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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.
//! The table implementation.
use std::collections::HashMap;
use std::str::FromStr;
use std::{any::Any, sync::Arc};
use super::helpers::{expr_applicable_for_cols, pruned_partition_list, split_files};
use super::PartitionedFile;
use super::ListingTableUrl;
use crate::datasource::{create_ordering, get_statistics_with_limit};
use crate::datasource::{
file_format::{file_compression_type::FileCompressionType, FileFormat},
physical_plan::{FileScanConfig, FileSinkConfig},
};
use crate::execution::context::SessionState;
use datafusion_catalog::TableProvider;
use datafusion_common::{DataFusionError, Result};
use datafusion_expr::{utils::conjunction, Expr, TableProviderFilterPushDown};
use datafusion_expr::{SortExpr, TableType};
use datafusion_physical_plan::{empty::EmptyExec, ExecutionPlan, Statistics};
use arrow::datatypes::{DataType, Field, SchemaBuilder, SchemaRef};
use arrow_schema::Schema;
use datafusion_common::{
config_datafusion_err, internal_err, plan_err, project_schema, Constraints,
SchemaExt, ToDFSchema,
};
use datafusion_execution::cache::cache_manager::FileStatisticsCache;
use datafusion_execution::cache::cache_unit::DefaultFileStatisticsCache;
use datafusion_physical_expr::{
create_physical_expr, LexOrdering, PhysicalSortRequirement,
};
use async_trait::async_trait;
use datafusion_catalog::Session;
use futures::{future, stream, StreamExt, TryStreamExt};
use itertools::Itertools;
use object_store::ObjectStore;
/// Configuration for creating a [`ListingTable`]
#[derive(Debug, Clone)]
pub struct ListingTableConfig {
/// Paths on the `ObjectStore` for creating `ListingTable`.
/// They should share the same schema and object store.
pub table_paths: Vec<ListingTableUrl>,
/// Optional `SchemaRef` for the to be created `ListingTable`.
pub file_schema: Option<SchemaRef>,
/// Optional `ListingOptions` for the to be created `ListingTable`.
pub options: Option<ListingOptions>,
}
impl ListingTableConfig {
/// Creates new [`ListingTableConfig`].
///
/// The [`SchemaRef`] and [`ListingOptions`] are inferred based on
/// the suffix of the provided `table_paths` first element.
pub fn new(table_path: ListingTableUrl) -> Self {
let table_paths = vec![table_path];
Self {
table_paths,
file_schema: None,
options: None,
}
}
/// Creates new [`ListingTableConfig`] with multiple table paths.
///
/// The [`SchemaRef`] and [`ListingOptions`] are inferred based on
/// the suffix of the provided `table_paths` first element.
pub fn new_with_multi_paths(table_paths: Vec<ListingTableUrl>) -> Self {
Self {
table_paths,
file_schema: None,
options: None,
}
}
/// Add `schema` to [`ListingTableConfig`]
pub fn with_schema(self, schema: SchemaRef) -> Self {
Self {
table_paths: self.table_paths,
file_schema: Some(schema),
options: self.options,
}
}
/// Add `listing_options` to [`ListingTableConfig`]
pub fn with_listing_options(self, listing_options: ListingOptions) -> Self {
Self {
table_paths: self.table_paths,
file_schema: self.file_schema,
options: Some(listing_options),
}
}
fn infer_file_extension(path: &str) -> Result<String> {
let mut exts = path.rsplit('.');
let mut splitted = exts.next().unwrap_or("");
let file_compression_type = FileCompressionType::from_str(splitted)
.unwrap_or(FileCompressionType::UNCOMPRESSED);
if file_compression_type.is_compressed() {
splitted = exts.next().unwrap_or("");
}
Ok(splitted.to_string())
}
/// Infer `ListingOptions` based on `table_path` suffix.
pub async fn infer_options(self, state: &SessionState) -> Result<Self> {
let store = if let Some(url) = self.table_paths.first() {
state.runtime_env().object_store(url)?
} else {
return Ok(self);
};
let file = self
.table_paths
.first()
.unwrap()
.list_all_files(state, store.as_ref(), "")
.await?
.next()
.await
.ok_or_else(|| DataFusionError::Internal("No files for table".into()))??;
let file_extension =
ListingTableConfig::infer_file_extension(file.location.as_ref())?;
let file_format = state
.get_file_format_factory(&file_extension)
.ok_or(config_datafusion_err!(
"No file_format found with extension {file_extension}"
))?
.create(state, &HashMap::new())?;
let listing_options = ListingOptions::new(file_format)
.with_file_extension(file_extension)
.with_target_partitions(state.config().target_partitions());
Ok(Self {
table_paths: self.table_paths,
file_schema: self.file_schema,
options: Some(listing_options),
})
}
/// Infer the [`SchemaRef`] based on `table_path` suffix. Requires `self.options` to be set prior to using.
pub async fn infer_schema(self, state: &SessionState) -> Result<Self> {
match self.options {
Some(options) => {
let schema = if let Some(url) = self.table_paths.first() {
options.infer_schema(state, url).await?
} else {
Arc::new(Schema::empty())
};
Ok(Self {
table_paths: self.table_paths,
file_schema: Some(schema),
options: Some(options),
})
}
None => internal_err!("No `ListingOptions` set for inferring schema"),
}
}
/// Convenience wrapper for calling `infer_options` and `infer_schema`
pub async fn infer(self, state: &SessionState) -> Result<Self> {
self.infer_options(state).await?.infer_schema(state).await
}
}
/// Options for creating a [`ListingTable`]
#[derive(Clone, Debug)]
pub struct ListingOptions {
/// A suffix on which files should be filtered (leave empty to
/// keep all files on the path)
pub file_extension: String,
/// The file format
pub format: Arc<dyn FileFormat>,
/// The expected partition column names in the folder structure.
/// See [Self::with_table_partition_cols] for details
pub table_partition_cols: Vec<(String, DataType)>,
/// Set true to try to guess statistics from the files.
/// This can add a lot of overhead as it will usually require files
/// to be opened and at least partially parsed.
pub collect_stat: bool,
/// Group files to avoid that the number of partitions exceeds
/// this limit
pub target_partitions: usize,
/// Optional pre-known sort order(s). Must be `SortExpr`s.
///
/// DataFusion may take advantage of this ordering to omit sorts
/// or use more efficient algorithms. Currently sortedness must be
/// provided if it is known by some external mechanism, but may in
/// the future be automatically determined, for example using
/// parquet metadata.
///
/// See <https://github.com/apache/datafusion/issues/4177>
/// NOTE: This attribute stores all equivalent orderings (the outer `Vec`)
/// where each ordering consists of an individual lexicographic
/// ordering (encapsulated by a `Vec<Expr>`). If there aren't
/// multiple equivalent orderings, the outer `Vec` will have a
/// single element.
pub file_sort_order: Vec<Vec<SortExpr>>,
}
impl ListingOptions {
/// Creates an options instance with the given format
/// Default values:
/// - no file extension filter
/// - no input partition to discover
/// - one target partition
/// - stat collection
pub fn new(format: Arc<dyn FileFormat>) -> Self {
Self {
file_extension: String::new(),
format,
table_partition_cols: vec![],
collect_stat: true,
target_partitions: 1,
file_sort_order: vec![],
}
}
/// Set file extension on [`ListingOptions`] and returns self.
