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feat: metadata columns #14057

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1 change: 1 addition & 0 deletions datafusion-examples/Cargo.toml
Original file line number Diff line number Diff line change
Expand Up @@ -70,6 +70,7 @@ datafusion-proto = { workspace = true }
datafusion-sql = { workspace = true }
env_logger = { workspace = true }
futures = { workspace = true }
itertools = { workspace = true }
log = { workspace = true }
mimalloc = { version = "0.1", default-features = false }
object_store = { workspace = true, features = ["aws", "http"] }
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330 changes: 330 additions & 0 deletions datafusion-examples/examples/metadata_columns.rs
Original file line number Diff line number Diff line change
@@ -0,0 +1,330 @@
// 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.

use std::any::Any;
use std::fmt::{self, Debug, Formatter};
use std::sync::{Arc, Mutex};
use std::time::Duration;

use arrow::array::{ArrayRef, StringArray, UInt64Array};
use arrow_schema::SchemaBuilder;
use async_trait::async_trait;
use datafusion::arrow::array::{UInt64Builder, UInt8Builder};
use datafusion::arrow::datatypes::{DataType, Field, Schema, SchemaRef};
use datafusion::arrow::record_batch::RecordBatch;
use datafusion::datasource::{TableProvider, TableType};
use datafusion::error::Result;
use datafusion::execution::context::TaskContext;
use datafusion::physical_expr::EquivalenceProperties;
use datafusion::physical_plan::execution_plan::{Boundedness, EmissionType};
use datafusion::physical_plan::memory::MemoryStream;
use datafusion::physical_plan::{
project_schema, DisplayAs, DisplayFormatType, ExecutionPlan, Partitioning,
PlanProperties, SendableRecordBatchStream,
};

use datafusion::prelude::*;

use datafusion::catalog::Session;
use datafusion_common::METADATA_OFFSET;
use itertools::Itertools;
use tokio::time::timeout;

/// This example demonstrates executing a simple query against a custom datasource
#[tokio::main]
async fn main() -> Result<()> {
// create our custom datasource and adding some users
let db = CustomDataSource::default();
db.populate_users();

search_accounts(db.clone(), "select * from accounts", 3).await?;
search_accounts(
db.clone(),
"select _rowid, _file, * from accounts where _rowid > 1",
1,
)
.await?;
search_accounts(
db.clone(),
"select _rowid, _file, * from accounts where _file = 'file-0'",
1,
)
.await?;

Ok(())
}

async fn search_accounts(
db: CustomDataSource,
sql: &str,
expected_result_length: usize,
) -> Result<()> {
// create local execution context
let ctx = SessionContext::new();
ctx.register_table("accounts", Arc::new(db)).unwrap();
let options = SQLOptions::new().with_allow_ddl(false);

timeout(Duration::from_secs(10), async move {
let dataframe = ctx.sql_with_options(sql, options).await.unwrap();
let result = dataframe.collect().await.unwrap();
let record_batch = result.first().unwrap();

assert_eq!(expected_result_length, record_batch.column(1).len());
dbg!(record_batch.columns());
})
.await
.unwrap();

Ok(())
}

/// A User, with an id and a bank account
#[derive(Clone, Debug)]
struct User {
id: u8,
bank_account: u64,
}

/// A custom datasource, used to represent a datastore with a single index
#[derive(Clone)]
pub struct CustomDataSource {
inner: Arc<Mutex<CustomDataSourceInner>>,
metadata_columns: SchemaRef,
}

struct CustomDataSourceInner {
data: Vec<User>,
}

impl Debug for CustomDataSource {
fn fmt(&self, f: &mut Formatter<'_>) -> fmt::Result {
f.write_str("custom_db")
}
}

impl CustomDataSource {
pub(crate) async fn create_physical_plan(
&self,
projections: Option<&Vec<usize>>,
schema: SchemaRef,
) -> Result<Arc<dyn ExecutionPlan>> {
Ok(Arc::new(CustomExec::new(projections, schema, self.clone())))
}

pub(crate) fn populate_users(&self) {
self.add_user(User {
id: 1,
bank_account: 9_000,
});
self.add_user(User {
id: 2,
bank_account: 100,
});
self.add_user(User {
id: 3,
bank_account: 1_000,
});
}

fn add_user(&self, user: User) {
let mut inner = self.inner.lock().unwrap();
inner.data.push(user);
}
}

impl Default for CustomDataSource {
fn default() -> Self {
CustomDataSource {
inner: Arc::new(Mutex::new(CustomDataSourceInner {
data: Default::default(),
})),
metadata_columns: Arc::new(Schema::new(vec![
Field::new("_rowid", DataType::UInt64, false),
Field::new("_file", DataType::Utf8, false),
])),
}
}
}

