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demux.rs
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demux.rs
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// 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.
//! Module containing helper methods/traits related to enabling
//! dividing input stream into multiple output files at execution time
use std::collections::HashMap;
use std::sync::Arc;
use crate::datasource::listing::ListingTableUrl;
use crate::error::Result;
use crate::physical_plan::SendableRecordBatchStream;
use arrow_array::builder::UInt64Builder;
use arrow_array::cast::AsArray;
use arrow_array::{downcast_dictionary_array, RecordBatch, StringArray, StructArray};
use arrow_schema::{DataType, Schema};
use chrono::NaiveDate;
use datafusion_common::cast::{
as_boolean_array, as_date32_array, as_date64_array, as_int32_array, as_int64_array,
as_string_array, as_string_view_array,
};
use datafusion_common::{exec_datafusion_err, DataFusionError};
use datafusion_common_runtime::SpawnedTask;
use datafusion_execution::TaskContext;
use futures::StreamExt;
use object_store::path::Path;
use rand::distributions::DistString;
use tokio::sync::mpsc::{self, Receiver, Sender, UnboundedReceiver, UnboundedSender};
type RecordBatchReceiver = Receiver<RecordBatch>;
type DemuxedStreamReceiver = UnboundedReceiver<(Path, RecordBatchReceiver)>;
/// Splits a single [SendableRecordBatchStream] into a dynamically determined
/// number of partitions at execution time. The partitions are determined by
/// factors known only at execution time, such as total number of rows and
/// partition column values. The demuxer task communicates to the caller
/// by sending channels over a channel. The inner channels send RecordBatches
/// which should be contained within the same output file. The outer channel
/// is used to send a dynamic number of inner channels, representing a dynamic
/// number of total output files. The caller is also responsible to monitor
/// the demux task for errors and abort accordingly. The single_file_output parameter
/// overrides all other settings to force only a single file to be written.
/// partition_by parameter will additionally split the input based on the unique
/// values of a specific column `<https://github.com/apache/datafusion/issues/7744>``
/// ┌───────────┐ ┌────────────┐ ┌─────────────┐
/// ┌──────▶ │ batch 1 ├────▶...──────▶│ Batch a │ │ Output File1│
/// │ └───────────┘ └────────────┘ └─────────────┘
/// │
/// ┌──────────┐ │ ┌───────────┐ ┌────────────┐ ┌─────────────┐
/// ┌───────────┐ ┌────────────┐ │ │ ├──────▶ │ batch a+1├────▶...──────▶│ Batch b │ │ Output File2│
/// │ batch 1 ├────▶...──────▶│ Batch N ├─────▶│ Demux ├────────┤ ... └───────────┘ └────────────┘ └─────────────┘
/// └───────────┘ └────────────┘ │ │ │
/// └──────────┘ │ ┌───────────┐ ┌────────────┐ ┌─────────────┐
/// └──────▶ │ batch d ├────▶...──────▶│ Batch n │ │ Output FileN│
/// └───────────┘ └────────────┘ └─────────────┘
pub(crate) fn start_demuxer_task(
input: SendableRecordBatchStream,
context: &Arc<TaskContext>,
partition_by: Option<Vec<(String, DataType)>>,
base_output_path: ListingTableUrl,
file_extension: String,
keep_partition_by_columns: bool,
) -> (SpawnedTask<Result<()>>, DemuxedStreamReceiver) {
let (tx, rx) = mpsc::unbounded_channel();
let context = context.clone();
let single_file_output = !base_output_path.is_collection();
let task = match partition_by {
Some(parts) => {
// There could be an arbitrarily large number of parallel hive style partitions being written to, so we cannot
// bound this channel without risking a deadlock.