///
/// # Example
/// ```
/// # use std::sync::Arc;
/// # use datafusion::prelude::SessionContext;
/// # use datafusion::datasource::{listing::ListingOptions, file_format::parquet::ParquetFormat};
///
/// let listing_options = ListingOptions::new(Arc::new(
/// ParquetFormat::default()
/// ))
/// .with_file_extension(".parquet");
///
/// assert_eq!(listing_options.file_extension, ".parquet");
/// ```
pub fn with_file_extension(mut self, file_extension: impl Into<String>) -> Self {
self.file_extension = file_extension.into();
self
}
/// Optionally set file extension on [`ListingOptions`] and returns self.
///
/// If `file_extension` is `None`, the file extension will not be changed
///
/// # Example
/// ```
/// # use std::sync::Arc;
/// # use datafusion::prelude::SessionContext;
/// # use datafusion::datasource::{listing::ListingOptions, file_format::parquet::ParquetFormat};
/// let extension = Some(".parquet");
/// let listing_options = ListingOptions::new(Arc::new(
/// ParquetFormat::default()
/// ))
/// .with_file_extension_opt(extension);
///
/// assert_eq!(listing_options.file_extension, ".parquet");
/// ```
pub fn with_file_extension_opt<S>(mut self, file_extension: Option<S>) -> Self
where
S: Into<String>,
{
if let Some(file_extension) = file_extension {
self.file_extension = file_extension.into();
}
self
}
/// Set `table partition columns` on [`ListingOptions`] and returns self.
///
/// "partition columns," used to support [Hive Partitioning], are
/// columns added to the data that is read, based on the folder
/// structure where the data resides.
///
/// For example, give the following files in your filesystem:
///
/// ```text
/// /mnt/nyctaxi/year=2022/month=01/tripdata.parquet
/// /mnt/nyctaxi/year=2021/month=12/tripdata.parquet
/// /mnt/nyctaxi/year=2021/month=11/tripdata.parquet
/// ```
///
/// A [`ListingTable`] created at `/mnt/nyctaxi/` with partition
/// columns "year" and "month" will include new `year` and `month`
/// columns while reading the files. The `year` column would have
/// value `2022` and the `month` column would have value `01` for
/// the rows read from
/// `/mnt/nyctaxi/year=2022/month=01/tripdata.parquet`
///
///# Notes
///
/// - If only one level (e.g. `year` in the example above) is
/// specified, the other levels are ignored but the files are
/// still read.
///
/// - Files that don't follow this partitioning scheme will be
/// ignored.
///
/// - Since the columns have the same value for all rows read from
/// each individual file (such as dates), they are typically
/// dictionary encoded for efficiency. You may use
/// [`wrap_partition_type_in_dict`] to request a
/// dictionary-encoded type.
///
/// - The partition columns are solely extracted from the file path. Especially they are NOT part of the parquet files itself.
///
/// # Example
///
/// ```
/// # use std::sync::Arc;
/// # use arrow::datatypes::DataType;
/// # use datafusion::prelude::col;
/// # use datafusion::datasource::{listing::ListingOptions, file_format::parquet::ParquetFormat};
///
/// // listing options for files with paths such as `/mnt/data/col_a=x/col_b=y/data.parquet`
/// // `col_a` and `col_b` will be included in the data read from those files
/// let listing_options = ListingOptions::new(Arc::new(
/// ParquetFormat::default()
/// ))
/// .with_table_partition_cols(vec![("col_a".to_string(), DataType::Utf8),
/// ("col_b".to_string(), DataType::Utf8)]);
///
/// assert_eq!(listing_options.table_partition_cols, vec![("col_a".to_string(), DataType::Utf8),
/// ("col_b".to_string(), DataType::Utf8)]);
/// ```
///
/// [Hive Partitioning]: https://docs.cloudera.com/HDPDocuments/HDP2/HDP-2.1.3/bk_system-admin-guide/content/hive_partitioned_tables.html
/// [`wrap_partition_type_in_dict`]: crate::datasource::physical_plan::wrap_partition_type_in_dict
pub fn with_table_partition_cols(
mut self,
table_partition_cols: Vec<(String, DataType)>,
) -> Self {
self.table_partition_cols = table_partition_cols;
self
}
/// Set stat collection on [`ListingOptions`] and returns self.
///
/// ```
/// # use std::sync::Arc;
/// # use datafusion::datasource::{listing::ListingOptions, file_format::parquet::ParquetFormat};
///
/// let listing_options = ListingOptions::new(Arc::new(
/// ParquetFormat::default()
/// ))
/// .with_collect_stat(true);
///
/// assert_eq!(listing_options.collect_stat, true);
/// ```
pub fn with_collect_stat(mut self, collect_stat: bool) -> Self {
self.collect_stat = collect_stat;
self
}
/// Set number of target partitions on [`ListingOptions`] and returns self.
///
/// ```
/// # use std::sync::Arc;
/// # use datafusion::datasource::{listing::ListingOptions, file_format::parquet::ParquetFormat};
///
/// let listing_options = ListingOptions::new(Arc::new(
/// ParquetFormat::default()
/// ))
/// .with_target_partitions(8);
///
/// assert_eq!(listing_options.target_partitions, 8);
/// ```
pub fn with_target_partitions(mut self, target_partitions: usize) -> Self {
self.target_partitions = target_partitions;
self
}
/// Set file sort order on [`ListingOptions`] and returns self.
///
/// ```
/// # use std::sync::Arc;
/// # use datafusion::prelude::col;
/// # use datafusion::datasource::{listing::ListingOptions, file_format::parquet::ParquetFormat};
///
/// // Tell datafusion that the files are sorted by column "a"
/// let file_sort_order = vec![vec![
/// col("a").sort(true, true)
/// ]];
///
/// let listing_options = ListingOptions::new(Arc::new(
/// ParquetFormat::default()
/// ))
/// .with_file_sort_order(file_sort_order.clone());
///
/// assert_eq!(listing_options.file_sort_order, file_sort_order);
/// ```
pub fn with_file_sort_order(mut self, file_sort_order: Vec<Vec<SortExpr>>) -> Self {
self.file_sort_order = file_sort_order;
self
}
/// Infer the schema of the files at the given path on the provided object store.
/// The inferred schema does not include the partitioning columns.
///
/// This method will not be called by the table itself but before creating it.
/// This way when creating the logical plan we can decide to resolve the schema
/// locally or ask a remote service to do it (e.g a scheduler).
pub async fn infer_schema<'a>(
&'a self,
state: &SessionState,
table_path: &'a ListingTableUrl,
) -> Result<SchemaRef> {
let store = state.runtime_env().object_store(table_path)?;
let files: Vec<_> = table_path
.list_all_files(state, store.as_ref(), &self.file_extension)
.await?
.try_collect()
.await?;
let schema = self.format.infer_schema(state, &store, &files).await?;
Ok(schema)
}
/// Infers the partition columns stored in `LOCATION` and compares
/// them with the columns provided in `PARTITIONED BY` to help prevent
/// accidental corrupts of partitioned tables.
///
/// Allows specifying partial partitions.
pub async fn validate_partitions(
&self,
state: &SessionState,
table_path: &ListingTableUrl,
) -> Result<()> {
if self.table_partition_cols.is_empty() {
return Ok(());
}
if !table_path.is_collection() {
return plan_err!(
"Can't create a partitioned table backed by a single file, \
perhaps the URL is missing a trailing slash?"
