berkaysynnada commented on code in PR #15770:
URL: https://github.com/apache/datafusion/pull/15770#discussion_r2083506643
##########
datafusion/physical-plan/src/topk/mod.rs:
##########
@@ -570,6 +680,47 @@ impl TopKHeap {
+ self.store.size()
+ self.owned_bytes
}
+
+ fn get_threshold_values(
+ &self,
+ sort_exprs: &[PhysicalSortExpr],
+ ) -> Result<Option<Vec<ScalarValue>>> {
+ // If the heap doesn't have k elements yet, we can't create thresholds
+ let max_row = match self.max() {
+ Some(row) => row,
+ None => return Ok(None),
+ };
+
+ // Get the batch that contains the max row
+ let batch_entry = match self.store.get(max_row.batch_id) {
+ Some(entry) => entry,
+ None => return internal_err!("Invalid batch ID in TopKRow"),
+ };
+
+ // Extract threshold values for each sort expression
+ let mut scalar_values = Vec::with_capacity(sort_exprs.len());
+ for sort_expr in sort_exprs {
Review Comment:
I wonder if it is worth to make this evaluates in a parallelized way 🤔
##########
datafusion/core/tests/fuzz_cases/topk_filter_pushdown.rs:
##########
@@ -0,0 +1,354 @@
+// 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::collections::HashMap;
+use std::sync::{Arc, LazyLock};
+
+use arrow::array::{Int32Array, StringArray, StringDictionaryBuilder};
+use arrow::datatypes::Int32Type;
+use arrow::record_batch::RecordBatch;
+use arrow::util::pretty::pretty_format_batches;
+use arrow_schema::{DataType, Field, Schema};
+use datafusion::datasource::listing::{ListingOptions, ListingTable,
ListingTableConfig};
+use datafusion::prelude::{SessionConfig, SessionContext};
+use datafusion_datasource::ListingTableUrl;
+use datafusion_datasource_parquet::ParquetFormat;
+use datafusion_execution::object_store::ObjectStoreUrl;
+use itertools::Itertools;
+use object_store::memory::InMemory;
+use object_store::path::Path;
+use object_store::{ObjectStore, PutPayload};
+use parquet::arrow::ArrowWriter;
+use rand::rngs::StdRng;
+use rand::{Rng, SeedableRng};
+use tokio::sync::Mutex;
+use tokio::task::JoinSet;
+
+#[derive(Clone)]
+struct TestDataSet {
+ store: Arc<dyn ObjectStore>,
+ schema: Arc<Schema>,
+}
+
+/// List of in memory parquet files with UTF8 data
+// Use a mutex rather than LazyLock to allow for async initialization
+static TESTFILES: LazyLock<Mutex<Vec<TestDataSet>>> =
+ LazyLock::new(|| Mutex::new(vec![]));
+
+async fn test_files() -> Vec<TestDataSet> {
+ let files_mutex = &TESTFILES;
+ let mut files = files_mutex.lock().await;
+ if !files.is_empty() {
+ return (*files).clone();
+ }
+
+ let mut rng = StdRng::seed_from_u64(0);
+
+ for nulls_in_ids in [false, true] {
+ for nulls_in_names in [false, true] {
+ for nulls_in_departments in [false, true] {
+ let store = Arc::new(InMemory::new());
+
+ let schema = Arc::new(Schema::new(vec![
+ Field::new("id", DataType::Int32, nulls_in_ids),
+ Field::new("name", DataType::Utf8, nulls_in_names),
+ Field::new(
+ "department",
+ DataType::Dictionary(
+ Box::new(DataType::Int32),
+ Box::new(DataType::Utf8),
+ ),
+ nulls_in_departments,
+ ),
+ ]));
+
+ let name_choices = if nulls_in_names {
+ [Some("Alice"), Some("Bob"), None, Some("David"), None]
+ } else {
+ [
+ Some("Alice"),