SpawnedTask::spawn(async move {
hive_style_partitions_demuxer(
tx,
input,
context,
parts,
base_output_path,
file_extension,
keep_partition_by_columns,
)
.await
})
}
None => SpawnedTask::spawn(async move {
row_count_demuxer(
tx,
input,
context,
base_output_path,
file_extension,
single_file_output,
)
.await
}),
};
(task, rx)
}
/// Dynamically partitions input stream to achieve desired maximum rows per file
async fn row_count_demuxer(
mut tx: UnboundedSender<(Path, Receiver<RecordBatch>)>,
mut input: SendableRecordBatchStream,
context: Arc<TaskContext>,
base_output_path: ListingTableUrl,
file_extension: String,
single_file_output: bool,
) -> Result<()> {
let exec_options = &context.session_config().options().execution;
let max_rows_per_file = exec_options.soft_max_rows_per_output_file;
let max_buffered_batches = exec_options.max_buffered_batches_per_output_file;
let minimum_parallel_files = exec_options.minimum_parallel_output_files;
let mut part_idx = 0;
let write_id =
rand::distributions::Alphanumeric.sample_string(&mut rand::thread_rng(), 16);
let mut open_file_streams = Vec::with_capacity(minimum_parallel_files);
let mut next_send_steam = 0;
let mut row_counts = Vec::with_capacity(minimum_parallel_files);
// Overrides if single_file_output is set
let minimum_parallel_files = if single_file_output {
1
} else {
minimum_parallel_files
};
let max_rows_per_file = if single_file_output {
usize::MAX
} else {
max_rows_per_file
};
while let Some(rb) = input.next().await.transpose()? {
// ensure we have at least minimum_parallel_files open
if open_file_streams.len() < minimum_parallel_files {
open_file_streams.push(create_new_file_stream(
&base_output_path,
&write_id,
part_idx,
&file_extension,
single_file_output,
max_buffered_batches,
&mut tx,
)?);
row_counts.push(0);
part_idx += 1;
} else if row_counts[next_send_steam] >= max_rows_per_file {
row_counts[next_send_steam] = 0;
open_file_streams[next_send_steam] = create_new_file_stream(
&base_output_path,
&write_id,
part_idx,
&file_extension,
single_file_output,
max_buffered_batches,
&mut tx,
)?;
part_idx += 1;
}
row_counts[next_send_steam] += rb.num_rows();
open_file_streams[next_send_steam]
.send(rb)
.await
.map_err(|_| {
DataFusionError::Execution(
"Error sending RecordBatch to file stream!".into(),
)
})?;
next_send_steam = (next_send_steam + 1) % minimum_parallel_files;
}
Ok(())
}
/// Helper for row count demuxer
fn generate_file_path(
base_output_path: &ListingTableUrl,
write_id: &str,
part_idx: usize,
file_extension: &str,
single_file_output: bool,
) -> Path {
if !single_file_output {
base_output_path
.prefix()
.child(format!("{}_{}.{}", write_id, part_idx, file_extension))
} else {
base_output_path.prefix().to_owned()
}
}
/// Helper for row count demuxer
fn create_new_file_stream(
base_output_path: &ListingTableUrl,
write_id: &str,
part_idx: usize,
file_extension: &str,
single_file_output: bool,
max_buffered_batches: usize,
tx: &mut UnboundedSender<(Path, Receiver<RecordBatch>)>,
) -> Result<Sender<RecordBatch>> {
let file_path = generate_file_path(
base_output_path,
write_id,
part_idx,
file_extension,
single_file_output,
);
let (tx_file, rx_file) = mpsc::channel(max_buffered_batches / 2);
tx.send((file_path, rx_file)).map_err(|_| {
DataFusionError::Execution("Error sending RecordBatch to file stream!".into())
})?;
Ok(tx_file)
}
/// Splits an input stream based on the distinct values of a set of columns
/// Assumes standard hive style partition paths such as
/// /col1=val1/col2=val2/outputfile.parquet
async fn hive_style_partitions_demuxer(
tx: UnboundedSender<(Path, Receiver<RecordBatch>)>,
mut input: SendableRecordBatchStream,
context: Arc<TaskContext>,
partition_by: Vec<(String, DataType)>,
base_output_path: ListingTableUrl,
file_extension: String,
keep_partition_by_columns: bool,
) -> Result<()> {
let write_id =
rand::distributions::Alphanumeric.sample_string(&mut rand::thread_rng(), 16);
let exec_options = &context.session_config().options().execution;
let max_buffered_recordbatches = exec_options.max_buffered_batches_per_output_file;
// To support non string partition col types, cast the type to &str first
let mut value_map: HashMap<Vec<String>, Sender<RecordBatch>> = HashMap::new();
while let Some(rb) = input.next().await.transpose()? {
// First compute partition key for each row of batch, e.g. (col1=val1, col2=val2, ...)
let all_partition_values = compute_partition_keys_by_row(&rb, &partition_by)?;
// Next compute how the batch should be split up to take each distinct key to its own batch
let take_map = compute_take_arrays(&rb, all_partition_values);
// Divide up the batch into distinct partition key batches and send each batch
for (part_key, mut builder) in take_map.into_iter() {
// Take method adapted from https://github.com/lancedb/lance/pull/1337/files
// TODO: upstream RecordBatch::take to arrow-rs
let take_indices = builder.finish();
let struct_array: StructArray = rb.clone().into();
let parted_batch = RecordBatch::from(
arrow::compute::take(&struct_array, &take_indices, None)?.as_struct(),
);
// Get or create channel for this batch
let part_tx = match value_map.get_mut(&part_key) {
Some(part_tx) => part_tx,
None => {
// Create channel for previously unseen distinct partition key and notify consumer of new file
let (part_tx, part_rx) = tokio::sync::mpsc::channel::<RecordBatch>(
max_buffered_recordbatches,
);
let file_path = compute_hive_style_file_path(
&part_key,
&partition_by,
&write_id,
&file_extension,
&base_output_path,
);
tx.send((file_path, part_rx)).map_err(|_| {
DataFusionError::Execution(
"Error sending new file stream!".into(),
)
})?;
value_map.insert(part_key.clone(), part_tx);
value_map
.get_mut(&part_key)
.ok_or(DataFusionError::Internal(
"Key must exist since it was just inserted!".into(),
))?
}
};
let final_batch_to_send = if keep_partition_by_columns {
parted_batch
} else {
remove_partition_by_columns(&parted_batch, &partition_by)?