);
}
let inferred = self.infer_partitions(state, table_path).await?;
// no partitioned files found on disk
if inferred.is_empty() {
return Ok(());
}
let table_partition_names = self
.table_partition_cols
.iter()
.map(|(col_name, _)| col_name.clone())
.collect_vec();
if inferred.len() < table_partition_names.len() {
return plan_err!(
"Inferred partitions to be {:?}, but got {:?}",
inferred,
table_partition_names
);
}
// match prefix to allow creating tables with partial partitions
for (idx, col) in table_partition_names.iter().enumerate() {
if &inferred[idx] != col {
return plan_err!(
"Inferred partitions to be {:?}, but got {:?}",
inferred,
table_partition_names
);
}
}
Ok(())
}
/// Infer the partitioning at the given path on the provided object store.
/// For performance reasons, it doesn't read all the files on disk
/// and therefore may fail to detect invalid partitioning.
async fn infer_partitions(
&self,
state: &SessionState,
table_path: &ListingTableUrl,
) -> Result<Vec<String>> {
let store = state.runtime_env().object_store(table_path)?;
// only use 10 files for inference
// This can fail to detect inconsistent partition keys
// A DFS traversal approach of the store can help here
let files: Vec<_> = table_path
.list_all_files(state, store.as_ref(), &self.file_extension)
.await?
.take(10)
.try_collect()
.await?;
let stripped_path_parts = files.iter().map(|file| {
table_path
.strip_prefix(&file.location)
.unwrap()
.collect_vec()
});
let partition_keys = stripped_path_parts
.map(|path_parts| {
path_parts
.into_iter()
.rev()
.skip(1) // get parents only; skip the file itself
.rev()
.map(|s| s.split('=').take(1).collect())
.collect_vec()
})
.collect_vec();
match partition_keys.into_iter().all_equal_value() {
Ok(v) => Ok(v),
Err(None) => Ok(vec![]),
Err(Some(diff)) => {
let mut sorted_diff = [diff.0, diff.1];
sorted_diff.sort();
plan_err!("Found mixed partition values on disk {:?}", sorted_diff)
}
}
}
}
/// Reads data from one or more files as a single table.
///
/// Implements [`TableProvider`], a DataFusion data source. The files are read
/// using an [`ObjectStore`] instance, for example from local files or objects
/// from AWS S3.
///
/// For example, given the `table1` directory (or object store prefix)
///
/// ```text
/// table1
/// ├── file1.parquet
/// └── file2.parquet
/// ```
///
/// A `ListingTable` would read the files `file1.parquet` and `file2.parquet` as
/// a single table, merging the schemas if the files have compatible but not
/// identical schemas.
///
/// Given the `table2` directory (or object store prefix)
///
/// ```text
/// table2
/// ├── date=2024-06-01
/// │ ├── file3.parquet
/// │ └── file4.parquet
/// └── date=2024-06-02
/// └── file5.parquet
/// ```
///
/// A `ListingTable` would read the files `file3.parquet`, `file4.parquet`, and
/// `file5.parquet` as a single table, again merging schemas if necessary.
///
/// Given the hive style partitioning structure (e.g,. directories named
/// `date=2024-06-01` and `date=2026-06-02`), `ListingTable` also adds a `date`
/// column when reading the table:
/// * The files in `table2/date=2024-06-01` will have the value `2024-06-01`
/// * The files in `table2/date=2024-06-02` will have the value `2024-06-02`.
///
/// If the query has a predicate like `WHERE date = '2024-06-01'`
/// only the corresponding directory will be read.
///
/// `ListingTable` also supports filter and projection pushdown for formats that
/// support it as such as Parquet.
///
/// # Example
///
/// Here is an example of reading a directory of parquet files using a
/// [`ListingTable`]:
///
/// ```no_run
/// # use datafusion::prelude::SessionContext;
/// # use datafusion::error::Result;
/// # use std::sync::Arc;
/// # use datafusion::datasource::{
/// # listing::{
/// # ListingOptions, ListingTable, ListingTableConfig, ListingTableUrl,
/// # },
/// # file_format::parquet::ParquetFormat,
/// # };
/// # #[tokio::main]
/// # async fn main() -> Result<()> {
/// let ctx = SessionContext::new();
/// let session_state = ctx.state();
/// let table_path = "/path/to/parquet";
///
/// // Parse the path
/// let table_path = ListingTableUrl::parse(table_path)?;
///
/// // Create default parquet options
/// let file_format = ParquetFormat::new();
/// let listing_options = ListingOptions::new(Arc::new(file_format))
/// .with_file_extension(".parquet");
///
/// // Resolve the schema
/// let resolved_schema = listing_options
/// .infer_schema(&session_state, &table_path)
/// .await?;
///
/// let config = ListingTableConfig::new(table_path)
/// .with_listing_options(listing_options)
/// .with_schema(resolved_schema);
///
/// // Create a new TableProvider
/// let provider = Arc::new(ListingTable::try_new(config)?);
///
/// // This provider can now be read as a dataframe:
/// let df = ctx.read_table(provider.clone());
///
/// // or registered as a named table:
/// ctx.register_table("my_table", provider);
///
/// # Ok(())
/// # }
/// ```
pub struct ListingTable {
table_paths: Vec<ListingTableUrl>,
/// File fields only
file_schema: SchemaRef,
/// File fields + partition columns
table_schema: SchemaRef,
options: ListingOptions,
definition: Option<String>,
collected_statistics: FileStatisticsCache,
constraints: Constraints,
column_defaults: HashMap<String, Expr>,
}
impl ListingTable {
/// Create new [`ListingTable`] that lists the FS to get the files
/// to scan. See [`ListingTable`] for and example.
///
/// Takes a `ListingTableConfig` as input which requires an `ObjectStore` and `table_path`.
/// `ListingOptions` and `SchemaRef` are optional. If they are not
/// provided the file type is inferred based on the file suffix.
/// If the schema is provided then it must be resolved before creating the table
/// and should contain the fields of the file without the table
/// partitioning columns.
///
pub fn try_new(config: ListingTableConfig) -> Result<Self> {
let file_schema = config
.file_schema
.ok_or_else(|| DataFusionError::Internal("No schema provided.".into()))?;
let options = config.options.ok_or_else(|| {
DataFusionError::Internal("No ListingOptions provided".into())
})?;
// Add the partition columns to the file schema
let mut builder = SchemaBuilder::from(file_schema.as_ref().to_owned());
for (part_col_name, part_col_type) in &options.table_partition_cols {
builder.push(Field::new(part_col_name, part_col_type.clone(), false));
}
let table = Self {
table_paths: config.table_paths,
file_schema,
table_schema: Arc::new(builder.finish()),
options,
definition: None,
collected_statistics: Arc::new(DefaultFileStatisticsCache::default()),
constraints: Constraints::empty(),
column_defaults: HashMap::new(),
};
Ok(table)
}
/// Assign constraints
pub fn with_constraints(mut self, constraints: Constraints) -> Self {
self.constraints = constraints;
self
}
/// Assign column defaults
pub fn with_column_defaults(
mut self,
column_defaults: HashMap<String, Expr>,
) -> Self {
self.column_defaults = column_defaults;
self
}
/// Set the [`FileStatisticsCache`] used to cache parquet file statistics.
///
/// Setting a statistics cache on the `SessionContext` can avoid refetching statistics
/// multiple times in the same session.
