Repository: duckdb/duckdb (language: python)

Issue:
Regression: query that runs instantly on GA release hangs indefinitely on the last few nightlies
### What happens?

A query of moderate complexity and based on small-ish datasets hangs indefinitely on the past few nighty builds. The same query finishes correctly instantly on the GA release.

### To Reproduce

```sql
  select      row_number() over ()
  ,           count(distinct upper_ch1_ch2) over()::int  as num_bigrams
  ,           sum(count_ch1_ch2) over ()            as bigram_count_all
  ,           count_ch1_ch2 / bigram_count_all         as actual_bigram_frequency
  ,           ch1.actual_frequency * ch2.actual_frequency as expected_bigram_frequency
  from        read_csv('stat_bigrams.csv')    as stat_bigrams
  inner join  read_csv('stat_stat_chars.csv') as ch1
  on          stat_bigrams.ascii_upper_ch1  = ch1.ascii_upper_ch
  inner join  read_csv('stat_stat_chars.csv') as ch2
  on          stat_bigrams.ascii_upper_ch2 = ch2.ascii_upper_ch;
```
[stat_bigrams.csv](https://github.com/user-attachments/files/16726998/stat_bigrams.csv)
[stat_stat_chars.csv](https://github.com/user-attachments/files/16726999/stat_stat_chars.csv)


### OS:

windows 11 enterprise

### DuckDB Version:

v1.0.0 1f98600c2c (duckdb_cli-windows-amd64)

### DuckDB Client:

CLI 

### Full Name:

Roland Bouman

### Affiliation:

EPAM Systems BV Netherlands

### What is the latest build you tested with? If possible, we recommend testing with the latest nightly build.

I have tested with a nightly build

### Did you include all relevant data sets for reproducing the issue?

Yes

### Did you include all code required to reproduce the issue?

- [X] Yes, I have

### Did you include all relevant configuration (e.g., CPU architecture, Python version, Linux distribution) to reproduce the issue?

- [X] Yes, I have


Discussion / hints:
Thanks, reproduced!
I have tried to peel off as much of the complexity as I could but I am at a loss to provide a clear definition of what triggers this.

I have a  few variations of the query that run as expected on both the nightly and the GA:

no1 lacks `expected_bigram_frequency` column

```
  select      row_number() over ()
  ,           count(distinct upper_ch1_ch2) over()::int  as num_bigrams
  ,           sum(count_ch1_ch2) over ()            as bigram_count_all
  ,           count_ch1_ch2 / bigram_count_all         as actual_bigram_frequency
  from        read_csv('C:\Users\Roland_Bouman\Downloads\stat_bigrams.csv')    as stat_bigrams
  inner join  read_csv('C:\Users\Roland_Bouman\Downloads\stat_stat_chars.csv') as ch1
  on          stat_bigrams.ascii_upper_ch1  = ch1.ascii_upper_ch
  inner join  read_csv('C:\Users\Roland_Bouman\Downloads\stat_stat_chars.csv') as ch2
  on          stat_bigrams.ascii_upper_ch2 = ch2.ascii_upper_ch
```

no2 lacks `row_number() over ()` expression
```
  select      count(distinct upper_ch1_ch2) over()::int  as num_bigrams
  ,           sum(count_ch1_ch2) over ()            as bigram_count_all
  ,           count_ch1_ch2 / bigram_count_all         as actual_bigram_frequency
  ,           ch1.actual_frequency * ch2.actual_frequency as expected_bigram_frequency
  from        read_csv('C:\Users\Roland_Bouman\Downloads\stat_bigrams.csv')    as stat_bigrams
  inner join  read_csv('C:\Users\Roland_Bouman\Downloads\stat_stat_chars.csv') as ch1
  on          stat_bigrams.ascii_upper_ch1  = ch1.ascii_upper_ch
  inner join  read_csv('C:\Users\Roland_Bouman\Downloads\stat_stat_chars.csv') as ch2
  on          stat_bigrams.ascii_upper_ch2 = ch2.ascii_upper_ch
```
However, a query that combines just those two columns executes instantly with expected results, so the cause is not just in combining these two columns.:
```
  select      count(distinct upper_ch1_ch2) over()::int  as num_bigrams
  ,           ch1.actual_frequency * ch2.actual_frequency as expected_bigram_frequency
  from        read_csv('C:\Users\Roland_Bouman\Downloads\stat_bigrams.csv')    as stat_bigrams
  inner join  read_csv('C:\Users\Roland_Bouman\Downloads\stat_stat_chars.csv') as ch1
  on          stat_bigrams.ascii_upper_ch1  = ch1.ascii_upper_ch
  inner join  read_csv('C:\Users\Roland_Bouman\Downloads\stat_stat_chars.csv') as ch2
  on          stat_bigrams.ascii_upper_ch2 = ch2.ascii_upper_ch

