would not place it in an example since we do not support it yet.

##########
File path: rust/datafusion/src/execution/dataframe_impl.rs
##########
@@ -111,6 +112,16 @@ impl DataFrame for DataFrameImpl {
         Ok(Arc::new(DataFrameImpl::new(self.ctx_state.clone(), &plan)))
     }
 
+    fn repartition(
+        &self,
+        partitioning_scheme: Partitioning,

Review comment:
       nit: this introduces a new naming, `partitioning_scheme`.
   
   We have:
   
   * `partition`
   * `partitioning`
   * `partitioning_scheme`
   * `repartition`
   * `part`
   
   I do not know the common notation, but we could try to reduce the number of different names we use.
   
   In my (little) understanding:
   * data is partitioned according to a `partition`
   * partitioned data is divided in `part`s 
   * we can `repartition` it according to a new `partition`.
   
   In this understanding, I would replace `partitioning` and `partitioning_scheme` by `partition`.
   
   Even if this understanding is not correct, maybe we could reduce the number of different names?

##########
File path: rust/datafusion/src/logical_plan/plan.rs
##########
@@ -198,6 +206,15 @@ impl LogicalPlan {
     }
 }
 
+/// Logical partitioning schemes supported by the repartition operator.
+#[derive(Debug, Clone)]
+pub enum Partitioning {
+    /// Allocate batches using a round-robin algorithm
+    RoundRobinBatch(usize),
+    /// Allocate rows based on a hash of one of more expressions

Review comment:
       Document `usize`? (Number of parts?)

##########
File path: rust/datafusion/src/physical_plan/repartition.rs
##########
@@ -0,0 +1,336 @@
+// 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.
+
+//! The repartition operator maps N input partitions to M output partitions based on a
+//! partitioning scheme.
+
+use std::any::Any;
+use std::pin::Pin;
+use std::sync::Arc;
+use std::task::{Context, Poll};
+
+use crate::error::{DataFusionError, Result};
+use crate::physical_plan::{ExecutionPlan, Partitioning};
+use arrow::datatypes::SchemaRef;
+use arrow::error::Result as ArrowResult;
+use arrow::record_batch::RecordBatch;
+
+use super::{RecordBatchStream, SendableRecordBatchStream};
+use async_trait::async_trait;
+
+use crossbeam::channel::{unbounded, Receiver, Sender};
+use futures::stream::Stream;
+use futures::StreamExt;
+use tokio::sync::Mutex;
+use tokio::task::JoinHandle;
+
+/// partition. No guarantees are made about the order of the resulting partition.
+#[derive(Debug)]
+pub struct RepartitionExec {
+    /// Input execution plan
+    input: Arc<dyn ExecutionPlan>,
+    /// Partitioning scheme to use
+    partitioning: Partitioning,
+    /// Receivers for output batches
+    rx: Arc<Mutex<Vec<Receiver<Option<ArrowResult<RecordBatch>>>>>>,
+    /// Senders for output batches
+    tx: Arc<Mutex<Vec<Sender<Option<ArrowResult<RecordBatch>>>>>>,
+}
+
+#[async_trait]
+impl ExecutionPlan for RepartitionExec {
+    /// Return a reference to Any that can be used for downcasting
+    fn as_any(&self) -> &dyn Any {
+        self
+    }
+
+    /// Get the schema for this execution plan
+    fn schema(&self) -> SchemaRef {
+        self.input.schema()
+    }
+
+    fn children(&self) -> Vec<Arc<dyn ExecutionPlan>> {
+        vec![self.input.clone()]
+    }
+
+    fn with_new_children(
+        &self,
+        children: Vec<Arc<dyn ExecutionPlan>>,
+    ) -> Result<Arc<dyn ExecutionPlan>> {
+        match children.len() {
+            1 => Ok(Arc::new(RepartitionExec::try_new(
+                children[0].clone(),
+                self.partitioning.clone(),
+            )?)),
+            _ => Err(DataFusionError::Internal(
+                "RepartitionExec wrong number of children".to_string(),
+            )),
+        }
+    }
+
+    fn output_partitioning(&self) -> Partitioning {
+        self.partitioning.clone()
+    }
+
+    async fn execute(&self, partition: usize) -> Result<SendableRecordBatchStream> {
+        // lock mutexes
+        let mut tx = self.tx.lock().await;
+        let mut rx = self.rx.lock().await;
+
+        let num_input_partitions = self.input.output_partitioning().partition_count();
+        let num_output_partitions = self.partitioning.partition_count();
+
+        // if this is the first partition to be invoked then we need to set up initial state
+        if tx.is_empty() {
+            // create one channel per *output* partition
+            for _ in 0..num_output_partitions {
+                // Note that this operator uses unbounded channels to avoid deadlocks because
+                // the output partitions can be read in any order and this could cause input
+                // partitions to be blocked when sending data to output receivers that are not
+                // being read yet. This may cause high memory usage if the next operator is
+                // reading output partitions in order rather than concurrently. One workaround
+                // for this would be to add spill-to-disk capabilities.
+                let (sender, receiver) = unbounded::<Option<ArrowResult<RecordBatch>>>();
+                tx.push(sender);
+                rx.push(receiver);
+            }
+            // launch one async task per *input* partition
+            for i in 0..num_input_partitions {
+                let input = self.input.clone();
+                let mut tx = tx.clone();
+                let partitioning = self.partitioning.clone();
+                let _: JoinHandle<Result<()>> = tokio::spawn(async move {
+                    let mut stream = input.execute(i).await?;
+                    let mut counter = 0;
+                    while let Some(result) = stream.next().await {
+                        match partitioning {
+                            Partitioning::RoundRobinBatch(_) => {

Review comment:
       I have an old hash repartitioning code in a branch around from a previous try. Quite old by now, but I can definitely put it together for this (like I did for the join). I think we now actually have the framework in place to use it.




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