///
/// If `None`, creates a new [`DefaultFileStatisticsCache`] scoped to this query.
pub fn with_cache(mut self, cache: Option<FileStatisticsCache>) -> Self {
self.collected_statistics =
cache.unwrap_or(Arc::new(DefaultFileStatisticsCache::default()));
self
}
/// Specify the SQL definition for this table, if any
pub fn with_definition(mut self, definition: Option<String>) -> Self {
self.definition = definition;
self
}
/// Get paths ref
pub fn table_paths(&self) -> &Vec<ListingTableUrl> {
&self.table_paths
}
/// Get options ref
pub fn options(&self) -> &ListingOptions {
&self.options
}
/// If file_sort_order is specified, creates the appropriate physical expressions
fn try_create_output_ordering(&self) -> Result<Vec<LexOrdering>> {
create_ordering(&self.table_schema, &self.options.file_sort_order)
}
}
#[async_trait]
impl TableProvider for ListingTable {
fn as_any(&self) -> &dyn Any {
self
}
fn schema(&self) -> SchemaRef {
Arc::clone(&self.table_schema)
}
fn constraints(&self) -> Option<&Constraints> {
Some(&self.constraints)
}
fn table_type(&self) -> TableType {
TableType::Base
}
async fn scan(
&self,
state: &dyn Session,
projection: Option<&Vec<usize>>,
filters: &[Expr],
limit: Option<usize>,
) -> Result<Arc<dyn ExecutionPlan>> {
// TODO (https://github.com/apache/datafusion/issues/11600) remove downcast_ref from here?
let session_state = state.as_any().downcast_ref::<SessionState>().unwrap();
let (mut partitioned_file_lists, statistics) = self
.list_files_for_scan(session_state, filters, limit)
.await?;
// if no files need to be read, return an `EmptyExec`
if partitioned_file_lists.is_empty() {
let projected_schema = project_schema(&self.schema(), projection)?;
return Ok(Arc::new(EmptyExec::new(projected_schema)));
}
let output_ordering = self.try_create_output_ordering()?;
match state
.config_options()
.execution
.split_file_groups_by_statistics
.then(|| {
output_ordering.first().map(|output_ordering| {
FileScanConfig::split_groups_by_statistics(
&self.table_schema,
&partitioned_file_lists,
output_ordering,
)
})
})
.flatten()
{
Some(Err(e)) => log::debug!("failed to split file groups by statistics: {e}"),
Some(Ok(new_groups)) => {
if new_groups.len() <= self.options.target_partitions {
partitioned_file_lists = new_groups;
} else {
log::debug!("attempted to split file groups by statistics, but there were more file groups than target_partitions; falling back to unordered")
}
}
None => {} // no ordering required
};
// extract types of partition columns
let table_partition_cols = self
.options
.table_partition_cols
.iter()
.map(|col| Ok(self.table_schema.field_with_name(&col.0)?.clone()))
.collect::<Result<Vec<_>>>()?;
let filters = if let Some(expr) = conjunction(filters.to_vec()) {
// NOTE: Use the table schema (NOT file schema) here because `expr` may contain references to partition columns.
let table_df_schema = self.table_schema.as_ref().clone().to_dfschema()?;
let filters =
create_physical_expr(&expr, &table_df_schema, state.execution_props())?;
Some(filters)
} else {
None
};
let object_store_url = if let Some(url) = self.table_paths.first() {
url.object_store()
} else {
return Ok(Arc::new(EmptyExec::new(Arc::new(Schema::empty()))));
};
// create the execution plan
self.options
.format
.create_physical_plan(
session_state,
FileScanConfig::new(object_store_url, Arc::clone(&self.file_schema))
.with_file_groups(partitioned_file_lists)
.with_statistics(statistics)
.with_projection(projection.cloned())
.with_limit(limit)
.with_output_ordering(output_ordering)
.with_table_partition_cols(table_partition_cols),
filters.as_ref(),
)
.await
}
fn supports_filters_pushdown(
&self,
filters: &[&Expr],
) -> Result<Vec<TableProviderFilterPushDown>> {
Ok(filters
.iter()
.map(|filter| {
if expr_applicable_for_cols(
&self
.options
.table_partition_cols
.iter()
.map(|x| x.0.as_str())
.collect::<Vec<_>>(),
filter,
) {
// if filter can be handled by partition pruning, it is exact
TableProviderFilterPushDown::Exact
} else {
// otherwise, we still might be able to handle the filter with file
// level mechanisms such as Parquet row group pruning.
TableProviderFilterPushDown::Inexact
}
})
.collect())
}
fn get_table_definition(&self) -> Option<&str> {
self.definition.as_deref()
}
async fn insert_into(
&self,
state: &dyn Session,
input: Arc<dyn ExecutionPlan>,
overwrite: bool,
) -> Result<Arc<dyn ExecutionPlan>> {
// Check that the schema of the plan matches the schema of this table.
if !self
.schema()
.logically_equivalent_names_and_types(&input.schema())
{
return plan_err!(
// Return an error if schema of the input query does not match with the table schema.
"Inserting query must have the same schema with the table."
);
}
let table_path = &self.table_paths()[0];
if !table_path.is_collection() {
return plan_err!(
"Inserting into a ListingTable backed by a single file is not supported, URL is possibly missing a trailing `/`. \
To append to an existing file use StreamTable, e.g. by using CREATE UNBOUNDED EXTERNAL TABLE"
);
}
// Get the object store for the table path.
let store = state.runtime_env().object_store(table_path)?;
// TODO (https://github.com/apache/datafusion/issues/11600) remove downcast_ref from here?
let session_state = state.as_any().downcast_ref::<SessionState>().unwrap();
let file_list_stream = pruned_partition_list(
session_state,
store.as_ref(),
table_path,
&[],
&self.options.file_extension,
&self.options.table_partition_cols,
)
.await?;
let file_groups = file_list_stream.try_collect::<Vec<_>>().await?;
let keep_partition_by_columns =
state.config_options().execution.keep_partition_by_columns;
// Sink related option, apart from format
let config = FileSinkConfig {
object_store_url: self.table_paths()[0].object_store(),
table_paths: self.table_paths().clone(),
file_groups,
output_schema: self.schema(),
table_partition_cols: self.options.table_partition_cols.clone(),
overwrite,
keep_partition_by_columns,
};
let order_requirements = if !self.options().file_sort_order.is_empty() {
// Multiple sort orders in outer vec are equivalent, so we pass only the first one
let ordering = self
.try_create_output_ordering()?
.first()
.ok_or(DataFusionError::Internal(
"Expected ListingTable to have a sort order, but none found!".into(),
))?
.clone();
// Converts Vec<Vec<SortExpr>> into type required by execution plan to specify its required input ordering
Some(
ordering
.into_iter()
.map(PhysicalSortRequirement::from)
.collect::<Vec<_>>(),
)
} else {
None
};
self.options()
.format
.create_writer_physical_plan(input, session_state, config, order_requirements)
.await
}
fn get_column_default(&self, column: &str) -> Option<&Expr> {
self.column_defaults.get(column)
}
}
impl ListingTable {
/// Get the list of files for a scan as well as the file level statistics.
/// The list is grouped to let the execution plan know how the files should
/// be distributed to different threads / executors.
async fn list_files_for_scan<'a>(
&'a self,
ctx: &'a SessionState,
filters: &'a [Expr],
limit: Option<usize>,
) -> Result<(Vec<Vec<PartitionedFile>>, Statistics)> {
let store = if let Some(url) = self.table_paths.first() {
ctx.runtime_env().object_store(url)?
