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csv.rs
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csv.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.
//! Execution plan for reading CSV files
use crate::error::{DataFusionError, Result};
use crate::execution::context::{SessionState, TaskContext};
use crate::physical_plan::expressions::PhysicalSortExpr;
use crate::physical_plan::file_format::delimited_stream::newline_delimited_stream;
use crate::physical_plan::file_format::file_stream::{
FileOpenFuture, FileOpener, FileStream,
};
use crate::physical_plan::file_format::FileMeta;
use crate::physical_plan::metrics::{BaselineMetrics, ExecutionPlanMetricsSet};
use crate::physical_plan::{
DisplayFormatType, ExecutionPlan, Partitioning, SendableRecordBatchStream, Statistics,
};
use arrow::csv;
use arrow::datatypes::SchemaRef;
use bytes::Buf;
use futures::{StreamExt, TryStreamExt};
use object_store::{GetResult, ObjectMeta, ObjectStore};
use std::any::Any;
use std::fs;
use std::path::Path;
use std::sync::Arc;
use tokio::task::{self, JoinHandle};
use super::FileScanConfig;
/// Execution plan for scanning a CSV file
#[derive(Debug, Clone)]
pub struct CsvExec {
base_config: FileScanConfig,
projected_statistics: Statistics,
projected_schema: SchemaRef,
has_header: bool,
delimiter: u8,
/// Execution metrics
metrics: ExecutionPlanMetricsSet,
}
impl CsvExec {
/// Create a new CSV reader execution plan provided base and specific configurations
pub fn new(base_config: FileScanConfig, has_header: bool, delimiter: u8) -> Self {
let (projected_schema, projected_statistics) = base_config.project();
Self {
base_config,
projected_schema,
projected_statistics,
has_header,
delimiter,
metrics: ExecutionPlanMetricsSet::new(),
}
}
/// Ref to the base configs
pub fn base_config(&self) -> &FileScanConfig {
&self.base_config
}
/// true if the first line of each file is a header
pub fn has_header(&self) -> bool {
self.has_header
}
/// A column delimiter
pub fn delimiter(&self) -> u8 {
self.delimiter
}
}
impl ExecutionPlan for CsvExec {
/// 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.projected_schema.clone()
}
/// Get the output partitioning of this plan
fn output_partitioning(&self) -> Partitioning {
Partitioning::UnknownPartitioning(self.base_config.file_groups.len())
}
fn relies_on_input_order(&self) -> bool {
false
}
fn output_ordering(&self) -> Option<&[PhysicalSortExpr]> {
None
}
fn children(&self) -> Vec<Arc<dyn ExecutionPlan>> {
// this is a leaf node and has no children
vec![]
}
fn with_new_children(
self: Arc<Self>,
_: Vec<Arc<dyn ExecutionPlan>>,
) -> Result<Arc<dyn ExecutionPlan>> {
Ok(self)
}
fn execute(
&self,
partition: usize,
context: Arc<TaskContext>,
) -> Result<SendableRecordBatchStream> {
let config = Arc::new(CsvConfig {
batch_size: context.session_config().batch_size(),
file_schema: Arc::clone(&self.base_config.file_schema),
file_projection: self.base_config.file_column_projection_indices(),
has_header: self.has_header,
delimiter: self.delimiter,
});
let opener = CsvOpener { config };
let stream = FileStream::new(
&self.base_config,
partition,
context,
opener,
BaselineMetrics::new(&self.metrics, partition),
)?;
Ok(Box::pin(stream) as SendableRecordBatchStream)
}
fn fmt_as(
&self,
t: DisplayFormatType,
f: &mut std::fmt::Formatter,
) -> std::fmt::Result {
match t {
DisplayFormatType::Default => {
write!(
f,
"CsvExec: files={}, has_header={}, limit={:?}, projection={}",
super::FileGroupsDisplay(&self.base_config.file_groups),
self.has_header,
self.base_config.limit,
super::ProjectSchemaDisplay(&self.projected_schema),
)
}
}
}
fn statistics(&self) -> Statistics {
self.projected_statistics.clone()
}
}
#[derive(Debug, Clone)]
struct CsvConfig {
batch_size: usize,
file_schema: SchemaRef,
file_projection: Option<Vec<usize>>,
has_header: bool,
delimiter: u8,
}
impl CsvConfig {
fn open<R: std::io::Read>(&self, reader: R, first_chunk: bool) -> csv::Reader<R> {
let datetime_format = None;
csv::Reader::new(
reader,
Arc::clone(&self.file_schema),
self.has_header && first_chunk,
Some(self.delimiter),