```


For me, the no2 variant (without the window function) runs instantly on both v1.0 and the latest `main`.
> For me, the no2 variant runs instantly on both v1.0 and the latest main.

That's interesting - do you mean the other variants also show the hanging behavior? For me all these variants run instantly on both nightly and GA, that's why I'm stumped to guess what the cause might be.
No, I was just reading too superficially. All your variants under this comment run quickly on my machine too:

> I have a few variations of the query that run as expected on both the nightly and the GA:

I've asked the team to take a look at what might be causing this regression.
Thanks @rpbouman - this is a recent issue with the new window threading system and the distinct aggregate accelerator. I really appreciate that someone is testing this stuff now!

Fix patch:
diff --git a/data/csv/issue_13525/stat_bigrams.csv b/data/csv/issue_13525/stat_bigrams.csv
new file mode 100644
index 000000000000..8d75d6f48a0e
--- /dev/null
+++ b/data/csv/issue_13525/stat_bigrams.csv
@@ -0,0 +1,3724 @@
+upper_ch1,ascii_upper_ch1,upper_ch2,ascii_upper_ch2,upper_ch1_ch2,count_ch1_ch2
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diff --git a/data/csv/issue_13525/stat_stat_chars.csv b/data/csv/issue_13525/stat_stat_chars.csv
new file mode 100644
index 000000000000..6a883b63210d
--- /dev/null
+++ b/data/csv/issue_13525/stat_stat_chars.csv
@@ -0,0 +1,117 @@
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diff --git a/src/execution/operator/aggregate/physical_window.cpp b/src/execution/operator/aggregate/physical_window.cpp
index 480939768246..bc2cb86104a2 100644
--- a/src/execution/operator/aggregate/physical_window.cpp
+++ b/src/execution/operator/aggregate/physical_window.cpp
@@ -345,6 +345,9 @@ class WindowGlobalSourceState : public GlobalSourceState {
 
 	WindowGlobalSourceState(ClientContext &context_p, WindowGlobalSinkState &gsink_p);
 
+	//! Build task list
+	void CreateTaskList();
+
 	//! Are there any more tasks?
 	bool HasMoreTasks() const {
 		return !stopped && next_task < tasks.size();
@@ -363,8 +366,12 @@ class WindowGlobalSourceState : public GlobalSourceState {
 	ClientContext &context;
 	//! All the sunk data
 	WindowGlobalSinkState &gsink;
+	//! The total number of blocks to process;
+	idx_t total_blocks = 0;
 	//! State mutex
 	mutable mutex lock;
+	//! The number of local states
+	atomic<idx_t> threads;
 	//! The list of tasks
 	vector<Task> tasks;
 	//! The the next task
@@ -380,12 +387,12 @@ class WindowGlobalSourceState : public GlobalSourceState {
 
 public:
 	idx_t MaxThreads() override {
-		return tasks.size();
+		return total_blocks;
 	}
 };
 
 WindowGlobalSourceState::WindowGlobalSourceState(ClientContext &context_p, WindowGlobalSinkState &gsink_p)
-    : context(context_p), gsink(gsink_p), next_task(0), finished(0), stopped(false), returned(0) {
+    : context(context_p), gsink(gsink_p), threads(0), next_task(0), finished(0), stopped(false), returned(0) {
 	auto &gpart = gsink.global_partition;
 	auto &window_hash_groups = gsink.global_partition->window_hash_groups;
 