} else {
return Ok((vec![], Statistics::new_unknown(&self.file_schema)));
};
// list files (with partitions)
let file_list = future::try_join_all(self.table_paths.iter().map(|table_path| {
pruned_partition_list(
ctx,
store.as_ref(),
table_path,
filters,
&self.options.file_extension,
&self.options.table_partition_cols,
)
}))
.await?;
let file_list = stream::iter(file_list).flatten();
// collect the statistics if required by the config
let files = file_list
.map(|part_file| async {
let part_file = part_file?;
if self.options.collect_stat {
let statistics =
self.do_collect_statistics(ctx, &store, &part_file).await?;
Ok((part_file, statistics))
} else {
Ok((
part_file,
Arc::new(Statistics::new_unknown(&self.file_schema)),
))
}
})
.boxed()
.buffered(ctx.config_options().execution.meta_fetch_concurrency);
let (files, statistics) = get_statistics_with_limit(
files,
self.schema(),
limit,
self.options.collect_stat,
)
.await?;
Ok((
split_files(files, self.options.target_partitions),
statistics,
))
}
/// Collects statistics for a given partitioned file.
///
/// This method first checks if the statistics for the given file are already cached.
/// If they are, it returns the cached statistics.
/// If they are not, it infers the statistics from the file and stores them in the cache.
async fn do_collect_statistics<'a>(
&'a self,
ctx: &SessionState,
store: &Arc<dyn ObjectStore>,
part_file: &PartitionedFile,
) -> Result<Arc<Statistics>> {
match self
.collected_statistics
.get_with_extra(&part_file.object_meta.location, &part_file.object_meta)
{
Some(statistics) => Ok(statistics),
None => {
let statistics = self
.options
.format
.infer_stats(
ctx,
store,
self.file_schema.clone(),
&part_file.object_meta,
)
.await?;
let statistics = Arc::new(statistics);
self.collected_statistics.put_with_extra(
&part_file.object_meta.location,
statistics.clone(),
&part_file.object_meta,
);
Ok(statistics)
}
}
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::datasource::file_format::avro::AvroFormat;
use crate::datasource::file_format::csv::CsvFormat;
use crate::datasource::file_format::json::JsonFormat;
#[cfg(feature = "parquet")]
use crate::datasource::file_format::parquet::ParquetFormat;
use crate::datasource::{provider_as_source, MemTable};
use crate::execution::options::ArrowReadOptions;
use crate::prelude::*;
use crate::{
assert_batches_eq,
test::{columns, object_store::register_test_store},
};
use datafusion_physical_plan::collect;
use arrow::record_batch::RecordBatch;
use arrow_schema::SortOptions;
use datafusion_common::stats::Precision;
use datafusion_common::{assert_contains, ScalarValue};
use datafusion_expr::{BinaryExpr, LogicalPlanBuilder, Operator};
use datafusion_physical_expr::PhysicalSortExpr;
use datafusion_physical_plan::ExecutionPlanProperties;
use tempfile::TempDir;
#[tokio::test]
async fn read_single_file() -> Result<()> {
let ctx = SessionContext::new();
let table = load_table(&ctx, "alltypes_plain.parquet").await?;
let projection = None;
let exec = table
.scan(&ctx.state(), projection, &[], None)
.await
.expect("Scan table");
assert_eq!(exec.children().len(), 0);
assert_eq!(exec.output_partitioning().partition_count(), 1);
// test metadata
assert_eq!(exec.statistics()?.num_rows, Precision::Exact(8));
assert_eq!(exec.statistics()?.total_byte_size, Precision::Exact(671));
Ok(())
}
#[cfg(feature = "parquet")]
#[tokio::test]
async fn load_table_stats_by_default() -> Result<()> {
use crate::datasource::file_format::parquet::ParquetFormat;
let testdata = crate::test_util::parquet_test_data();
let filename = format!("{}/{}", testdata, "alltypes_plain.parquet");
let table_path = ListingTableUrl::parse(filename).unwrap();
let ctx = SessionContext::new();
let state = ctx.state();
let opt = ListingOptions::new(Arc::new(ParquetFormat::default()));
let schema = opt.infer_schema(&state, &table_path).await?;
let config = ListingTableConfig::new(table_path)
.with_listing_options(opt)
.with_schema(schema);
let table = ListingTable::try_new(config)?;
let exec = table.scan(&state, None, &[], None).await?;
assert_eq!(exec.statistics()?.num_rows, Precision::Exact(8));
assert_eq!(exec.statistics()?.total_byte_size, Precision::Exact(671));
Ok(())
}
#[cfg(feature = "parquet")]
#[tokio::test]
async fn load_table_stats_when_no_stats() -> Result<()> {
use crate::datasource::file_format::parquet::ParquetFormat;
let testdata = crate::test_util::parquet_test_data();
let filename = format!("{}/{}", testdata, "alltypes_plain.parquet");
let table_path = ListingTableUrl::parse(filename).unwrap();
let ctx = SessionContext::new();
let state = ctx.state();
let opt = ListingOptions::new(Arc::new(ParquetFormat::default()))
.with_collect_stat(false);
let schema = opt.infer_schema(&state, &table_path).await?;
let config = ListingTableConfig::new(table_path)
.with_listing_options(opt)
.with_schema(schema);
let table = ListingTable::try_new(config)?;
let exec = table.scan(&state, None, &[], None).await?;
assert_eq!(exec.statistics()?.num_rows, Precision::Absent);
assert_eq!(exec.statistics()?.total_byte_size, Precision::Absent);
Ok(())
}
#[cfg(feature = "parquet")]
#[tokio::test]
async fn test_try_create_output_ordering() {
let testdata = crate::test_util::parquet_test_data();
let filename = format!("{}/{}", testdata, "alltypes_plain.parquet");
let table_path = ListingTableUrl::parse(filename).unwrap();
let ctx = SessionContext::new();
let state = ctx.state();
let options = ListingOptions::new(Arc::new(ParquetFormat::default()));
let schema = options.infer_schema(&state, &table_path).await.unwrap();
use crate::datasource::file_format::parquet::ParquetFormat;
use datafusion_physical_plan::expressions::col as physical_col;
use std::ops::Add;
// (file_sort_order, expected_result)
let cases = vec![
(vec![], Ok(vec![])),
// sort expr, but non column
(
vec![vec![
col("int_col").add(lit(1)).sort(true, true),
]],
Err("Expected single column references in output_ordering, got int_col + Int32(1)"),
),
// ok with one column
(
vec![vec![col("string_col").sort(true, false)]],
Ok(vec![vec![PhysicalSortExpr {
expr: physical_col("string_col", &schema).unwrap(),
options: SortOptions {
descending: false,
nulls_first: false,
},
}]])
),
// ok with two columns, different options