self.batch_size,
None,
self.file_projection.clone(),
datetime_format,
)
}
}
struct CsvOpener {
config: Arc<CsvConfig>,
}
impl FileOpener for CsvOpener {
fn open(
&self,
store: Arc<dyn ObjectStore>,
file_meta: FileMeta,
) -> Result<FileOpenFuture> {
let config = self.config.clone();
Ok(Box::pin(async move {
match store.get(file_meta.location()).await? {
GetResult::File(file, _) => {
Ok(futures::stream::iter(config.open(file, true)).boxed())
}
GetResult::Stream(s) => {
let mut first_chunk = true;
Ok(newline_delimited_stream(s.map_err(Into::into))
.map_ok(move |bytes| {
let reader = config.open(bytes.reader(), first_chunk);
first_chunk = false;
futures::stream::iter(reader)
})
.try_flatten()
.boxed())
}
}
}))
}
}
pub async fn plan_to_csv(
state: &SessionState,
plan: Arc<dyn ExecutionPlan>,
path: impl AsRef<str>,
) -> Result<()> {
let path = path.as_ref();
// create directory to contain the CSV files (one per partition)
let fs_path = Path::new(path);
match fs::create_dir(fs_path) {
Ok(()) => {
let mut tasks = vec![];
for i in 0..plan.output_partitioning().partition_count() {
let plan = plan.clone();
let filename = format!("part-{}.csv", i);
let path = fs_path.join(&filename);
let file = fs::File::create(path)?;
let mut writer = csv::Writer::new(file);
let task_ctx = Arc::new(TaskContext::from(state));
let stream = plan.execute(i, task_ctx)?;
let handle: JoinHandle<Result<()>> = task::spawn(async move {
stream
.map(|batch| writer.write(&batch?))
.try_collect()
.await
.map_err(DataFusionError::from)
});
tasks.push(handle);
}
futures::future::join_all(tasks).await;
Ok(())
}
Err(e) => Err(DataFusionError::Execution(format!(
"Could not create directory {}: {:?}",
path, e
))),
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::physical_plan::file_format::chunked_store::ChunkedStore;
use crate::prelude::*;
use crate::test::partitioned_csv_config;
use crate::test_util::aggr_test_schema_with_missing_col;
use crate::{scalar::ScalarValue, test_util::aggr_test_schema};
use arrow::datatypes::*;
use futures::StreamExt;
use object_store::local::LocalFileSystem;
use std::fs::File;
use std::io::Write;
use tempfile::TempDir;
#[tokio::test]
async fn csv_exec_with_projection() -> Result<()> {
let session_ctx = SessionContext::new();
let task_ctx = session_ctx.task_ctx();
let file_schema = aggr_test_schema();
let filename = "aggregate_test_100.csv";
let mut config = partitioned_csv_config(filename, file_schema, 1)?;
config.projection = Some(vec![0, 2, 4]);
let csv = CsvExec::new(config, true, b',');
assert_eq!(13, csv.base_config.file_schema.fields().len());
assert_eq!(3, csv.projected_schema.fields().len());
assert_eq!(3, csv.schema().fields().len());
let mut stream = csv.execute(0, task_ctx)?;
let batch = stream.next().await.unwrap()?;
assert_eq!(3, batch.num_columns());
assert_eq!(100, batch.num_rows());
// slice of the first 5 lines
let expected = vec![
"+----+-----+------------+",
"| c1 | c3 | c5 |",
"+----+-----+------------+",
"| c | 1 | 2033001162 |",
"| d | -40 | 706441268 |",
"| b | 29 | 994303988 |",
"| a | -85 | 1171968280 |",
"| b | -82 | 1824882165 |",
"+----+-----+------------+",
];
crate::assert_batches_eq!(expected, &[batch.slice(0, 5)]);
Ok(())
}
#[tokio::test]
async fn csv_exec_with_limit() -> Result<()> {
let session_ctx = SessionContext::new();
let task_ctx = session_ctx.task_ctx();
let file_schema = aggr_test_schema();
let filename = "aggregate_test_100.csv";
let mut config = partitioned_csv_config(filename, file_schema, 1)?;
config.limit = Some(5);
let csv = CsvExec::new(config, true, b',');
assert_eq!(13, csv.base_config.file_schema.fields().len());
assert_eq!(13, csv.projected_schema.fields().len());
assert_eq!(13, csv.schema().fields().len());
let mut it = csv.execute(0, task_ctx)?;
let batch = it.next().await.unwrap()?;
assert_eq!(13, batch.num_columns());
assert_eq!(5, batch.num_rows());
let expected = vec![
"+----+----+-----+--------+------------+----------------------+-----+-------+------------+----------------------+-------------+---------------------+--------------------------------+",