+ Some("Bob"),
+ Some("Charlie"),
+ Some("David"),
+ Some("Eve"),
+ ]
+ };
+
+ let department_choices = if nulls_in_departments {
+ [
+ Some("Theater"),
+ Some("Engineering"),
+ None,
+ Some("Arts"),
+ None,
+ ]
+ } else {
+ [
+ Some("Theater"),
+ Some("Engineering"),
+ Some("Healthcare"),
+ Some("Arts"),
+ Some("Music"),
+ ]
+ };
+
+ // Generate 5 files, some with overlapping or repeated ids
some without
+ for i in 0..5 {
+ let num_batches = rng.gen_range(1..3);
+ let mut batches = Vec::with_capacity(num_batches);
+ for _ in 0..num_batches {
+ let num_rows = 25;
+ let ids =
Int32Array::from_iter((0..num_rows).map(|file| {
+ if nulls_in_ids {
+ if rng.gen_bool(1.0 / 10.0) {
+ None
+ } else {
+ Some(rng.gen_range(file..file + 5))
+ }
+ } else {
+ Some(rng.gen_range(file..file + 5))
+ }
+ }));
+ let names =
StringArray::from_iter((0..num_rows).map(|_| {
+ // randomly select a name
+ let idx = rng.gen_range(0..name_choices.len());
+ name_choices[idx].map(|s| s.to_string())
+ }));
+ let mut departments =
StringDictionaryBuilder::<Int32Type>::new();
+ for _ in 0..num_rows {
+ // randomly select a department
+ let idx =
rng.gen_range(0..department_choices.len());
+
departments.append_option(department_choices[idx].as_ref());
+ }
+ let batch = RecordBatch::try_new(
+ schema.clone(),
+ vec![
+ Arc::new(ids),
+ Arc::new(names),
+ Arc::new(departments.finish()),
+ ],
+ )
+ .unwrap();
+ batches.push(batch);
+ }
+ let mut buf = vec![];
+ {
+ let mut writer =
+ ArrowWriter::try_new(&mut buf, schema.clone(),
None).unwrap();
+ for batch in batches {
+ writer.write(&batch).unwrap();
+ writer.flush().unwrap();
+ }
+ writer.flush().unwrap();
+ writer.finish().unwrap();
+ }
+ let payload = PutPayload::from(buf);
+ let path = Path::from(format!("file_{i}.parquet"));
+ store.put(&path, payload).await.unwrap();
+ }
+ files.push(TestDataSet { store, schema });
+ }
+ }
+ }
+ (*files).clone()
+}
+
+async fn run_query_with_config(
+ query: &str,
+ config: SessionConfig,
+ dataset: TestDataSet,
+) -> Vec<RecordBatch> {
+ let store = dataset.store;
+ let schema = dataset.schema;
+ let ctx = SessionContext::new_with_config(config);
+ let url = ObjectStoreUrl::parse("memory://").unwrap();
+ ctx.register_object_store(url.as_ref(), store.clone());
+
+ let format = Arc::new(
+ ParquetFormat::default()
+ .with_options(ctx.state().table_options().parquet.clone()),
+ );
+ let options = ListingOptions::new(format);
+ let table_path = ListingTableUrl::parse("memory:///").unwrap();
+ let config = ListingTableConfig::new(table_path)
+ .with_listing_options(options)
+ .with_schema(schema);
+ let table = Arc::new(ListingTable::try_new(config).unwrap());
+
+ ctx.register_table("test_table", table).unwrap();
+
+ ctx.sql(query).await.unwrap().collect().await.unwrap()
+}
+
+#[derive(Debug)]
+struct RunQueryResult {
+ query: String,
+ result: Vec<RecordBatch>,
+ expected: Vec<RecordBatch>,
+}
+
+impl RunQueryResult {
+ fn expected_formated(&self) -> String {
+ format!("{}", pretty_format_batches(&self.expected).unwrap())
+ }
+
+ fn result_formated(&self) -> String {
+ format!("{}", pretty_format_batches(&self.result).unwrap())
+ }
+