(
vec![vec![
col("string_col").sort(true, false),
col("int_col").sort(false, true),
]],
Ok(vec![vec![
PhysicalSortExpr {
expr: physical_col("string_col", &schema).unwrap(),
options: SortOptions {
descending: false,
nulls_first: false,
},
},
PhysicalSortExpr {
expr: physical_col("int_col", &schema).unwrap(),
options: SortOptions {
descending: true,
nulls_first: true,
},
},
]])
),
];
for (file_sort_order, expected_result) in cases {
let options = options.clone().with_file_sort_order(file_sort_order);
let config = ListingTableConfig::new(table_path.clone())
.with_listing_options(options)
.with_schema(schema.clone());
let table =
ListingTable::try_new(config.clone()).expect("Creating the table");
let ordering_result = table.try_create_output_ordering();
match (expected_result, ordering_result) {
(Ok(expected), Ok(result)) => {
assert_eq!(expected, result);
}
(Err(expected), Err(result)) => {
// can't compare the DataFusionError directly
let result = result.to_string();
let expected = expected.to_string();
assert_contains!(result.to_string(), expected);
}
(expected_result, ordering_result) => {
panic!(
"expected: {expected_result:#?}\n\nactual:{ordering_result:#?}"
);
}
}
}
}
#[tokio::test]
async fn read_empty_table() -> Result<()> {
let ctx = SessionContext::new();
let path = String::from("table/p1=v1/file.avro");
register_test_store(&ctx, &[(&path, 100)]);
let opt = ListingOptions::new(Arc::new(AvroFormat {}))
.with_file_extension(AvroFormat.get_ext())
.with_table_partition_cols(vec![(String::from("p1"), DataType::Utf8)])
.with_target_partitions(4);
let table_path = ListingTableUrl::parse("test:///table/").unwrap();
let file_schema =
Arc::new(Schema::new(vec![Field::new("a", DataType::Boolean, false)]));
let config = ListingTableConfig::new(table_path)
.with_listing_options(opt)
.with_schema(file_schema);
let table = ListingTable::try_new(config)?;
assert_eq!(
columns(&table.schema()),
vec!["a".to_owned(), "p1".to_owned()]
);
// this will filter out the only file in the store
let filter = Expr::not_eq(col("p1"), lit("v1"));
let scan = table
.scan(&ctx.state(), None, &[filter], None)
.await
.expect("Empty execution plan");
assert!(scan.as_any().is::<EmptyExec>());
assert_eq!(
columns(&scan.schema()),
vec!["a".to_owned(), "p1".to_owned()]
);
Ok(())
}
#[tokio::test]
async fn test_assert_list_files_for_scan_grouping() -> Result<()> {
// more expected partitions than files
assert_list_files_for_scan_grouping(
&[
"bucket/key-prefix/file0",
"bucket/key-prefix/file1",
"bucket/key-prefix/file2",
"bucket/key-prefix/file3",
"bucket/key-prefix/file4",
],
"test:///bucket/key-prefix/",
12,
5,
)
.await?;
// as many expected partitions as files
assert_list_files_for_scan_grouping(
&[
"bucket/key-prefix/file0",
"bucket/key-prefix/file1",
"bucket/key-prefix/file2",
"bucket/key-prefix/file3",
],
"test:///bucket/key-prefix/",
4,
4,
)
.await?;
// more files as expected partitions
assert_list_files_for_scan_grouping(
&[
"bucket/key-prefix/file0",
"bucket/key-prefix/file1",
"bucket/key-prefix/file2",
"bucket/key-prefix/file3",
"bucket/key-prefix/file4",
],
"test:///bucket/key-prefix/",
2,
2,
)
.await?;
// no files => no groups
assert_list_files_for_scan_grouping(&[], "test:///bucket/key-prefix/", 2, 0)
.await?;
// files that don't match the prefix
assert_list_files_for_scan_grouping(
&[
"bucket/key-prefix/file0",
"bucket/key-prefix/file1",
"bucket/other-prefix/roguefile",
],
"test:///bucket/key-prefix/",
10,
2,
)
.await?;
Ok(())
}
#[tokio::test]
async fn test_assert_list_files_for_multi_path() -> Result<()> {
// more expected partitions than files
assert_list_files_for_multi_paths(
&[
"bucket/key1/file0",
"bucket/key1/file1",
"bucket/key1/file2",
"bucket/key2/file3",
"bucket/key2/file4",
"bucket/key3/file5",
],
&["test:///bucket/key1/", "test:///bucket/key2/"],
12,
5,
)
.await?;
// as many expected partitions as files
assert_list_files_for_multi_paths(
&[
"bucket/key1/file0",
"bucket/key1/file1",
"bucket/key1/file2",
"bucket/key2/file3",
"bucket/key2/file4",
"bucket/key3/file5",
],
&["test:///bucket/key1/", "test:///bucket/key2/"],
5,
5,
)
.await?;
// more files as expected partitions
assert_list_files_for_multi_paths(
&[
"bucket/key1/file0",
"bucket/key1/file1",
"bucket/key1/file2",
"bucket/key2/file3",
"bucket/key2/file4",
"bucket/key3/file5",
],
&["test:///bucket/key1/"],
2,
2,
)
.await?;
// no files => no groups
assert_list_files_for_multi_paths(&[], &["test:///bucket/key1/"], 2, 0).await?;
// files that don't match the prefix
assert_list_files_for_multi_paths(
&[
"bucket/key1/file0",
"bucket/key1/file1",
"bucket/key1/file2",
"bucket/key2/file3",
"bucket/key2/file4",
"bucket/key3/file5",
],
&["test:///bucket/key3/"],
2,
1,
)
.await?;
Ok(())
}
async fn load_table(
ctx: &SessionContext,
name: &str,
) -> Result<Arc<dyn TableProvider>> {
let testdata = crate::test_util::parquet_test_data();
let filename = format!("{testdata}/{name}");
let table_path = ListingTableUrl::parse(filename).unwrap();
let config = ListingTableConfig::new(table_path)
.infer(&ctx.state())
.await?;
let table = ListingTable::try_new(config)?;
Ok(Arc::new(table))
}
/// Check that the files listed by the table match the specified `output_partitioning`
/// when the object store contains `files`.
async fn assert_list_files_for_scan_grouping(
files: &[&str],
table_prefix: &str,
target_partitions: usize,
output_partitioning: usize,
) -> Result<()> {
let ctx = SessionContext::new();
register_test_store(&ctx, &files.iter().map(|f| (*f, 10)).collect::<Vec<_>>());
let format = AvroFormat {};
let opt = ListingOptions::new(Arc::new(format))
.with_file_extension("")
.with_target_partitions(target_partitions);
let schema = Schema::new(vec![Field::new("a", DataType::Boolean, false)]);
let table_path = ListingTableUrl::parse(table_prefix).unwrap();
let config = ListingTableConfig::new(table_path)
.with_listing_options(opt)
.with_schema(Arc::new(schema));
let table = ListingTable::try_new(config)?;
let (file_list, _) = table.list_files_for_scan(&ctx.state(), &[], None).await?;
assert_eq!(file_list.len(), output_partitioning);
Ok(())
}
/// Check that the files listed by the table match the specified `output_partitioning`
/// when the object store contains `files`.