"| c1 | c2 | c3 | c4 | c5 | c6 | c7 | c8 | c9 | c10 | c11 | c12 | c13 |",
"+----+----+-----+--------+------------+----------------------+-----+-------+------------+----------------------+-------------+---------------------+--------------------------------+",
"| c | 2 | 1 | 18109 | 2033001162 | -6513304855495910254 | 25 | 43062 | 1491205016 | 5863949479783605708 | 0.110830784 | 0.9294097332465232 | 6WfVFBVGJSQb7FhA7E0lBwdvjfZnSW |",
"| d | 5 | -40 | 22614 | 706441268 | -7542719935673075327 | 155 | 14337 | 3373581039 | 11720144131976083864 | 0.69632107 | 0.3114712539863804 | C2GT5KVyOPZpgKVl110TyZO0NcJ434 |",
"| b | 1 | 29 | -18218 | 994303988 | 5983957848665088916 | 204 | 9489 | 3275293996 | 14857091259186476033 | 0.53840446 | 0.17909035118828576 | AyYVExXK6AR2qUTxNZ7qRHQOVGMLcz |",
"| a | 1 | -85 | -15154 | 1171968280 | 1919439543497968449 | 77 | 52286 | 774637006 | 12101411955859039553 | 0.12285209 | 0.6864391962767343 | 0keZ5G8BffGwgF2RwQD59TFzMStxCB |",
"| b | 5 | -82 | 22080 | 1824882165 | 7373730676428214987 | 208 | 34331 | 3342719438 | 3330177516592499461 | 0.82634634 | 0.40975383525297016 | Ig1QcuKsjHXkproePdERo2w0mYzIqd |",
"+----+----+-----+--------+------------+----------------------+-----+-------+------------+----------------------+-------------+---------------------+--------------------------------+",
];
crate::assert_batches_eq!(expected, &[batch]);
Ok(())
}
#[tokio::test]
async fn csv_exec_with_missing_column() -> Result<()> {
let session_ctx = SessionContext::new();
let task_ctx = session_ctx.task_ctx();
let file_schema = aggr_test_schema_with_missing_col();
let filename = "aggregate_test_100.csv";
let mut config = partitioned_csv_config(filename, file_schema, 1)?;
config.limit = Some(5);
let csv = CsvExec::new(config, true, b',');
assert_eq!(14, csv.base_config.file_schema.fields().len());
assert_eq!(14, csv.projected_schema.fields().len());
assert_eq!(14, csv.schema().fields().len());
let mut it = csv.execute(0, task_ctx)?;
let batch = it.next().await.unwrap()?;
assert_eq!(14, batch.num_columns());
assert_eq!(5, batch.num_rows());
let expected = vec![
"+----+----+-----+--------+------------+----------------------+-----+-------+------------+----------------------+-------------+---------------------+--------------------------------+-------------+",
"| c1 | c2 | c3 | c4 | c5 | c6 | c7 | c8 | c9 | c10 | c11 | c12 | c13 | missing_col |",
"+----+----+-----+--------+------------+----------------------+-----+-------+------------+----------------------+-------------+---------------------+--------------------------------+-------------+",
"| c | 2 | 1 | 18109 | 2033001162 | -6513304855495910254 | 25 | 43062 | 1491205016 | 5863949479783605708 | 0.110830784 | 0.9294097332465232 | 6WfVFBVGJSQb7FhA7E0lBwdvjfZnSW | |",
"| d | 5 | -40 | 22614 | 706441268 | -7542719935673075327 | 155 | 14337 | 3373581039 | 11720144131976083864 | 0.69632107 | 0.3114712539863804 | C2GT5KVyOPZpgKVl110TyZO0NcJ434 | |",
"| b | 1 | 29 | -18218 | 994303988 | 5983957848665088916 | 204 | 9489 | 3275293996 | 14857091259186476033 | 0.53840446 | 0.17909035118828576 | AyYVExXK6AR2qUTxNZ7qRHQOVGMLcz | |",
"| a | 1 | -85 | -15154 | 1171968280 | 1919439543497968449 | 77 | 52286 | 774637006 | 12101411955859039553 | 0.12285209 | 0.6864391962767343 | 0keZ5G8BffGwgF2RwQD59TFzMStxCB | |",
"| b | 5 | -82 | 22080 | 1824882165 | 7373730676428214987 | 208 | 34331 | 3342719438 | 3330177516592499461 | 0.82634634 | 0.40975383525297016 | Ig1QcuKsjHXkproePdERo2w0mYzIqd | |",
"+----+----+-----+--------+------------+----------------------+-----+-------+------------+----------------------+-------------+---------------------+--------------------------------+-------------+",
];
crate::assert_batches_eq!(expected, &[batch]);
Ok(())
}
#[tokio::test]
async fn csv_exec_with_partition() -> Result<()> {
let session_ctx = SessionContext::new();
let task_ctx = session_ctx.task_ctx();
let file_schema = aggr_test_schema();
let filename = "aggregate_test_100.csv";
let mut config = partitioned_csv_config(filename, file_schema.clone(), 1)?;
// Add partition columns