};
// Finally send the partial batch partitioned by distinct value!
part_tx.send(final_batch_to_send).await.map_err(|_| {
DataFusionError::Internal("Unexpected error sending parted batch!".into())
})?;
}
}
Ok(())
}
fn compute_partition_keys_by_row<'a>(
rb: &'a RecordBatch,
partition_by: &'a [(String, DataType)],
) -> Result<Vec<Vec<String>>> {
let mut all_partition_values = vec![];
// For the purposes of writing partitioned data, we can rely on schema inference
// to determine the type of the partition cols in order to provide a more ergonomic
// UI which does not require specifying DataTypes manually. So, we ignore the
// DataType within the partition_by array and infer the correct type from the
// batch schema instead.
let schema = rb.schema();
for (col, _) in partition_by.iter() {
let mut partition_values = vec![];
let dtype = schema.field_with_name(col)?.data_type();
let col_array = rb.column_by_name(col).ok_or(exec_datafusion_err!(
"PartitionBy Column {} does not exist in source data! Got schema {schema}.",
col
))?;
match dtype {
DataType::Utf8 => {
let array = as_string_array(col_array)?;
for i in 0..rb.num_rows() {
partition_values.push(array.value(i).to_string());
}
}
DataType::Utf8View => {
let array = as_string_view_array(col_array)?;
for i in 0..rb.num_rows() {
partition_values.push(array.value(i).to_string());
}
}
DataType::Boolean => {
let array = as_boolean_array(col_array)?;
for i in 0..rb.num_rows() {
partition_values.push(array.value(i).to_string());
}
}
DataType::Date32 => {
let array = as_date32_array(col_array)?;
// ISO-8601/RFC3339 format - yyyy-mm-dd
let format = "%Y-%m-%d";
for i in 0..rb.num_rows() {
let date =
NaiveDate::from_num_days_from_ce_opt(*&array.value(i)).unwrap();
partition_values.push(date.format(format).to_string());
}
}
DataType::Date64 => {
let array = as_date64_array(col_array)?;
// ISO-8601/RFC3339 format - yyyy-mm-dd
let format = "%Y-%m-%d";
for i in 0..rb.num_rows() {
let date = NaiveDate::from_num_days_from_ce_opt(
(*&array.value(i) / 86_400_000) as i32,
)
.unwrap();
partition_values.push(date.format(format).to_string());
}
}
DataType::Int32 => {
let array = as_int32_array(col_array)?;
for i in 0..rb.num_rows() {
partition_values.push(array.value(i).to_string());
}
}
DataType::Int64 => {
let array = as_int64_array(col_array)?;
for i in 0..rb.num_rows() {
partition_values.push(array.value(i).to_string());
}
}
DataType::Dictionary(_, _) => {
downcast_dictionary_array!(
col_array => {
let array = col_array.downcast_dict::<StringArray>()
.ok_or(exec_datafusion_err!("it is not yet supported to write to hive partitions with datatype {}",
dtype))?;
for val in array.values() {
partition_values.push(
val.ok_or(exec_datafusion_err!("Cannot partition by null value for column {}", col))?.to_string()
);
}
},
_ => unreachable!(),
)
}
_ => {
return Err(DataFusionError::NotImplemented(format!(
"it is not yet supported to write to hive partitions with datatype {}",
dtype
)))
}
}
all_partition_values.push(partition_values);
}
Ok(all_partition_values)
}
fn compute_take_arrays(
rb: &RecordBatch,
all_partition_values: Vec<Vec<String>>,
) -> HashMap<Vec<String>, UInt64Builder> {
let mut take_map = HashMap::new();
for i in 0..rb.num_rows() {
let mut part_key = vec![];
for vals in all_partition_values.iter() {
part_key.push(vals[i].to_owned());
}
let builder = take_map.entry(part_key).or_insert(UInt64Builder::new());
builder.append_value(i as u64);
}
take_map
}
fn remove_partition_by_columns(
parted_batch: &RecordBatch,
partition_by: &[(String, DataType)],
) -> Result<RecordBatch> {
let partition_names: Vec<_> = partition_by.iter().map(|(s, _)| s).collect();
let (non_part_cols, non_part_fields): (Vec<_>, Vec<_>) = parted_batch
.columns()
.iter()
.zip(parted_batch.schema().fields())
.filter_map(|(a, f)| {
if !partition_names.contains(&f.name()) {
Some((a.clone(), (**f).clone()))
} else {
None
}
})
.unzip();
let non_part_schema = Schema::new(non_part_fields);
let final_batch_to_send =
RecordBatch::try_new(Arc::new(non_part_schema), non_part_cols)?;
Ok(final_batch_to_send)
}
fn compute_hive_style_file_path(
part_key: &[String],
partition_by: &[(String, DataType)],
write_id: &str,
file_extension: &str,
base_output_path: &ListingTableUrl,
) -> Path {
let mut file_path = base_output_path.prefix().clone();
for j in 0..part_key.len() {
file_path = file_path.child(format!("{}={}", partition_by[j].0, part_key[j]));
}
file_path.child(format!("{}.{}", write_id, file_extension))
}