async fn assert_list_files_for_multi_paths(
files: &[&str],
table_prefix: &[&str],
target_partitions: usize,
output_partitioning: usize,
) -> Result<()> {
let ctx = SessionContext::new();
register_test_store(&ctx, &files.iter().map(|f| (*f, 10)).collect::<Vec<_>>());
let format = AvroFormat {};
let opt = ListingOptions::new(Arc::new(format))
.with_file_extension("")
.with_target_partitions(target_partitions);
let schema = Schema::new(vec![Field::new("a", DataType::Boolean, false)]);
let table_paths = table_prefix
.iter()
.map(|t| ListingTableUrl::parse(t).unwrap())
.collect();
let config = ListingTableConfig::new_with_multi_paths(table_paths)
.with_listing_options(opt)
.with_schema(Arc::new(schema));
let table = ListingTable::try_new(config)?;
let (file_list, _) = table.list_files_for_scan(&ctx.state(), &[], None).await?;
assert_eq!(file_list.len(), output_partitioning);
Ok(())
}
#[tokio::test]
async fn test_insert_into_append_new_json_files() -> Result<()> {
let mut config_map: HashMap<String, String> = HashMap::new();
config_map.insert("datafusion.execution.batch_size".into(), "10".into());
config_map.insert(
"datafusion.execution.soft_max_rows_per_output_file".into(),
"10".into(),
);
helper_test_append_new_files_to_table(
JsonFormat::default().get_ext(),
FileCompressionType::UNCOMPRESSED,
Some(config_map),
2,
)
.await?;
Ok(())
}
#[tokio::test]
async fn test_insert_into_append_new_csv_files() -> Result<()> {
let mut config_map: HashMap<String, String> = HashMap::new();
config_map.insert("datafusion.execution.batch_size".into(), "10".into());
config_map.insert(
"datafusion.execution.soft_max_rows_per_output_file".into(),
"10".into(),
);
helper_test_append_new_files_to_table(
CsvFormat::default().get_ext(),
FileCompressionType::UNCOMPRESSED,
Some(config_map),
2,
)
.await?;
Ok(())
}
#[tokio::test]
async fn test_insert_into_append_2_new_parquet_files_defaults() -> Result<()> {
let mut config_map: HashMap<String, String> = HashMap::new();
config_map.insert("datafusion.execution.batch_size".into(), "10".into());
config_map.insert(
"datafusion.execution.soft_max_rows_per_output_file".into(),
"10".into(),
);
helper_test_append_new_files_to_table(
ParquetFormat::default().get_ext(),
FileCompressionType::UNCOMPRESSED,
Some(config_map),
2,
)
.await?;
Ok(())
}
#[tokio::test]
async fn test_insert_into_append_1_new_parquet_files_defaults() -> Result<()> {
let mut config_map: HashMap<String, String> = HashMap::new();
config_map.insert("datafusion.execution.batch_size".into(), "20".into());
config_map.insert(
"datafusion.execution.soft_max_rows_per_output_file".into(),
"20".into(),
);
helper_test_append_new_files_to_table(
ParquetFormat::default().get_ext(),
FileCompressionType::UNCOMPRESSED,
Some(config_map),
1,
)
.await?;
Ok(())
}
#[tokio::test]
async fn test_insert_into_sql_csv_defaults() -> Result<()> {
helper_test_insert_into_sql("csv", FileCompressionType::UNCOMPRESSED, "", None)
.await?;
Ok(())
}
#[tokio::test]
async fn test_insert_into_sql_csv_defaults_header_row() -> Result<()> {
helper_test_insert_into_sql(
"csv",
FileCompressionType::UNCOMPRESSED,
"",
Some(HashMap::from([("has_header".into(), "true".into())])),
)
.await?;
Ok(())
}
#[tokio::test]
async fn test_insert_into_sql_json_defaults() -> Result<()> {
helper_test_insert_into_sql("json", FileCompressionType::UNCOMPRESSED, "", None)
.await?;
Ok(())
}
#[tokio::test]
async fn test_insert_into_sql_parquet_defaults() -> Result<()> {
helper_test_insert_into_sql(
"parquet",
FileCompressionType::UNCOMPRESSED,
"",
None,
)
.await?;
Ok(())
}
#[tokio::test]
async fn test_insert_into_sql_parquet_session_overrides() -> Result<()> {
let mut config_map: HashMap<String, String> = HashMap::new();
config_map.insert(
"datafusion.execution.parquet.compression".into(),
"zstd(5)".into(),
);
config_map.insert(
"datafusion.execution.parquet.dictionary_enabled".into(),
"false".into(),
);
config_map.insert(
"datafusion.execution.parquet.dictionary_page_size_limit".into(),
"100".into(),
);
config_map.insert(
"datafusion.execution.parquet.staistics_enabled".into(),
"none".into(),
);
config_map.insert(
"datafusion.execution.parquet.max_statistics_size".into(),
"10".into(),
);
config_map.insert(
"datafusion.execution.parquet.max_row_group_size".into(),
"5".into(),
);
config_map.insert(
"datafusion.execution.parquet.created_by".into(),
"datafusion test".into(),
);
config_map.insert(
"datafusion.execution.parquet.column_index_truncate_length".into(),
"50".into(),
);
config_map.insert(
"datafusion.execution.parquet.data_page_row_count_limit".into(),
"50".into(),
);
config_map.insert(
"datafusion.execution.parquet.bloom_filter_on_write".into(),
"true".into(),
);
config_map.insert(
"datafusion.execution.parquet.bloom_filter_fpp".into(),
"0.01".into(),
);
config_map.insert(
"datafusion.execution.parquet.bloom_filter_ndv".into(),
"1000".into(),
);
config_map.insert(
"datafusion.execution.parquet.writer_version".into(),
"2.0".into(),
);
config_map.insert(
"datafusion.execution.parquet.write_batch_size".into(),
"5".into(),
);
helper_test_insert_into_sql(
"parquet",
FileCompressionType::UNCOMPRESSED,
"",
Some(config_map),
)
.await?;
Ok(())
}
#[tokio::test]
async fn test_insert_into_append_new_parquet_files_session_overrides() -> Result<()> {
let mut config_map: HashMap<String, String> = HashMap::new();
config_map.insert("datafusion.execution.batch_size".into(), "10".into());
config_map.insert(
"datafusion.execution.soft_max_rows_per_output_file".into(),
"10".into(),
);
config_map.insert(
"datafusion.execution.parquet.compression".into(),
"zstd(5)".into(),
);
config_map.insert(
"datafusion.execution.parquet.dictionary_enabled".into(),
"false".into(),
);
config_map.insert(
"datafusion.execution.parquet.dictionary_page_size_limit".into(),
"100".into(),
);
config_map.insert(
"datafusion.execution.parquet.staistics_enabled".into(),
"none".into(),
);
config_map.insert(
"datafusion.execution.parquet.max_statistics_size".into(),
"10".into(),
);
config_map.insert(
"datafusion.execution.parquet.max_row_group_size".into(),
"5".into(),
);
config_map.insert(
"datafusion.execution.parquet.created_by".into(),
"datafusion test".into(),
);
config_map.insert(
"datafusion.execution.parquet.column_index_truncate_length".into(),
"50".into(),
);
config_map.insert(
"datafusion.execution.parquet.data_page_row_count_limit".into(),
"50".into(),
);
config_map.insert(
"datafusion.execution.parquet.encoding".into(),
"delta_binary_packed".into(),
);
config_map.insert(
"datafusion.execution.parquet.bloom_filter_on_write".into(),
"true".into(),
);
config_map.insert(
"datafusion.execution.parquet.bloom_filter_fpp".into(),
"0.01".into(),
);
config_map.insert(
"datafusion.execution.parquet.bloom_filter_ndv".into(),
"1000".into(),
);
config_map.insert(
"datafusion.execution.parquet.writer_version".into(),
"2.0".into(),
);
config_map.insert(
"datafusion.execution.parquet.write_batch_size".into(),
"5".into(),
);
config_map.insert("datafusion.execution.batch_size".into(), "1".into());
helper_test_append_new_files_to_table(
ParquetFormat::default().get_ext(),
FileCompressionType::UNCOMPRESSED,
Some(config_map),
2,
)
.await?;
Ok(())
}
#[tokio::test]
async fn test_insert_into_append_new_parquet_files_invalid_session_fails(
) -> Result<()> {
let mut config_map: HashMap<String, String> = HashMap::new();
config_map.insert(
"datafusion.execution.parquet.compression".into(),
"zstd".into(),
);
let e = helper_test_append_new_files_to_table(
ParquetFormat::default().get_ext(),
FileCompressionType::UNCOMPRESSED,
Some(config_map),
2,
)
.await
.expect_err("Example should fail!");
assert_eq!(e.strip_backtrace(), "Invalid or Unsupported Configuration: zstd compression requires specifying a level such as zstd(4)");
Ok(())
}
async fn helper_test_append_new_files_to_table(
file_type_ext: String,
file_compression_type: FileCompressionType,
session_config_map: Option<HashMap<String, String>>,
expected_n_files_per_insert: usize,
) -> Result<()> {
// Create the initial context, schema, and batch.