config.table_partition_cols = vec!["date".to_owned()];
config.file_groups[0][0].partition_values =
vec![ScalarValue::Utf8(Some("2021-10-26".to_owned()))];
// We should be able to project on the partition column
// Which is supposed to be after the file fields
config.projection = Some(vec![0, file_schema.fields().len()]);
// we don't have `/date=xx/` in the path but that is ok because
// partitions are resolved during scan anyway
let csv = CsvExec::new(config, true, b',');
assert_eq!(13, csv.base_config.file_schema.fields().len());
assert_eq!(2, csv.projected_schema.fields().len());
assert_eq!(2, csv.schema().fields().len());
let mut it = csv.execute(0, task_ctx)?;
let batch = it.next().await.unwrap()?;
assert_eq!(2, batch.num_columns());
assert_eq!(100, batch.num_rows());
// slice of the first 5 lines
let expected = vec![
"+----+------------+",
"| c1 | date |",
"+----+------------+",
"| c | 2021-10-26 |",
"| d | 2021-10-26 |",
"| b | 2021-10-26 |",
"| a | 2021-10-26 |",
"| b | 2021-10-26 |",
"+----+------------+",
];
crate::assert_batches_eq!(expected, &[batch.slice(0, 5)]);
Ok(())
}
/// Generate CSV partitions within the supplied directory
fn populate_csv_partitions(
tmp_dir: &TempDir,
partition_count: usize,
file_extension: &str,
) -> Result<SchemaRef> {
// define schema for data source (csv file)
let schema = Arc::new(Schema::new(vec![
Field::new("c1", DataType::UInt32, false),
Field::new("c2", DataType::UInt64, false),
Field::new("c3", DataType::Boolean, false),
]));
// generate a partitioned file
for partition in 0..partition_count {
let filename = format!("partition-{}.{}", partition, file_extension);
let file_path = tmp_dir.path().join(&filename);
let mut file = File::create(file_path)?;
// generate some data
for i in 0..=10 {
let data = format!("{},{},{}\n", partition, i, i % 2 == 0);
file.write_all(data.as_bytes())?;
}
}
Ok(schema)
}
#[tokio::test]
async fn test_chunked() {
let ctx = SessionContext::new();
let chunk_sizes = [10, 20, 30, 40];
for chunk_size in chunk_sizes {
ctx.runtime_env().register_object_store(
"file",
"",
Arc::new(ChunkedStore::new(
Arc::new(LocalFileSystem::new()),
chunk_size,
)),
);
let task_ctx = ctx.task_ctx();
let filename = "aggregate_test_100.csv";
let file_schema = aggr_test_schema();
let config =
partitioned_csv_config(filename, file_schema.clone(), 1).unwrap();
let csv = CsvExec::new(config, true, b',');
let it = csv.execute(0, task_ctx).unwrap();
let batches: Vec<_> = it.try_collect().await.unwrap();
let total_rows = batches.iter().map(|b| b.num_rows()).sum::<usize>();
assert_eq!(total_rows, 100);
}
}
#[tokio::test]
async fn write_csv_results() -> Result<()> {
// create partitioned input file and context
let tmp_dir = TempDir::new()?;
let ctx =
SessionContext::with_config(SessionConfig::new().with_target_partitions(8));
let schema = populate_csv_partitions(&tmp_dir, 8, ".csv")?;
// register csv file with the execution context
ctx.register_csv(
"test",
tmp_dir.path().to_str().unwrap(),
CsvReadOptions::new().schema(&schema),
)
.await?;
// execute a simple query and write the results to CSV
let out_dir = tmp_dir.as_ref().to_str().unwrap().to_string() + "/out";
let df = ctx.sql("SELECT c1, c2 FROM test").await?;
df.write_csv(&out_dir).await?;
// create a new context and verify that the results were saved to a partitioned csv file
let ctx = SessionContext::new();
let schema = Arc::new(Schema::new(vec![
Field::new("c1", DataType::UInt32, false),
Field::new("c2", DataType::UInt64, false),
]));
// register each partition as well as the top level dir
let csv_read_option = CsvReadOptions::new().schema(&schema);
ctx.register_csv(
"part0",
&format!("{}/part-0.csv", out_dir),
csv_read_option.clone(),
)
.await?;
ctx.register_csv("allparts", &out_dir, csv_read_option)
.await?;
let part0 = ctx.sql("SELECT c1, c2 FROM part0").await?.collect().await?;
let allparts = ctx
.sql("SELECT c1, c2 FROM allparts")
.await?
.collect()
.await?;
let allparts_count: usize = allparts.iter().map(|batch| batch.num_rows()).sum();
assert_eq!(part0[0].schema(), allparts[0].schema());
assert_eq!(allparts_count, 80);
Ok(())
}
}