+ fn is_ok(&self) -> bool {
+ self.expected_formated() == self.result_formated()
+ }
+}
+
+async fn run_query(
+ query: String,
+ cfg: SessionConfig,
+ dataset: TestDataSet,
+) -> RunQueryResult {
+ let cfg_with_dynamic_filters = cfg
+ .clone()
+ .set_bool("datafusion.optimizer.enable_dynamic_filter_pushdown", true);
+ let cfg_without_dynamic_filters = cfg
+ .clone()
+ .set_bool("datafusion.optimizer.enable_dynamic_filter_pushdown",
false);
+
+ let expected_result =
+ run_query_with_config(&query, cfg_without_dynamic_filters,
dataset.clone()).await;
+ let result =
+ run_query_with_config(&query, cfg_with_dynamic_filters,
dataset.clone()).await;
+
+ RunQueryResult {
+ query: query.to_string(),
+ result,
+ expected: expected_result,
+ }
+}
+
+struct TestCase {
+ query: String,
+ cfg: SessionConfig,
+ dataset: TestDataSet,
+}
+
+#[tokio::test(flavor = "multi_thread")]
+async fn test_fuzz_topk_filter_pushdown() {
+ let order_columns = ["id", "name", "department"];
+ let order_directions = ["ASC", "DESC"];
+ let null_orders = ["NULLS FIRST", "NULLS LAST"];
+
+ let start = datafusion_common::instant::Instant::now();
+ let mut orders: HashMap<String, Vec<String>> = HashMap::new();
+ for order_column in &order_columns {
+ for order_direction in &order_directions {
+ for null_order in &null_orders {
+ // if there is a vec for this column insert the order,
otherwise create a new vec
+ let ordering =
+ format!("{} {} {}", order_column, order_direction,
null_order);
+ match orders.get_mut(*order_column) {
+ Some(order_vec) => {
+ order_vec.push(ordering);
+ }
+ None => {
+ orders.insert(order_column.to_string(),
vec![ordering]);
+ }
+ }
+ }
+ }
+ }
+
+ let mut queries = vec![];
+
+ for limit in [1, 10] {
+ for num_order_by_columns in [1, 2, 3] {
+ for order_columns in ["id", "name", "department"]
+ .iter()
+ .combinations(num_order_by_columns)
+ {
+ for orderings in order_columns
+ .iter()
+ .map(|col| orders.get(**col).unwrap())
+ .multi_cartesian_product()
+ {
+ let query = format!(
+ "SELECT * FROM test_table ORDER BY {} LIMIT {}",
+ orderings.into_iter().join(", "),
+ limit
+ );
+ queries.push(query);
+ }
+ }
+ }
+ }
+
+ queries.sort_unstable();
+ println!(
+ "Generated {} queries in {:?}",
+ queries.len(),
+ start.elapsed()
+ );
+
+ let start = datafusion_common::instant::Instant::now();
+ let datasets = test_files().await;
+ println!("Generated test files in {:?}", start.elapsed());
+
+ let mut test_cases = vec![];
+ for enable_filter_pushdown in [true, false] {
+ for query in &queries {
+ for dataset in &datasets {
+ let mut cfg = SessionConfig::new();
+ cfg = cfg.set_bool(
+ "datafusion.optimizer.enable_dynamic_filter_pushdown",
+ enable_filter_pushdown,
+ );
+ test_cases.push(TestCase {
+ query: query.to_string(),
+ cfg,
+ dataset: dataset.clone(),
+ });
+ }
+ }
+ }
+
+ let start = datafusion_common::instant::Instant::now();
+ let mut join_set = JoinSet::new();
+ for tc in test_cases {
+ join_set.spawn(run_query(tc.query, tc.cfg, tc.dataset));
Review Comment:
can we somehow assert that the filter is pushed down to the source
dynamically in these tests?
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