let session_ctx = match session_config_map {
Some(cfg) => {
let config = SessionConfig::from_string_hash_map(&cfg)?;
SessionContext::new_with_config(config)
}
None => SessionContext::new(),
};
// Create a new schema with one field called "a" of type Int32
let schema = Arc::new(Schema::new(vec![Field::new(
"column1",
DataType::Int32,
false,
)]));
let filter_predicate = Expr::BinaryExpr(BinaryExpr::new(
Box::new(Expr::Column("column1".into())),
Operator::GtEq,
Box::new(Expr::Literal(ScalarValue::Int32(Some(0)))),
));
// Create a new batch of data to insert into the table
let batch = RecordBatch::try_new(
schema.clone(),
vec![Arc::new(arrow_array::Int32Array::from(vec![
1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
]))],
)?;
// Register appropriate table depending on file_type we want to test
let tmp_dir = TempDir::new()?;
match file_type_ext.as_str() {
"csv" => {
session_ctx
.register_csv(
"t",
tmp_dir.path().to_str().unwrap(),
CsvReadOptions::new()
.schema(schema.as_ref())
.file_compression_type(file_compression_type),
)
.await?;
}
"json" => {
session_ctx
.register_json(
"t",
tmp_dir.path().to_str().unwrap(),
NdJsonReadOptions::default()
.schema(schema.as_ref())
.file_compression_type(file_compression_type),
)
.await?;
}
"parquet" => {
session_ctx
.register_parquet(
"t",
tmp_dir.path().to_str().unwrap(),
ParquetReadOptions::default().schema(schema.as_ref()),
)
.await?;
}
"avro" => {
session_ctx
.register_avro(
"t",
tmp_dir.path().to_str().unwrap(),
AvroReadOptions::default().schema(schema.as_ref()),
)
.await?;
}
"arrow" => {
session_ctx
.register_arrow(
"t",
tmp_dir.path().to_str().unwrap(),
ArrowReadOptions::default().schema(schema.as_ref()),
)
.await?;
}
_ => panic!("Unrecognized file extension {file_type_ext}"),
}
// Create and register the source table with the provided schema and inserted data
let source_table = Arc::new(MemTable::try_new(
schema.clone(),
vec![vec![batch.clone(), batch.clone()]],
)?);
session_ctx.register_table("source", source_table.clone())?;
// Convert the source table into a provider so that it can be used in a query
let source = provider_as_source(source_table);
// Create a table scan logical plan to read from the source table
let scan_plan = LogicalPlanBuilder::scan("source", source, None)?
.filter(filter_predicate)?
.build()?;
// Since logical plan contains a filter, increasing parallelism is helpful.
// Therefore, we will have 8 partitions in the final plan.
// Create an insert plan to insert the source data into the initial table
let insert_into_table =
LogicalPlanBuilder::insert_into(scan_plan, "t", &schema, false)?.build()?;
// Create a physical plan from the insert plan
let plan = session_ctx
.state()
.create_physical_plan(&insert_into_table)
.await?;
// Execute the physical plan and collect the results
let res = collect(plan, session_ctx.task_ctx()).await?;
// Insert returns the number of rows written, in our case this would be 6.
let expected = [
"+-------+",
"| count |",
"+-------+",
"| 20 |",
"+-------+",
];
// Assert that the batches read from the file match the expected result.
assert_batches_eq!(expected, &res);
// Read the records in the table
let batches = session_ctx
.sql("select count(*) as count from t")
.await?
.collect()
.await?;
let expected = [
"+-------+",
"| count |",
"+-------+",
"| 20 |",
"+-------+",
];
// Assert that the batches read from the file match the expected result.
assert_batches_eq!(expected, &batches);
// Assert that `target_partition_number` many files were added to the table.
let num_files = tmp_dir.path().read_dir()?.count();
assert_eq!(num_files, expected_n_files_per_insert);
// Create a physical plan from the insert plan
let plan = session_ctx
.state()
.create_physical_plan(&insert_into_table)
.await?;
// Again, execute the physical plan and collect the results
let res = collect(plan, session_ctx.task_ctx()).await?;
// Insert returns the number of rows written, in our case this would be 6.
let expected = [
"+-------+",
"| count |",
"+-------+",
"| 20 |",
"+-------+",
];
// Assert that the batches read from the file match the expected result.
assert_batches_eq!(expected, &res);
// Read the contents of the table
let batches = session_ctx
.sql("select count(*) AS count from t")
.await?
.collect()
.await?;
// Define the expected result after the second append.
let expected = [
"+-------+",
"| count |",
"+-------+",
"| 40 |",
"+-------+",
];
// Assert that the batches read from the file after the second append match the expected result.
assert_batches_eq!(expected, &batches);
// Assert that another `target_partition_number` many files were added to the table.
let num_files = tmp_dir.path().read_dir()?.count();
assert_eq!(num_files, expected_n_files_per_insert * 2);
// Return Ok if the function
Ok(())
}
/// tests insert into with end to end sql
/// create external table + insert into statements
async fn helper_test_insert_into_sql(
file_type: &str,
// TODO test with create statement options such as compression
_file_compression_type: FileCompressionType,
external_table_options: &str,
session_config_map: Option<HashMap<String, String>>,
) -> Result<()> {
// Create the initial context
let session_ctx = match session_config_map {
Some(cfg) => {
let config = SessionConfig::from_string_hash_map(&cfg)?;
SessionContext::new_with_config(config)
}
None => SessionContext::new(),
};
// create table
let tmp_dir = TempDir::new()?;
let tmp_path = tmp_dir.into_path();
let str_path = tmp_path.to_str().expect("Temp path should convert to &str");
session_ctx
.sql(&format!(
"create external table foo(a varchar, b varchar, c int) \
stored as {file_type} \
location '{str_path}' \
{external_table_options}"
))
.await?
.collect()
.await?;
// insert data
session_ctx.sql("insert into foo values ('foo', 'bar', 1),('foo', 'bar', 2), ('foo', 'bar', 3)")
.await?
.collect()
.await?;
// check count
let batches = session_ctx
.sql("select * from foo")
.await?
.collect()
.await?;
let expected = [
"+-----+-----+---+",
"| a | b | c |",
"+-----+-----+---+",
"| foo | bar | 1 |",
"| foo | bar | 2 |",
"| foo | bar | 3 |",
"+-----+-----+---+",
];
assert_batches_eq!(expected, &batches);
Ok(())
}
}