#[async_trait]
impl TableProvider for CustomDataSource {
fn as_any(&self) -> &dyn Any {
self
}

fn schema(&self) -> SchemaRef {
SchemaRef::new(Schema::new(vec![
Field::new("id", DataType::UInt8, false),
Field::new("bank_account", DataType::UInt64, true),
]))
}

fn metadata_columns(&self) -> Option<SchemaRef> {
Some(self.metadata_columns.clone())
}

fn table_type(&self) -> TableType {
TableType::Base
}

async fn scan(
&self,
_state: &dyn Session,
projection: Option<&Vec<usize>>,
// filters and limit can be used here to inject some push-down operations if needed
_filters: &[Expr],
_limit: Option<usize>,
) -> Result<Arc<dyn ExecutionPlan>> {
let mut schema = self.schema();
let size = schema.fields.len();
if let Some(metadata) = self.metadata_columns() {
let mut builder = SchemaBuilder::from(schema.as_ref());
for f in metadata.fields.iter() {
builder.try_merge(f)?;
}
schema = Arc::new(builder.finish());
}

let projection = match projection {
Some(projection) => {
let projection = projection
.iter()
.map(|idx| {
if *idx >= METADATA_OFFSET {
*idx - METADATA_OFFSET + size
} else {
*idx
}
})
.collect_vec();
Some(projection)
}
None => None,
};
return self.create_physical_plan(projection.as_ref(), schema).await;
}
}

#[derive(Debug, Clone)]
struct CustomExec {
db: CustomDataSource,
projected_schema: SchemaRef,
cache: PlanProperties,
}

impl CustomExec {
fn new(
projections: Option<&Vec<usize>>,
schema: SchemaRef,
db: CustomDataSource,
) -> Self {
let projected_schema = project_schema(&schema, projections).unwrap();
let cache = Self::compute_properties(projected_schema.clone());
Self {
db,
projected_schema,
cache,
}
}

/// This function creates the cache object that stores the plan properties such as schema, equivalence properties, ordering, partitioning, etc.
fn compute_properties(schema: SchemaRef) -> PlanProperties {
let eq_properties = EquivalenceProperties::new(schema);
PlanProperties::new(
eq_properties,
Partitioning::UnknownPartitioning(1),
EmissionType::Incremental,
Boundedness::Bounded,
)
}
}

impl DisplayAs for CustomExec {
fn fmt_as(&self, _t: DisplayFormatType, f: &mut Formatter) -> fmt::Result {
write!(f, "CustomExec")
}
}

impl ExecutionPlan for CustomExec {
fn name(&self) -> &'static str {
"CustomExec"
}

fn as_any(&self) -> &dyn Any {
self
}

fn properties(&self) -> &PlanProperties {
&self.cache
}

fn children(&self) -> Vec<&Arc<dyn ExecutionPlan>> {
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 users: Vec<User> = {
let db = self.db.inner.lock().unwrap();
db.data.clone()
};

let mut id_array = UInt8Builder::with_capacity(users.len());
let mut account_array = UInt64Builder::with_capacity(users.len());
let len = users.len() as u64;

for user in users {
id_array.append_value(user.id);
account_array.append_value(user.bank_account);
}

let id_array = id_array.finish();
let account_array = account_array.finish();
let rowid_array = UInt64Array::from_iter_values(0_u64..len);
let file_array =
StringArray::from_iter_values((0_u64..len).map(|i| format!("file-{}", i)));

let arrays = self
.projected_schema
.fields
.iter()
.map(|f| match f.name().as_str() {
"_rowid" => Arc::new(rowid_array.clone()) as ArrayRef,
"id" => Arc::new(id_array.clone()) as ArrayRef,
"bank_account" => Arc::new(account_array.clone()) as ArrayRef,
"_file" => Arc::new(file_array.clone()) as ArrayRef,
_ => panic!("cannot reach here"),
})
.collect();

Ok(Box::pin(MemoryStream::try_new(
vec![RecordBatch::try_new(self.projected_schema.clone(), arrays)?],
self.schema(),
None,
)?))
}
}
10 changes: 10 additions & 0 deletions datafusion/catalog/src/table.rs
Original file line number Diff line number Diff line change
Expand Up @@ -55,6 +55,16 @@ pub trait TableProvider: Debug + Sync + Send {
/// Get a reference to the schema for this table
fn schema(&self) -> SchemaRef;

/// Get metadata columns of this table.
/// See Also: [`datafusion_common::DFSchema::metadata`]
///
/// Returns:
/// - `None` for tables that do not have metadata columns.
/// - `Some(SchemaRef)` for tables having metadata columns.
fn metadata_columns(&self) -> Option<SchemaRef> {
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Contributor

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Can you please document this better -- specifically:

  1. A link to the prior art (spark metadata columns)
  2. A brief summary of what metadata columns are used for and an example (you can copy the content from the spark docs)

None
}

/// Get a reference to the constraints of the table.
/// Returns:
/// - `None` for tables that do not support constraints.
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