@@ -394,6 +401,7 @@ WindowGlobalSourceState::WindowGlobalSourceState(ClientContext &context_p, Windo
 		if (gpart->rows && !gpart->rows->blocks.empty()) {
 			// We need to construct the single WindowHashGroup here because the sort tasks will not be run.
 			window_hash_groups.emplace_back(make_uniq<WindowHashGroup>(gsink, idx_t(0)));
+			total_blocks = gpart->rows->blocks.size();
 		}
 	} else {
 		idx_t batch_base = 0;
@@ -410,6 +418,21 @@ WindowGlobalSourceState::WindowGlobalSourceState(ClientContext &context_p, Windo
 			window_hash_group->batch_base = batch_base;
 			batch_base += block_count;
 		}
+		total_blocks = batch_base;
+	}
+}
+
+void WindowGlobalSourceState::CreateTaskList() {
+	//	Check whether we have a task list outside the mutex.
+	if (next_task.load()) {
+		return;
+	}
+
+	lock_guard<mutex> task_guard(lock);
+
+	auto &window_hash_groups = gsink.global_partition->window_hash_groups;
+	if (!tasks.empty()) {
+		return;
 	}
 
 	//    Sort the groups from largest to smallest
@@ -426,7 +449,6 @@ WindowGlobalSourceState::WindowGlobalSourceState(ClientContext &context_p, Windo
 	std::sort(partition_blocks.begin(), partition_blocks.end(), std::greater<PartitionBlock>());
 
 	//	Schedule the largest group on as many threads as possible
-	const auto threads = idx_t(TaskScheduler::GetScheduler(context).NumberOfThreads());
 	const auto &max_block = partition_blocks.front();
 	const auto per_thread = (max_block.first + threads - 1) / threads;
 
@@ -682,6 +704,8 @@ WindowLocalSourceState::WindowLocalSourceState(WindowGlobalSourceState &gsource)
 		output_types.emplace_back(wexpr.return_type);
 	}
 	output_chunk.Initialize(global_partition.allocator, output_types);
+
+	++gsource.threads;
 }
 
 bool WindowGlobalSourceState::TryNextTask(TaskPtr &task) {
@@ -873,6 +897,8 @@ SourceResultType PhysicalWindow::GetData(ExecutionContext &context, DataChunk &c
 	auto &gsource = input.global_state.Cast<WindowGlobalSourceState>();
 	auto &lsource = input.local_state.Cast<WindowLocalSourceState>();
 
+	gsource.CreateTaskList();
+
 	while (gsource.HasUnfinishedTasks() && chunk.size() == 0) {
 		if (!lsource.TaskFinished() || lsource.TryAssignTask()) {
 			try {


Test patch:
diff --git a/test/sql/window/test_thread_count.test b/test/sql/window/test_thread_count.test
new file mode 100644
index 000000000000..517521fd3313
--- /dev/null
+++ b/test/sql/window/test_thread_count.test
@@ -0,0 +1,15 @@
+# name: test/sql/window/test_thread_count.test
+# description: Check the task thread allocation does not exceed what is provided
+# group: [window]
+
+statement ok
+select      row_number() over ()
+,           count(distinct upper_ch1_ch2) over()::int  as num_bigrams
+,           sum(count_ch1_ch2) over ()            as bigram_count_all
+,           count_ch1_ch2 / bigram_count_all         as actual_bigram_frequency
+,           ch1.actual_frequency * ch2.actual_frequency as expected_bigram_frequency
+from        read_csv('data/csv/issue_13525/stat_bigrams.csv')    as stat_bigrams
+inner join  read_csv('data/csv/issue_13525/stat_stat_chars.csv') as ch1
+on          stat_bigrams.ascii_upper_ch1  = ch1.ascii_upper_ch
+inner join  read_csv('data/csv/issue_13525/stat_stat_chars.csv') as ch2
+on          stat_bigrams.ascii_upper_ch2 = ch2.ascii_upper_ch;